From 602216816eb2f65603d4ef6d55edd51337cabbff Mon Sep 17 00:00:00 2001 From: luissian Date: Thu, 2 Nov 2023 16:53:07 +0100 Subject: [PATCH 001/214] first change to new format --- .gitignore | 1 + MANIFEST.in | 3 + allele_calling.py | 2484 ---------------------------------- analyze_schema.py | 715 ---------- create_schema.py | 40 - distance_matrix.py | 323 ----- reference_alleles.py | 219 --- requirements.txt | 4 + setup.py | 38 + taranis.py | 282 ---- taranis/__init__.py | 3 + taranis/__main__.py | 143 ++ taranis/reference_alleles.py | 149 ++ taranis/utils.py | 131 ++ taranis_configuration.py | 13 - tox.ini | 6 + 16 files changed, 478 insertions(+), 4076 deletions(-) create mode 100644 MANIFEST.in delete mode 100755 allele_calling.py delete mode 100755 analyze_schema.py delete mode 100644 create_schema.py delete mode 100755 distance_matrix.py delete mode 100755 reference_alleles.py create mode 100644 requirements.txt create mode 100644 setup.py delete mode 100755 taranis.py create mode 100644 taranis/__init__.py create mode 100644 taranis/__main__.py create mode 100644 taranis/reference_alleles.py create mode 100644 taranis/utils.py delete mode 100644 taranis_configuration.py create mode 100644 tox.ini diff --git a/.gitignore b/.gitignore index 190f57d..423e48b 100644 --- a/.gitignore +++ b/.gitignore @@ -90,6 +90,7 @@ venv/ ENV/ env.bak/ venv.bak/ +virtualenv/ # Spyder project settings .spyderproject diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 0000000..7b74127 --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,3 @@ +include LICENSE +include README.md +include requirements.txt diff --git a/allele_calling.py b/allele_calling.py deleted file mode 100755 index 72d3294..0000000 --- a/allele_calling.py +++ /dev/null @@ -1,2484 +0,0 @@ -#!/usr/bin/env python3 - -import argparse -import sys -import io -import os -import re -import statistics -import logging -from logging.handlers import RotatingFileHandler -from datetime import datetime -import glob -import pickle -from Bio import SeqIO -from Bio.SeqRecord import SeqRecord -from Bio import Seq -from Bio import pairwise2 -from Bio.pairwise2 import format_alignment -from Bio.Blast.Applications import NcbiblastnCommandline -from io import StringIO -from Bio.Blast import NCBIXML -import pandas as pd -import shutil -from progressbar import ProgressBar -from utils.taranis_utils import * -import math -import csv -import plotly.graph_objects as go - - -def check_blast (reference_allele, sample_files, db_name, logger) : ## N - for s_file in sample_files: - f_name = os.path.basename(s_file).split('.') - dir_name = os.path.dirname(s_file) - blast_dir = os.path.join(dir_name, db_name,f_name[0]) - blast_db = os.path.join(blast_dir,f_name[0]) - if not os.path.exists(blast_dir) : - logger.error('Blast db folder for sample %s does not exist', f_name) - return False - cline = NcbiblastnCommandline(db=blast_db, evalue=0.001, outfmt=5, max_target_seqs=10, max_hsps=10,num_threads=1, query=reference_allele) - out, err = cline() - - psiblast_xml = StringIO(out) - blast_records = NCBIXML.parse(psiblast_xml) - - for blast_record in blast_records: - locationcontigs = [] - for alignment in blast_record.alignments: - # select the best match - for match in alignment.hsps: - alleleMatchid = int((blast_record.query_id.split("_"))[-1]) - return True - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # -# Parse samples and core genes schema fasta files to dictionary # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # - -def parsing_fasta_file_to_dict (fasta_file, logger): - fasta_dict = {} - fasta_dict_ordered = {} - for contig in SeqIO.parse(fasta_file, "fasta"): - fasta_dict[str(contig.id)] = str(contig.seq.upper()) - logger.debug('file %s parsed to dictionary', fasta_file) - - for key in sorted(list(fasta_dict.keys())): - fasta_dict_ordered[key] = fasta_dict[key] - return fasta_dict_ordered - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # -# Get core genes schema info before allele calling analysis # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # - -#def prepare_core_gene (core_gene_file_list, store_dir, ref_alleles_dir, logger): -def prepare_core_gene (core_gene_file_list, store_dir, ref_alleles_dir, genus, species, usegenus, logger): - - ## Initialize dict for keeping id-allele, quality, length variability, length statistics and annotation info for each schema core gene - alleles_in_locus_dict = {} - schema_quality = {} - annotation_core_dict = {} - schema_variability = {} - schema_statistics = {} - - - ## Process each schema core gene - blast_dir = os.path.join(store_dir,'blastdb') - logger.info('start preparation of core genes files') - for fasta_file in core_gene_file_list: - - f_name = os.path.basename(fasta_file).split('.') - - # Parse core gene fasta file and keep id-sequence info in dictionary - fasta_file_parsed_dict = parsing_fasta_file_to_dict(fasta_file, logger) - if f_name[0] not in alleles_in_locus_dict.keys(): - alleles_in_locus_dict[f_name[0]] = {} - alleles_in_locus_dict[f_name[0]] = fasta_file_parsed_dict - - # dump fasta file into pickle file - #with open (file_list[-1],'wb') as f: - # pickle.dump(fasta_file_parsed_dict, f) - - # Get core gene alleles quality - locus_quality = check_core_gene_quality(fasta_file, logger) - if f_name[0] not in schema_quality.keys(): - schema_quality[f_name[0]] = {} - schema_quality[f_name[0]] = locus_quality - - # Get gene and product annotation for core gene using reference allele(s) - ref_allele = os.path.join(ref_alleles_dir, f_name[0] + '.fasta') - - gene_annot, product_annot = get_gene_annotation (ref_allele, store_dir, genus, species, usegenus, logger) - #gene_annot, product_annot = get_gene_annotation (ref_allele, store_dir, logger) - if f_name[0] not in annotation_core_dict.keys(): - annotation_core_dict[f_name[0]] = {} - annotation_core_dict[f_name[0]] = [gene_annot, product_annot] - - # Get core gene alleles length to keep length variability and statistics info - alleles_len = [] - for allele in fasta_file_parsed_dict : - alleles_len.append(len(fasta_file_parsed_dict[allele])) - - #alleles_in_locus = list (SeqIO.parse(fasta_file, "fasta")) ## parse - #for allele in alleles_in_locus : ## parse - #alleles_len.append(len(str(allele.seq))) ## parse - - schema_variability[f_name[0]]=list(set(alleles_len)) - - if len(alleles_len) == 1: - stdev = 0 - else: - stdev = statistics.stdev(alleles_len) - schema_statistics[f_name[0]]=[statistics.mean(alleles_len), stdev, min(alleles_len), max(alleles_len)] - - return alleles_in_locus_dict, annotation_core_dict, schema_variability, schema_statistics, schema_quality - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # -# Get Prodigal training file from reference genome for samples gene prediction # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # - -def prodigal_training(reference_genome_file, prodigal_dir, logger): - - f_name = os.path.basename(reference_genome_file).split('.')[0] - prodigal_train_dir = os.path.join(prodigal_dir, 'training') - - output_prodigal_train_dir = os.path.join(prodigal_train_dir, f_name + '.trn') - - if not os.path.exists(prodigal_train_dir): - try: - os.makedirs(prodigal_train_dir) - logger.debug('Created prodigal directory for training file %s', f_name) - except: - logger.info('Cannot create prodigal directory for training file %s', f_name) - print ('Error when creating the directory %s for training file', prodigal_train_dir) - exit(0) - - prodigal_command = ['prodigal' , '-i', reference_genome_file, '-t', output_prodigal_train_dir] - prodigal_result = subprocess.run(prodigal_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) - - # if prodigal_result.stderr: - # logger.error('cannot create training file for %s', f_name) - # logger.error('prodigal returning error code %s', prodigal_result.stderr) - # return False - else: - logger.info('Skeeping prodigal training file creation for %s, as it has already been created', f_name) - - return output_prodigal_train_dir - - -# · * · * · * · * · * · * · * · * · * # -# Get Prodigal sample gene prediction # -# · * · * · * · * · * · * · * · * · * # - -def prodigal_prediction(file_name, prodigal_dir, prodigal_train_dir, logger): - - f_name = '.'.join(os.path.basename(file_name).split('.')[:-1]) - prodigal_dir_sample = os.path.join(prodigal_dir,f_name) - - output_prodigal_coord = os.path.join(prodigal_dir_sample, f_name + '_coord.gff') ## no - output_prodigal_prot = os.path.join(prodigal_dir_sample, f_name + '_prot.faa') ## no - output_prodigal_dna = os.path.join(prodigal_dir_sample, f_name + '_dna.faa') - - if not os.path.exists(prodigal_dir_sample): - try: - os.makedirs(prodigal_dir_sample) - logger.debug('Created prodigal directory for Core Gene %s', f_name) - except: - logger.info('Cannot create prodigal directory for Core Gene %s' , f_name) - print ('Error when creating the directory %s for prodigal genes prediction', prodigal_dir_sample) - exit(0) - - prodigal_command = ['prodigal' , '-i', file_name , '-t', prodigal_train_dir, '-f', 'gff', '-o', output_prodigal_coord, '-a', output_prodigal_prot, '-d', output_prodigal_dna] - prodigal_result = subprocess.run(prodigal_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) - - # if prodigal_result.stderr: - # logger.error('cannot predict genes for %s ', f_name) - # logger.error('prodigal returning error code %s', prodigal_result.stderr) - #return False - else: - logger.info('Skeeping prodigal genes prediction for %s, as it has already been made', f_name) - - return True - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # -# Get Prodigal predicted gene sequence equivalent to BLAST result matching bad quality allele or to no Exact Match BLAST result in allele calling analysis # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # - -def get_prodigal_sequence(blast_sseq, contig_blast_id, prodigal_directory, sample_name, blast_parameters, logger): - - prodigal_directory_sample = os.path.join(prodigal_directory, sample_name) - genes_file = os.path.join(prodigal_directory_sample, sample_name + '_dna.faa') - - ## Create directory for storing prodigal genes prediction per contig BLAST databases - blastdb_per_contig_directory = 'blastdb_per_contig' - full_path_blastdb_per_contig = os.path.join(prodigal_directory_sample, blastdb_per_contig_directory) - if not os.path.exists(full_path_blastdb_per_contig): - try: - os.makedirs(full_path_blastdb_per_contig) - logger.info('Directory %s has been created', full_path_blastdb_per_contig) - except: - print ('Cannot create the directory ', full_path_blastdb_per_contig) - logger.info('Directory %s cannot be created', full_path_blastdb_per_contig) - exit (0) - - ## Create directory for storing prodigal genes prediction sequences per contig - prodigal_genes_per_contig_directory = 'prodigal_genes_per_contig' - full_path_prodigal_genes_per_contig = os.path.join(prodigal_directory_sample, prodigal_genes_per_contig_directory) - if not os.path.exists(full_path_prodigal_genes_per_contig): - try: - os.makedirs(full_path_prodigal_genes_per_contig) - logger.info('Directory %s has been created', full_path_prodigal_genes_per_contig) - except: - print ('Cannot create the directory ', full_path_prodigal_genes_per_contig) - logger.info('Directory %s cannot be created', full_path_prodigal_genes_per_contig) - exit (0) - - ## Parse prodigal genes prediction fasta file - predicted_genes = SeqIO.parse(genes_file, "fasta") - - ## Create fasta file containing Prodigal predicted genes sequences for X contig in sample - contig_genes_path = os.path.join(full_path_prodigal_genes_per_contig, contig_blast_id + '.fasta') - with open (contig_genes_path, 'w') as out_fh: - for rec in predicted_genes: - contig_prodigal_id = '_'.join((rec.id).split("_")[:-1]) - if contig_prodigal_id == contig_blast_id: - out_fh.write ('>' + str(rec.description) + '\n' + str(rec.seq) + '\n') - - ## Create local BLAST database for Prodigal predicted genes sequences for X contig in sample - if not create_blastdb(contig_genes_path, full_path_blastdb_per_contig, 'nucl', logger): - print('Error when creating the blastdb for samples files. Check log file for more information. \n ') - return False - - ## Local BLAST Prodigal predicted genes sequences database VS BLAST sequence obtained in sample in allele calling analysis - blast_db_name = os.path.join(full_path_blastdb_per_contig, contig_blast_id, contig_blast_id) - - cline = NcbiblastnCommandline(db=blast_db_name, evalue=0.001, perc_identity = 90, outfmt= blast_parameters, max_target_seqs=10, max_hsps=10, num_threads=1) - out, err = cline(stdin = blast_sseq) - out_lines = out.splitlines() - - bigger_bitscore = 0 - if len (out_lines) > 0 : - for line in out_lines : - values = line.split('\t') - if float(values[8]) > bigger_bitscore: - qseqid , sseqid , pident , qlen , s_length , mismatch , r_gapopen , r_evalue , bitscore , sstart , send , qstart , qend ,sseq , qseq = values - bigger_bitscore = float(bitscore) - - ## Get complete Prodigal sequence matching allele calling BLAST sequence using ID - predicted_genes_in_contig = SeqIO.parse(contig_genes_path, "fasta") - - for rec in predicted_genes_in_contig: - if rec.id == sseqid: - predicted_gene_sequence = str(rec.seq) - start_prodigal = str(rec.description.split( '#')[1]) - end_prodigal = str(rec.description.split('#')[2]) - break - - ## Sequence not found by Prodigal when there are no BLAST results matching allele calling BLAST sequence - if len (out_lines) == 0: - predicted_gene_sequence = 'Sequence not found by Prodigal' - start_prodigal = '-' - end_prodigal = '-' - - return predicted_gene_sequence, start_prodigal, end_prodigal ### start_prodigal y end_prodigal para report prodigal - - -# · * · * · * · * · * · * · * · * · * · * · * · * # -# Get samples info before allele calling analysis # -# · * · * · * · * · * · * · * · * · * · * · * · * # - -def prepare_samples(sample_file_list, store_dir, reference_genome_file, logger): - - ## Initialize dictionary for keeping id-contig - contigs_in_sample_dict = {} - - ## Paths for samples blastdb, Prodigal genes prediction and BLAST results - blast_dir = os.path.join(store_dir,'blastdb') - prodigal_dir = os.path.join(store_dir,'prodigal') - blast_results_seq_dir = os.path.join(store_dir,'blast_results', 'blast_results_seq') - - ## Get training file for Prodigal genes prediction - output_prodigal_train_dir = prodigal_training(reference_genome_file, prodigal_dir, logger) - if not output_prodigal_train_dir: - print('Error when creating training file for genes prediction. Check log file for more information. \n ') - return False - - for fasta_file in sample_file_list: - f_name = '.'.join(os.path.basename(fasta_file).split('.')[:-1]) - - # Get samples id-contig dictionary - fasta_file_parsed_dict = parsing_fasta_file_to_dict(fasta_file, logger) - if f_name not in contigs_in_sample_dict.keys(): - contigs_in_sample_dict[f_name] = {} - contigs_in_sample_dict[f_name] = fasta_file_parsed_dict - - # dump fasta file into pickle file - #with open (file_list[-1],'wb') as f: # generación de diccionarios de contigs para cada muestra - # pickle.dump(fasta_file_parsed_dict, f) - - # Create directory for storing BLAST results using reference allele(s) - blast_results_seq_per_sample_dir = os.path.join(blast_results_seq_dir, f_name) - - if not os.path.exists(blast_results_seq_per_sample_dir): - try: - os.makedirs(blast_results_seq_per_sample_dir) - logger.debug('Created blast results directory for sample %s', f_name) - except: - logger.info('Cannot create blast results directory for sample %s', f_name) - print ('Error when creating the directory for blast results', blast_results_seq_per_sample_dir) - exit(0) - - # Prodigal genes prediction for each sample - if not prodigal_prediction(fasta_file, prodigal_dir, output_prodigal_train_dir, logger): - print('Error when predicting genes for samples files. Check log file for more information. \n ') - return False - - # Create local BLAST db for each sample fasta file - if not create_blastdb(fasta_file, blast_dir, 'nucl', logger): - print('Error when creating the blastdb for samples files. Check log file for more information. \n ') - return False - - return contigs_in_sample_dict - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # -# Get established length thresholds for allele tagging in allele calling analysis # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # - -def length_thresholds(core_name, schema_statistics, percent): ### logger - - locus_mean = int(schema_statistics[core_name][0]) - - if percent != "SD": - max_length_threshold = math.ceil(locus_mean + ((locus_mean * float(percent)) / 100)) - min_length_threshold = math.floor(locus_mean - ((locus_mean * float(percent)) / 100)) - else: - percent = float(schema_statistics[core_name][1]) - - max_length_threshold = math.ceil(locus_mean + (locus_mean * percent)) - min_length_threshold = math.floor(locus_mean - (locus_mean * percent)) - - return max_length_threshold, min_length_threshold - - -# · * · * · * · * · * · * · * · * · * · * · # -# Convert dna sequence to protein sequence # -# · * · * · * · * · * · * · * · * · * · * · # - -def convert_to_protein (sequence) : - - seq = Seq.Seq(sequence) - protein = str(seq.translate()) - - return protein - -# · * · * · * · * · * · * · * · * · * · * · * · * · * # -# Get SNPs between BLAST sequence and matching allele # -# · * · * · * · * · * · * · * · * · * · * · * · * · * # - -def get_snp (sample, query) : - - prot_annotation = {'S': 'polar' ,'T': 'polar' ,'Y': 'polar' ,'Q': 'polar' ,'N': 'polar' ,'C': 'polar' ,'S': 'polar' , - 'F': 'nonpolar' ,'L': 'nonpolar','I': 'nonpolar','M': 'nonpolar','P': 'nonpolar','V': 'nonpolar','A': 'nonpolar','W': 'nonpolar','G': 'nonpolar', - 'D' : 'acidic', 'E' :'acidic', - 'H': 'basic' , 'K': 'basic' , 'R' : 'basic', - '-': '-----', '*' : 'Stop codon'} - snp_list = [] - sample = sample.replace('-','') - #length = max(len(sample), len(query)) - length = len(query) - # normalize the length of the sample for the iteration - if len(sample) < length : - need_to_add = length - len(sample) - sample = sample + need_to_add * '-' - - # convert to Seq class to translate to protein - seq_sample = Seq.Seq(sample) - seq_query = Seq.Seq(query) - - for index in range(length): - if seq_query[index] != seq_sample[index] : - triple_index = index - (index % 3) - codon_seq = seq_sample[triple_index : triple_index + 3] - codon_que = seq_query[triple_index : triple_index + 3] - if not '-' in str(codon_seq) : - prot_seq = str(codon_seq.translate()) - prot_que = str(codon_que.translate()) - else: - prot_seq = '-' - prot_que = str(seq_query[triple_index: ].translate()) - if prot_annotation[prot_que[0]] == prot_annotation[prot_seq[0]] : - missense_synonym = 'Synonymous' - elif prot_seq == '*' : - missense_synonym = 'Nonsense' - else: - missense_synonym = 'Missense' - #snp_list.append([str(index+1),str(seq_sample[index]) + '/' + str(seq_query[index]), str(codon_seq) + '/'+ str(codon_que), - snp_list.append([str(index+1),str(seq_query[index]) + '/' + str(seq_sample[index]), str(codon_que) + '/'+ str(codon_seq), - # when one of the sequence ends but not the other we will translate the remain sequence to proteins - # in that case we will only annotate the first protein. Using [0] as key of the dictionary annotation - prot_que + '/' + prot_seq, missense_synonym, prot_annotation[prot_que[0]] + ' / ' + prot_annotation[prot_seq[0]]]) - if '-' in str(codon_seq) : - break - return snp_list - - -def nucleotide_to_protein_alignment (sample_seq, query_seq ) : ### Sustituido por get_alignment - aligment = [] - sample_prot = convert_to_protein(sample_seq) - query_prot = convert_to_protein(query_seq) - minimun_length = min(len(sample_prot), len(query_prot)) - for i in range(minimun_length): - if sample_prot[i] == query_prot[i] : - aligment.append('|') - else: - aligment.append(' ') - protein_alignment = [['sample', sample_prot],['match', ''.join(aligment)], ['schema', query_prot]] - - return protein_alignment - - -def get_alignment_for_indels (blast_db_name, qseq) : ### Sustituido por get_alignment - #match_alignment =[] - cline = NcbiblastnCommandline(db=blast_db_name, evalue=0.001, perc_identity = 80, outfmt= 5, max_target_seqs=10, max_hsps=10,num_threads=1) - out, err = cline(stdin = qseq) - psiblast_xml = StringIO(out) - blast_records = NCBIXML.parse(psiblast_xml) - for blast_record in blast_records: - for alignment in blast_record.alignments: - for match in alignment.hsps: - match_alignment = [['sample', match.sbjct],['match', match.match], ['schema',match.query]] - return match_alignment - - -def get_alignment_for_deletions (sample_seq, query_seq): ### Sustituido por get_alignment - index_found = False - alignments = pairwise2.align.globalxx(sample_seq, query_seq) - for index in range(len(alignments)) : - if alignments[index][4] == len(query_seq) : - index_found = True - break - if not index_found : - index = 0 - values = format_alignment(*alignments[index]).split('\n') - match_alignment = [['sample', values[0]],['match', values[1]], ['schema',values[2]]] - return match_alignment - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # -# Get DNA and protein alignment between the final sequence found in the sample and the matching allele # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # - -def get_alignment (sample_seq, query_seq, reward, penalty, gapopen, gapextend, seq_type = "dna"): - - ## If sequences alignment type desired is "protein" convert dna sequences to protein - if seq_type == "protein": - sample_seq = convert_to_protein(sample_seq) - query_seq = convert_to_protein(query_seq) - - ## Get dna/protein alignment between final sequence found and matching allele - # arguments pairwise2.align.globalms: match, mismatch, gap opening, gap extending - alignments = pairwise2.align.localms(sample_seq, query_seq, reward, penalty, -gapopen, -gapextend) - values = format_alignment(*alignments[0]).split('\n') - match_alignment = [['sample', values[0]],['match', values[1]], ['schema',values[2]]] - - return match_alignment - - -# · * · * · * · * · * · * · * · * # -# Tag LNF cases and keep LNF info # -# · * · * · * · * · * · * · * · * # - -def lnf_tpr_tag(core_name, sample_name, alleles_in_locus_dict, samples_matrix_dict, lnf_tpr_dict, schema_statistics, locus_alleles_path, qseqid, pident, s_length, new_sequence_length, perc_identity_ref, coverage, schema_quality, annotation_core_dict, count_dict, logger): - - gene_annot, product_annot = annotation_core_dict[core_name] - - if qseqid == '-': - samples_matrix_dict[sample_name].append('LNF') - tag_report = 'LNF' - matching_allele_length = '-' - - else: - if new_sequence_length == '-': - samples_matrix_dict[sample_name].append('LNF_' + str(qseqid)) - tag_report = 'LNF' - else: - samples_matrix_dict[sample_name].append('TPR_' + str(qseqid)) - tag_report = 'TPR' - - matching_allele_seq = alleles_in_locus_dict[core_name][qseqid] - matching_allele_length = len(matching_allele_seq) - - #alleles_in_locus = list (SeqIO.parse(locus_alleles_path, "fasta")) ## parse - #for allele in alleles_in_locus : ## parse - #if allele.id == qseqid : ## parse - #break ## parse - #matching_allele_seq = str(allele.seq) ## parse - #matching_allele_length = len(matching_allele_seq) ## parse - - if pident == '-': - # (los dos BLAST sin resultado) - coverage_blast = '-' - coverage_new_sequence = '-' - add_info = 'Locus not found' - logger.info('Locus not found at sample %s, for gene %s', sample_name, core_name) - - # Get allele quality - allele_quality = '-' - - # (recuento tags para plot) - count_dict[sample_name]['not_found'] += 1 - count_dict[sample_name]['total'] += 1 - - elif 90 > float(pident): - # (BLAST 90 sin resultado y BLAST 70 con resultado) - coverage_blast = '-' - coverage_new_sequence = '-' - add_info = 'BLAST sequence ID under threshold: {}%'.format(perc_identity_ref) - logger.info('BLAST sequence ID %s under threshold at sample %s, for gene %s', pident, sample_name, core_name) - - # Get allele quality - allele_quality = '-' - - # (recuento tags para plot) - count_dict[sample_name]['low_id'] += 1 - count_dict[sample_name]['total'] += 1 - - elif 90 <= float(pident) and new_sequence_length == '-': - # (BLAST 90 con resultado, bajo coverage BLAST) - locus_mean = int(schema_statistics[core_name][0]) - coverage_blast = int(s_length) / locus_mean - #coverage_blast = int(s_length) / matching_allele_length - coverage_new_sequence = '-' - if coverage_blast < 1: - add_info = 'BLAST sequence coverage under threshold: {}%'.format(coverage) - else: - add_info = 'BLAST sequence coverage above threshold: {}%'.format(coverage) - logger.info('BLAST sequence coverage %s under threshold at sample %s, for gene %s', coverage_blast, sample_name, core_name) - - # Get allele quality - allele_quality = '-' - - # (recuento tags para plot) - count_dict[sample_name]['low_coverage'] += 1 - count_dict[sample_name]['total'] += 1 - - elif 90 <= float(pident) and new_sequence_length != '-': - # (BLAST 90 con resultado, buen coverage BLAST, bajo coverage new_sseq) - locus_mean = int(schema_statistics[core_name][0]) - coverage_blast = int(s_length) / locus_mean * 100 - #coverage_blast = int(s_length) / matching_allele_length - coverage_new_sequence = new_sequence_length / matching_allele_length * 100 - if coverage_new_sequence < 1: - add_info = 'New sequence coverage under threshold: {}%'.format(coverage) - else: - add_info = 'New sequence coverage above threshold: {}%'.format(coverage) - logger.info('New sequence coverage %s under threshold at sample %s, for gene %s', coverage_new_sequence, sample_name, core_name) - - # Get allele quality - allele_quality = schema_quality[core_name][qseqid] - - # (recuento tags para plot) - count_dict[sample_name]['total'] += 1 - for count_class in count_dict[sample_name]: - if count_class in allele_quality: - count_dict[sample_name][count_class] += 1 - #if "bad_quality" in allele_quality: - # count_dict[sample_name]['bad_quality'] += 1 - - ## Keeping LNF and TPR report info - if not core_name in lnf_tpr_dict: - lnf_tpr_dict[core_name] = {} - if not sample_name in lnf_tpr_dict[core_name]: - lnf_tpr_dict[core_name][sample_name] = [] - - lnf_tpr_dict[core_name][sample_name].append([gene_annot, product_annot, tag_report, qseqid, allele_quality, pident, str(coverage_blast), str(coverage_new_sequence), str(matching_allele_length), str(s_length), str(new_sequence_length), add_info]) ### Meter secuencias alelo, blast y new_sseq (si las hay)? - - return True - - -# · * · * · * · * · * · * · * · * · * · * · * · * # -# Tag paralog and exact match cases and keep info # -# · * · * · * · * · * · * · * · * · * · * · * · * # - -def paralog_exact_tag(sample_name, core_name, tag, schema_quality, matching_genes_dict, samples_matrix_dict, allele_found, tag_dict, prodigal_report, prodigal_directory, blast_parameters, annotation_core_dict, count_dict, logger): - - logger.info('Found %s at sample %s for core gene %s ', tag, sample_name, core_name) - - paralog_quality_count = [] # (lista para contabilizar parálogos debido a bad o good quality) - - gene_annot, product_annot = annotation_core_dict[core_name] - - if not sample_name in tag_dict : - tag_dict[sample_name] = {} - if not core_name in tag_dict[sample_name] : - tag_dict[sample_name][core_name]= [] - - if tag == 'EXACT': - allele = list(allele_found.keys())[0] - qseqid = allele_found[allele][0] - tag = qseqid - - samples_matrix_dict[sample_name].append(tag) - - for sequence in allele_found: - qseqid, sseqid, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart, send, qstart, qend, sseq, qseq = allele_found[sequence] - sseq = sseq.replace('-', '') - - # Get allele quality - allele_quality = schema_quality[core_name][qseqid] - - if len(allele_found) > 1: - paralog_quality_count.append(allele_quality) - - # Get prodigal gene prediction if allele quality is 'bad_quality' - if 'bad_quality' in allele_quality: - complete_predicted_seq, start_prodigal, end_prodigal = get_prodigal_sequence(sseq, sseqid, prodigal_directory, sample_name, blast_parameters, logger) - - ##### informe prodigal ##### - prodigal_report.append([core_name, sample_name, qseqid, tag, sstart, send, start_prodigal, end_prodigal, sseq, complete_predicted_seq]) - - else: - complete_predicted_seq = '-' - - if not sseqid in matching_genes_dict[sample_name] : - matching_genes_dict[sample_name][sseqid] = [] - if sstart > send : - #matching_genes_dict[sample_name][sseqid].append([core_name, sstart, send,'-', tag]) - matching_genes_dict[sample_name][sseqid].append([core_name, qseqid, sstart, send,'-', tag]) - else: - #matching_genes_dict[sample_name][sseqid].append([core_name, sstart, send,'+', tag]) - matching_genes_dict[sample_name][sseqid].append([core_name, qseqid, sstart, send,'+', tag]) - - ## Keeping paralog NIPH/NIPHEM report info - if tag == 'NIPH' or tag == 'NIPHEM': - tag_dict[sample_name][core_name].append([gene_annot, product_annot, tag, pident, qseqid, allele_quality, sseqid, bitscore, sstart, send, sseq, complete_predicted_seq]) - else: - tag_dict[sample_name][core_name] = [gene_annot, product_annot, qseqid, allele_quality, sseqid, s_length, sstart, send, sseq, complete_predicted_seq] - - # (recuento tags para plot) - count_dict[sample_name]['total'] += 1 - for count_class in count_dict[sample_name]: - if count_class in allele_quality: - if "no_start_stop" not in count_class and "no_start_stop" in allele_quality: - if count_class == "bad_quality": - count_dict[sample_name][count_class] += 1 - else: - count_dict[sample_name][count_class] += 1 - - # (recuento tags para plot (parálogos)) - if len(allele_found) > 0: - count = 0 - for paralog_quality in paralog_quality_count: - count += 1 - if "bad_quality" in paralog_quality: - count_dict[sample_name]['total'] += 1 - for count_class in count_dict[sample_name]: - if count_class in paralog_quality: - if "no_start_stop" not in count_class and "no_start_stop" in paralog_quality: - if count_class == "bad_quality": - count_dict[sample_name][count_class] += 1 - else: - next - else: - count_dict[sample_name][count_class] += 1 - break - - else: - if count == len(paralog_quality_count): - count_dict[sample_name]['total'] += 1 - count_dict[sample_name]['good_quality'] += 1 - - return True - - -# · * · * · * · * · * · * · * · * · * · * # -# Tag INF/ASM/ALM/PLOT cases and keep info # -# · * · * · * · * · * · * · * · * · * · * # - -def inf_asm_alm_tag(core_name, sample_name, tag, blast_values, allele_quality, new_sseq, matching_allele_length, tag_dict, list_tag, samples_matrix_dict, matching_genes_dict, prodigal_report, start_prodigal, end_prodigal, complete_predicted_seq, annotation_core_dict, count_dict, logger): - - gene_annot, product_annot = annotation_core_dict[core_name] - - qseqid, sseqid, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart, send, qstart, qend, sseq, qseq = blast_values - - sseq = sseq.replace('-', '') - s_length = len(sseq) - new_sequence_length = len(new_sseq) - - logger.info('Found %s at sample %s for core gene %s ', tag, sample_name, core_name) - - if tag == 'PLOT': - tag_allele = tag + '_' + str(qseqid) - else: - # Adding ASM/ALM/INF allele to the allele_matrix if it is not already include - if not core_name in tag_dict: - tag_dict[core_name] = [] - if not new_sseq in tag_dict[core_name] : - tag_dict[core_name].append(new_sseq) - # Find the index of ASM/ALM/INF to include it in the sample matrix dict - index_tag = tag_dict[core_name].index(new_sseq) - - tag_allele = tag + '_' + core_name + '_' + str(qseqid) + '_' + str(index_tag) - - samples_matrix_dict[sample_name].append(tag_allele) - - # Keeping INF/ASM/ALM/PLOT report info - if not core_name in list_tag : - list_tag[core_name] = {} - if not sample_name in list_tag[core_name] : - list_tag[core_name][sample_name] = {} - - if tag == 'INF': - list_tag[core_name][sample_name][tag_allele] = [gene_annot, product_annot, qseqid, allele_quality, sseqid, bitscore, str(matching_allele_length), str(s_length), str(new_sequence_length), mismatch , r_gapopen, sstart, send, new_sseq, complete_predicted_seq] - - # (recuento tags para plots) - count_dict[sample_name]['total'] += 1 - for count_class in count_dict[sample_name]: - if count_class in allele_quality: - count_dict[sample_name][count_class] += 1 - #if "bad_quality" in allele_quality: - # count_dict[sample_name]['bad_quality'] += 1 - - elif tag == 'PLOT': - list_tag[core_name][sample_name] = [gene_annot, product_annot, qseqid, allele_quality, sseqid, bitscore, sstart, send, sseq, new_sseq] - - # (recuento tags para plots) - count_dict[sample_name]['total'] += 1 - - else : - if tag == 'ASM': - newsseq_vs_blastseq = 'shorter' - elif tag == 'ALM': - newsseq_vs_blastseq = 'longer' - - if len(sseq) < matching_allele_length: - add_info = 'Global effect: DELETION. BLAST sequence length shorter than matching allele sequence length / Net result: ' + tag + '. Final gene sequence length ' + newsseq_vs_blastseq + ' than matching allele sequence length' - - elif len(sseq) == matching_allele_length: - add_info = 'Global effect: BASE SUBSTITUTION. BLAST sequence length equal to matching allele sequence length / Net result: ' + tag + '. Final gene sequence length ' + newsseq_vs_blastseq + ' than matching allele sequence length' - - elif len(sseq) > matching_allele_length: - add_info = 'Global effect: INSERTION. BLAST sequence length longer than matching allele sequence length / Net result: ' + tag + '. Final gene sequence length ' + newsseq_vs_blastseq + ' than matching allele sequence length' - - list_tag[core_name][sample_name][tag_allele] = [gene_annot, product_annot, qseqid, allele_quality, sseqid, bitscore, str(matching_allele_length), str(s_length), str(new_sequence_length), mismatch , r_gapopen, sstart, send, new_sseq, add_info, complete_predicted_seq] - - # (recuento tags para plots) - if tag == 'ASM': - count_dict[sample_name]['total'] += 1 - for mut_type in count_dict[sample_name]: - if mut_type in add_info.lower(): - count_dict[sample_name][mut_type] += 1 - - elif tag == 'ALM': - count_dict[sample_name]['total'] += 1 - for mut_type in count_dict[sample_name]: - if mut_type in add_info.lower(): - count_dict[sample_name][mut_type] += 1 - - if not sseqid in matching_genes_dict[sample_name] : - matching_genes_dict[sample_name][sseqid] = [] - if sstart > send : - #matching_genes_dict[sample_name][sseqid].append([core_name, str(int(sstart)-new_sequence_length -1), sstart,'-', tag_allele]) - matching_genes_dict[sample_name][sseqid].append([core_name, qseqid, str(int(sstart)-new_sequence_length -1), sstart,'-', tag_allele]) - else: - #matching_genes_dict[sample_name][sseqid].append([core_name, sstart, str(int(sstart)+ new_sequence_length),'+', tag_allele]) - matching_genes_dict[sample_name][sseqid].append([core_name, qseqid, sstart, str(int(sstart)+ new_sequence_length),'+', tag_allele]) - - ##### informe prodigal ##### - prodigal_report.append([core_name, sample_name, qseqid, tag_allele, sstart, send, start_prodigal, end_prodigal, sseq, complete_predicted_seq]) - - return True - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # -# Keep best results info after BLAST using results from previous reference allele BLAST as database VS ALL alleles in locus as query in allele calling analysis # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # - -def get_blast_results (sample_name, values, contigs_in_sample_dict, allele_found, logger) : - - qseqid, sseqid, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart, send, qstart, qend, sseq, qseq = values - - ## Get contig ID and BLAST sequence - sseqid_blast = "_".join(sseqid.split('_')[1:]) - sseq_no_gaps = sseq.replace('-', '') - - - ## Get start and end positions in contig searching for BLAST sequence index in contig sequence - - # Get contig sequence - accession_sequence = contigs_in_sample_dict[sample_name][sseqid_blast] - - #for record in sample_contigs: ## parse - #if record.id == sseqid_blast : ## parse - #break ## parse - #accession_sequence = str(record.seq) ## parse - - # Try to get BLAST sequence index in contig. If index -> error because different contig sequence and BLAST sequence - # direction, obtain reverse complement BLAST sequence and try again. - try: - sseq_index_1 = int(accession_sequence.index(sseq_no_gaps)) + 1 - - except: - sseq_no_gaps = str(Seq.Seq(sseq_no_gaps).reverse_complement()) - sseq_index_1 = int(accession_sequence.index(sseq_no_gaps)) + 1 - - sseq_index_2 = int(sseq_index_1) + len(sseq_no_gaps) - 1 - - # Assign found indexes to start and end possitions depending on BLAST sequence and allele sequence direction - if int(sstart) < int(send): - sstart_new = str(min(sseq_index_1, sseq_index_2)) - send_new = str(max(sseq_index_1, sseq_index_2)) - else: - sstart_new = str(max(sseq_index_1, sseq_index_2)) - send_new = str(min(sseq_index_1, sseq_index_2)) - - - ## Keep BLAST results info discarding subsets - allele_is_subset = False - - if len(allele_found) > 0 : - for allele_id in allele_found : - min_index = min(int(allele_found[allele_id][9]), int(allele_found[allele_id][10])) - max_index = max(int(allele_found[allele_id][9]), int(allele_found[allele_id][10])) - if int(sstart_new) in range(min_index, max_index + 1) or int(send_new) in range(min_index, max_index + 1): # if both genome locations overlap - if sseqid_blast == allele_found[allele_id][1]: # if both sequences are in the same contig - logger.info('Found allele %s that starts or ends at the same position as %s ' , qseqid, allele_id) - allele_is_subset = True - break - - if len(allele_found) == 0 or not allele_is_subset : - contig_id_start = str(sseqid_blast + '_'+ sstart_new) - - # Skip the allele found in the 100% identity and 100% alignment - if not contig_id_start in allele_found: - allele_found[contig_id_start] = [qseqid, sseqid_blast, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart_new, send_new, '-', '-', sseq, qseq] - - return True - - -# · * · * · * · * · * · * · * · * · * · # -# Get SNPs and ADN and protein alignment # -# · * · * · * · * · * · * · * · * · * · # - -def keep_snp_alignment_info(sseq, new_sseq, matching_allele_seq, qseqid, query_direction, core_name, sample_name, reward, penalty, gapopen, gapextend, snp_dict, match_alignment_dict, protein_dict, logger): - - ## Check allele sequence direction - if query_direction == 'reverse': - matching_allele_seq = str(Seq.Seq(matching_allele_seq).reverse_complement()) - else: - matching_allele_seq = str(matching_allele_seq) - - ## Get the SNP information - snp_information = get_snp(sseq, matching_allele_seq) - if len(snp_information) > 0 : - if not core_name in snp_dict : - snp_dict[core_name] = {} - if not sample_name in snp_dict[core_name] : - snp_dict[core_name][sample_name] = {} - snp_dict[core_name][sample_name][qseqid]= snp_information - - ## Get new sequence-allele sequence dna alignment - if not core_name in match_alignment_dict : - match_alignment_dict[core_name] = {} - if not sample_name in match_alignment_dict[core_name] : - match_alignment_dict[core_name][sample_name] = get_alignment (new_sseq, matching_allele_seq, reward, penalty, gapopen, gapextend) - - ## Get new sequence-allele sequence protein alignment - if not core_name in protein_dict : - protein_dict[core_name] = {} - if not sample_name in protein_dict[core_name] : - protein_dict[core_name][sample_name] = [] - protein_dict[core_name][sample_name] = get_alignment (new_sseq, matching_allele_seq, reward, penalty, gapopen, gapextend, "protein") - - return True - - -# · * · * · * · * · * · * · * · * · * · * · # -# Create allele tag summary for each sample # -# · * · * · * · * · * · * · * · * · * · * · # - -def create_summary (samples_matrix_dict, logger) : - - summary_dict = {} - summary_result_list = [] - summary_heading_list = ['Exact match', 'INF', 'ASM', 'ALM', 'LNF', 'TPR', 'NIPH', 'NIPHEM', 'PLOT', 'ERROR'] - summary_result_list.append('File\t' + '\t'.join(summary_heading_list)) - for key in sorted (samples_matrix_dict) : - - summary_dict[key] = {'Exact match':0, 'INF':0, 'ASM':0, 'ALM':0, 'LNF':0, 'TPR':0,'NIPH':0, 'NIPHEM':0, 'PLOT':0, 'ERROR':0} - for values in samples_matrix_dict[key] : - if 'INF_' in values : - summary_dict[key]['INF'] += 1 - elif 'ASM_' in values : - summary_dict[key]['ASM'] += 1 - elif 'ALM_' in values : - summary_dict[key]['ALM'] += 1 - elif 'LNF' in values : - summary_dict[key]['LNF'] += 1 - elif 'TPR' in values : - summary_dict[key]['TPR'] += 1 - elif 'NIPH' == values : - summary_dict[key]['NIPH'] += 1 - elif 'NIPHEM' == values : - summary_dict[key]['NIPHEM'] += 1 - elif 'PLOT' in values : - summary_dict[key]['PLOT'] += 1 - elif 'ERROR' in values : - summary_dict[key]['ERROR'] += 1 - else: - try: - number = int(values) - summary_dict[key]['Exact match'] +=1 - except: - if '_' in values : - tmp_value = values - try: - number = int(tmp_value[-1]) - summary_dict[key]['Exact match'] +=1 - except: - logger.debug('The value %s, was found when collecting summary information for the %s', values, summary_dict[key] ) - else: - logger.debug('The value %s, was found when collecting summary information for the %s', values, summary_dict[key] ) - summary_sample_list = [] - for item in summary_heading_list : - summary_sample_list.append(str(summary_dict[key][item])) - summary_result_list.append(key + '\t' +'\t'.join(summary_sample_list)) - return summary_result_list - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # -# Get gene and product annotation for core gene using Prokka # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # - -### (tsv para algunos locus? Utils para analyze schema?) -def get_gene_annotation (annotation_file, annotation_dir, genus, species, usegenus, logger) : - - name_file = os.path.basename(annotation_file).split('.') - annotation_dir = os.path.join (annotation_dir, 'annotation', name_file[0]) - - if usegenus == 'true': - annotation_result = subprocess.run (['prokka', annotation_file, '--outdir', annotation_dir, - '--genus', genus, '--species', species, '--usegenus', - '--gcode', '11', '--prefix', name_file[0], '--quiet']) - - elif usegenus == 'false': - annotation_result = subprocess.run (['prokka', annotation_file, '--outdir', annotation_dir, - '--genus', genus, '--species', species, - '--gcode', '11', '--prefix', name_file[0], '--quiet']) - - annot_tsv = [] - tsv_path = os.path.join (annotation_dir, name_file[0] + '.tsv') - - try: - with open(tsv_path) as tsvfile: - tsvreader = csv.reader(tsvfile, delimiter="\t") - for line in tsvreader: - annot_tsv.append(line) - - if len(annot_tsv) > 1: - - gene_index = annot_tsv[0].index("gene") - product_index = annot_tsv[0].index("product") - - try: - if '_' in annot_tsv[1][2]: - gene_annot = annot_tsv[1][gene_index].split('_')[0] - else: - gene_annot = annot_tsv[1][gene_index] - except: - gene_annot = 'Not found by Prokka' - - try: - product_annot = annot_tsv[1][product_index] - except: - product_annot = 'Not found by Prokka' - else: - gene_annot = 'Not found by Prokka' - product_annot = 'Not found by Prokka' - except: - gene_annot = 'Not found by Prokka' - product_annot = 'Not found by Prokka' - - return gene_annot, product_annot - -""" -def get_gene_annotation (annotation_file, annotation_dir, logger) : - name_file = os.path.basename(annotation_file).split('.') - annotation_dir = os.path.join (annotation_dir, 'annotation', name_file[0]) - - annotation_result = subprocess.run (['prokka', annotation_file, '--outdir', annotation_dir, - '--prefix', name_file[0], '--quiet']) - - annot_tsv = [] - tsv_path = os.path.join (annotation_dir, name_file[0] + '.tsv') - - try: - with open(tsv_path) as tsvfile: - tsvreader = csv.reader(tsvfile, delimiter="\t") - for line in tsvreader: - annot_tsv.append(line) - - if len(annot_tsv) > 1: - try: - if '_' in annot_tsv[1][2]: - gene_annot = annot_tsv[1][2].split('_')[0] - else: - gene_annot = annot_tsv[1][2] - except: - gene_annot = 'Not found by Prokka' - - try: - product_annot = annot_tsv[1][4] - except: - product_annot = 'Not found by Prokka' - else: - gene_annot = 'Not found by Prokka' - product_annot = 'Not found by Prokka' - except: - gene_annot = 'Not found by Prokka' - product_annot = 'Not found by Prokka' - - return gene_annot, product_annot -""" - -""" -def get_gene_annotation (annotation_file, annotation_dir, logger) : - name_file = os.path.basename(annotation_file).split('.') - annotation_dir = os.path.join (annotation_dir, 'annotation', name_file[0]) - - annotation_result = subprocess.run (['prokka', annotation_file, '--outdir', annotation_dir, - '--prefix', name_file[0], '--quiet']) - - annot_tsv = [] - tsv_path = os.path.join (annotation_dir, name_file[0] + '.tsv') - with open(tsv_path) as tsvfile: - tsvreader = csv.reader(tsvfile, delimiter="\t") - for line in tsvreader: - annot_tsv.append(line) - - if len(annot_tsv) > 1: - try: - if '_' in annot_tsv[1][2]: - gene_annot = annot_tsv[1][2].split('_')[0] - else: - gene_annot = annot_tsv[1][2] - except: - gene_annot = 'Not found by Prokka' - - try: - product_annot = annot_tsv[1][4] - except: - product_annot = 'Not found by Prokka' - else: - gene_annot = 'Not found by Prokka' - product_annot = 'Not found by Prokka' - - return gene_annot, product_annot -""" - - -def analize_annotation_files (in_file, logger) : ## N - examiner = GFF.GFFExaminer() - file_fh = open(in_file) - datos = examiner.available_limits(in_file) - file_fh.close() - return True - - -def get_inferred_allele_number(core_dict, logger): ## N - #This function will look for the highest locus number and it will return a safe high value - # that will be added to the schema database - logger.debug('running get_inferred_allele_number function') - int_keys = [] - for key in core_dict.keys(): - int_keys.append(key) - max_value = max(int_keys) - digit_length = len(str(max_value)) - return True #str 1 ( #'1'+ '0'*digit_length + 2) - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # -# Get ST profile for each samples based on allele calling results # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # - -def get_ST_profile(outputdir, profile_csv_path, exact_dict, inf_dict, core_gene_list_files, sample_list_files, logger): - ## logger - - csv_read = [] - ST_profiles_dict = {} - samples_profiles_dict = {} - analysis_profiles_dict = {} - inf_ST = {} - count_st = {} - - with open(profile_csv_path) as csvfile: - csvreader = csv.reader(csvfile, delimiter="\t") - for line in csvreader: - csv_read.append(line) - - profile_header = csv_read[0][1:len(core_gene_list_files) + 1] - - for ST_index in range(1, len(csv_read)): - ST_profiles_dict[csv_read[ST_index][0]] = {} - for core_index in range(len(profile_header)): - ST_profiles_dict[csv_read[ST_index][0]][profile_header[core_index]] = csv_read[ST_index][core_index + 1] - - for sample_file in sample_list_files: - sample_name = '.'.join(os.path.basename(sample_file).split('.')[:-1]) - - st_counter = 0 - for ST in ST_profiles_dict: - core_counter = 0 - - for core_name in profile_header: - allele_in_ST = ST_profiles_dict[ST][core_name] - exact_gene = True - - if sample_name in exact_dict: - if core_name in exact_dict[sample_name]: - allele_in_sample = exact_dict[sample_name][core_name][2] - - if not '_' in allele_in_ST: - if '_' in allele_in_sample: - allele_in_sample = allele_in_sample.split('_')[1] - - if st_counter == 0: - if sample_name not in analysis_profiles_dict: - analysis_profiles_dict[sample_name] = {} - analysis_profiles_dict[sample_name][core_name] = allele_in_sample - - if allele_in_sample == allele_in_ST: - core_counter += 1 - - else: - exact_gene = False - - else: - exact_gene = False - - if exact_gene == False: - if sample_name in inf_dict: - if core_name in inf_dict[sample_name]: - if st_counter == 0: - allele_in_sample = inf_dict[sample_name][core_name][2] - if sample_name not in analysis_profiles_dict: - analysis_profiles_dict[sample_name] = {} - analysis_profiles_dict[sample_name][core_name] = allele_in_sample - - else: - if st_counter == 0: - if sample_name not in analysis_profiles_dict: - analysis_profiles_dict[sample_name] = {} - - if allele_in_ST == 'N' and "allele_in_sample" not in locals(): - core_counter += 1 - - st_counter += 1 - if core_counter == len(profile_header): - samples_profiles_dict[sample_name] = ST - - if "_INF" in ST: - if "New" not in count_st: - count_st["New"] = {} - if ST not in count_st["New"]: - count_st["New"][ST] = 0 - count_st["New"][ST] += 1 - - else: - if "Known" not in count_st: - count_st["Known"] = {} - if ST not in count_st["Known"]: - count_st["Known"][ST] = 0 - count_st["Known"][ST] += 1 - - break - - if sample_name not in samples_profiles_dict: - if sample_name in analysis_profiles_dict: - if len(analysis_profiles_dict[sample_name]) == len(profile_header): - new_st_id = str(len(ST_profiles_dict) + 1) - ST_profiles_dict[new_st_id + "_INF"] = analysis_profile_dict[sample_name] - inf_ST[new_st_id] = analysis_profile_dict[sample_name] - - samples_profiles_dict[sample_name]=new_st_id + "_INF" - - if "New" not in count_st: - count_st["New"] = {} - if new_st_id not in count_st["New"]: - count_st["New"][new_st_id] = 0 - count_st["New"][new_st_id] += 1 - - else: - samples_profiles_dict[sample_name] = '-' - - if "Unknown" not in count_st: - count_st["Unknown"] = 0 - count_st["Unknown"] += 1 - else: - samples_profiles_dict[sample_name] = '-' - - if "Unknown" not in count_st: - count_st["Unknown"] = 0 - count_st["Unknown"] += 1 - - ## Create ST profile results report - save_st_profile_results (outputdir, samples_profiles_dict, logger) - - ## Obtain interactive piechart - logger.info('Creating interactive ST results piechart') - create_sunburst_plot_st (outputdir, count_st, logger) - - return True, inf_ST - - -# · * · * · * · * · * · * # -# Create ST results report # -# · * · * · * · * · * · * # - -def save_st_profile_results (outputdir, samples_profiles_dict, logger): - - header_stprofile = ['Sample Name', 'ST'] - - if samples_profiles_dict != '': - ## Saving ST profile to file - logger.info('Saving ST profile information to file..') - stprofile_file = os.path.join(outputdir, 'stprofile.tsv') - with open (stprofile_file , 'w') as st_fh : - st_fh.write('\t'.join(header_stprofile)+ '\n') - for sample in sorted(samples_profiles_dict): - st_fh.write(sample + '\t' + samples_profiles_dict[sample] + '\n') - - return True - - -def create_sunburst_plot_st (outputdir, count_st, logger): - ### logger? - counts = [] - st_ids = ["ST"] - st_labels = ["ST"] - st_parents = [""] - - total_samples = 0 - - for st_type in count_st: - - if type(count_st[st_type]) == dict: - total_st_type_count = sum(count_st[st_type].values()) - else: - total_st_type_count = count_st[st_type] - - counts.append(total_st_type_count) - st_ids.append(st_type) - st_labels.append(st_type) - st_parents.append("ST") - - total_samples += total_st_type_count - - if type(count_st[st_type]) == dict: - for st in count_st[st_type]: - counts.append(count_st[st_type][st]) - st_ids.append(st + " - " + st_type) - st_labels.append(st) - st_parents.append(st_type) - - counts.insert(0, total_samples) - - fig = go.Figure(go.Sunburst( - ids = st_ids, - labels = st_labels, - parents = st_parents, - values = counts, - branchvalues = "total", - )) - - fig.update_layout(margin = dict(t=0, l=0, r=0, b=0)) - - plotsdir = os.path.join(outputdir, 'plots', 'samples_st.html') - - fig.write_html(plotsdir) - - return True - - -# · * · * · * · * · * · * · * · * · * · * · # -# Update ST profile file adding new ST found # -# · * · * · * · * · * · * · * · * · * · * · # - -def update_st_profile (updateprofile, profile_csv_path, outputdir, inf_ST, core_gene_list_files, logger): - - ## Create a copy of ST profile file if updateprofile = 'new' - if updateprofile == 'new': - no_updated_profile_csv_path = profile_csv_path - profile_csv_path_name = os.path.basename(no_updated_profile_csv_path).split('.')[0] - profile_csv_path = os.path.join(outputdir, profile_csv_path_name + '_updated' + '.csv') - shutil.copyfile(no_updated_profile_csv_path, profile_csv_path) - logger.info('Copying ST profile file to update profiles') - - ## Update ST profile file - logger.info('Updating ST profile file adding new INF ST') - - with open (profile_csv_path, 'r') as csvfile: - csvreader = csv.reader(csvfile, delimiter="\t") - for line in csvreader: - profile_header = line[0][1:len(core_gene_list_files) + 1] - break - - with open (profile_csv_path, 'a') as profile_fh: - for ST in inf_ST: - locus_ST_list = [] - locus_ST_list.append(ST) - for locus in profile_header: - locus_ST_list.append(inf_ST[ST][locus]) - profile_fh.write ('\t'.join(locus_ST_list)+ '\n') - - return True - - -# · * · * · * · * · * · * · * · * · * · # -# Create allele calling results reports # -# · * · * · * · * · * · * · * · * · * · # - -def save_allele_call_results (outputdir, full_gene_list, samples_matrix_dict, exact_dict, paralog_dict, inf_dict, plot_dict, matching_genes_dict, list_asm, list_alm, lnf_tpr_dict, snp_dict, match_alignment_dict, protein_dict, prodigal_report, shorter_seq_coverage, longer_seq_coverage, equal_seq_coverage, shorter_blast_seq_coverage, longer_blast_seq_coverage, equal_blast_seq_coverage, logger): - header_matching_alleles_contig = ['Sample Name', 'Contig', 'Core Gene', 'Allele', 'Contig Start', 'Contig Stop', 'Direction', 'Codification'] - header_exact = ['Core Gene', 'Sample Name', 'Gene Annotation', 'Product Annotation', 'Allele', 'Allele Quality', 'Contig', 'Query length', 'Contig start', 'Contig end', 'Sequence', 'Predicted Sequence'] - header_paralogs = ['Core Gene','Sample Name', 'Gene Annotation', 'Product Annotation', 'Paralog Tag', 'ID %', 'Allele', 'Allele Quality', 'Contig', 'Bitscore', 'Contig start', 'Contig end', 'Sequence', 'Predicted Sequence'] - header_inferred = ['Core Gene','Sample Name', 'INF tag', 'Gene Annotation', 'Product Annotation', 'Allele', 'Allele Quality', 'Contig', 'Bitscore', 'Query length', 'Contig length', 'New sequence length' , 'Mismatch' , 'gaps', 'Contig start', 'Contig end', 'New sequence', 'Predicted Sequence'] - header_asm = ['Core Gene', 'Sample Name', 'ASM tag', 'Gene Annotation', 'Product Annotation', 'Allele', 'Allele Quality', 'Contig', 'Bitscore', 'Query length', 'Contig length', 'New sequence length' , 'Mismatch' , 'gaps', 'Contig start', 'Contig end', 'New sequence', 'Additional info', 'Predicted Sequence'] - header_alm = ['Core Gene', 'Sample Name', 'ALM tag', 'Gene Annotation', 'Product Annotation', 'Allele', 'Allele Quality', 'Contig', 'Bitscore', 'Query length', 'Contig length', 'New sequence length' , 'Mismatch' , 'gaps', 'Contig start', 'Contig end', 'New sequence', 'Additional info', 'Predicted Sequence'] - header_plot = ['Core Gene', 'Sample Name', 'Gene Annotation', 'Product Annotation', 'Allele', 'Allele Quality', 'Contig','Bitscore', 'Contig start', 'Contig end', 'Sequence', 'Predicted Sequence'] - header_lnf_tpr = ['Core Gene', 'Sample Name', 'Gene Annotation', 'Product Annotation', 'Tag', 'Allele', 'Allele Quality', 'ID %', 'Blast sequence coverage %', 'New sequence coverage %', 'Query length', 'Contig length', 'New sequence length', 'Additional info'] - header_snp = ['Core Gene', 'Sample Name', 'Allele', 'Position', 'Mutation Schema/Sample', 'Codon Schema/Sample','Amino acid in Schema/Sample', 'Mutation type','Annotation Schema/Sample'] - header_protein = ['Core Gene','Sample Name', 'Protein in ' , 'Protein sequence'] - header_match_alignment = ['Core Gene','Sample Name','Alignment', 'Sequence'] - header_stprofile = ['Sample Name', 'ST'] - - - # Añadido header_prodigal_report para report prodigal -# header_prodigal_report = ['Core gene', 'Sample Name', 'Allele', 'Sequence type', 'BLAST start', 'BLAST end', 'Prodigal start', 'Prodigal end', 'BLAST sequence', 'Prodigal sequence'] - # Añadido header_newsseq_coverage_report para determinar coverage threshold a imponer -# header_newsseq_coverage_report = ['Core gene', 'Sample Name', 'Query length', 'New sequence length', 'Locus mean', 'Coverage (new sequence/allele)', 'Coverage (new sequence/locus mean)'] - # Añadido header_blast_coverage_report para determinar coverage threshold a imponer -# header_blast_coverage_report = ['Core gene', 'Sample Name', 'Query length', 'Blast sequence length', 'Locus mean', 'Coverage (blast sequence/allele)', 'Coverage (blast sequence/locus mean)'] - - ## Saving the result information to file - print ('Saving results to files \n') - result_file = os.path.join ( outputdir, 'result.tsv') - logger.info('Saving result information to file..') - with open (result_file, 'w') as out_fh: - out_fh.write ('Sample Name\t'+'\t'.join( full_gene_list) + '\n') - for key in sorted (samples_matrix_dict): - out_fh.write (key + '\t' + '\t'.join(samples_matrix_dict[key])+ '\n') - - ## Saving exact matches to file - logger.info('Saving exact matches information to file..') - exact_file = os.path.join(outputdir, 'exact.tsv') - with open (exact_file , 'w') as exact_fh : - exact_fh.write('\t'.join(header_exact)+ '\n') - for sample in sorted(exact_dict): - for core in sorted(exact_dict[sample]): - exact_fh.write(core + '\t' + sample + '\t' + '\t'.join(exact_dict[sample][core]) + '\n') - - ## Saving paralog alleles to file - logger.info('Saving paralog information to file..') - paralog_file = os.path.join(outputdir, 'paralog.tsv') - with open (paralog_file , 'w') as paralog_fh : - paralog_fh.write('\t'.join(header_paralogs) + '\n') - for sample in sorted (paralog_dict) : - for core in sorted (paralog_dict[sample]): - for paralog in paralog_dict[sample][core] : - paralog_fh.write(core + '\t' + sample + '\t' + '\t'.join (paralog) + '\n') - - ## Saving inferred alleles to file - logger.info('Saving inferred alleles information to file..') - inferred_file = os.path.join(outputdir, 'inferred_alleles.tsv') - with open (inferred_file , 'w') as infer_fh : - infer_fh.write('\t'.join(header_inferred) + '\n') - for core in sorted (inf_dict) : - for sample in sorted (inf_dict[core]) : - for inferred in inf_dict[core][sample]: - # seq_in_inferred_allele = '\t'.join (inf_dict[sample]) - infer_fh.write(core + '\t' + sample + '\t' + inferred + '\t' + '\t'.join(inf_dict[core][sample][inferred]) + '\n') - - ## Saving PLOTs to file - logger.info('Saving PLOT information to file..') - plot_file = os.path.join(outputdir, 'plot.tsv') - with open (plot_file , 'w') as plot_fh : - plot_fh.write('\t'.join(header_plot) + '\n') - for core in sorted (plot_dict) : - for sample in sorted (plot_dict[core]): - plot_fh.write(core + '\t' + sample + '\t' + '\t'.join(plot_dict[core][sample]) + '\n') - - ## Saving matching contigs to file - logger.info('Saving matching information to file..') - matching_file = os.path.join(outputdir, 'matching_contigs.tsv') - with open (matching_file , 'w') as matching_fh : - matching_fh.write('\t'.join(header_matching_alleles_contig) + '\n') - for samples in sorted (matching_genes_dict) : - for contigs in matching_genes_dict[samples] : - for contig in matching_genes_dict[samples] [contigs]: - matching_alleles = '\t'.join (contig) - matching_fh.write(samples + '\t' + contigs +'\t' + matching_alleles + '\n') - - ## Saving ASMs to file - logger.info('Saving asm information to file..') - asm_file = os.path.join(outputdir, 'asm.tsv') - with open (asm_file , 'w') as asm_fh : - asm_fh.write('\t'.join(header_asm)+ '\n') - for core in list_asm : - for sample in list_asm[core] : - for asm in list_asm[core][sample]: - asm_fh.write(core + '\t' + sample + '\t' + asm + '\t' + '\t'.join(list_asm[core][sample][asm]) + '\n') - - ## Saving ALMs to file - logger.info('Saving alm information to file..') - alm_file = os.path.join(outputdir, 'alm.tsv') - with open (alm_file , 'w') as alm_fh : - alm_fh.write('\t'.join(header_alm)+ '\n') - for core in list_alm : - for sample in list_alm[core] : - for alm in list_alm[core][sample]: - alm_fh.write(core + '\t' + sample + '\t' + alm + '\t' + '\t'.join(list_alm[core][sample][alm]) + '\n') - - ## Saving LNFs to file - logger.info('Saving lnf information to file..') - lnf_file = os.path.join(outputdir, 'lnf_tpr.tsv') - with open (lnf_file , 'w') as lnf_fh : - lnf_fh.write('\t'.join(header_lnf_tpr)+ '\n') - for core in lnf_tpr_dict : - for sample in lnf_tpr_dict[core] : - for lnf in lnf_tpr_dict[core][sample] : - lnf_fh.write(core + '\t' + sample + '\t' + '\t'.join(lnf) + '\n') - - ## Saving SNPs information to file - logger.info('Saving SNPs information to file..') - snp_file = os.path.join(outputdir, 'snp.tsv') - with open (snp_file , 'w') as snp_fh : - snp_fh.write('\t'.join(header_snp) + '\n') - for core in sorted (snp_dict) : - for sample in sorted (snp_dict[core]): - for allele_id_snp in snp_dict[core][sample] : - for snp in snp_dict[core][sample][allele_id_snp] : - snp_fh.write(core + '\t' + sample + '\t' + allele_id_snp + '\t' + '\t'.join (snp) + '\n') - - ## Saving DNA sequences alignments to file - logger.info('Saving matching alignment information to files..') - alignment_dir = os.path.join(outputdir,'alignments') - if os.path.exists(alignment_dir) : - shutil.rmtree(alignment_dir) - logger.info('deleting the alignment files from previous execution') - os.makedirs(alignment_dir) - for core in sorted(match_alignment_dict) : - for sample in sorted (match_alignment_dict[core]) : - match_alignment_file = os.path.join(alignment_dir, str('match_alignment_' + core + '_' + sample + '.txt')) - with open(match_alignment_file, 'w') as match_alignment_fh : - match_alignment_fh.write( '\t'.join(header_match_alignment) + '\n') - for match_align in match_alignment_dict[core][sample] : - match_alignment_fh.write(core + '\t'+ sample +'\t'+ '\t'.join(match_align) + '\n') - - ## Saving protein sequences alignments to file - logger.info('Saving protein information to files..') - protein_dir = os.path.join(outputdir,'proteins') - if os.path.exists(protein_dir) : - shutil.rmtree(protein_dir) - logger.info('deleting the proteins files from previous execution') - os.makedirs(protein_dir) - for core in sorted(protein_dict) : - for sample in sorted (protein_dict[core]) : - protein_file = os.path.join(protein_dir, str('protein_' + core + '_' + sample + '.txt')) - with open(protein_file, 'w') as protein_fh : - protein_fh.write( '\t'.join(header_protein) + '\n') - for protein in protein_dict[core][sample] : - protein_fh.write(core + '\t'+ sample +'\t'+ '\t'.join(protein) + '\n') - - ## Saving summary information to file - logger.info('Saving summary information to file..') - summary_result = create_summary (samples_matrix_dict, logger) - summary_file = os.path.join( outputdir, 'summary_result.tsv') - with open (summary_file , 'w') as summ_fh: - for line in summary_result : - summ_fh.write(line + '\n') - - ## Modify the result file to remove the PLOT_ string for creating the file to use in the tree diagram -# logger.info('Saving result information for tree diagram') -# tree_diagram_file = os.path.join ( outputdir, 'result_for_tree_diagram.tsv') -# with open (result_file, 'r') as result_fh: -# with open(tree_diagram_file, 'w') as td_fh: -# for line in result_fh: -# tree_line = line.replace('PLOT_','') -# td_fh.write(tree_line) - - ########################################################################################### - # Guardando report de prodigal. Temporal -# prodigal_report_file = os.path.join (outputdir, 'prodigal_report.tsv') - # saving prodigal predictions to file -# with open (prodigal_report_file, 'w') as out_fh: -# out_fh.write ('\t'.join(header_prodigal_report)+ '\n') -# for prodigal_result in prodigal_report: -# out_fh.write ('\t'.join(prodigal_result)+ '\n') - - # Guardando coverage de new_sseq para estimar el threshold a establecer. Temporal -# newsseq_coverage_file = os.path.join (outputdir, 'newsseq_coverage_report.tsv') - # saving the coverage information to file -# with open (newsseq_coverage_file, 'w') as out_fh: -# out_fh.write ('\t' + '\t'.join(header_newsseq_coverage_report)+ '\n') -# for coverage in shorter_seq_coverage: -# out_fh.write ('Shorter new sequence' + '\t' + '\t'.join(coverage)+ '\n') -# for coverage in longer_seq_coverage: -# out_fh.write ('Longer new sequence' + '\t' + '\t'.join(coverage)+ '\n') -# for coverage in equal_seq_coverage: -# out_fh.write ('Same length new sequence' + '\t' + '\t'.join(coverage)+ '\n') - - # Guardando coverage de la sseq obtenida tras blast para estimar el threshold a establecer. Temporal -# blast_coverage_file = os.path.join (outputdir, 'blast_coverage_report.tsv') - # saving the result information to file -# with open (blast_coverage_file, 'w') as out_fh: -# out_fh.write ('\t' + '\t'.join(header_blast_coverage_report)+ '\n') -# for coverage in shorter_blast_seq_coverage: -# out_fh.write ('Shorter blast sequence' + '\t' + '\t'.join(coverage)+ '\n') -# for coverage in longer_blast_seq_coverage: -# out_fh.write ('Longer blast sequence' + '\t' + '\t'.join(coverage)+ '\n') -# for coverage in equal_blast_seq_coverage: -# out_fh.write ('Same length blast sequence' + '\t' + '\t'.join(coverage)+ '\n') - ########################################################################################### - - return True - - - -def save_allele_calling_plots (outputdir, sample_list_files, count_exact, count_inf, count_asm, count_alm, count_lnf, count_tpr, count_plot, count_niph, count_niphem, count_error, logger): - - ## Create result plots directory - plots_dir = os.path.join(outputdir,'plots') - try: - os.makedirs(plots_dir) - except: - logger.info('Deleting the results plots directory for a previous execution without cleaning up') - shutil.rmtree(os.path.join(outputdir, 'plots')) - try: - os.makedirs(plots_dir) - logger.info ('Results plots folder %s has been created again', plots_dir) - except: - logger.info('Unable to create again the results plots directory %s', plots_dir) - print('Cannot create Results plots directory on ', plots_dir) - exit(0) - - for sample_file in sample_list_files: - sample_name = '.'.join(os.path.basename(sample_file).split('.')[:-1]) - - ## Obtain interactive piechart - logger.info('Creating interactive results piecharts') - create_sunburst_allele_call (outputdir, sample_name, count_exact[sample_name], count_inf[sample_name], count_asm[sample_name], count_alm[sample_name], count_lnf[sample_name], count_tpr[sample_name], count_plot[sample_name], count_niph[sample_name], count_niphem[sample_name], count_error[sample_name]) - - return True - - - -def create_sunburst_allele_call (outputdir, sample_name, count_exact, count_inf, count_asm, count_alm, count_lnf, count_tpr, count_plot, count_niph, count_niphem, count_error): - ### logger - - total_locus = count_exact['total'] + count_inf['total'] + count_asm['total'] + count_alm['total'] + count_lnf['total'] + count_tpr['total'] + count_plot['total'] \ - + count_niph['total'] + count_niphem['total'] + count_error['total'] - - tag_counts = [total_locus, count_exact['total'], count_exact['good_quality'], count_exact['bad_quality'], count_exact['no_start'], count_exact['no_start_stop'], - count_exact['no_stop'], count_exact['multiple_stop'], count_inf['total'], count_inf['good_quality'], count_inf['bad_quality'], count_inf['no_start'], - count_inf['no_start_stop'], count_inf['no_stop'], count_inf['multiple_stop'], count_asm['total'], count_asm['insertion'], count_asm['deletion'], - count_asm['substitution'], count_alm['total'], count_alm['insertion'], count_alm['deletion'], count_alm['substitution'], count_plot['total'], - count_niph['total'], count_niph['good_quality'], count_niph['bad_quality'], count_niph['no_start'], count_niph['no_start_stop'], count_niph['no_stop'], - count_niph['multiple_stop'], count_niphem['total'], count_niphem['good_quality'], count_niphem['bad_quality'], count_niphem['no_start'], - count_niphem['no_start_stop'], count_niphem['no_stop'], count_niphem['multiple_stop'], count_lnf['total'], count_lnf['not_found'], count_lnf['low_id'], - count_lnf['low_coverage'], count_tpr['total'], count_tpr['good_quality'], count_tpr['bad_quality'], count_tpr['no_start'], count_tpr['no_start_stop'], - count_tpr['no_stop'], count_tpr['multiple_stop'], count_error['total'], count_error['good_quality'], count_error['bad_quality'], count_error['no_start'], - count_error['no_start_stop'], count_error['no_stop'], count_error['multiple_stop']] - - fig=go.Figure(go.Sunburst( - ids=[ - sample_name, "Exact Match", "Good Quality - Exact Match", "Bad Quality - Exact Match", - "No start - Bad Quality - Exact Match", "No start-stop - Bad Quality - Exact Match", - "No stop - Bad Quality - Exact Match", "Multiple stop - Bad Quality - Exact Match", - "INF", "Good Quality - INF", "Bad Quality - INF", "No start - Bad Quality - INF", - "No start-stop - Bad Quality - INF", "No stop - Bad Quality - INF", "Multiple stop - Bad Quality - INF", - "ASM", "Insertion - ASM", "Deletion - ASM", "Substitution - ASM", "ALM", "Insertion - ALM", - "Deletion - ALM", "Substitution - ALM", "PLOT", "NIPH", "Good Quality - NIPH", - "Bad Quality - NIPH", "No start - Bad Quality - NIPH", "No start-stop - Bad Quality - NIPH", - "No stop - Bad Quality - NIPH", "Multiple stop - Bad Quality - NIPH", "NIPHEM", - "Good Quality - NIPHEM", "Bad Quality - NIPHEM", "No start - Bad Quality - NIPHEM", - "No start-stop - Bad Quality - NIPHEM", "No stop - Bad Quality - NIPHEM", - "Multiple stop - Bad Quality - NIPHEM", "LNF", "Not found", - "Low ID", "Low coverage", "TPR", "Good Quality - TPR", "Bad Quality - TPR", - "No start - Bad Quality - TPR", "No start-stop - Bad Quality - TPR", "No stop - Bad Quality - TPR", - "Multiple stop - Bad Quality - TPR", "Error", "Good Quality - Error", "Bad Quality - Error", - "No start - Bad Quality - Error", "No start-stop - Bad Quality - Error", - "No stop - Bad Quality - Error", "Multiple stop - Bad Quality - Error" - ], - labels= [ - sample_name, "Exact
Match", "Good
Quality", "Bad
Quality", - "No
start", "No
start-stop", "No
stop", "Multiple
stop", - "INF", "Good
Quality", "Bad
Quality", "No
start", - "No
start-stop", "No
stop", "Multiple
stop", "ASM", "Insertion", - "Deletion", "Substitution", "ALM", "Insertion", "Deletion", - "Substitution", "PLOT", "NIPH", "Good
Quality", "Bad
Quality", - "No
start", "No
start-stop", "No
stop", "Multiple
stop", - "NIPHEM", "Good
Quality", "Bad
Quality", "No
start", - "No
start-stop", "No
stop", "Multiple
stop", "LNF", "Not
found", - "Low
ID", "Low
coverage", "TPR", "Good
Quality", "Bad
Quality", - "No
start", "No
start-stop", "No
stop", "Multiple
stop", - "Error", "Good
Quality", "Bad
Quality","No
start", - "No
start-stop", "No
stop", "Multiple
stop" - ], - parents=[ - "", sample_name, "Exact Match", "Exact Match", "Bad Quality - Exact Match", - "Bad Quality - Exact Match", "Bad Quality - Exact Match", "Bad Quality - Exact Match", - sample_name, "INF", "INF", "Bad Quality - INF", "Bad Quality - INF", "Bad Quality - INF", - "Bad Quality - INF", sample_name, "ASM", "ASM", "ASM", sample_name, "ALM", "ALM", "ALM", sample_name, - sample_name, "NIPH", "NIPH", "Bad Quality - NIPH", "Bad Quality - NIPH", "Bad Quality - NIPH", - "Bad Quality - NIPH", sample_name, "NIPHEM", "NIPHEM", "Bad Quality - NIPHEM", - "Bad Quality - NIPHEM", "Bad Quality - NIPHEM", "Bad Quality - NIPHEM", sample_name, "LNF", - "LNF", "LNF", sample_name, "TPR", "TPR", "Bad Quality - TPR", "Bad Quality - TPR", - "Bad Quality - TPR", "Bad Quality - TPR", sample_name, "Error", "Error", "Bad Quality - Error", - "Bad Quality - Error", "Bad Quality - Error", "Bad Quality - Error" - ], - values=tag_counts, - branchvalues="total", - )) - - fig.update_layout(margin = dict(t=0, l=0, r=0, b=0)) - - plotsdir = os.path.join(outputdir, 'plots', sample_name + '.html') - - fig.write_html(plotsdir) - - return True - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # -# Update core genes schema adding new inferred alleles found for each locus in allele calling analysis # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # - -def update_schema (updateschema, schemadir, outputdir, core_gene_list_files, inferred_alleles_dict, alleles_in_locus_dict, logger): - - if len(inferred_alleles_dict) > 0: - ## Create a copy of core genes schema if updateschema = 'new' / 'New' - if updateschema == 'new': - no_updated_schemadir = schemadir - ##schemadir_name = os.path.dirname(no_updated_schemadir) ---> se puede usar si guardo finalmente el nuevo esquema en el mismo directorio que el antiguo esquema, pero para ello debo verificar si termina o no el path en / para eliminarlo o no del path antes de hacer el dirname - schemadir_name = no_updated_schemadir.split("/") - if no_updated_schemadir.endswith("/"): - schemadir_name = schemadir_name[-2] - else: - schemadir_name = schemadir_name[-1] - - schemadir = os.path.join(outputdir, schemadir_name + '_updated') - - try: - shutil.copytree(no_updated_schemadir, schemadir) - logger.info ('Schema copy %s has been created to update schema', schemadir) - except: - logger.info('Deleting preexisting directory') - shutil.rmtree(schemadir) - try: - shutil.copytree(no_updated_schemadir, schemadir) - logger.info ('Schema copy %s has been created to update schema', schemadir) - except: - logger.info('Unable to create schema copy %s', schemadir) - print('Cannot create schema copy on ', schemadir) - exit(0) - - ## Get INF alleles for each core gene and update each locus fasta file - for core_file in core_gene_list_files: - core_name = os.path.basename(core_file).split('.')[0] - if core_name in inferred_alleles_dict: - logger.info('Updating core gene file %s adding new INF alleles', core_file) - - inf_list = inferred_alleles_dict[core_name] - - try: - alleles_ids = [int(allele) for allele in alleles_in_locus_dict[core_name]] - allele_number = max(alleles_ids) - - locus_schema_file = os.path.join(schemadir, core_name + '.fasta') - with open (locus_schema_file, 'a') as core_fh: - for inf in inf_list: - allele_number += 1 - core_fh.write('\n' + '>' + str(allele_number) + ' # ' + 'INF by Taranis' + '\n' + inf + '\n') - except: - alleles_ids = [int(allele.split('_')[-1]) for allele in alleles_in_locus_dict[core_name]] - allele_number = max(alleles_ids) - - locus_schema_file = os.path.join(schemadir, core_name + '.fasta') - with open (locus_schema_file, 'a') as core_fh: - for inf in inf_list: - allele_number += 1 - complete_inf_id = core_name + '_' + str(allele_number) - core_fh.write('\n' + '>' + complete_inf_id + ' # ' + 'INF by Taranis' + '\n' + inf + '\n') - - return True - -""" -def update_schema (updateschema, schemadir, storedir, core_gene_list_files, inferred_alleles_dict, alleles_in_locus_dict, logger): - - ## Create a copy of core genes schema if updateschema = 'new' / 'New' - if updateschema == 'new' or updateschema == 'New': - no_updated_schemadir = schemadir - schemadir_name = os.path.basename(no_updated_schemadir) - schemadir = os.path.join(storedir, schemadir_name + '_updated') - shutil.copytree(no_updated_schemadir, schemadir) - logger.info('Copying core genes fasta files to update schema') - - ## Get INF alleles for each core gene and update each locus fasta file - for core_file in core_gene_list_files: - core_name = os.path.basename(core_file).split('.')[0] - if core_name in inferred_alleles_dict: - logger.info('Updating core gene file %s adding new INF alleles', core_file) - - inf_list = inferred_alleles_dict[core_name] - - alleles_ids = [int(allele) for allele in alleles_in_locus_dict[core_name]] - allele_number = max(alleles_ids) - - locus_schema_file = os.path.join(schemadir, core_name + '.fasta') - with open (locus_schema_file, 'a') as core_fh: - for inf in inf_list: - allele_number += 1 - core_fh.write('\n' + '>' + str(allele_number) + ' # ' + 'INF by Taranis' + '\n' + inf + '\n') - - return True -""" - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # -# Allele calling analysis to find each core gene in schema and its variants in samples # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # - -def allele_call_nucleotides (core_gene_list_files, sample_list_files, alleles_in_locus_dict, contigs_in_sample_dict, query_directory, reference_alleles_directory, blast_db_directory, prodigal_directory, blast_results_seq_directory, blast_results_db_directory, inputdir, outputdir, cpus, percentlength, coverage, evalue, perc_identity_ref, perc_identity_loc, reward, penalty, gapopen, gapextend, max_target_seqs, max_hsps, num_threads, flankingnts, schema_variability, schema_statistics, schema_quality, annotation_core_dict, profile_csv_path, logger ): - - prodigal_report = [] # TEMPORAL. prodigal_report para checkear las secuencias obtenidas con prodigal vs blast y las posiciones sstart y send - # listas añadidas para calcular coverage medio de new_sseq con respecto a alelo para establecer coverage mínimo por debajo del cual considerar LNF - shorter_seq_coverage = [] # TEMPORAL - longer_seq_coverage = [] # TEMPORAL - equal_seq_coverage = [] # TEMPORAL - # listas añadidas para calcular coverage medio de sseq con respecto a alelo tras blast para establecer coverage mínimo por debajo del cual considerar LNF - shorter_blast_seq_coverage = [] # TEMPORAL - longer_blast_seq_coverage = [] # TEMPORAL - equal_blast_seq_coverage = [] # TEMPORAL - - full_gene_list = [] - samples_matrix_dict = {} # to keep allele number - matching_genes_dict = {} # to keep start and stop positions - exact_dict = {} # c/m: to keep exact matches found for each sample - inferred_alleles_dict = {} # to keep track of the new inferred alleles - inf_dict = {} # to keep inferred alleles found for each sample - paralog_dict = {} # to keep paralogs found for each sample - asm_dict = {} # c/m: to keep track of asm - alm_dict = {} # c/m: to keep track of alm - list_asm = {} # c/m: to keep asm found for each sample - list_alm = {} # c/m: to keep alm found for each sample - lnf_tpr_dict = {} # c/m: to keep locus not found for each sample - plot_dict = {} # c/m: to keep plots for each sample - snp_dict = {} # c/m: to keep snp information for each sample - protein_dict = {} - match_alignment_dict = {} - - # (recuento tags para plots) - count_exact = {} - count_inf = {} - count_asm = {} - count_alm = {} - count_lnf = {} - count_tpr = {} - count_plot = {} - count_niph = {} - count_niphem = {} - count_error = {} - - blast_parameters = '"6 , qseqid , sseqid , pident , qlen , length , mismatch , gapopen , evalue , bitscore , sstart , send , qstart , qend , sseq , qseq"' - - print('Allele calling starts') - pbar = ProgressBar () - - - ## # # # # # # # # # # # # # # # # # # # # # # # # ## - ## Processing the search for each schema core gene ## - ## # # # # # # # # # # # # # # # # # # # # # # # # ## - - for core_file in pbar(core_gene_list_files) : - core_name = os.path.basename(core_file).split('.')[0] - full_gene_list.append(core_name) - logger.info('Processing core gene file %s ', core_file) - - # Get path to this locus fasta file - locus_alleles_path = os.path.join(query_directory, str(core_name + '.fasta')) - - # Get path to reference allele fasta file for this locus - core_reference_allele_path = os.path.join(reference_alleles_directory, core_name + '.fasta') - - # Get length thresholds for INF, ASM and ALM classification - max_length_threshold, min_length_threshold = length_thresholds(core_name, schema_statistics, percentlength) - - # Get length thresholds for LNF, ASM and ALM classification - max_coverage_threshold, min_coverage_threshold = length_thresholds(core_name, schema_statistics, coverage) - - ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## - ## Processing the search for each schema core gene in each sample ## - ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## - - for sample_file in sample_list_files: - logger.info('Processing sample file %s ', sample_file) - - sample_name = '.'.join(os.path.basename(sample_file).split('.')[:-1]) - - # (recuento tags para plots) - if sample_name not in count_exact: - count_exact[sample_name] = {"good_quality" : 0, "bad_quality" : 0, "no_start" : 0, "no_start_stop" : 0, "no_stop" : 0, "multiple_stop" : 0, "total" : 0} - - if sample_name not in count_inf: - count_inf[sample_name] = {"good_quality" : 0, "bad_quality" : 0, "no_start" : 0, "no_start_stop" : 0, "no_stop" : 0, "multiple_stop" : 0, "total" : 0} - - if sample_name not in count_asm: - count_asm[sample_name] = {"insertion" : 0, "deletion" : 0, "substitution" : 0, "total" : 0} - - if sample_name not in count_alm: - count_alm[sample_name] = {"insertion" : 0, "deletion" : 0, "substitution" : 0, "total" : 0} - - if sample_name not in count_lnf: - count_lnf[sample_name] = {"not_found" : 0, "low_id" : 0, "low_coverage" : 0, "total" : 0} - - if sample_name not in count_tpr: - count_tpr[sample_name] = {"good_quality" : 0, "bad_quality" : 0, "no_start" : 0, "no_start_stop" : 0, "no_stop" : 0, "multiple_stop" : 0, "total" : 0} - - if sample_name not in count_plot: - count_plot[sample_name] = {"total" : 0} - - if sample_name not in count_niph: - count_niph[sample_name] = {"good_quality" : 0, "bad_quality" : 0, "no_start" : 0, "no_start_stop" : 0, "no_stop" : 0, "multiple_stop" : 0, "total" : 0} - - if sample_name not in count_niphem: - count_niphem[sample_name] = {"good_quality" : 0, "bad_quality" : 0, "no_start" : 0, "no_start_stop" : 0, "no_stop" : 0, "multiple_stop" : 0, "total" : 0} - - if sample_name not in count_error: - count_error[sample_name] = {"good_quality" : 0, "bad_quality" : 0, "no_start" : 0, "no_start_stop" : 0, "no_stop" : 0, "multiple_stop" : 0, "total" : 0} - - - # Initialize the sample list to add the number of alleles and the start, stop positions - if not sample_name in samples_matrix_dict: - samples_matrix_dict[sample_name] = [] - matching_genes_dict[sample_name] = {} - - # Path to this sample BLAST database created when processing samples - blast_db_name = os.path.join(blast_db_directory, sample_name, sample_name) - - # * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * # - # Sample contigs VS reference allele(s) BLAST for locus detection in sample # - # * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * # - - cline = NcbiblastnCommandline(db=blast_db_name, evalue=evalue, perc_identity=perc_identity_ref, reward=reward, penalty=penalty, gapopen=gapopen, gapextend=gapextend, outfmt=blast_parameters, max_target_seqs=max_target_seqs, max_hsps=max_hsps, num_threads=num_threads, query=core_reference_allele_path) - out, err = cline() - out_lines = out.splitlines() - - bigger_bitscore = 0 - - # ······························································ # - # LNF if there are no BLAST results for this gene in this sample # - # ······························································ # - if len (out_lines) == 0: - - # Trying to get the allele number to avoid that a bad quality assembly impact on the tree diagram - cline = NcbiblastnCommandline(db=blast_db_name, evalue=evalue, perc_identity = 70, reward=reward, penalty=penalty, gapopen=gapopen, gapextend=gapextend, outfmt=blast_parameters, max_target_seqs=1, max_hsps=1, num_threads=1, query=core_reference_allele_path) - out, err = cline() - out_lines = out.splitlines() - - if len (out_lines) > 0 : - - for line in out_lines : - values = line.split('\t') - if float(values[8]) > bigger_bitscore: - qseqid , sseqid , pident , qlen , s_length , mismatch , r_gapopen , r_evalue , bitscore , sstart , send , qstart , qend ,sseq , qseq = values - bigger_bitscore = float(bitscore) - - # Keep LNF info - lnf_tpr_tag(core_name, sample_name, alleles_in_locus_dict, samples_matrix_dict, lnf_tpr_dict, schema_statistics, locus_alleles_path, qseqid, pident, '-', '-', perc_identity_ref, '-', schema_quality, annotation_core_dict, count_lnf, logger) - - else: - # Keep LNF info - lnf_tpr_tag(core_name, sample_name, '-', samples_matrix_dict, lnf_tpr_dict, schema_statistics, locus_alleles_path, '-', '-', '-', '-', '-', '-', schema_quality, annotation_core_dict, count_lnf, logger) - - continue - - ## Continue classification process if the core gene has been detected in sample after BLAST search - if len (out_lines) > 0: - - # Parse contigs for this sample - #contig_file = os.path.join(inputdir, sample_name + ".fasta") ## parse - #records = list(SeqIO.parse(contig_file, "fasta")) ## parse - - ## Keep BLAST results after locus detection in sample using reference allele - - # Path to BLAST results fasta file - path_to_blast_seq = os.path.join(blast_results_seq_directory, sample_name, core_name + "_blast.fasta") - - with open (path_to_blast_seq, 'w') as outblast_fh: - seq_number = 1 - for line in out_lines : - values = line.split('\t') - qseqid = values[0] - if values[1] not in contigs_in_sample_dict[sample_name]: - sseqid = '|'.join(values[1].split('|')[1:-1]) - else: - sseqid = values[1] - sstart = values[9] - send = values[10] - - # Get flanked BLAST sequences from contig for correct allele tagging - - accession_sequence = contigs_in_sample_dict[sample_name][sseqid] - #for record in records: ## parse - #if record.id == sseqid : ## parse - #break ## parse - #accession_sequence = str(record.seq) ## parse - - if int(send) > int(sstart): - max_index = int(send) - min_index = int(sstart) - else: - max_index = int(sstart) - min_index = int(send) - - if (flankingnts + 1) <= min_index: - if flankingnts <= (len(accession_sequence) - max_index): - flanked_sseq = accession_sequence[ min_index -1 -flankingnts : max_index + flankingnts ] - else: - flanked_sseq = accession_sequence[ min_index -1 -flankingnts : ] - else: - flanked_sseq = accession_sequence[ : max_index + flankingnts ] - - seq_id = str(seq_number) + '_' + sseqid - outblast_fh.write('>' + seq_id + ' # ' + ' # '.join(values[0:13]) + '\n' + flanked_sseq + '\n' + '\n' ) - - seq_number += 1 - - ## Create local BLAST database for BLAST results after locus detection in sample using reference allele - db_name = os.path.join(blast_results_db_directory, sample_name) - if not create_blastdb(path_to_blast_seq, db_name, 'nucl', logger): - print('Error when creating the blastdb for blast results file for locus %s at sample %s. Check log file for more information. \n ', core_name, sample_name) - return False - - # Path to local BLAST database for BLAST results after locus detection in sample using reference allele - locus_blast_db_name = os.path.join(blast_results_db_directory, sample_name, os.path.basename(core_name) + '_blast', os.path.basename(core_name) + '_blast') - - - # * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * # - # BLAST result sequences VS ALL alleles in locus BLAST for allele identification detection in sample # - # * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * # - - cline = NcbiblastnCommandline(db=locus_blast_db_name, evalue=evalue, perc_identity=perc_identity_loc, reward=reward, penalty=penalty, gapopen=gapopen, gapextend=gapextend, outfmt = blast_parameters, max_target_seqs=max_target_seqs, max_hsps=max_hsps, num_threads=num_threads, query=locus_alleles_path) - - out, err = cline() - out_lines = out.splitlines() - - allele_found = {} # To keep filtered BLAST results - - ## Check if there is any BLAST result with ID = 100 ## - for line in out_lines: - - values = line.split('\t') - pident = values[2] - - if float(pident) == 100: - - qseqid, sseqid, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart, send, qstart, qend, sseq, qseq = values - - # Parse core gene fasta file to get matching allele sequence and length - #alleles_in_locus = list (SeqIO.parse(locus_alleles_path, "fasta")) ## parse - #for allele in alleles_in_locus : ## parse - #if allele.id == qseqid : ## parse - #break ## comentar parse - #matching_allele_seq = str(allele.seq) ## parse - #matching_allele_length = len(matching_allele_seq) ## parse - - matching_allele_seq = alleles_in_locus_dict[core_name][qseqid] - matching_allele_length = len(matching_allele_seq) - - # Keep BLAST results with ID = 100 and same length as matching allele - if int(s_length) == matching_allele_length: - #get_blast_results (values, records, allele_found, logger) - get_blast_results (sample_name, values, contigs_in_sample_dict, allele_found, logger) - - # ·································································································································· # - # NIPHEM (paralog) if there are multiple BLAST results with ID = 100 and same length as matching allele for this gene in this sample # - # ·································································································································· # - if len(allele_found) > 1: - - # Keep NIPHEM info - paralog_exact_tag(sample_name, core_name, 'NIPHEM', schema_quality, matching_genes_dict, samples_matrix_dict, allele_found, paralog_dict, prodigal_report, prodigal_directory, blast_parameters, annotation_core_dict, count_niphem, logger) - - continue - - ## Check for possible paralogs with ID < 100 if there is only one BLAST result with ID = 100 and same length as matching allele - elif len(allele_found) == 1 : - - for line in out_lines : - values = line.split('\t') - - sseq_no_gaps = values[13].replace('-', '') - s_length_no_gaps = len(sseq_no_gaps) - - # Keep BLAST result if its coverage is within min and max thresholds - if min_length_threshold <= s_length_no_gaps <= max_length_threshold: - #get_blast_results (values, records, allele_found, logger) - get_blast_results (sample_name, values, contigs_in_sample_dict, allele_found, logger) - - # ································································ # - # EXACT MATCH if there is any paralog for this gene in this sample # - # ································································ # - if len(allele_found) == 1 : - - paralog_exact_tag(sample_name, core_name, 'EXACT', schema_quality, matching_genes_dict, samples_matrix_dict, allele_found, exact_dict, prodigal_report, prodigal_directory, blast_parameters, annotation_core_dict, count_exact, logger) - - continue - - # ··········································································· # - # NIPH if there there are paralogs with ID < 100 for this gene in this sample # - # ··········································································· # - else: - - paralog_exact_tag(sample_name, core_name, 'NIPH', schema_quality, matching_genes_dict, samples_matrix_dict, allele_found, paralog_dict, prodigal_report, prodigal_directory, blast_parameters, annotation_core_dict, count_niph, logger) - - continue - - ## Look for the best BLAST result if there are no results with ID = 100 ## - elif len(allele_found) == 0: - - bigger_bitscore_seq_values = [] - - for line in out_lines : - values = line.split('\t') - - if float(values[8]) > bigger_bitscore: - s_length_no_gaps = len(values[13].replace('-', '')) - - # Keep BLAST result if its coverage is within min and max thresholds and its bitscore is bigger than the one previously kept - if min_coverage_threshold <= s_length_no_gaps <= max_coverage_threshold: - bigger_bitscore_seq_values = values - bigger_bitscore = float(bigger_bitscore_seq_values[8]) - - ## Check if best BLAST result out of coverage thresholds is a possible PLOT or LNF due to low coverage ## - #if len(allele_found) == 0: - if len(bigger_bitscore_seq_values) == 0: - - # Look for best bitscore BLAST result out of coverage thresholds to check possible PLOT or reporting LNF due to low coverage - - for line in out_lines : - values = line.split('\t') - - if float(values[8]) > bigger_bitscore: - qseqid, sseqid, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart, send, qstart, qend, sseq, qseq = values - bigger_bitscore_seq_values_out_cov = values ### - bigger_bitscore = float(bitscore) - - # Get BLAST values relatives to contig for bigger bitscore result - lnf_plot_found = {} ### - - get_blast_results (sample_name, bigger_bitscore_seq_values_out_cov, contigs_in_sample_dict, lnf_plot_found, logger) ### - - allele_id = str(list(lnf_plot_found.keys())[0]) ### - qseqid, sseqid, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart, send, qstart, qend, sseq, qseq = lnf_plot_found[allele_id] - - # Get contig sequence and length for best bitscore BLAST result ID - - #for record in records: ## parse - #if record.id == sseqid : ## parse - #break ## parse - #accession_sequence = record.seq ## parse - #length_sseqid = len(accession_sequence) ## parse - - accession_sequence = contigs_in_sample_dict[sample_name][sseqid] - length_sseqid = len(accession_sequence) - - # Check if best BLAST result out of coverage thresholds is a possible PLOT. If so, keep result info for later PLOT classification - if int(sstart) == length_sseqid or int(send) == length_sseqid or int(sstart) == 1 or int(send) == 1: - bigger_bitscore_seq_values = bigger_bitscore_seq_values_out_cov ### - - # ·············································································································································· # - # LNF if there are no BLAST results within coverage thresholds for this gene in this sample and best out threshold result is not a possible PLOT # - # ·············································································································································· # - else: - # Get sequence length - s_length_no_gaps = len(bigger_bitscore_seq_values_out_cov[13].replace('-', '')) - - # Keep LNF info - lnf_tpr_tag(core_name, sample_name, alleles_in_locus_dict, samples_matrix_dict, lnf_tpr_dict, schema_statistics, locus_alleles_path, qseqid, pident, s_length_no_gaps, '-', '-', coverage, schema_quality, annotation_core_dict, count_lnf, logger) - - ## Keep result with bigger bitscore in allele_found dict and look for possible paralogs ## - if len(bigger_bitscore_seq_values) > 0: - - qseqid, sseqid, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart, send, qstart, qend, sseq, qseq = bigger_bitscore_seq_values - - #get_blast_results (bigger_bitscore_seq_values, records, allele_found, logger) - get_blast_results (sample_name, bigger_bitscore_seq_values, contigs_in_sample_dict, allele_found, logger) - - # Possible paralogs search - for line in out_lines : - values = line.split('\t') - - qseqid, sseqid, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart, send, qstart, qend, sseq, qseq = values - sseq_no_gaps = sseq.replace('-', '') - s_length_no_gaps = len(sseq_no_gaps) - - if min_length_threshold <= s_length_no_gaps <= max_length_threshold: - - #get_blast_results (values, records, allele_found, logger) - get_blast_results (sample_name, values, contigs_in_sample_dict, allele_found, logger) - - # ····························································· # - # NIPH if there there are paralogs for this gene in this sample # - # ····························································· # - if len(allele_found) > 1 : - - paralog_exact_tag(sample_name, core_name, 'NIPH', schema_quality, matching_genes_dict, samples_matrix_dict, allele_found, paralog_dict, prodigal_report, prodigal_directory, blast_parameters, annotation_core_dict, count_niph, logger) - - continue - - ## Continue classification if there are no paralogs ## - elif len(allele_found) == 1 : - - allele_id = str(list(allele_found.keys())[0]) - qseqid, sseqid, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart, send, qstart, qend, sseq, qseq = allele_found[allele_id] - - sseq_no_gaps = sseq.replace('-', '') - s_length_no_gaps = len(sseq_no_gaps) - - # Get matching allele quality - allele_quality = schema_quality[core_name][qseqid] - - # Get matching allele sequence and length - - #alleles_in_locus = list (SeqIO.parse(locus_alleles_path, "fasta")) ## parse - #for allele in alleles_in_locus : ## parse - #if allele.id == qseqid : ## parse - #break ## parse - #matching_allele_seq = allele.seq ## parse - #matching_allele_length = len(matching_allele_seq) ## parse - - matching_allele_seq = alleles_in_locus_dict [core_name][qseqid] - matching_allele_length = len(matching_allele_seq) - - # Get contig sequence and length for ID found in BLAST - - #for record in records: ## parse - #if record.id == sseqid : ## parse - #break ## parse - #accession_sequence = record.seq ## parse - #length_sseqid = len(accession_sequence) ## parse - - accession_sequence = contigs_in_sample_dict[sample_name][sseqid] - length_sseqid = len(accession_sequence) - - # ········································································································· # - # PLOT if found sequence is shorter than matching allele and it is located on the edge of the sample contig # - # ········································································································· # - if int(sstart) == length_sseqid or int(send) == length_sseqid or int(sstart) == 1 or int(send) == 1: - if int(s_length) < matching_allele_length: - - ### sacar sec prodigal para PLOT? - # Get prodigal predicted sequence if matching allele quality is "bad quality" - if 'bad_quality' in allele_quality: - complete_predicted_seq, start_prodigal, end_prodigal = get_prodigal_sequence(sseq_no_gaps, sseqid, prodigal_directory, sample_name, blast_parameters, logger) - - # Keep info for prodigal report - prodigal_report.append([core_name, sample_name, qseqid, 'PLOT', sstart, send, start_prodigal, end_prodigal, sseq_no_gaps, complete_predicted_seq]) - - else: - complete_predicted_seq = '-' - start_prodigal = '-' - end_prodigal = '-' - - # Keep PLOT info - inf_asm_alm_tag(core_name, sample_name, 'PLOT', allele_found[allele_id], allele_quality, '-', matching_allele_length, '-', plot_dict, samples_matrix_dict, matching_genes_dict, prodigal_report, start_prodigal, end_prodigal, complete_predicted_seq, annotation_core_dict, count_plot, logger) - - continue - - # * * * * * * * * * * * * * * * * * * * * # - # Search for complete final new sequence # - # * * * * * * * * * * * * * * * * * * * * # - - ## Get Prodigal predicted sequence ## - complete_predicted_seq, start_prodigal, end_prodigal = get_prodigal_sequence(sseq_no_gaps, sseqid, prodigal_directory, sample_name, blast_parameters, logger) - - ## Search for new codon stop using contig sequence info ## - - # Check matching allele sequence direction - query_direction = check_sequence_order(matching_allele_seq, logger) - - # Get extended BLAST sequence for stop codon search - if query_direction == 'reverse': - if int(send) > int (sstart): - sample_gene_sequence = accession_sequence[ : int(send) ] - sample_gene_sequence = str(Seq.Seq(sample_gene_sequence).reverse_complement()) - else: - sample_gene_sequence = accession_sequence[ int(send) -1 : ] - - else: - if int(sstart) > int (send): - sample_gene_sequence = accession_sequence[ : int(sstart) ] - sample_gene_sequence = str(Seq.Seq(sample_gene_sequence).reverse_complement()) - else: - sample_gene_sequence = accession_sequence[ int(sstart) -1 : ] - - # Get new stop codon index - stop_index = get_stop_codon_index(sample_gene_sequence) - - ## Classification of final new sequence if it is found ## - if stop_index != False: - new_sequence_length = stop_index +3 - new_sseq = str(sample_gene_sequence[0:new_sequence_length]) - - ######################################################################################################################### - ### c/m: introducido para determinar qué umbral de coverage poner. TEMPORAL - new_sseq_coverage = new_sequence_length/matching_allele_length ### introduciendo coverage new_sseq /// debería ser con respecto a la media? - - if new_sseq_coverage < 1: - shorter_seq_coverage.append([core_name, sample_name, str(matching_allele_length), str(new_sequence_length), str(schema_statistics[core_name][0]), str(new_sseq_coverage), str(new_sequence_length/schema_statistics[core_name][0])]) - elif new_sseq_coverage > 1: - longer_seq_coverage.append([core_name, sample_name, str(matching_allele_length), str(new_sequence_length), str(schema_statistics[core_name][0]), str(new_sseq_coverage), str(new_sequence_length/schema_statistics[core_name][0])]) - elif new_sseq_coverage == 1: - equal_seq_coverage.append([core_name, sample_name, str(matching_allele_length), str(new_sequence_length), str(schema_statistics[core_name][0]), str(new_sseq_coverage), str(new_sequence_length/schema_statistics[core_name][0])]) - ######################################################################################################################### - - # Get and keep SNP and DNA and protein alignment - keep_snp_alignment_info(sseq, new_sseq, matching_allele_seq, qseqid, query_direction, core_name, sample_name, reward, penalty, gapopen, gapextend, snp_dict, match_alignment_dict, protein_dict, logger) - - # ····································································································· # - # INF if final new sequence length is within min and max length thresholds for this gene in this sample # - # ····································································································· # - if min_length_threshold <= new_sequence_length <= max_length_threshold: - - # Keep INF info - inf_asm_alm_tag(core_name, sample_name, 'INF', allele_found[allele_id], allele_quality, new_sseq, matching_allele_length, inferred_alleles_dict, inf_dict, samples_matrix_dict, matching_genes_dict, prodigal_report, start_prodigal, end_prodigal, complete_predicted_seq, annotation_core_dict, count_inf, logger) ### introducido start_prodigal, end_prodigal, complete_predicted_seq, prodigal_report como argumento a inf_asm_alm_tag para report prodigal, temporal - - # ············································································································································ # - # ASM if final new sequence length is under min length threshold but its coverage is above min coverage threshold for this gene in this sample # - # ············································································································································ # - elif min_coverage_threshold <= new_sequence_length < min_length_threshold: - - # Keep ASM info - inf_asm_alm_tag(core_name, sample_name, 'ASM', allele_found[allele_id], allele_quality, new_sseq, matching_allele_length, asm_dict, list_asm, samples_matrix_dict, matching_genes_dict, prodigal_report, start_prodigal, end_prodigal, complete_predicted_seq, annotation_core_dict, count_asm, logger) - - # ············································································································································ # - # ALM if final new sequence length is above max length threshold but its coverage is under max coverage threshold for this gene in this sample # - # ············································································································································ # - elif max_length_threshold < new_sequence_length <= max_coverage_threshold: - - # Keep ALM info - inf_asm_alm_tag(core_name, sample_name, 'ALM', allele_found[allele_id], allele_quality, new_sseq, matching_allele_length, alm_dict, list_alm, samples_matrix_dict, matching_genes_dict, prodigal_report, start_prodigal, end_prodigal, complete_predicted_seq, annotation_core_dict, count_alm, logger) ### introducido start_prodigal, end_prodigal, complete_predicted_seq, prodigal_report como argumento a inf_asm_alm_tag para report prodigal, temporal - - # ························································································· # - # TPR if final new sequence coverage is not within thresholds for this gene in this sample # - # ························································································· # - else: - - # Keep TPR info - lnf_tpr_tag(core_name, sample_name, alleles_in_locus_dict, samples_matrix_dict, lnf_tpr_dict, schema_statistics, locus_alleles_path, qseqid, pident, s_length_no_gaps, new_sequence_length, '-', coverage, schema_quality, annotation_core_dict, count_tpr, logger) - - # ········································ # - # ERROR if final new sequence is not found # - # ········································ # - else: - logger.error('ERROR : Stop codon was not found for the core %s and the sample %s', core_name, sample_name) - samples_matrix_dict[sample_name].append('ERROR not stop codon') - if not sseqid in matching_genes_dict[sample_name] : - matching_genes_dict[sample_name][sseqid] = [] - if sstart > send : - #matching_genes_dict[sample_name][sseqid].append([core_name, sstart, send,'-', 'ERROR']) - matching_genes_dict[sample_name][sseqid].append([core_name, qseqid, sstart, send,'-', 'ERROR']) - else: - #matching_genes_dict[sample_name][sseqid].append([core_name, sstart,send,'+', 'ERROR']) - matching_genes_dict[sample_name][sseqid].append([core_name, qseqid, sstart, send,'+', 'ERROR']) - - # (recuento tags para plot) - count_error[sample_name]['total'] += 1 - for count_class in count_error[sample_name]: - if count_class in allele_quality: - if "no_start_stop" not in count_class and "no_start_stop" in allele_quality: - if count_class == "bad_quality": - count_error[sample_name][count_class] += 1 - else: - count_error[sample_name][count_class] += 1 - - - ## Save results and create reports - - if not save_allele_call_results (outputdir, full_gene_list, samples_matrix_dict, exact_dict, paralog_dict, inf_dict, plot_dict, matching_genes_dict, list_asm, list_alm, lnf_tpr_dict, snp_dict, match_alignment_dict, protein_dict, prodigal_report, shorter_seq_coverage, longer_seq_coverage, equal_seq_coverage, shorter_blast_seq_coverage, longer_blast_seq_coverage, equal_blast_seq_coverage, logger): - print('There is an error while saving the allele calling results. Check the log file to get more information \n') - # exit(0) - - - ## Saving sample results plots - - if not save_allele_calling_plots (outputdir, sample_list_files, count_exact, count_inf, count_asm, count_alm, count_lnf, count_tpr, count_plot, count_niph, count_niphem, count_error, logger): - print('There is an error while saving the allele calling results plots. Check the log file to get more information \n') - - - return True, inferred_alleles_dict, inf_dict, exact_dict - - -# * * * * * * * * * * * * * * * * * * * # -# Processing gene by gene allele calling # -# * * * * * * * * * * * * * * * * * * * # - -def processing_allele_calling (arguments) : - ''' - Description: - This is the main function for allele calling. - With the support of additional functions it will create the output files - with the summary report. - Input: - arguments # Input arguments given on command line - Functions: - ???? - Variables: - ???? - Return: - ???? - ''' - - start_time = datetime.now() - print('Start the execution at :', start_time ) - - # Open log file - logger = open_log ('taranis_wgMLST.log') - #print('Checking the pre-requisites.') - - ############################################################ - ## Check additional programs are installed in your system ## - ############################################################ - #pre_requisites_list = [['blastp', '2.9'], ['makeblastdb', '2.9']] - #if not check_prerequisites (pre_requisites_list, logger): - # print ('your system does not fulfill the pre-requistes to run the script ') - # exit(0) - - ###################################################### - ## Check that given directories contain fasta files ## - ###################################################### - print('Validating schema fasta files in ' , arguments.coregenedir , '\n') - valid_core_gene_files = get_fasta_file_list(arguments.coregenedir, logger) - if not valid_core_gene_files : - print ('There are not valid fasta files in ', arguments.coregenedir , ' directory. Check log file for more information ') - exit(0) - - print('Validating reference alleles fasta files in ' , arguments.refalleles , '\n') - valid_reference_alleles_files = get_fasta_file_list(arguments.refalleles, logger) - if not valid_reference_alleles_files : - print ('There are not valid reference alleles fasta files in ', arguments.refalleles, ' directory. Check log file for more information ') - exit(0) - - print('Validating sample fasta files in ' , arguments.inputdir , '\n') - valid_sample_files = get_fasta_file_list(arguments.inputdir, logger) - if not valid_sample_files : - print ('There are not valid fasta files in ', arguments.inputdir , ' directory. Check log file for more information ') - exit(0) - - ################################# - ## Prepare the coreMLST schema ## - ################################# - tmp_core_gene_dir = os.path.join(arguments.outputdir,'tmp','cgMLST') - try: - os.makedirs(tmp_core_gene_dir) - except: - logger.info('Deleting the temporary directory for a previous execution without cleaning up') - shutil.rmtree(os.path.join(arguments.outputdir, 'tmp')) - try: - os.makedirs(tmp_core_gene_dir) - logger.info ('Temporary folder %s has been created again', tmp_core_gene_dir) - except: - logger.info('Unable to create again the temporary directory %s', tmp_core_gene_dir) - print('Cannot create temporary directory on ', tmp_core_gene_dir) - exit(0) - - alleles_in_locus_dict, annotation_core_dict, schema_variability, schema_statistics, schema_quality = prepare_core_gene (valid_core_gene_files, tmp_core_gene_dir, arguments.refalleles, arguments.genus, arguments.species, str(arguments.usegenus).lower(), logger) - #alleles_in_locus_dict, annotation_core_dict, schema_variability, schema_statistics, schema_quality = prepare_core_gene (valid_core_gene_files, tmp_core_gene_dir, arguments.refalleles, arguments.outputdir, logger) - if not alleles_in_locus_dict: - print('There is an error while processing the schema preparation phase. Check the log file to get more information \n') - logger.info('Deleting the temporary directory to clean up the temporary files created') - shutil.rmtree(os.path.join(arguments.outputdir, 'tmp')) - exit(0) - - ############################### - ## Prepare the samples files ## - ############################### - tmp_samples_dir = os.path.join(arguments.outputdir,'tmp','samples') - try: - os.makedirs(tmp_samples_dir) - except: - logger.info('Deleting the temporary directory for a previous execution without properly cleaning up') - shutil.rmtree(tmp_samples_dir) - try: - os.makedirs(tmp_samples_dir) - logger.info('Temporary folder %s has been created again', tmp_samples_dir) - except: - logger.info('Unable to create again the temporary directory %s', tmp_samples_dir) - shutil.rmtree(os.path.join(arguments.outputdir, 'tmp')) - logger.info('Cleaned up temporary directory ', ) - print('Cannot create temporary directory on ', tmp_samples_dir, 'Check the log file to get more information \n') - exit(0) - - contigs_in_sample_dict = prepare_samples(valid_sample_files, tmp_samples_dir, arguments.refgenome, logger) - if not contigs_in_sample_dict : - print('There is an error while processing the saving temporary files. Check the log file to get more information \n') - logger.info('Deleting the temporary directory to clean up the temporary files created') - shutil.rmtree(os.path.join(arguments.outputdir, 'tmp')) - exit(0) - - ################################## - ## Run allele callling analysis ## - ################################## - query_directory = arguments.coregenedir - reference_alleles_directory = arguments.refalleles - blast_db_directory = os.path.join(tmp_samples_dir,'blastdb') - prodigal_directory = os.path.join(tmp_samples_dir,'prodigal') - blast_results_seq_directory = os.path.join(tmp_samples_dir,'blast_results', 'blast_results_seq') ### path a directorio donde guardar secuencias encontradas tras blast con alelo de referencia - blast_results_db_directory = os.path.join(tmp_samples_dir,'blast_results', 'blast_results_db') ### path a directorio donde guardar db de secuencias encontradas tras blast con alelo de referencia - - complete_allele_call, inferred_alleles_dict, inf_dict, exact_dict = allele_call_nucleotides(valid_core_gene_files, valid_sample_files, alleles_in_locus_dict, contigs_in_sample_dict, query_directory, reference_alleles_directory, blast_db_directory, prodigal_directory, blast_results_seq_directory, blast_results_db_directory, arguments.inputdir, arguments.outputdir, int(arguments.cpus), arguments.percentlength, arguments.coverage, float(arguments.evalue), int(arguments.perc_identity_ref), int(arguments.perc_identity_loc), int(arguments.reward), int(arguments.penalty), int(arguments.gapopen), int(arguments.gapextend), int(arguments.max_target_seqs), int(arguments.max_hsps), int(arguments.num_threads), int(arguments.flankingnts), schema_variability, schema_statistics, schema_quality, annotation_core_dict, arguments.profile, logger) - if not complete_allele_call: - print('There is an error while processing the allele calling. Check the log file to get more information \n') - exit(0) - - ######################################################### - ## Update core gene schema adding new inferred alleles ## - ######################################################### - if inferred_alleles_dict: - if str(arguments.updateschema).lower() == 'true' or str(arguments.updateschema).lower() == 'new': - if not update_schema (str(arguments.updateschema).lower(), arguments.coregenedir, arguments.outputdir, valid_core_gene_files, inferred_alleles_dict, alleles_in_locus_dict, logger): - print('There is an error adding new inferred alleles found to the core genes schema. Check the log file to get more information \n') - exit(0) - - if str(arguments.profile).lower() != 'false': - - ############################ - ## Get ST for each sample ## - ############################ - complete_ST, inf_ST = get_ST_profile(arguments.outputdir, arguments.profile, exact_dict, inf_dict, valid_core_gene_files, valid_sample_files, logger) - - if not complete_ST: - print('There is an error while processing ST analysis. Check the log file to get more information \n') - exit(0) - - ########################################### - ## Update ST profile file adding new STs ## - ########################################### - if str(arguments.updateprofile).lower() == 'true' or str(arguments.updateprofile).lower() == 'new': - if len(inf_ST) > 0: - if not update_st_profile (str(arguments.updateprofile).lower(), arguments.profile, arguments.outputdir, inf_ST, valid_core_gene_files, logger): - print('There is an error adding new STs found to the ST profile file. Check the log file to get more information \n') - exit(0) - - shutil.rmtree(os.path.join(arguments.outputdir, 'tmp')) - - end_time = datetime.now() - print('completed execution at :', end_time ) - - return True - - - diff --git a/analyze_schema.py b/analyze_schema.py deleted file mode 100755 index 5e35083..0000000 --- a/analyze_schema.py +++ /dev/null @@ -1,715 +0,0 @@ -#!/usr/bin/env python3 -import argparse -import os -import shutil -import sys -import glob -from datetime import datetime -import statistics -from collections import Counter -#import matplotlib.pyplot as plt -import plotly.graph_objs as go -import plotly.io as pio -#import numpy as np -#import logging -#from logging.handlers import RotatingFileHandler -from Bio import SeqIO -from Bio.SeqRecord import SeqRecord -from Bio import Seq -#from Bio.Blast.Applications import NcbiblastnCommandline -from io import StringIO -#from Bio.Blast import NCBIXML -#from BCBio import GFF -from progressbar import ProgressBar -from utils.taranis_utils import * - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # -# Extract info from schema: duplicates, subsets, quality, lenght statistics, annotation and general info # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # - -def extract_info_schema (schema_files, outputdir, genus, species, usegenus, logger) : - schema_info_dict = {} - protein_dict = {} - schema_sequence_dict ={} ## auxiliar duplicados - allele_duplicated = {} - allele_subsets = {} - schema_quality_per_class_ids = {} - schema_statistics = {} - schema_variability_count = {} ## auxiliar estadística longitud - annotation_core_dict = {} - - print('Analyzing schema...') - pbar = ProgressBar () - for schema_file in pbar (schema_files) : - - gene_name = os.path.basename(schema_file).split('.')[0] - - protein_dict[gene_name] = {} - schema_info_dict[gene_name] = {} - schema_sequence_dict[gene_name] = {} - schema_quality_per_class_ids[gene_name] = {'good_quality': [], 'bad_quality: no_start': [], 'bad_quality: no_stop': [], 'bad_quality: no_start_stop': [], 'bad_quality: multiple_stop': []} - schema_statistics[gene_name] = [] - schema_variability_count[gene_name] = {} ## auxiliar estadística longitud - - alleles_len = [] - - - # ········································ # - # Get schema alleles quality for core gene # - # ········································ # - - locus_quality = check_core_gene_quality(schema_file, logger) - - for allele in locus_quality: - schema_quality_per_class_ids[gene_name][locus_quality[allele]].append(allele) - - - # ············································· # - # Get gene and product annotation for core gene # - # ············································· # - - gene_annot, product_annot = get_gene_annotation (gene_name, outputdir, genus, species, usegenus, logger) - if gene_name not in annotation_core_dict.keys(): - annotation_core_dict[gene_name] = {} - annotation_core_dict[gene_name] = [gene_annot, product_annot] - - - alleles_in_locus = list(SeqIO.parse(schema_file, "fasta")) - - for allele_1 in alleles_in_locus: - - allele_1_id = allele_1.id - - - # ··························································· # - # Get alleles which are subsets of other locus schema alleles # - # ··························································· # - - for allele_2 in alleles_in_locus : - - if str(allele_1.seq) in str(allele_2.seq) or str(allele_2.seq) in str(allele_1.seq) : - if len(str(allele_1.seq)) != len(str(allele_2.seq)) : - - allele_2_id = allele_2.id - - if len(str(allele_1.seq)) > len(str(allele_2.seq)) : - no_subset = allele_1_id - subset = allele_2_id - - else: - no_subset = allele_2_id - subset = allele_1_id - - - if not gene_name in allele_subsets : - allele_subsets [gene_name] = {} - - if not no_subset in allele_subsets [gene_name] : - allele_subsets [gene_name][no_subset] = [] - if not subset in allele_subsets [gene_name][no_subset]: - allele_subsets [gene_name][no_subset].append(subset) - - - sequence = allele_1.seq - sequence_str = str(sequence) - - - # ··············································································· # - # Get protein sequence for each CDS encoding allele sequence in each schema locus # - # ··············································································· # - - query_direction = check_sequence_order(sequence_str, logger) - if query_direction == 'reverse' : - sequence = sequence.reverse_complement() - - try: - protein = str(sequence.translate(cds=True, table = 11)) - protein_dict[gene_name][allele_1_id] = protein - coding_cds = 'Yes' - except Exception as error: - logger.error('Not CDS for gene %s in the allele %s ', gene_name, allele_1_id) - protein = '-' - coding_cds = 'No' - protein_dict[gene_name][allele_1_id] = 'NOT CDS' - - - # ························································································································································································ # - # Create schema info summary including for each allele in each locus: nucleotide sequence, protein sequence, nucleotide sequence length, CDS encoding, allele quality and allele direction # - # ························································································································································································ # - - for quality_class in schema_quality_per_class_ids[gene_name]: - if str(allele_1_id) in schema_quality_per_class_ids[gene_name][quality_class]: - allele_quality = quality_class - - schema_info_dict[gene_name][allele_1_id] = [sequence_str, str(len(sequence_str)), coding_cds, allele_quality, query_direction, protein] - - # Get core gene alleles length to keep length variability and statistics info - alleles_len.append(len(sequence_str)) - - - # ··········································· # - # Get duplicated alleles in each locus schema # - # ··········································· # - if not sequence_str in schema_sequence_dict[gene_name] : - schema_sequence_dict [gene_name][sequence_str]= [allele_1_id] - else: - schema_sequence_dict [gene_name][sequence_str].append(allele_1_id) - - for allele in schema_sequence_dict [gene_name] : - if len(schema_sequence_dict [gene_name][allele]) > 1 : - if not gene_name in allele_duplicated : - allele_duplicated[gene_name] = [] - allele_duplicated [gene_name].append(sorted(schema_sequence_dict [gene_name][allele])) - - - # ······························································· # - # Get length variability and statistics for alleles in this locus # - # ······························································· # - - if len(alleles_len) == 1: - stdev = 0 - else: - stdev = statistics.stdev(alleles_len) - - #schema_statistics[gene_name]=[statistics.mode(alleles_len), statistics.mean(alleles_len), stdev, min(alleles_len), max(alleles_len)] - schema_statistics[gene_name]=[list(Counter(alleles_len).most_common(1)[0])[0], statistics.mean(alleles_len), stdev, min(alleles_len), max(alleles_len)] - - for length in list(set(alleles_len)): - schema_variability_count[gene_name][str(length)] = str(alleles_len.count(length)) - - return schema_info_dict, schema_quality_per_class_ids, allele_duplicated, allele_subsets, schema_statistics, schema_variability_count, annotation_core_dict, protein_dict - - -def create_bar_graphic (x_data, y_data, x_label, x_prefix ,y_label, title , rotation, file_name) : ## X - ''' - index = np.arange(len(x_data)) - plt.bar(index, y_data) - plt.xlabel(x_label, fontsize=5) - plt.ylabel(y_label, fontsize=5) - plt.xticks(index, x_data, fontsize= 7, rotation=rotation) - - plt.title(title) - #plt.show() - plt.savefig(file_name) - plt.close() - ''' - - trace0 = go.Bar( - #x=['Product A', 'Product B', 'Product C'], - #y=[20, 14, 23], - x = x_data, - y = y_data, - text = y_data, - - #text=['27% market share', '24% market share', '19% market share'], - textposition = 'auto', - marker=dict( color='rgb(158,202,225)', - line=dict( - color='rgb(8,48,107)', - width=1.5, ) - ), - opacity=0.6 - ) - - data = [trace0] - #import pdb; pdb.set_trace() - layout = go.Layout( title=title, - xaxis = dict(title = x_label, - tickformat = '%' +x_prefix), - yaxis = dict(title = y_label), - ) - fig = go.Figure(data=data, layout=layout) - pio.write_image(fig, file_name) - return True - - -def find_proteins_in_gene (raw_proteins_per_genes, logger) : ## X - proteins_sequence_per_gene ={} - proteins_percent_per_gene ={} - logger.info('Start handling the raw_proteins to get the unique coding proteins') - for gene in raw_proteins_per_genes : - proteins = [] - - #num_alleles = len (proteins_per_genes[gene]) - for allele, value in sorted(raw_proteins_per_genes[gene].items()) : - if value != 'NOT CDS' : - proteins.append(value) - proteins_sequence_per_gene[gene] = list(set(proteins)) - if len(proteins) == 0 : - proteins_percent_per_gene[gene] = '0' - else: - proteins_percent_per_gene[gene] = format(len(list(set(proteins))) / len(proteins) , '.2f') - - logger.info('Complete the protein handling') - return proteins_sequence_per_gene, proteins_percent_per_gene - - -def summary_schema_info (schema_info_dict, output_dir, logger) : ## X - logger.info('Start processing the information in schema info') - header_variability_length = ['Gene name', 'Length variability'] - header_gene_length = ['Gene name', 'Length'] - header_percent_allele_not_cds = ['Gene name', 'Allele Percentage that is not coding CDS'] - summary_info = {} - variability_length = {} - coding_cds = {} - #error_type = {} - allele_quality = {} - gene_length = {} - direction = {} - - # join all individual information to one item per gene - for gene in sorted(schema_info_dict) : - - g_length = [] # longitud - coding_cds[gene] = {} # coding cds - allele_quality[gene] = {} # tipo de error --> CALIDAD - direction[gene] = {} # dirección - - logger.debug('dumping g_length for gene %s ' ,gene) - for allele in schema_info_dict[gene] : - values = schema_info_dict[gene][allele] - g_length.append(int(values[1])) - #g_coding.append(values[1]) - if not values[2] in coding_cds[gene] : - coding_cds[gene][values[2]] = 0 - coding_cds[gene][values[2]] += 1 - - if not values[3] in allele_quality[gene] : - allele_quality[gene][values[3]] = 0 - allele_quality[gene][values[3]] += 1 - - if not values[4] in direction [gene]: - direction[gene][values[4]] = 0 - direction[gene][values[4]] += 1 - - - mode_length=statistics.mode(g_length) - min_length = min(g_length) - max_length = max(g_length) - gene_length[gene] = mode_length - variability_length[gene]=format(max((mode_length-min_length), (max_length-mode_length))/mode_length, '.2f') - - logger.info('Create the summary folder') - os.makedirs(os.path.join(output_dir, 'summary')) - - - #logger.info('Dumping the variability length from the schema to file') - #variability_length_file = os.path.join(output_dir, 'summary' , 'variability_length.tsv') - #save_simple_dict_to_file (variability_length, header_variability_length, variability_length_file, logger) - - ''' - with open (variability_length_file , 'w') as variability_length_fh : - variability_length_fh.write('\t'.join(header_variability_length) + '\n') - for gene, value in sorted (variability_length.items()) : - variability_length_fh.write(gene + '\t' + value + '\n') - ''' - - logger.info('Dumping completed') - #logger.info('Dumping the gene length from the schema to file') - #gene_length_file = os.path.join(output_dir, 'summary' , 'gene_length.tsv') - #save_simple_dict_to_file (gene_length, header_gene_length, gene_length_file, logger) - - logger.info('Processing the picture for gene length') - # Length of the gene will be clustered in 10 groups to be presented in the graphic bar - x_axis = [150, 250, 500, 1000, 1500, 2000, 2500, 3000, 4000 , 5000] - gene_length_values = 10 *[0] - #summary_length = {} - #set_of_length = [] - #number_of_set_length = [] - - for value in gene_length.values() : - if value > 5000 : - # if gene length is bigger than 5000 it will be assigned to 5000 - gene_length_values[len(x_axis)-1] += 1 - else: - for index in range(len(x_axis)) : - if value <= x_axis[index] : - gene_length_values[index] += 1 - break - - - x_axis_label = ['<= {0}'.format(element) for element in x_axis] - - length_graphic_file = os.path.join(output_dir, 'graphic_gene_length.png') - rotation = 30 - x_prefix = '' - create_bar_graphic (x_axis_label, gene_length_values, 'Gene length', x_prefix ,'Number of gene with the same length', 'Sequence length for genes defined in the schema ' , rotation, length_graphic_file) - #create_bar_graphic (set_of_length, number_of_set_length, 'length of gene', 'Number of gene with the same length', 'Length of the sequence for each gene defined in the schema ' , rotation, length_graphic_file) - - logger.info('Processing the picture for variablity length') - variation_lenght = {} - index_variation = [] - value_varation = [] - for gene, v_length in variability_length.items() : - if not v_length in variation_lenght : - variation_lenght[v_length] = 0 - variation_lenght [v_length] += 1 - for index, value in sorted(variation_lenght.items()): - index_variation.append(index) - value_varation.append(value) - - x_axis_label = ['{0}%'.format(int(float(element)*100)) for element in index_variation] - varation_length_graphic_file = os.path.join(output_dir, 'graphic_varation_length.png') - rotation = 30 - x_prefix ='' - create_bar_graphic (x_axis_label, value_varation, 'length variability of gene', x_prefix, 'Numbers of gene variability', 'Variability length of the sequence for each gene defined in the schema ' , rotation, varation_length_graphic_file) - logger.info('Complete picture for variability length') - - # combine the number of times that an allele is not protein coding - summary_coding_cds = {} - #count_conting_cds = {} - percents = [] - percent_value = [] - for gene in coding_cds : - - if 'Yes' in coding_cds[gene] : - allele_coding_cds = coding_cds[gene]['Yes'] - else: - allele_coding_cds = 0 - if 'No' in coding_cds[gene] : - allele_no_coding_cds = coding_cds[gene]['No'] - else: - allele_no_coding_cds = 0 - percent_not_coding = format(allele_no_coding_cds/(allele_no_coding_cds + allele_coding_cds), '.2f') - summary_coding_cds[gene] = percent_not_coding - - #logger.info('Dumping the allele percentage that are not codings CDS to file') - #percent_allele_not_coding_file = os.path.join(output_dir, 'summary' , 'percent_allele_not_coding.tsv') - #save_simple_dict_to_file (summary_coding_cds, header_percent_allele_not_cds, percent_allele_not_coding_file, logger) - - - # Create the plot file for the (cdc/non cds) percent relation - percent_coding_one_decimal = [] - for per_values in summary_coding_cds.values() : - percent_coding_one_decimal.append(str(round(float(per_values), 1))) - - percent_number = [] - percent_list = sorted(list(set(percent_coding_one_decimal))) - for item in percent_list : - percent_number.append(percent_coding_one_decimal.count(item)) - - x_axis_label = ['{0}%'.format(int(float(element)*100)) for element in percent_list] - - percent_not_contig_graphic_file = os.path.join(output_dir, 'graphic_allele_percent_not_coding.png') - rotation = 30 - x_prefix = '' - - create_bar_graphic (x_axis_label, percent_number, 'Percent of non coding CDS', x_prefix, 'Number of genes ', 'Alleles that are not coding CDS ( in % ) ' , rotation, percent_not_contig_graphic_file) - - # Combine the number of times that the error codo arise when trying to conver to cds - summary_allele_quality = {} - error_name = [] - error_value = [] - for gene, errors in allele_quality.items() : - for error_code , value_error in errors.items() : - if error_code != 'No error' : - - if 'start codon' in error_code : - error_code = 'not start codon' - elif 'Extra in frame stop' in error_code : - error_code = 'extra stop codon' - elif 'not a stop codon' in error_code : - error_code = 'not stop codon' - else: - pass - if not error_code in summary_allele_quality : - summary_allele_quality[error_code] = 0 - summary_allele_quality[error_code] += value_error - for error , value in summary_allele_quality.items(): - error_name.append(error) - error_value.append(value) - - # Create the plot file for error types when trying to convert to cds - allele_quality_graphic_file = os.path.join(output_dir, 'graphic_allele_quality_cds.png') - rotation = 0 - x_prefix = '' - create_bar_graphic (error_name, error_value, 'Error type when converting to CDS', x_prefix, 'Number of errors', 'Type of errors that are generated when trying to convert to CDS ' , rotation , allele_quality_graphic_file) - - logger.info('Schema info has been completed processed ') - - return True - - -def save_simple_dict_list_to_files (dict_to_save, heading_text, folder_name ,file_name, logger) : ## X - logger.info('Saving file %s', file_name) - for gene , value_list in sorted(dict_to_save.items()): - f_name = os.path.join(folder_name, str(gene + file_name)) - with open (f_name , 'w') as f_name_fh : - f_name_fh.write('\t'.join(heading_text) + '\n') - for item in value_list : - f_name_fh.write(gene + '\t' + item + '\n') - logger.info('Saved file %s', file_name) - return True - - -def save_simple_dict_to_file (dict_to_save, heading_text, file_name, logger) : ## X - logger.info('Saving file %s', file_name) - with open (file_name , 'w') as file_name_fh : - file_name_fh.write('\t'.join(heading_text) + '\n') - for gene , value in sorted (dict_to_save.items()) : - file_name_fh.write(gene + '\t' + str(value) + '\n') - logger.info('Saved file %s', file_name) - return True - - -def summary_proteins (raw_proteins_per_genes, output_dir, logger) : ## X - logger.info('Start handling protein from the raw information') - heading_summary_proteins_sequence = ['Gene Name', 'Protein sequence'] - heading_summary_proteins_percent = ['Gene Name', 'Percent of different proteins in the gene'] - proteins_sequence_per_gene, proteins_percent_per_gene = find_proteins_in_gene (raw_proteins_per_genes, logger) - # Save proteins sequences proteins to file - os.makedirs(os.path.join(output_dir, 'summary', 'proteins')) - folder_summary_proteins = os.path.join(output_dir, 'summary', 'proteins') - proteins_sequence_file = '_summary_protein_sequence.tsv' - save_simple_dict_list_to_files (proteins_sequence_per_gene, heading_summary_proteins_sequence, folder_summary_proteins, proteins_sequence_file, logger) - # Save proteins percent to file - proteins_percent_file = os.path.join(output_dir, 'summary' , 'proteins_percent.tsv') - save_simple_dict_to_file (proteins_percent_per_gene, heading_summary_proteins_percent, proteins_percent_file ,logger) - - # create the diagram to display the percent proteins for each gene - # round number to 1 decimal to show the graphic - all_percent = [] - percent_values = proteins_percent_per_gene.values() - for percent_value in percent_values : - all_percent.append(str(round(float(percent_value), 1))) - - #all_percent = list(proteins_percent_per_gene.values() ) - percent_list = sorted(list(set(all_percent))) - percent_number = [] - for item in percent_list : - percent_number.append(all_percent.count(item)) - x_axis_label = ['{0}%'.format(int(float(element)*100)) for element in percent_list] - protein_percent_graphic_file = os.path.join(output_dir, 'graphic_protein_percent.png') - rotation = 30 - x_prefix ='' - create_bar_graphic (x_axis_label, percent_number, 'Percent of proteins ', x_prefix , - 'Number of genes', 'Percent of Alleles that coding for the same protein (in %)' - , rotation, protein_percent_graphic_file) - - return True - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # -# Keep info extracted from schema: duplicates, subsets, quality, lenght statistics, annotation and general info # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # - -#def analyze_schema (inputdir, outputdir, alt_codon_start, logger) : -def analyze_schema (inputdir, outputdir, genus, species, usegenus, logger) : - - header_schema_quality = ['Core Gene', 'Good quality', 'Bad quality: no start', 'Bad quality: no stop', 'Bad quality: no start stop', 'Bad quality: multiple stop', 'Total'] - header_schema_statistics = ['Core Gene', 'Mode', 'Mean', 'Standard Deviation', 'Min Length', 'Max Length', 'Schema Variability', 'Total'] - header_annotation = ['Core Gene', 'Gene Annotation', 'Product Annotation'] #### - header_alleles_duplicated = ['Core Gene', 'Duplicated alleles group IDs' ] - header_alleles_subsets = ['Core Gene', 'Allele', 'Subsets alleles group IDs' ] - header_schema_info = ['Core Gene', 'Allele', 'Nucleotides Sequence', 'Length', 'Encoding CDS' , 'Allele Quality', 'Direction', 'Protein Sequence'] - - schema_files = get_fasta_file_list(inputdir, logger) - - logger.info('Extract the raw information for each gene in the schema') - - #schema_info_dict, schema_quality_per_class_ids, allele_duplicated, allele_subsets, raw_proteins_per_genes = extract_info_schema (schema_files, logger) - schema_info_dict, schema_quality_per_class_ids, allele_duplicated, allele_subsets, schema_statistics, schema_variability_count, annotation_core_dict, raw_proteins_per_genes = extract_info_schema (schema_files, outputdir, genus, species, usegenus, logger) - - print('Saving data to ', outputdir ) - logger.info('Start dumping the raw information to files') - os.makedirs(os.path.join(outputdir, 'raw_info')) - - # Saving schema info to file - logger.info('Saving schema info to file..') - os.makedirs(os.path.join(outputdir, 'raw_info', 'raw_schema_information')) - for core in sorted(schema_info_dict) : - schema_info_file = os.path.join(outputdir, 'raw_info', 'raw_schema_information', str(core + '_schema_information.tsv')) - with open (schema_info_file , 'w') as schema_info_fh : - schema_info_fh.write('\t'.join(header_schema_info) + '\n') - for allele in (schema_info_dict[core]) : - schema_info_fh.write(core + '\t' + allele + '\t' + '\t'.join(schema_info_dict[core][allele]) + '\n') - - # Saving duplicated alleles to file - logger.info('Saving duplicated alleles to file..') - allele_duplicated_file = os.path.join(outputdir, 'raw_info' , 'duplicated_alleles.tsv') - with open (allele_duplicated_file , 'w') as allele_duplicated_fh : - allele_duplicated_fh.write('\t'.join(header_alleles_duplicated) + '\n') - for core in sorted(allele_duplicated) : - for duplication in (allele_duplicated[core]): - allele_duplicated_fh.write(core + '\t' + ', '.join(map(str, list(duplication))) + '\n') - - # Saving alleles subsets to file - logger.info('Saving subsets alleles to file..') - #os.makedirs(os.path.join(outputdir, 'raw_info', 'subsets_alleles')) - allele_subsets_file = os.path.join(outputdir, 'raw_info' , 'alleles_subsets.tsv') - with open (allele_subsets_file , 'w') as allele_subsets_fh : - allele_subsets_fh.write('\t'.join(header_alleles_subsets) + '\n') - for core in sorted(allele_subsets) : - for allele_id in allele_subsets[core]: - allele_subsets_fh.write(core + '\t' + allele_id + '\t' + ', '.join(map(str, list(allele_subsets[core][allele_id]))) + '\n') - - # Saving schema quality to file - logger.info('Saving schema quality information to file..') - quality_file = os.path.join(outputdir, 'raw_info', 'schema_quality.tsv') - with open (quality_file , 'w') as quality_fh : - quality_fh.write('\t'.join(header_schema_quality) + '\n') - for core in sorted(schema_quality_per_class_ids) : - len_quality_class_type = [len(schema_quality_per_class_ids[core]['good_quality']), len(schema_quality_per_class_ids[core]['bad_quality: no_start']), \ - len(schema_quality_per_class_ids[core]['bad_quality: no_stop']), len(schema_quality_per_class_ids[core]['bad_quality: no_start_stop']), \ - len(schema_quality_per_class_ids[core]['bad_quality: multiple_stop'])] - - ### orden alfabético? (['bad_quality: multiple_stop', 'bad_quality: no_start', 'bad_quality: no_start_stop', 'bad_quality: no_stop', 'good_quality']) - #len_quality_class_type = [] - #for quality_class in sorted(schema_quality_per_class_ids[core]): - # len_quality_class_type.append(len(quality_class)) - #len_quality_class_type = [len(value) for value in list(schema_quality_per_class_ids[core].values())] - - quality_fh.write(core + '\t' + '\t'.join (map(str, len_quality_class_type)) + '\t' + str(sum(len_quality_class_type)) + '\n') - - # Saving length statistics to file - logger.info('Saving schema length statistics information to file..') - statistics_file = os.path.join(outputdir, 'raw_info', 'length_statistics.tsv') - with open (statistics_file , 'w') as stat_fh : - stat_fh.write('\t'.join(header_schema_statistics) + '\n') - for core in sorted (schema_statistics): - length_number = [] - total_alleles = 0 - for length in schema_variability_count[core]: - length_number.append(length + ': ' + schema_variability_count[core][length]) - total_alleles += int(schema_variability_count[core][length]) - - stat_fh.write(core + '\t' + '\t'.join (map(str,schema_statistics[core])) + '\t' + ', '.join(length_number) + '\t' + str(total_alleles) + '\n') - #stat_fh.write(core + '\t' + ', '.join(map(str,schema_statistics[core][0])) + '\t' + '\t'.join (map(str,schema_statistics[core][1::])) + '\t' + ', '.join(length_number) + '\t' + str(total_alleles) + '\n') - - # Saving schema annotation to file - #logger.info('Saving core gene schema annotation to file..') - #annotation_file = os.path.join(outputdir, 'raw_info' , 'annotation.tsv') - #with open (annotation_file , 'w') as annot_fh : - # annot_fh.write('\t'.join(header_annotation) + '\n') - # for core in sorted(annotation_core_dict) : - # annot_fh.write(core + '\t' + '\t'.join(annotation_core_dict[core]) + '\n') - - - logger.info('Completed dumped raw information to files') - - #summary_schema_info(schema_info_dict, outputdir, logger) - #summary_proteins (raw_proteins_per_genes, outputdir, logger) - - return schema_quality_per_class_ids, allele_duplicated, allele_subsets, schema_files - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # -# Filter schema removing subsets, duplicates and bad quality alleles from each locus # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # - -def remove_alleles_from_schema (schema_files, remove_subsets, remove_duplicates, remove_no_cds, new_schema, allele_subsets, allele_duplicated, schema_quality_per_class, schema_dir, outputdir, logger): - ## logger? - - ## Create a copy of core genes schema if updateschema = 'new' / 'New' - if new_schema == 'true': - # print("Ha entrado a true new schema para filtrar el esquema", '\n') - no_filtered_schema_dir = schema_dir - schema_dir_name = os.path.basename(no_filtered_schema_dir) - schema_dir = os.path.join(outputdir, schema_dir_name + '_filtered') - shutil.copytree(no_filtered_schema_dir, schema_dir) - logger.info('Copying core genes fasta files to filter schema') - - schema_files = get_fasta_file_list(schema_dir, logger) - - - print('\n', 'Filtering schema...') - pbar = ProgressBar () - for schema_file in pbar (schema_files) : - core_name = os.path.basename (schema_file).split('.') [0] - - alleles_to_remove = [] - - if remove_subsets == 'true': - if core_name in allele_subsets: - subsets_alleles = sum(list(allele_subsets [core_name].values()), []) - alleles_to_remove += [x for x in subsets_alleles] - - if remove_duplicates == 'true': - if core_name in allele_duplicated: - for duplicates_group in allele_duplicated[core_name]: - for id_index in range(1, len(duplicates_group)): - alleles_to_remove += duplicates_group[id_index] - - if remove_no_cds == 'true': - for quality_class in schema_quality_per_class [core_name]: - if 'bad_quality' in quality_class: - alleles_to_remove += schema_quality_per_class [core_name][quality_class] - - alleles_to_remove_unique = list(set(alleles_to_remove)) - - alleles_in_locus_dict = {} - allele_str_id = '' - - for allele in SeqIO.parse(schema_file, 'fasta'): - if '_' in str(allele.id): - split_id = str(allele.id).split('_') - allele_id = int(split_id[-1]) - allele_str_id = '_'.join(split_id[0:len(split_id)]) - else: - allele_id = int(allele.id) - - alleles_in_locus_dict[allele_id] = str(allele.seq) - - - with open(schema_file, 'w') as schema_fh : - for allele_id in sorted(alleles_in_locus_dict): - if str(allele_id) not in alleles_to_remove_unique: - if len(allele_str_id) > 0: - allele_id_comp = allele_str_id + '_' + str(allele_id) - else: - allele_id_comp = str(allele_id) - schema_fh.write('>' + allele_id_comp + '\n' + alleles_in_locus_dict[allele_id] + '\n' + '\n' ) - - return True - - -# · * · * · * · * · * · * · * · * · * · * · * · * # -# Processing schema analysis and schema filtering # -# · * · * · * · * · * · * · * · * · * · * · * · * # - -def processing_analyze_schema(arguments) : - - start_time = datetime.now() - print('Start the execution at :', start_time ) - - # Open log file - logger = open_log ('analyze_schema.log') - - ############################# - ## Create output directory ## - ############################# - try: - os.makedirs(arguments.outputdir) - except: - logger.info('Deleting the result directory for a previous execution without cleaning up') - shutil.rmtree(arguments.outputdir) - try: - os.makedirs(arguments.outputdir) - logger.info ( 'Results folder %s has been created again', arguments.outputdir) - except: - logger.info('Unable to create again the result directory %s', arguments.outputdir) - print('Cannot create result directory on ', arguments.outputdir) - exit(0) - - ######################### - ## Get schema analysis ## - ######################### - #analyze_schema (arguments.inputdir, arguments.outputdir, arguments.alt, logger) - schema_quality_per_class_ids, allele_duplicated, allele_subsets, schema_files = analyze_schema (arguments.inputdir, arguments.outputdir, arguments.genus, arguments.species, arguments.usegenus, logger) - if not schema_quality_per_class_ids: - print('There is an error while processing the schema analysis. Check the log file to get more information \n') - exit(0) - - ################################################################################### - ## Remove allele subsets, duplicated alleles and bad quality alleles from schema ## - ################################################################################### - if str(arguments.removesubsets).lower() == 'true' or str(arguments.removeduplicates).lower() == 'true' or str(arguments.removenocds).lower() == 'true' : - if not remove_alleles_from_schema (schema_files, str(arguments.removesubsets).lower(), str(arguments.removeduplicates).lower(), str(arguments.removenocds).lower(), str(arguments.newschema).lower(), allele_subsets, allele_duplicated, schema_quality_per_class_ids, arguments.inputdir, arguments.outputdir, logger): - print('There is an error while processing the schema allele filtering. Check the log file to get more information \n') - exit(0) - - end_time = datetime.now() - print('completed execution at :', end_time ) - - return True diff --git a/create_schema.py b/create_schema.py deleted file mode 100644 index a759705..0000000 --- a/create_schema.py +++ /dev/null @@ -1,40 +0,0 @@ -#!/usr/bin/env python3 -from utils.taranis_utils import * - - - -def processing_create_schema(arguments): - ''' - Description: - - - Input: - - Variables: - - - Return: - - ''' - xls_file = arguments.xlsfile - output_dir = arguments.outputdir - logger = open_log('create_schema') - if logger != 'Error' : - gene_list = read_xls_file(xls_file, logger) - if 'Error' not in gene_list : - for gene, protein in gene_list : - #curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=nuccore;id=NP_214515.1;retmode=text;rettype=fasta" - pass - else: - print('There was an error when accessing the excel file ') - print(gene_list ) - return gene_list - #return 'Error when reading the excel file' - logger.info('test') - logger.error('error_test ') - else : - print ('Exiting the create schema utility because the log file') - print ('could not be created') - return 'Error' - - return True diff --git a/distance_matrix.py b/distance_matrix.py deleted file mode 100755 index e4da300..0000000 --- a/distance_matrix.py +++ /dev/null @@ -1,323 +0,0 @@ -#!/usr/bin/env python3 - -import argparse -import sys -import io -import os -import logging -from logging.handlers import RotatingFileHandler -from datetime import datetime -from io import StringIO -import pandas as pd -import shutil -import csv -import plotly.graph_objects as go -from utils.taranis_utils import * - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # -# Filter and remove samples with missing percentage above certain specified threshold from allele calling comparison table for distance matrix calculation # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # - -def missing_filter_row(pd_matrix, missing_values_dict, sample_missing_threshold, logger): - - logger.info('Filtering samples with missing values in more than %s loci', sample_missing_threshold) - - num_cols = len(pd_matrix.columns) - num_rows = len(pd_matrix.index) - - missing_values_all_samples_dict = {} - sample_index_to_ignore = [] - - - ## Count unique tags to get missing values tag percentage for each sample or row - for index, row in pd_matrix.iterrows(): - - missing_values_per_sample_dict = dict(missing_values_dict) - row_elements = list(row) - row_elements_unique = list(set(row_elements)) - - for tag in row_elements_unique: - - for missing_tag in missing_values_per_sample_dict: - if missing_tag in tag: - missing_values_per_sample_dict[missing_tag] += row_elements.count(tag) - - missing_percent = (sum(missing_values_per_sample_dict.values())/num_cols) * 100 - - if sample_missing_threshold < missing_percent : - - sample_index_to_ignore.append(index) - missing_values_all_samples_dict[index] = missing_values_per_sample_dict - - ## Remove samples with missing value percentage above specified missing threshold - for sample_index in sample_index_to_ignore: - - pd_matrix = pd_matrix.drop(sample_index, axis = 0) - - return pd_matrix, missing_values_all_samples_dict, num_rows, num_cols - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # -# Filter and remove loci with missing percentage above certain specified threshold from allele calling comparison table for distance matrix calculation # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # - -def missing_filter_col(pd_matrix, missing_values_dict, locus_missing_threshold, logger): - - logger.info('Filtering loci with missing values in more than %s samples', locus_missing_threshold) - - num_rows = len(pd_matrix.index) - num_cols = len(pd_matrix.columns) - - pd_matrix_cols = list(pd_matrix) - index = 0 - - missing_values_all_loci_dict = {} - locus_index_to_ignore = [] - - ## Count unique tags to get missing values tag percentage for each locus or column - for col in pd_matrix_cols: - - missing_values_per_locus_dict = dict(missing_values_dict) - col_list = [] - - for row in range(0, num_rows): - - col_list.append(pd_matrix[col][row]) - - col_elements_count_dict = {} - col_elements_unique = list(set(col_list)) - - for tag in col_elements_unique: - for missing_tag in missing_values_per_locus_dict: - if missing_tag in tag: - missing_values_per_locus_dict[missing_tag] += col_list.count(tag) - - missing_percent = (sum(missing_values_per_locus_dict.values())/num_rows) * 100 - - if locus_missing_threshold < missing_percent : - - locus_index_to_ignore.append(pd_matrix_cols[index]) - missing_values_all_loci_dict[list(pd_matrix.columns)[index]] = missing_values_per_locus_dict - - index += 1 - - ## Remove loci with missing value percentage above specified missing threshold - for locus_index in locus_index_to_ignore: - - pd_matrix = pd_matrix.drop(locus_index, axis = 1) - - return pd_matrix, missing_values_all_loci_dict, num_rows, num_cols - - -# · * · * · * · * · * · * · * # -# Find hamming distance matrix # -# · * · * · * · * · * · * · * # - -def hamming_distance (pd_matrix): - ''' - The function is used to find the hamming distance matrix - Input: - pd_matrix # Contains the panda dataFrame - Variables: - unique_values # contains the array with the unique values in the dataFrame - U # Is the boolean matrix of differences - H # It is accumulative values of U - Return: - H where the number of columns have been subtracted - ''' - - unique_values = pd.unique(pd_matrix[list(pd_matrix.keys())].values.ravel('K')) - # Create binary matrix ('1' or '0' ) matching the input matrix vs the unique_values[0] - # astype(int) is used to transform the boolean matrix into integer - U = pd_matrix.eq(unique_values[0]).astype(int) - # multiply the matrix with the transpose - H = U.dot(U.T) - - # Repeat for each unique value - for unique_val in range(1,len(unique_values)): - U = pd_matrix.eq(unique_values[unique_val]).astype(int) - # Add the value of the binary matrix with the previous stored values - H = H.add(U.dot(U.T)) - - return len(pd_matrix.columns) - H - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # -# Create samples distance matrix from filtered allele calling comparison table and get samples and loci filtering report # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # - -def create_distance_matrix (alleles_matrix, outputdir, locus_missing_threshold, sample_missing_threshold, paralog_filter, lnf_filter, plot_filter, logger): - - ## Read the allele calling comparison table file - try: - pd_matrix = pd.read_csv(alleles_matrix, sep='\t', header=0, index_col=0) - - except Exception as e: - logger.info('Unable to open the distance matrix file %s', result_file) - - print('------------- ERROR --------------') - print('Unable to open the allele calling comparison table file') - print('Check in the logging configuration file') - print('------------------------------------------') - return 'Error' - - ## Get list of tags to be considered missing values if required - if paralog_filter != "false" or lnf_filter != "false" or plot_filter != "false": - - missing_values_dict = {} - if lnf_filter == "true": - missing_values_dict["LNF"] = 0 - - if paralog_filter == "true": - missing_values_dict["NIPH"] = 0 - - if plot_filter == "true": - missing_values_dict["PLOT"] = 0 - - - ## Filter samples and loci with missing values percentage obove specified threshold if required - if sample_missing_threshold < 100 or locus_missing_threshold < 100: - - filter_report_file = os.path.join(outputdir, 'matrix_distance_filter_report.tsv') - - if sample_missing_threshold < 100: ### ESTO MIRAR CÓMO LO VOY A INDICAR AL FINAL ##### - - # Filter samples from allele calling comparison table file - pd_matrix, missing_values_all_samples_dict, total_samples, total_loci = missing_filter_row(pd_matrix, missing_values_dict, sample_missing_threshold, logger) - - # Get samples filtering report - logger.info('Saving distance matrix filter information to file after samples filtering..') - - header_samples_filter = ["Sample Name", "LNF", "NIPH + NIPHEM", "PLOT", "Total missing values %"] - - #with open (filter_report_file, 'a') as filter_fh: - - if len(missing_values_all_samples_dict) > 0: - - filter_fh.write(str(len(missing_values_all_samples_dict)) + "/" + str(total_samples) + " samples with missing values in more than " + str(sample_missing_threshold) + "% loci removed: " + '\n') - filter_fh.write('\n' + '\t'.join(header_samples_filter) + '\n') - - for sample in sorted(missing_values_all_samples_dict): - - missing_values = [str(missing_values_all_samples_dict[sample][missing_tag]) + "/" + str(total_loci) for missing_tag in sorted(missing_values_all_samples_dict[sample])] - filter_fh.write(sample + '\t' + '\t'.join(missing_values) + '\t' + str(sum(missing_values_all_samples_dict[sample].values())/total_loci*100) + '\n') - filter_fh.write('\n' + '\n' + '\n') - - - if locus_missing_threshold < 100: - - # Filter loci from allele calling comparison table file - pd_matrix, missing_values_all_loci_dict, total_samples, total_loci = missing_filter_col(pd_matrix, missing_values_dict, locus_missing_threshold, logger) - - # Get samples filtering report - logger.info('Saving distance matrix filter information to file after loci filtering..') - - header_loci_filter = ["Core Gene", "LNF", "NIPH + NIPHEM", "PLOT", "Total missing values %"] - - with open (filter_report_file, 'a') as filter_fh: - - #if len(missing_values_all_loci_dict) > 0: - - filter_fh.write(str(len(missing_values_all_loci_dict)) + "/" + str(total_loci) + " loci with missing values in more than " + str(locus_missing_threshold) + "% of samples removed: " + '\n') - - if len(missing_values_all_loci_dict)/total_loci > 0.1: - - filter_fh.write("WARNING! Less than 90% of cgMLST schema loci kept to create distance matrix." + '\n') - - filter_fh.write('\n' + '\t'.join(header_loci_filter) + '\n') - - for locus in sorted(missing_values_all_loci_dict): - - missing_values = [str(missing_values_all_loci_dict[locus][missing_tag]) + "/" + str(total_samples) for missing_tag in sorted(missing_values_all_loci_dict[locus])] - filter_fh.write(locus + '\t' + '\t'.join(missing_values) + '\t' + str(sum(missing_values_all_loci_dict[locus].values())/total_samples*100) + '\n') - - ## Save filtered allele calling results - out_filtered_results_file = os.path.join(outputdir, 'filtered_result.tsv') - pd_matrix.to_csv(out_filtered_results_file, sep = '\t') - - else: - print("WARNING: Samples or loci filtering not specified. Cannot filter missing values") - logger.info('WARNING: Samples or loci filtering not specified. Cannot filter missing values..') - - else: - if sample_missing_threshold < 100 or locus_missing_threshold < 100: - print("WARNING: Missing values to be filtered not specified. Cannot filter missing values") - logger.info('WARNING: Missing values to be filtered not specified. Cannot filter missing values..') - - ## Get and keep hamming distance matrix - distance_matrix = hamming_distance (pd_matrix) - out_distance_file = os.path.join(outputdir, 'matrix_distance.tsv') - - try: - distance_matrix.to_csv(out_distance_file, sep = '\t') - except Exception as e: - logger.info('Unable to create the distance matrix file %s', out_file) - - print('------------- ERROR --------------') - print('Unable to create the distance matrix file') - print('Check in the logging configuration file') - print('------------------------------------------') - return 'Error' - - return True - - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # -# Get samples distance matrix from allele calling comparison table # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # - -def processing_distance_matrix (arguments) : - ''' - Description: - This is the main function for getting distance matrix. - With the support of additional functions it will obtain the samples distance matrix from allele calling comparison table. - Input: - arguments # Input arguments given on command line - Functions: - - Variables: ????? - - Return: ????? - ''' - - start_time = datetime.now() - print('Start the execution at :', start_time ) - # Open log file - logger = open_log ('taranis_distance_matrix.log') - - ############################# - ## Create output directory ## - ############################# - try: - os.makedirs(arguments.outputdir) - except: - logger.info('Deleting the output directory for a previous execution without cleaning up') - shutil.rmtree(arguments.outputdir) - try: - os.makedirs(arguments.outputdir) - logger.info ('Output folder %s has been created again', arguments.outputdir) - except: - logger.info('Unable to create again the output directory %s', arguments.outputdir) - print('Cannot create output directory on ', arguments.outputdir) - exit(0) - - ################################ - ## Create the distance matrix ## - ################################ - try: - logger.info('Creating distance matrix') - print ('Creating distance matrix\n') - create_distance_matrix(arguments.alleles_matrix, arguments.outputdir, int(arguments.locus_missing_threshold), int(arguments.sample_missing_threshold), str(arguments.paralog_filter).lower(), str(arguments.lnf_filter).lower(), str(arguments.plot_filter).lower(), logger) - - except: - logger.info('There was an error when creating distance matrix') - print('There was an error when creating distance matrix\n') - shutil.rmtree(os.path.join(arguments.outputdir)) - exit(0) - - end_time = datetime.now() - print('Completed execution at :', end_time ) - - return True \ No newline at end of file diff --git a/reference_alleles.py b/reference_alleles.py deleted file mode 100755 index 262f8c3..0000000 --- a/reference_alleles.py +++ /dev/null @@ -1,219 +0,0 @@ -#!/usr/bin/env python3 - -from datetime import datetime -from progressbar import ProgressBar -from Bio.Blast.Applications import NcbiblastnCommandline -import numpy as np -from utils.taranis_utils import * - - -# · * · * · * · * · * · * · * · * · * · * · * · * · # -# Get reference alleles for one locus in the schema # -# · * · * · * · * · * · * · * · * · * · * · * · * · # - -def get_reference_allele(locus_quality, fasta_file, store_dir, evalue, perc_identity, reward, penalty, gapopen, gapextend, num_threads, logger): - - logger.info('Searching reference alleles') - - ## Create mash directory where to store temporal files - f_name = os.path.basename(fasta_file).split('.') - #full_path_reference_allele = os.path.join(store_dir, 'reference_alleles') - #full_path_mash = os.path.join(full_path_reference_allele, 'mash') - full_path_mash = os.path.join(store_dir, 'mash') - full_path_locus_mash = os.path.join(full_path_mash, f_name[0]) - - if not os.path.exists(full_path_locus_mash): - try: - os.makedirs(full_path_locus_mash) - logger.info('Directory %s has been created', full_path_locus_mash) - except: - print ('Cannot create the directory ', full_path_locus_mash) - logger.info('Directory %s cannot be created', full_path_locus_mash) - exit (0) - - - ## Split locus multifasta into fastas containing one allele sequence each - alleles_in_locus_number = 0 # Get alleles in locus number to set max_target_seqs value in final BLAST search - alleles_in_locus = [] # List to store alleles in locus IDs to intersect final BLAST search results - for record in list(SeqIO.parse(fasta_file, "fasta")): - alleles_in_locus.append(str(record.id)) - split_fasta_path = os.path.join(full_path_locus_mash, str(record.id) + ".fasta") - alleles_in_locus_number += 1 - with open (split_fasta_path, 'w') as out_fh: - out_fh.write ('>' + str(record.id) + '\n' + str(record.seq)) - - - ## Get mash sketch file to get pairwise sequences distances at a time - sketch_path = os.path.join(full_path_locus_mash, "reference.msh") - mash_sketch_command = ["mash", "sketch", "-o", sketch_path] - - # Get file paths to include in mash sketch file - split_multifasta_files_list = get_fasta_file_list(full_path_locus_mash, logger) - - for fasta_path in split_multifasta_files_list: - mash_sketch_command.append(fasta_path) - - mash_sketch_result = subprocess.run(mash_sketch_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) - - - ## Get pairwise allele sequences mash distances - mash_distance_command = ["mash", "dist", sketch_path, sketch_path] - mash_distance_result = subprocess.Popen(mash_distance_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) - - out, err = mash_distance_result.communicate() - out = out.decode('UTF-8').split('\n') - - comp_dist_list = [] - for n in range(len(out)-1): - comp = out[n].split('\t') - comp_dist = float(comp[2]) - comp_dist_list.append(comp_dist) - - - ## Get distances matrix and mean distances matrix - comp_dist_list_per_allele = [] - alleles_number = len(split_multifasta_files_list) - - for index_distance in range(0, len(comp_dist_list), alleles_number): - dist_per_allele = comp_dist_list[index_distance : index_distance + alleles_number] - comp_dist_list_per_allele.append(dist_per_allele) - - comp_dist_arr_per_allele = np.asarray(comp_dist_list_per_allele) - allele_mean_distance = np.mean(comp_dist_arr_per_allele, 0) - - - ## Get reference allele (centroid): max average distance allele tagged as 'good_quality' - min_mean = max(allele_mean_distance) - ref_allele_id = str() - - for mean_index in range(len(allele_mean_distance)): - if allele_mean_distance[mean_index] <= min_mean: - allele_path = split_multifasta_files_list[mean_index] - allele_id = os.path.basename(split_multifasta_files_list[mean_index]).split('.')[0] - if locus_quality[allele_id] == 'good_quality': - min_mean = allele_mean_distance[mean_index] - ref_allele_id = allele_id - - - ## Check that chosen reference allele represents every allele in the locus - - # Create local BLAST database for all alleles in the locus - db_name = os.path.join(store_dir, 'locus_blastdb') - if not create_blastdb(fasta_file, db_name, 'nucl', logger): - print('Error when creating the blastdb for locus %s. Check log file for more information. \n ', f_name[0]) - return False - - locus_db_name = os.path.join(db_name, f_name[0], f_name[0]) - - # All alleles in locus VS reference allele chosen (centroid) BLAST - blast_parameters = '"6 , qseqid , sseqid , pident , qlen , length , mismatch , gapopen , evalue , bitscore , sstart , send , qstart , qend , sseq , qseq"' - cline = NcbiblastnCommandline(db=locus_db_name, evalue=evalue, perc_identity=perc_identity, reward=reward, penalty=penalty, gapopen=gapopen, gapextend=gapextend, outfmt=blast_parameters, max_target_seqs=alleles_in_locus_number, max_hsps=alleles_in_locus_number, num_threads=num_threads, query=allele_path) - - out, err = cline() - out_lines = out.splitlines() - - # Keep not represented alleles along with the centroid as reference alleles of themselves - alleles_in_blast = [] - - for line in out_lines: - values = line.split('\t') - alleles_in_blast.append(values[1]) - - ids_intersect = list(set(alleles_in_locus) - set(alleles_in_blast)) - ids_intersect.insert(0, ref_allele_id) - - reference_file_path = os.path.join(store_dir, os.path.basename(fasta_file)) - with open (reference_file_path, 'w') as out_fh: - for record in list(SeqIO.parse(fasta_file, "fasta")): - if record.id in ids_intersect: - out_fh.write ('>' + str(record.id) + '\n' + str(record.seq) + '\n') - - shutil.rmtree(full_path_locus_mash) - shutil.rmtree(db_name) - - return True, full_path_mash - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * # -# Get reference alleles for every locus in the schema # -# · * · * · * · * · * · * · * · * · * · * · * · * · * # - -def processing_reference_alleles (arguments) : - ''' - Description: - This is the main function for getting reference alleles. - With the support of additional functions it will obtain reference alleles for each locus in the schema. - Input: - arguments # Input arguments given on command line - Functions: - - Variables: ????? - - Return: ????? - ''' - - start_time = datetime.now() - print('Start the execution at :', start_time ) - # Open log file - logger = open_log ('taranis_ref_alleles.log') - print('Checking the pre-requisites.') - - ############################################################ - ## Check additional programs are installed in your system ## - ############################################################ - #pre_requisite_list = [['blastp', '2.9'], ['makeblastdb' , '2.9'], ['mash', '2']] - #if not check_prerequisites (pre_requisite_list, logger): - # print ('your system does not fulfill the pre-requistes to run the script ') - # exit(0) - - ###################################################### - ## Check that given directories contain fasta files ## - ###################################################### - print('Validating schema fasta files in ' , arguments.coregenedir , '\n') - core_gene_files_list = get_fasta_file_list(arguments.coregenedir, logger) - if not core_gene_files_list : - print ('There are not valid fasta files in ', arguments.coregenedir , ' directory. Check log file for more information ') - exit(0) - - ############################# - ## Create output directory ## - ############################# - try: - os.makedirs(arguments.outputdir) - except: - logger.info('Deleting the output directory for a previous execution without cleaning up') - shutil.rmtree(arguments.outputdir) - try: - os.makedirs(arguments.outputdir) - logger.info ('Output folder %s has been created again', arguments.outputdir) - except: - logger.info('Unable to create again the output directory %s', arguments.outputdir) - print('Cannot create output directory on ', arguments.outputdir) - exit(0) - - ####################################################### - ## Obtain reference alleles for each locus in schema ## - ####################################################### - logger.info('Getting reference alleles for each locus in schema') - print('Getting reference alleles...') - - pbar = ProgressBar () - for fasta_file in pbar(core_gene_files_list): - - # Get core gene alleles quality - locus_quality = check_core_gene_quality(fasta_file, logger) - - # Get core gene reference alleles - complete_reference_alleles, full_path_mash = get_reference_allele(locus_quality, fasta_file, arguments.outputdir, float(arguments.evalue), float(arguments.perc_identity), int(arguments.reward), int(arguments.penalty), int(arguments.gapopen), int(arguments.gapextend), int(arguments.num_threads), logger) - if not complete_reference_alleles: - print('There is an error while processing reference alleles. Check the log file to get more information \n') - logger.info('Deleting the directory to clean up the temporary files created') - shutil.rmtree(os.path.join(arguments.outputdir)) - exit(0) - - shutil.rmtree(full_path_mash) - - end_time = datetime.now() - print('Completed execution at :', end_time ) - - return True \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..ea5b59f --- /dev/null +++ b/requirements.txt @@ -0,0 +1,4 @@ +click +questionary +bio +scikit-learn \ No newline at end of file diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..363fec6 --- /dev/null +++ b/setup.py @@ -0,0 +1,38 @@ +#!/usr/bin/env python + +from setuptools import setup, find_packages + +version = "2.1.0" + +with open("README.md") as f: + readme = f.read() + +with open("requirements.txt") as f: + required = f.read().splitlines() + +setup( + name="taranis", + version=version, + description="Tools for gene-by-gene allele calling analysis", + long_description=readme, + long_description_content_type="text/markdown", + keywords=[ + "buisciii", + "bioinformatics", + "pipeline", + "sequencing", + "NGS", + "next generation sequencing", + ], + author="Sara Monzon", + author_email="smonzon@isciii.es", + url="https://github.com/BU-ISCIII/taranis", + license="GNU GENERAL PUBLIC LICENSE v.3", + entry_points={ + "console_scripts": ["taranis=taranis.__main__:run_taranis"] + }, + install_requires=required, + packages=find_packages(exclude=("docs")), + include_package_data=True, + zip_safe=False, +) diff --git a/taranis.py b/taranis.py deleted file mode 100755 index 706aa9b..0000000 --- a/taranis.py +++ /dev/null @@ -1,282 +0,0 @@ -#!/usr/bin/env python3 - -import sys -import argparse -import create_schema -import analyze_schema -import reference_alleles -import allele_calling -import distance_matrix -from taranis_configuration import * - -def check_arg (args=None) : - ''' - The function is used for parsing the input parameters form the command line - using the standard python package argparse. The package itself is handling - the validation and the return errors messages - Input: - args # Contains the arguments from the command line - Variables: - allele_calling_parser # It is used for the allele calling input parameters - analyze_schema_parser # It is used for the schema evaluation input parameters - compare_schema_parser # It is used for schema comparison input parameters - create_schema_parser # It is used for create an schema input parameters - Return: - parser.parse_args() # The variable contains the valid parameters - ''' - parser = argparse.ArgumentParser(prog = 'taranis.py', - formatter_class=argparse.RawDescriptionHelpFormatter, - description= 'Taranis is a set of utilities related to cgMSLST ') - - parser.add_argument('--version', action='version', version='%(prog)s 0.3.5') - - subparser = parser.add_subparsers(help = 'analyze_schema, reference_alleles, allele_calling, distance_matrix ' - + 'are the available actions to execute taranis', - dest = 'chosen_action') - - - ### Input parameters for analyze schema option - analyze_schema_parser = subparser.add_parser('analyze_schema', - help = 'Analyze the schema.') - analyze_schema_parser.add_argument('-inputdir',required = True, - help = 'Directory where are the schema files.') - analyze_schema_parser.add_argument('-outputdir', required = True, - help = 'Directory where the result files will be stored.') - analyze_schema_parser.add_argument('-removesubsets', required = False, - help = 'Remove allele subsequences from the schema.' - + 'True: Remove subsets.' - + 'False: Do not remove subsets.' - + 'Default is False.', - default = False) - analyze_schema_parser.add_argument('-removeduplicates', required = False, - help = 'Remove duplicated alleles from the schema.' - + 'True: Remove duplicates.' - + 'False: Do not remove duplicates.' - + 'Default is False.', - default = False) - analyze_schema_parser.add_argument('-removenocds', required = False, - help = 'Remove no CDS alleles from the schema.' - + 'True: Remove no CDS alleles.' - + 'False: Do not remove no CDS alleles.' - + 'Default is False.', - default = False) - analyze_schema_parser.add_argument('-newschema', required = False, - help = 'Filter a copy of the core genes schema preserving the analysis core genes schema.' - #Create an analysis core genes schema copy for filtering alleles when this option is selected.' - + 'True: Create a copy of the core genes schema for filtering.' - + 'False: Do not create a copy of the core genes schema for filtering.' - + 'Default is False.', - default = False) - analyze_schema_parser.add_argument('-genus' , required = False, - help = 'Genus name for Prokka schema genes annotation. Default is Genus. ', - default = 'Genus') - analyze_schema_parser.add_argument('-species' , required = False, - help = 'Species name for Prokka schema genes annotation. Default is species. ', - default = 'species') - analyze_schema_parser.add_argument('-usegenus' , required = False, - help = 'Use genus-specific BLAST databases for Prokka schema genes annotation (needs --genus). Default is False. ', - default = 'False') - analyze_schema_parser.add_argument('-cpus', required = False, - help = 'Number of CPUS to be used in the program. Default is 1.', - default = 1) - - - ### Input parameters for reference alleles options - reference_alleles_parser = subparser.add_parser('reference_alleles', help = 'Obtain reference allele(s) for each locus.') - reference_alleles_parser.add_argument('-coregenedir', required = True, - help = 'Directory where the core gene files are located. ') - reference_alleles_parser.add_argument('-outputdir', required = True, - help = 'Directory where the result files will be stored. ') - reference_alleles_parser.add_argument('-evalue', required = False, - help = 'E-value in BLAST searches. Default is 0.001.', - default = 0.001) - reference_alleles_parser.add_argument('-perc_identity', required = False, - help = 'Identity percent in BLAST searches. Default is 90. ', - default = 90) - reference_alleles_parser.add_argument('-reward', required = False, - help = 'Match reward in BLAST searches. Default is 1. ', - default = 1) - reference_alleles_parser.add_argument('-penalty', required = False, - help = 'Mismatch penalty in BLAST searches. Default is -2. ', - default = -2) - reference_alleles_parser.add_argument('-gapopen', required = False, - help = 'Gap open penalty in BLAST searches. Default is 1. ', - default = 1) - reference_alleles_parser.add_argument('-gapextend', required = False, - help = 'Gap extension penalty in BLAST searches. Default is 1. ', - default = 1) - reference_alleles_parser.add_argument('-num_threads', required = False, - help = 'num_threads in BLAST searches. Default is 1. ', - default = 1) - reference_alleles_parser.add_argument('-cpus', required = False, - help = 'Number of CPUS to be used in the program. Default is 1.', - default = 1) - - - ### Input parameters for allele calling option - allele_calling_parser = subparser.add_parser('allele_calling', - help = 'Gene by gene allele calling.') - allele_calling_parser.add_argument('-coregenedir', required = True, - help = 'Directory where the core gene files are located ') - allele_calling_parser.add_argument('-refalleles', required = True, - help = 'Directory where the core gene references files are located ') - allele_calling_parser.add_argument('-inputdir', required = True, - help ='Directory where are located the sample fasta files') - allele_calling_parser.add_argument('-refgenome', required = True, - help = 'Reference genome file for genes prediction') - allele_calling_parser.add_argument('-outputdir', required = True, - help = 'Directory where the result files will be stored') - allele_calling_parser.add_argument('-percentlength', required = False, - help = 'Allowed length percentage to be considered as INF. ' - + 'Outside of this limit it is considered as ASM or ALM. Default is SD.', - default = 'SD') - allele_calling_parser.add_argument('-coverage', required = False, - help = 'Coverage threshold to exclude found sequences. ' - + 'Outside of this limit it is considered LNF. Default is 50.', - default = 50) - allele_calling_parser.add_argument('-evalue', required = False, - help = 'E-value in BLAST searches. Default is 0.001. ', - default = 0.001) - allele_calling_parser.add_argument('-perc_identity_ref', required = False, - help = 'Identity percentage in BLAST searches using reference alleles for each locus detection in samples. Default is 90.', - default = 85) - allele_calling_parser.add_argument('-perc_identity_loc', required = False, - help = 'Identity percentage in BLAST searches using all alleles in each locus for allele identification in samples. Default is 90.', - default = 90) - allele_calling_parser.add_argument('-reward', required = False, - help = 'Match reward in BLAST searches. Default is 1. ', - default = 1) - allele_calling_parser.add_argument('-penalty', required = False, - help = 'Mismatch penalty in BLAST searches. Default is -2. ', - default = -2) - allele_calling_parser.add_argument('-gapopen', required = False, - help = 'Gap open penalty in BLAST searches. Default is 1. ', - default = 1) - allele_calling_parser.add_argument('-gapextend', required = False, - help = 'Gap extension penalty in BLAST searches. Default is 1. ', - default = 1) - allele_calling_parser.add_argument('-max_target_seqs', required = False, - help = 'max_target_seqs in BLAST searches. Default is 10. ', - default = 10) - allele_calling_parser.add_argument('-max_hsps', required = False, - help = 'max_hsps in BLAST searches. Default is 10. ', - default = 10) - allele_calling_parser.add_argument('-num_threads', required = False, - help = 'num_threads in BLAST searches. Default is 1. ', - default = 1) - allele_calling_parser.add_argument('-flankingnts' , required = False, - help = 'Number of flanking nucleotides to add to each BLAST result obtained after locus detection in sample using reference allele for correct allele identification. Default is 100. ', - default = 100) - allele_calling_parser.add_argument('-updateschema' , required = False, - help = 'Add INF alleles found for each locus to the core genes schema. ' - + 'True: Add INF alleles to the analysis core genes schema. ' - + 'New: Add INF alleles to a copy of the core genes schema preserving the analysis core genes schema. ' - + 'False: Do not update the core gene schema adding new INF alleles found. ' - + 'Default is True. ', - default = True) - allele_calling_parser.add_argument('-profile' , required = False, - help = 'ST profile file based on core genes schema file to get ST for each sample. Default is empty and Taranis does not calculate samples ST. ', - default = 'False') - allele_calling_parser.add_argument('-updateprofile' , required = False, - help = 'Add new ST profiles found to the ST profile file. ' - + 'True: Add new ST profiles to the analysis ST profile file. ' - + 'New: Add Add new ST profiles to a copy of the ST profile file preserving the analysis ST file. ' - + 'False: Do not update the ST profile file adding new ST profiles found. ' - + 'Default is True. ', - default = True) - allele_calling_parser.add_argument('-cpus', required = False, - help = 'Number of CPUS to be used in the program. Default is 1.', - default = 1) - allele_calling_parser.add_argument('-genus' , required = False, - help = 'Genus name for Prokka schema genes annotation. Default is Genus. ', - default = 'Genus') - allele_calling_parser.add_argument('-species' , required = False, - help = 'Species name for Prokka schema genes annotation. Default is species. ', - default = 'species') - allele_calling_parser.add_argument('-usegenus' , required = False, - help = 'Use genus-specific BLAST databases for Prokka schema genes annotation (needs --genus). Default is False. ', - default = 'False') - - - ### Input parameters for distance matrix option - distance_matrix_parser = subparser.add_parser('distance_matrix', - help = 'Get samples distance matrix from allele calling comparison table.') - distance_matrix_parser.add_argument('-alleles_matrix', required = True, - help = 'Alleles matrix file from which to obtain distances between samples') - distance_matrix_parser.add_argument('-locus_missing_threshold', required = False, - help = 'Missing values percentage threshold above which loci are excluded for distance matrix creation. Default is 100.', - default = 100) - distance_matrix_parser.add_argument('-sample_missing_threshold', required = False, - help = 'Missing values percentage threshold above which samples are excluded for distance matrix creation. Default is 100.', - default = 20) - distance_matrix_parser.add_argument('-paralog_filter', required = False, - help = 'Consider paralog tags (NIPH, NIPHEM) as missing values. Default is True', - default = True) - distance_matrix_parser.add_argument('-lnf_filter', required = False, - help = 'Consider locus not found tag (LNF) as missing value. Default is True', - default = True) - distance_matrix_parser.add_argument('-plot_filter', required = False, - help = 'Consider incomplete alleles found on the tip of a contig tag (PLOT) as missing value. Default is True', - default = True) - distance_matrix_parser.add_argument('-outputdir', required = True, - help = 'Directory where the result files will be stored') - - - ### Input parameters for schema comparison options - #compare_schema_parser = subparser.add_parser('compare_schema', help = 'Compare 2 schema.') - #compare_schema_parser.add_argument('-scheme1', - # help = 'Directory where are the schema files for the schema 1.') - #compare_schema_parser.add_argument('-scheme2', - # help = 'Directory where are the schema files for the schema 2.') - - - ### Input parameters for schema creation options - #create_schema_parser = subparser.add_parser('create_schema', help = 'Create a schema.') - #create_schema_parser.add_argument('-xlsfile', - # help = 'xls file name which contains the list of the core genes.') - #create_schema_parser.add_argument('-outputdir', help = 'Directory where the core gene files ' - # + 'will be stored. If directory exists it will be prompt for ' - # + 'deletion confirmation.') - - return parser.parse_args() - - -#def processing_compare_schema (arguments) : - # print ('compare_schema') - # return True - -#def processing_create_schema (arguments) : - # print ('create_schema') - # create_schema.processing_create_schema(arguments) - # return True - -if __name__ == '__main__' : - version = 'taranis version 0.2.2' - if len(sys.argv) == 1 : - print( 'Mandatory parameters are missing to execute the program. \n ' ,'Usage: "taranis.py -help " for more information \n') - exit (0) - - arguments = check_arg(sys.argv[1:]) - - if arguments.chosen_action == 'allele_calling' : - result = allele_calling.processing_allele_calling(arguments) - elif arguments.chosen_action == 'analyze_schema': - result = analyze_schema.processing_analyze_schema(arguments) - elif arguments.chosen_action == 'reference_alleles' : - result = reference_alleles.processing_reference_alleles(arguments) - elif arguments.chosen_action == 'distance_matrix' : - result = distance_matrix.processing_distance_matrix(arguments) - #elif arguments.chosen_action == 'compare_schema' : - # result = processing_compare_schema(arguments) - #elif arguments.chosen_action == 'create_schema' : - # result = processing_create_schema(arguments) - else: - print('not allow') - result = 'Error' - ''' - if 'Error' in result : - print('Exiting the code with errors. ') - print('Check the log for more information') - else: - ''' - print('completed') diff --git a/taranis/__init__.py b/taranis/__init__.py new file mode 100644 index 0000000..c968626 --- /dev/null +++ b/taranis/__init__.py @@ -0,0 +1,3 @@ +import pkg_resources + +__version__ = pkg_resources.get_distribution("taranis").version \ No newline at end of file diff --git a/taranis/__main__.py b/taranis/__main__.py new file mode 100644 index 0000000..548d014 --- /dev/null +++ b/taranis/__main__.py @@ -0,0 +1,143 @@ +import logging + +import click +import os +import rich.console +import rich.logging +import rich.traceback +import sys + +import taranis.utils +import taranis.reference_alleles + +log = logging.getLogger() + +# Set up rich stderr console +stderr = rich.console.Console( + stderr=True, force_terminal=taranis.utils.rich_force_colors() +) + + +def run_taranis(): + # Set up the rich traceback + rich.traceback.install(console=stderr, width=200, word_wrap=True, extra_lines=1) + + # Print taranis header + # stderr.print("\n[green]{},--.[grey39]/[green],-.".format(" " * 42), highlight=False) + stderr.print("[blue] ______ ___ ___ ", highlight=False, ) + stderr.print("[blue] \ |-[grey39]-| [blue] |__--__| /\ | \ /\ |\ | | | ", highlight=False,) + stderr.print("[blue] \ \ [grey39]/ [blue] || / \ |__ / / \ | \ | | |___ ", highlight=False,) + stderr.print("[blue] / [grey39] / [blue] \ || /____\ | \ /____\ | \ | | |", highlight=False, ) + stderr.print("[blue] / [grey39] |-[blue]-| || / \ | \ / \ | \| | ___| ", highlight=False,) + + # stderr.print("[green] `._,._,'\n", highlight=False) + __version__ = "2.1.0" + stderr.print( + "\n" "[grey39] Taranis version {}".format(__version__), highlight=False + ) + + # Lanch the click cli + taranis_cli() + + +# Customise the order of subcommands for --help +class CustomHelpOrder(click.Group): + def __init__(self, *args, **kwargs): + self.help_priorities = {} + super(CustomHelpOrder, self).__init__(*args, **kwargs) + + def get_help(self, ctx): + self.list_commands = self.list_commands_for_help + return super(CustomHelpOrder, self).get_help(ctx) + + def list_commands_for_help(self, ctx): + """reorder the list of commands when listing the help""" + commands = super(CustomHelpOrder, self).list_commands(ctx) + return ( + c[1] + for c in sorted( + (self.help_priorities.get(command, 1000), command) + for command in commands + ) + ) + + def command(self, *args, **kwargs): + """Behaves the same as `click.Group.command()` except capture + a priority for listing command names in help. + """ + help_priority = kwargs.pop("help_priority", 1000) + help_priorities = self.help_priorities + + def decorator(f): + cmd = super(CustomHelpOrder, self).command(*args, **kwargs)(f) + help_priorities[cmd.name] = help_priority + return cmd + + return decorator + + +@click.group(cls=CustomHelpOrder) +@click.version_option(taranis.__version__) +@click.option( + "-v", + "--verbose", + is_flag=True, + default=False, + help="Print verbose output to the console.", +) +@click.option( + "-l", "--log-file", help="Save a verbose log to a file.", metavar="" +) +def taranis_cli(verbose, log_file): + # Set the base logger to output DEBUG + log.setLevel(logging.DEBUG) + + # Set up logs to a file if we asked for one + if log_file: + log_fh = logging.FileHandler(log_file, encoding="utf-8") + log_fh.setLevel(logging.DEBUG) + log_fh.setFormatter( + logging.Formatter( + "[%(asctime)s] %(name)-20s [%(levelname)-7s] %(message)s" + ) + ) + log.addHandler(log_fh) + +# Reference alleles +@taranis_cli.command(help_priority=2) +@click.option("-s", "--schema", required=True, multiple=False, type=click.Path(), help="Directory where the schema with the core gene files are located. ") +@click.option("-o", "--output", required=True, multiple=False, type=click.Path(), help="Output folder to save reference alleles") +def reference_alleles( + schema, + output, +): + # taranis reference-alleles -s ../../documentos_antiguos/datos_prueba/schema_test/ -o ../../new_taranis_result_code + if not taranis.utils.folder_exists(schema): + log.error("schema folder %s does not exists", schema) + stderr.print( + "[red] Schema folder does not exist. " + schema + "!" + ) + sys.exit(1) + schema_files = taranis.utils.get_files_in_folder(schema, "fasta") + if len(schema_files) == 0: + log.error("Schema folder %s does not have any fasta file", schema) + stderr.print("[red] Schema folder does not have any fasta file") + sys.exit(1) + # Check if output folder exists + if taranis.utils.folder_exists(output): + q_question = "Folder " + output + " already exists. Files will be overwritten. Do you want to continue?" + if "no" in taranis.utils.query_user_yes_no(q_question, "no"): + log.info("Aborting code by user request") + stderr.print("[red] Exiting code. ") + sys.exit(1) + else: + try: + os.makedirs(output) + except OSError as e: + log.info("Unable to create folder at %s", output) + stderr.print("[red] ERROR. Unable to create folder " + output) + sys.exit(1) + """Create the reference alleles from the schema """ + for f_file in schema_files: + ref_alleles = taranis.reference_alleles.ReferenceAlleles(f_file, output) + _ = ref_alleles.create_ref_alleles() \ No newline at end of file diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py new file mode 100644 index 0000000..4a6ed91 --- /dev/null +++ b/taranis/reference_alleles.py @@ -0,0 +1,149 @@ +import logging +import os +import re +import rich.console +import sys +import subprocess + +# from Bio import SeqIO +from Bio.Seq import Seq + +import taranis.utils + +import pdb + +log = logging.getLogger(__name__) +stderr = rich.console.Console( + stderr=True, + style="dim", + highlight=False, + force_terminal=taranis.utils.rich_force_colors(), +) + +class ReferenceAlleles: + def __init__(self, fasta_file, output): + """ + self.schema_dir = schema_dir + self.out_dir = out_dir + if self.schema_dir is None: + self.schema_dir = taranis.utils.prompt_text("Write the path of the schema`s files") + if not taranis.utils.folder_exists(self.schema_dir): + log.error("schema folder %s does not exists", self.schema_dir) + stderr.print( + "[red] Schema folder does not exist. " + self.schema_dir + "!" + ) + sys.exit(1) + if out_dir is None: + self.out_dir = taranis.utils.prompt_text("Define the the directory to save results") + # Check if folder exists + if taranis.utils.folder_exists(self.out_dir): + q_question = "Folder " + self.out_dir + " already exists. Files will be overwritten. Do you want to continue?" + if "no" in taranis.utils.query_user_yes_no(q_question, "no"): + log.info("Aborting code by user request") + stderr.print("[red] Exiting code. ") + sys.exit(1) + else: + try: + os.makedirs(self.out_dir) + except OSError as e: + log.info("Unable to create folder at %s", self.out_dir) + stderr.print("[red] ERROR. Unable to create folder " + self.out_dir) + sys.exit(1) + """ + self.fasta_file = fasta_file + self.output = output + self.records = None + self.locus_quality = {} + self.selected_locus = {} + + def check_locus_quality(self): + # START_CODONS_FORWARD = ['ATG', 'ATA', 'ATT', 'GTG', 'TTG', 'CTG'] + # START_CODONS_REVERSE = ['CAT', 'TAT', 'AAT', 'CAC', 'CAA', 'CAG'] + + STOP_CODONS_FORWARD = ["TAA", "TAG", "TGA"] + STOP_CODONS_REVERSE = ["TTA", "CTA", "TCA"] + for record in self.records: + # Check if start condon forward + seq = str(record.seq) + s_codon_f = re.match(r"^(ATG|ATA|ATT|GTG|TTG|CTG).+(\w{3})$", seq) + if s_codon_f: + # Check if last 3 characters are codon stop forward + if s_codon_f.group(2) in STOP_CODONS_FORWARD: + # Check if multiple stop codon by translating to protein and + # comparing length + locus_prot = Seq(record.seq).translate() + if len(locus_prot) == int(len(seq)/3): + self.locus_quality[record.id] = "good quality" + self.selected_locus[record.id] = seq + else: + self.locus_quality[record.id] = "bad quality: multiple_stop" + else: + self.locus_quality[record.id] = "bad quality: no_stop" + else: + # Check if start codon reverse + s_codon_r = re.match(r"^(\w{3}).+ (CAT|TAT|AAT|CAC|CAA|CAG)$", seq) + if s_codon_r: + # Matched reverse start codon + if s_codon_f.group(1) in STOP_CODONS_REVERSE: + locus_prot = Seq(record.seq).reverse_complement().translate() + if len(locus_prot) == int(len(record.seq)/3): + self.locus_quality[record.id] = "good quality" + self.selected_locus[record.id] = seq + else: + self.locus_quality[record.id] = "bad quality: multiple_stop" + else: + self.locus_quality[record.id] = "bad quality: no_stop" + else: + self.locus_quality[record.id] = "bad_quality: no_start" + return + + def create_matrix_distance(self): + f_name = os.path.basename(self.fasta_file).split('.')[0] + mash_folder = os.path.join(self.output, "mash", f_name ) + _ = taranis.utils.write_fasta_file(mash_folder, self.selected_locus, multiple_files=True, extension=False) + # save directory to return after mash + working_dir = os.getcwd() + os.chdir(mash_folder) + # run mash sketch command + sketch_path = "reference.msh" + mash_sketch_command = ["mash", "sketch", "-o", sketch_path] + mash_sketch_command += list(self.selected_locus.keys()) + + mash_sketch_result = subprocess.run(mash_sketch_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) + # Get pairwise allele sequences mash distances + mash_distance_command = ["mash", "dist", sketch_path, sketch_path] + mash_distance_result = subprocess.Popen(mash_distance_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) + + out, err = mash_distance_result.communicate() + with open ("matrix_distance.tsv", "w") as fo: + fo.write(out.decode("UTF-8")) + import pandas as pd + locus_num = len(self.selected_locus) + + matrix_df = pd.read_csv("matrix_distance.tsv", sep="\t", header=None) + list_alleles = matrix_df.iloc[0:locus_num,0] + values_np = matrix_df.iloc[:,2].to_numpy() + + matrix_np = values_np.reshape(locus_num, locus_num) + # out = out.decode('UTF-8').split('\n') + from sklearn.cluster import AgglomerativeClustering + clusterer = AgglomerativeClustering(n_clusters=3, metric="precomputed", linkage="average", distance_threshold=None) + clusters = clusterer.fit_predict(matrix_np) + # clustering = AgglomerativeClustering(affinity="precomputed").fit(matrix_np) + pdb.set_trace() + + # from sklearn.cluster import AgglomerativeClustering + # import numpy as np + # X = np.array([[0, 2, 3], [2, 0, 3], [3, 3, 0]]) + # clustering = AgglomerativeClustering(affinity="precomputed").fit(X) + + + def create_ref_alleles(self): + self.records = taranis.utils.read_fasta_file(self.fasta_file) + _ = self.check_locus_quality() + pdb.set_trace() + # Prepare data to use mash to create the distance matrix + _ = self.create_matrix_distance() + + + pass \ No newline at end of file diff --git a/taranis/utils.py b/taranis/utils.py new file mode 100644 index 0000000..9ca894f --- /dev/null +++ b/taranis/utils.py @@ -0,0 +1,131 @@ +#!/usr/bin/env python +""" +Common utility function used for relecov_tools package. +""" + +import glob +# import hashlib +import logging +import questionary +# import json +# import openpyxl +# import yaml +# from itertools import islice + +import os +from rich.console import Console +import sys + +from Bio import SeqIO + +log = logging.getLogger(__name__) + + +def get_files_in_folder(folder, extension=None): + """get the list of files, filtered by extension in the input folder. If + extension is not set, then all files in folder are returned + + Args: + folder (string): folder path + extension (string, optional): extension for filtering. Defaults to None. + + Returns: + list: list of files + """ + if extension is None: + extension = "*" + return glob.glob(folder + "*." + extension) + + +def file_exists(file_to_check): + """Checks if input file exists + + Args: + file_to_check (string): file name including path of the file + + Returns: + boolean: True if exists + """ + if os.path.isfile(file_to_check): + return True + return False + +def folder_exists(folder_to_check): + """Checks if input folder exists + + Args: + folder_to_check (string): folder name including path + + Returns: + boolean: True if exists + """ + if os.path.isdir(folder_to_check): + return True + return False + +def prompt_text(msg): + source = questionary.text(msg).unsafe_ask() + return source + +def query_user_yes_no(question, default): + """Query the user to choose yes or no for the query question + + Args: + question (string): Text message + default (string): default option to be used: yes or no + + Returns: + user select: True continue with code + """ + valid = {"yes": True, "y": True, "ye": True, "no": False, "n": False} + if default is None: + prompt = " [y/n] " + elif default == "yes": + prompt = " [Y/n] " + elif default == "no": + prompt = " [y/N] " + else: + raise ValueError("invalid default answer: '%s'" % default) + while True: + sys.stdout.write(question + prompt) + choice = input().lower() + if default is not None and choice == "": + return valid[default] + elif choice in valid: + if "y" in choice: + return "yes" + else: + return "no" + else: + sys.stdout.write("Please respond with 'yes' or 'no' " "(or 'y' or 'n').\n") + +def read_fasta_file(fasta_file): + return SeqIO.parse(fasta_file, "fasta") + +def rich_force_colors(): + """ + Check if any environment variables are set to force Rich to use coloured output + """ + if ( + os.getenv("GITHUB_ACTIONS") + or os.getenv("FORCE_COLOR") + or os.getenv("PY_COLORS") + ): + return True + return None + +def write_fasta_file(out_folder, seq_data, multiple_files=False, extension=True): + try: + os.makedirs(out_folder, exist_ok=True) + except OSError as e: + sys.exit(1) + if isinstance(seq_data, dict): + for key, seq in seq_data.items(): + if extension: + f_name = os.path.join(out_folder, key + ".fasta") + else: + f_name = os.path.join(out_folder, key) + with open (f_name, "w") as fo: + fo.write(">" + key + "\n") + fo.write(seq) + \ No newline at end of file diff --git a/taranis_configuration.py b/taranis_configuration.py deleted file mode 100644 index 3dfd550..0000000 --- a/taranis_configuration.py +++ /dev/null @@ -1,13 +0,0 @@ -#!/usr/bin/env python3 - -import os - -### Settings configuration for logging -taranis_dir = os.path.dirname(os.path.realpath(__file__)) -###LOGGING_CONFIGURATION = taranis_dir -###LOGGING_CONFIGURATION = taranis_dir + '/logging_config.ini' -LOGGING_CONFIGURATION = os.path.join(taranis_dir,'logging_config.ini') -print(LOGGING_CONFIGURATION) - -## Settings configuration for create schema functionality - diff --git a/tox.ini b/tox.ini new file mode 100644 index 0000000..5135173 --- /dev/null +++ b/tox.ini @@ -0,0 +1,6 @@ +## According to black coding style: https://black.readthedocs.io/en/stable/the_black_code_style/current_style.html +[flake8] +max-line-length = 88 + +select = C,E,F,W,B,B950 +extend-ignore = E203, E501, W605 From 5c7e3e3a260819ed12d5e23a416db9bb44d35528 Mon Sep 17 00:00:00 2001 From: luissian Date: Tue, 19 Dec 2023 23:55:13 +0100 Subject: [PATCH 002/214] First changes to the new refactorization --- environment.yml | 2 +- setup.py | 2 +- taranis/__main__.py | 74 +- taranis/allele_calling.py | 93 ++ taranis/allele_calling_old.py | 2484 +++++++++++++++++++++++++++++++++ taranis/blast.py | 67 + taranis/prediction.py | 65 + taranis/reference_alleles.py | 223 ++- taranis/utils.py | 46 +- 9 files changed, 2998 insertions(+), 58 deletions(-) create mode 100644 taranis/allele_calling.py create mode 100644 taranis/allele_calling_old.py create mode 100644 taranis/blast.py create mode 100644 taranis/prediction.py diff --git a/environment.yml b/environment.yml index 653ff2c..d50b6a5 100644 --- a/environment.yml +++ b/environment.yml @@ -7,10 +7,10 @@ dependencies: - conda-forge::python>=3.6 - conda-forge::biopython==1.72 - conda-forge::pandas==1.2.4 - - conda-forge::progressbar==2.5 - conda-forge::openpyxl==3.0.7 - conda-forge::plotly==5.0.0 - conda-forge::numpy==1.20.3 + - conda-forge::rich==13.7.0 - bioconda::prokka>=1.14 - bioconda::blast>=2.9 - bioconda::mash>=2 diff --git a/setup.py b/setup.py index 363fec6..4b6fad4 100644 --- a/setup.py +++ b/setup.py @@ -2,7 +2,7 @@ from setuptools import setup, find_packages -version = "2.1.0" +version = "2.2.0" with open("README.md") as f: readme = f.read() diff --git a/taranis/__main__.py b/taranis/__main__.py index 548d014..9affab9 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -1,14 +1,17 @@ import logging import click +import glob import os import rich.console import rich.logging import rich.traceback import sys +import taranis.prediction import taranis.utils import taranis.reference_alleles +import taranis.allele_calling log = logging.getLogger() @@ -86,7 +89,7 @@ def decorator(f): help="Print verbose output to the console.", ) @click.option( - "-l", "--log-file", help="Save a verbose log to a file.", metavar="" + "-l", "--log-file", help="Save a verbose log to a file.", metavar="filename" ) def taranis_cli(verbose, log_file): # Set the base logger to output DEBUG @@ -111,6 +114,7 @@ def reference_alleles( schema, output, ): + # taranis reference-alleles -s ../../documentos_antiguos/datos_prueba/schema_1_locus/ -o ../../new_taranis_result_code # taranis reference-alleles -s ../../documentos_antiguos/datos_prueba/schema_test/ -o ../../new_taranis_result_code if not taranis.utils.folder_exists(schema): log.error("schema folder %s does not exists", schema) @@ -140,4 +144,70 @@ def reference_alleles( """Create the reference alleles from the schema """ for f_file in schema_files: ref_alleles = taranis.reference_alleles.ReferenceAlleles(f_file, output) - _ = ref_alleles.create_ref_alleles() \ No newline at end of file + _ = ref_alleles.create_ref_alleles() + + +# Allele calling +# taranis -l ../../test/taranis.log allele-calling -s ../../documentos_antiguos/datos_prueba/schema_test/ -r ../../documentos_antiguos/datos_prueba/reference_alleles/ -g ../../taranis_data/listeria_genoma_referencia/listeria.fasta -a ../../taranis_data/listeria_sampled/RA-L2073_R1.fasta -o ../../test/ +# taranis allele-calling -s ../../documentos_antiguos/datos_prueba/schema_test/ -r ../../documentos_antiguos/datos_prueba/reference_alleles/ -g ../../taranis_data/listeria_genoma_referencia/listeria.fasta -a ../../taranis_data/listeria_sampled/RA-L2073_R1.fasta -o ../../test/ +# taranis allele-calling -s ../../documentos_antiguos/datos_prueba/schema_test/ -r ../../documentos_antiguos/datos_prueba/reference_alleles/ -g ../../taranis_data/listeria_genoma_referencia/listeria.fasta -a ../../taranis_data/muestras_listeria_servicio_fasta/3789/assembly.fasta -o ../../test/ + +@taranis_cli.command(help_priority=3) +@click.option("-s", "--schema", required=True, multiple=False, type=click.Path(), help="Directory where the schema with the core gene files are located. ") +@click.option("-r", "--reference", required=True, multiple=False, type=click.Path(), help="Directory where the schema reference allele files are located. ") +@click.option("-g", "--genome", required=True, multiple=False, type=click.Path(), help="Genome reference file") +@click.option("-a", "--sample", required=True, multiple=False, type=click.Path(), help="Sample location file in fasta format. ") +@click.option("-o", "--output", required=True, multiple=False, type=click.Path(), help="Output folder to save reference alleles") +def allele_calling( + schema, + reference, + genome, + sample, + output, +): + folder_to_check = [schema, reference] + for folder in folder_to_check: + if not taranis.utils.folder_exists(folder): + log.error("folder %s does not exists", folder) + stderr.print( + "[red] Folder does not exist. " + folder + "!" + ) + sys.exit(1) + if not taranis.utils.file_exists(sample): + log.error("file %s does not exists", sample) + stderr.print( + "[red] File does not exist. " + sample + "!" + ) + sys.exit(1) + schema_files = taranis.utils.get_files_in_folder(schema, "fasta") + if len(schema_files) == 0: + log.error("Schema folder %s does not have any fasta file", schema) + stderr.print("[red] Schema folder does not have any fasta file") + sys.exit(1) + + # Check if output folder exists + if taranis.utils.folder_exists(output): + q_question = "Folder " + output + " already exists. Files will be overwritten. Do you want to continue?" + if "no" in taranis.utils.query_user_yes_no(q_question, "no"): + log.info("Aborting code by user request") + stderr.print("[red] Exiting code. ") + sys.exit(1) + else: + try: + os.makedirs(output) + except OSError as e: + log.info("Unable to create folder at %s", output) + stderr.print("[red] ERROR. Unable to create folder " + output) + sys.exit(1) + # Filter fasta files from reference folder + ref_alleles = glob.glob(os.path.join(reference, "*.fasta")) + # Create predictions + pred_out = os.path.join(output, "prediction" ) + pred_sample = taranis.prediction.Prediction(genome, sample, pred_out) + pred_sample.training() + pred_sample.prediction() + + """Analyze the sample file against schema to identify outbreakers + """ + sample_allele = taranis.allele_calling.Sample(pred_sample, sample, schema, ref_alleles ,output) + sample_allele.analyze_sample() diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py new file mode 100644 index 0000000..1759c48 --- /dev/null +++ b/taranis/allele_calling.py @@ -0,0 +1,93 @@ +import logging +import os +import rich.console + + +import taranis.utils +import taranis.blast +import numpy +from pathlib import Path + + +import pdb +log = logging.getLogger(__name__) +stderr = rich.console.Console( + stderr=True, + style="dim", + highlight=False, + force_terminal=taranis.utils.rich_force_colors(), +) + +class Sample: + def __init__(self, prediction, sample_file, schema, reference_alleles, out_folder): + self.prediction = prediction + self.sample_file = sample_file + self.schema = schema + self.ref_alleles = reference_alleles + self.out_folder = out_folder + self.s_name = Path(sample_file).stem + self.blast_dir = os.path.join(out_folder,"blastdb") + self.blast_sample = os.path.join(self.blast_dir, self.s_name) + + def assign_abbreviation(self, query_seq, allele_name, sample_contig, schema_gene): + s_alleles_blast = taranis.blast.Blast("nucl") + ref_allele_blast_dir = os.path.join(self.blast_dir, "ref_alleles") + query_path = os.path.join(self.out_folder, "tmp", allele_name) + # Write to file the sequence to find out the loci name that fully match + f_name = taranis.utils.write_fasta_file(query_path, query_seq, allele_name) + query_file = os.path.join(query_path, f_name) + _ = s_alleles_blast.create_blastdb(schema_gene, ref_allele_blast_dir) + # Blast with sample sequence to find the allele in the schema + seq_blast_match = s_alleles_blast.run_blast(query_file, perc_identity=100) + pdb.set_trace() + if len(seq_blast_match) == 1: + + # Hacer un blast con la query esta secuencia y la database del alelo + # Create blast db with sample file + + + pass + + + def catalog_alleles (self, ref_allele): + allele_name = Path(ref_allele).stem + schema_gene = os.path.join(self.schema, allele_name + ".fasta") + allele_name = Path(ref_allele).stem + # run blast with sample as db and reference allele as query + sample_blast_match = self.sample_blast.run_blast(ref_allele) + if len(sample_blast_match) > 0 : + s_lines = [] + for out_line in sample_blast_match: + s_lines.append(out_line.split("\t")) + np_lines = numpy.array(s_lines) + # convert to float the perc_identity to find out the max value + max_val = numpy.max(np_lines[:,2].astype(float)) + mask = np_lines[:, 2] ==str(max_val) + # Select rows that match the percent identity. Index 2 in blast results + sel_row = np_lines[mask, :] = np_lines[mask, :] + query_seq = sel_row[0,14] + sample_contig = sel_row[0,1] + abbr = self.assign_abbreviation(query_seq, allele_name, sample_contig, schema_gene) + else: + # Sample does not have a reference allele to be matched + # Keep LNF info + # ver el codigo de espe + #lnf_tpr_tag() + pass + pdb.set_trace() + + + def analyze_sample(self): + # Create blast db with sample file + self.sample_blast = taranis.blast.Blast("nucl") + _ = self.sample_blast.create_blastdb(self.sample_file, self.blast_dir) + result = {} + pdb.set_trace() + for ref_allele in self.ref_alleles: + # schema_alleles = os.path.join(self.schema, ref_allele) + # parallel in all CPUs in cluster node + result[ref_allele] = self.catalog_alleles(ref_allele) + + pdb.set_trace() + return + diff --git a/taranis/allele_calling_old.py b/taranis/allele_calling_old.py new file mode 100644 index 0000000..72d3294 --- /dev/null +++ b/taranis/allele_calling_old.py @@ -0,0 +1,2484 @@ +#!/usr/bin/env python3 + +import argparse +import sys +import io +import os +import re +import statistics +import logging +from logging.handlers import RotatingFileHandler +from datetime import datetime +import glob +import pickle +from Bio import SeqIO +from Bio.SeqRecord import SeqRecord +from Bio import Seq +from Bio import pairwise2 +from Bio.pairwise2 import format_alignment +from Bio.Blast.Applications import NcbiblastnCommandline +from io import StringIO +from Bio.Blast import NCBIXML +import pandas as pd +import shutil +from progressbar import ProgressBar +from utils.taranis_utils import * +import math +import csv +import plotly.graph_objects as go + + +def check_blast (reference_allele, sample_files, db_name, logger) : ## N + for s_file in sample_files: + f_name = os.path.basename(s_file).split('.') + dir_name = os.path.dirname(s_file) + blast_dir = os.path.join(dir_name, db_name,f_name[0]) + blast_db = os.path.join(blast_dir,f_name[0]) + if not os.path.exists(blast_dir) : + logger.error('Blast db folder for sample %s does not exist', f_name) + return False + cline = NcbiblastnCommandline(db=blast_db, evalue=0.001, outfmt=5, max_target_seqs=10, max_hsps=10,num_threads=1, query=reference_allele) + out, err = cline() + + psiblast_xml = StringIO(out) + blast_records = NCBIXML.parse(psiblast_xml) + + for blast_record in blast_records: + locationcontigs = [] + for alignment in blast_record.alignments: + # select the best match + for match in alignment.hsps: + alleleMatchid = int((blast_record.query_id.split("_"))[-1]) + return True + +# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # +# Parse samples and core genes schema fasta files to dictionary # +# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # + +def parsing_fasta_file_to_dict (fasta_file, logger): + fasta_dict = {} + fasta_dict_ordered = {} + for contig in SeqIO.parse(fasta_file, "fasta"): + fasta_dict[str(contig.id)] = str(contig.seq.upper()) + logger.debug('file %s parsed to dictionary', fasta_file) + + for key in sorted(list(fasta_dict.keys())): + fasta_dict_ordered[key] = fasta_dict[key] + return fasta_dict_ordered + + +# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # +# Get core genes schema info before allele calling analysis # +# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # + +#def prepare_core_gene (core_gene_file_list, store_dir, ref_alleles_dir, logger): +def prepare_core_gene (core_gene_file_list, store_dir, ref_alleles_dir, genus, species, usegenus, logger): + + ## Initialize dict for keeping id-allele, quality, length variability, length statistics and annotation info for each schema core gene + alleles_in_locus_dict = {} + schema_quality = {} + annotation_core_dict = {} + schema_variability = {} + schema_statistics = {} + + + ## Process each schema core gene + blast_dir = os.path.join(store_dir,'blastdb') + logger.info('start preparation of core genes files') + for fasta_file in core_gene_file_list: + + f_name = os.path.basename(fasta_file).split('.') + + # Parse core gene fasta file and keep id-sequence info in dictionary + fasta_file_parsed_dict = parsing_fasta_file_to_dict(fasta_file, logger) + if f_name[0] not in alleles_in_locus_dict.keys(): + alleles_in_locus_dict[f_name[0]] = {} + alleles_in_locus_dict[f_name[0]] = fasta_file_parsed_dict + + # dump fasta file into pickle file + #with open (file_list[-1],'wb') as f: + # pickle.dump(fasta_file_parsed_dict, f) + + # Get core gene alleles quality + locus_quality = check_core_gene_quality(fasta_file, logger) + if f_name[0] not in schema_quality.keys(): + schema_quality[f_name[0]] = {} + schema_quality[f_name[0]] = locus_quality + + # Get gene and product annotation for core gene using reference allele(s) + ref_allele = os.path.join(ref_alleles_dir, f_name[0] + '.fasta') + + gene_annot, product_annot = get_gene_annotation (ref_allele, store_dir, genus, species, usegenus, logger) + #gene_annot, product_annot = get_gene_annotation (ref_allele, store_dir, logger) + if f_name[0] not in annotation_core_dict.keys(): + annotation_core_dict[f_name[0]] = {} + annotation_core_dict[f_name[0]] = [gene_annot, product_annot] + + # Get core gene alleles length to keep length variability and statistics info + alleles_len = [] + for allele in fasta_file_parsed_dict : + alleles_len.append(len(fasta_file_parsed_dict[allele])) + + #alleles_in_locus = list (SeqIO.parse(fasta_file, "fasta")) ## parse + #for allele in alleles_in_locus : ## parse + #alleles_len.append(len(str(allele.seq))) ## parse + + schema_variability[f_name[0]]=list(set(alleles_len)) + + if len(alleles_len) == 1: + stdev = 0 + else: + stdev = statistics.stdev(alleles_len) + schema_statistics[f_name[0]]=[statistics.mean(alleles_len), stdev, min(alleles_len), max(alleles_len)] + + return alleles_in_locus_dict, annotation_core_dict, schema_variability, schema_statistics, schema_quality + + +# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # +# Get Prodigal training file from reference genome for samples gene prediction # +# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # + +def prodigal_training(reference_genome_file, prodigal_dir, logger): + + f_name = os.path.basename(reference_genome_file).split('.')[0] + prodigal_train_dir = os.path.join(prodigal_dir, 'training') + + output_prodigal_train_dir = os.path.join(prodigal_train_dir, f_name + '.trn') + + if not os.path.exists(prodigal_train_dir): + try: + os.makedirs(prodigal_train_dir) + logger.debug('Created prodigal directory for training file %s', f_name) + except: + logger.info('Cannot create prodigal directory for training file %s', f_name) + print ('Error when creating the directory %s for training file', prodigal_train_dir) + exit(0) + + prodigal_command = ['prodigal' , '-i', reference_genome_file, '-t', output_prodigal_train_dir] + prodigal_result = subprocess.run(prodigal_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) + + # if prodigal_result.stderr: + # logger.error('cannot create training file for %s', f_name) + # logger.error('prodigal returning error code %s', prodigal_result.stderr) + # return False + else: + logger.info('Skeeping prodigal training file creation for %s, as it has already been created', f_name) + + return output_prodigal_train_dir + + +# · * · * · * · * · * · * · * · * · * # +# Get Prodigal sample gene prediction # +# · * · * · * · * · * · * · * · * · * # + +def prodigal_prediction(file_name, prodigal_dir, prodigal_train_dir, logger): + + f_name = '.'.join(os.path.basename(file_name).split('.')[:-1]) + prodigal_dir_sample = os.path.join(prodigal_dir,f_name) + + output_prodigal_coord = os.path.join(prodigal_dir_sample, f_name + '_coord.gff') ## no + output_prodigal_prot = os.path.join(prodigal_dir_sample, f_name + '_prot.faa') ## no + output_prodigal_dna = os.path.join(prodigal_dir_sample, f_name + '_dna.faa') + + if not os.path.exists(prodigal_dir_sample): + try: + os.makedirs(prodigal_dir_sample) + logger.debug('Created prodigal directory for Core Gene %s', f_name) + except: + logger.info('Cannot create prodigal directory for Core Gene %s' , f_name) + print ('Error when creating the directory %s for prodigal genes prediction', prodigal_dir_sample) + exit(0) + + prodigal_command = ['prodigal' , '-i', file_name , '-t', prodigal_train_dir, '-f', 'gff', '-o', output_prodigal_coord, '-a', output_prodigal_prot, '-d', output_prodigal_dna] + prodigal_result = subprocess.run(prodigal_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) + + # if prodigal_result.stderr: + # logger.error('cannot predict genes for %s ', f_name) + # logger.error('prodigal returning error code %s', prodigal_result.stderr) + #return False + else: + logger.info('Skeeping prodigal genes prediction for %s, as it has already been made', f_name) + + return True + + +# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # +# Get Prodigal predicted gene sequence equivalent to BLAST result matching bad quality allele or to no Exact Match BLAST result in allele calling analysis # +# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # + +def get_prodigal_sequence(blast_sseq, contig_blast_id, prodigal_directory, sample_name, blast_parameters, logger): + + prodigal_directory_sample = os.path.join(prodigal_directory, sample_name) + genes_file = os.path.join(prodigal_directory_sample, sample_name + '_dna.faa') + + ## Create directory for storing prodigal genes prediction per contig BLAST databases + blastdb_per_contig_directory = 'blastdb_per_contig' + full_path_blastdb_per_contig = os.path.join(prodigal_directory_sample, blastdb_per_contig_directory) + if not os.path.exists(full_path_blastdb_per_contig): + try: + os.makedirs(full_path_blastdb_per_contig) + logger.info('Directory %s has been created', full_path_blastdb_per_contig) + except: + print ('Cannot create the directory ', full_path_blastdb_per_contig) + logger.info('Directory %s cannot be created', full_path_blastdb_per_contig) + exit (0) + + ## Create directory for storing prodigal genes prediction sequences per contig + prodigal_genes_per_contig_directory = 'prodigal_genes_per_contig' + full_path_prodigal_genes_per_contig = os.path.join(prodigal_directory_sample, prodigal_genes_per_contig_directory) + if not os.path.exists(full_path_prodigal_genes_per_contig): + try: + os.makedirs(full_path_prodigal_genes_per_contig) + logger.info('Directory %s has been created', full_path_prodigal_genes_per_contig) + except: + print ('Cannot create the directory ', full_path_prodigal_genes_per_contig) + logger.info('Directory %s cannot be created', full_path_prodigal_genes_per_contig) + exit (0) + + ## Parse prodigal genes prediction fasta file + predicted_genes = SeqIO.parse(genes_file, "fasta") + + ## Create fasta file containing Prodigal predicted genes sequences for X contig in sample + contig_genes_path = os.path.join(full_path_prodigal_genes_per_contig, contig_blast_id + '.fasta') + with open (contig_genes_path, 'w') as out_fh: + for rec in predicted_genes: + contig_prodigal_id = '_'.join((rec.id).split("_")[:-1]) + if contig_prodigal_id == contig_blast_id: + out_fh.write ('>' + str(rec.description) + '\n' + str(rec.seq) + '\n') + + ## Create local BLAST database for Prodigal predicted genes sequences for X contig in sample + if not create_blastdb(contig_genes_path, full_path_blastdb_per_contig, 'nucl', logger): + print('Error when creating the blastdb for samples files. Check log file for more information. \n ') + return False + + ## Local BLAST Prodigal predicted genes sequences database VS BLAST sequence obtained in sample in allele calling analysis + blast_db_name = os.path.join(full_path_blastdb_per_contig, contig_blast_id, contig_blast_id) + + cline = NcbiblastnCommandline(db=blast_db_name, evalue=0.001, perc_identity = 90, outfmt= blast_parameters, max_target_seqs=10, max_hsps=10, num_threads=1) + out, err = cline(stdin = blast_sseq) + out_lines = out.splitlines() + + bigger_bitscore = 0 + if len (out_lines) > 0 : + for line in out_lines : + values = line.split('\t') + if float(values[8]) > bigger_bitscore: + qseqid , sseqid , pident , qlen , s_length , mismatch , r_gapopen , r_evalue , bitscore , sstart , send , qstart , qend ,sseq , qseq = values + bigger_bitscore = float(bitscore) + + ## Get complete Prodigal sequence matching allele calling BLAST sequence using ID + predicted_genes_in_contig = SeqIO.parse(contig_genes_path, "fasta") + + for rec in predicted_genes_in_contig: + if rec.id == sseqid: + predicted_gene_sequence = str(rec.seq) + start_prodigal = str(rec.description.split( '#')[1]) + end_prodigal = str(rec.description.split('#')[2]) + break + + ## Sequence not found by Prodigal when there are no BLAST results matching allele calling BLAST sequence + if len (out_lines) == 0: + predicted_gene_sequence = 'Sequence not found by Prodigal' + start_prodigal = '-' + end_prodigal = '-' + + return predicted_gene_sequence, start_prodigal, end_prodigal ### start_prodigal y end_prodigal para report prodigal + + +# · * · * · * · * · * · * · * · * · * · * · * · * # +# Get samples info before allele calling analysis # +# · * · * · * · * · * · * · * · * · * · * · * · * # + +def prepare_samples(sample_file_list, store_dir, reference_genome_file, logger): + + ## Initialize dictionary for keeping id-contig + contigs_in_sample_dict = {} + + ## Paths for samples blastdb, Prodigal genes prediction and BLAST results + blast_dir = os.path.join(store_dir,'blastdb') + prodigal_dir = os.path.join(store_dir,'prodigal') + blast_results_seq_dir = os.path.join(store_dir,'blast_results', 'blast_results_seq') + + ## Get training file for Prodigal genes prediction + output_prodigal_train_dir = prodigal_training(reference_genome_file, prodigal_dir, logger) + if not output_prodigal_train_dir: + print('Error when creating training file for genes prediction. Check log file for more information. \n ') + return False + + for fasta_file in sample_file_list: + f_name = '.'.join(os.path.basename(fasta_file).split('.')[:-1]) + + # Get samples id-contig dictionary + fasta_file_parsed_dict = parsing_fasta_file_to_dict(fasta_file, logger) + if f_name not in contigs_in_sample_dict.keys(): + contigs_in_sample_dict[f_name] = {} + contigs_in_sample_dict[f_name] = fasta_file_parsed_dict + + # dump fasta file into pickle file + #with open (file_list[-1],'wb') as f: # generación de diccionarios de contigs para cada muestra + # pickle.dump(fasta_file_parsed_dict, f) + + # Create directory for storing BLAST results using reference allele(s) + blast_results_seq_per_sample_dir = os.path.join(blast_results_seq_dir, f_name) + + if not os.path.exists(blast_results_seq_per_sample_dir): + try: + os.makedirs(blast_results_seq_per_sample_dir) + logger.debug('Created blast results directory for sample %s', f_name) + except: + logger.info('Cannot create blast results directory for sample %s', f_name) + print ('Error when creating the directory for blast results', blast_results_seq_per_sample_dir) + exit(0) + + # Prodigal genes prediction for each sample + if not prodigal_prediction(fasta_file, prodigal_dir, output_prodigal_train_dir, logger): + print('Error when predicting genes for samples files. Check log file for more information. \n ') + return False + + # Create local BLAST db for each sample fasta file + if not create_blastdb(fasta_file, blast_dir, 'nucl', logger): + print('Error when creating the blastdb for samples files. Check log file for more information. \n ') + return False + + return contigs_in_sample_dict + +# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # +# Get established length thresholds for allele tagging in allele calling analysis # +# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # + +def length_thresholds(core_name, schema_statistics, percent): ### logger + + locus_mean = int(schema_statistics[core_name][0]) + + if percent != "SD": + max_length_threshold = math.ceil(locus_mean + ((locus_mean * float(percent)) / 100)) + min_length_threshold = math.floor(locus_mean - ((locus_mean * float(percent)) / 100)) + else: + percent = float(schema_statistics[core_name][1]) + + max_length_threshold = math.ceil(locus_mean + (locus_mean * percent)) + min_length_threshold = math.floor(locus_mean - (locus_mean * percent)) + + return max_length_threshold, min_length_threshold + + +# · * · * · * · * · * · * · * · * · * · * · # +# Convert dna sequence to protein sequence # +# · * · * · * · * · * · * · * · * · * · * · # + +def convert_to_protein (sequence) : + + seq = Seq.Seq(sequence) + protein = str(seq.translate()) + + return protein + +# · * · * · * · * · * · * · * · * · * · * · * · * · * # +# Get SNPs between BLAST sequence and matching allele # +# · * · * · * · * · * · * · * · * · * · * · * · * · * # + +def get_snp (sample, query) : + + prot_annotation = {'S': 'polar' ,'T': 'polar' ,'Y': 'polar' ,'Q': 'polar' ,'N': 'polar' ,'C': 'polar' ,'S': 'polar' , + 'F': 'nonpolar' ,'L': 'nonpolar','I': 'nonpolar','M': 'nonpolar','P': 'nonpolar','V': 'nonpolar','A': 'nonpolar','W': 'nonpolar','G': 'nonpolar', + 'D' : 'acidic', 'E' :'acidic', + 'H': 'basic' , 'K': 'basic' , 'R' : 'basic', + '-': '-----', '*' : 'Stop codon'} + snp_list = [] + sample = sample.replace('-','') + #length = max(len(sample), len(query)) + length = len(query) + # normalize the length of the sample for the iteration + if len(sample) < length : + need_to_add = length - len(sample) + sample = sample + need_to_add * '-' + + # convert to Seq class to translate to protein + seq_sample = Seq.Seq(sample) + seq_query = Seq.Seq(query) + + for index in range(length): + if seq_query[index] != seq_sample[index] : + triple_index = index - (index % 3) + codon_seq = seq_sample[triple_index : triple_index + 3] + codon_que = seq_query[triple_index : triple_index + 3] + if not '-' in str(codon_seq) : + prot_seq = str(codon_seq.translate()) + prot_que = str(codon_que.translate()) + else: + prot_seq = '-' + prot_que = str(seq_query[triple_index: ].translate()) + if prot_annotation[prot_que[0]] == prot_annotation[prot_seq[0]] : + missense_synonym = 'Synonymous' + elif prot_seq == '*' : + missense_synonym = 'Nonsense' + else: + missense_synonym = 'Missense' + #snp_list.append([str(index+1),str(seq_sample[index]) + '/' + str(seq_query[index]), str(codon_seq) + '/'+ str(codon_que), + snp_list.append([str(index+1),str(seq_query[index]) + '/' + str(seq_sample[index]), str(codon_que) + '/'+ str(codon_seq), + # when one of the sequence ends but not the other we will translate the remain sequence to proteins + # in that case we will only annotate the first protein. Using [0] as key of the dictionary annotation + prot_que + '/' + prot_seq, missense_synonym, prot_annotation[prot_que[0]] + ' / ' + prot_annotation[prot_seq[0]]]) + if '-' in str(codon_seq) : + break + return snp_list + + +def nucleotide_to_protein_alignment (sample_seq, query_seq ) : ### Sustituido por get_alignment + aligment = [] + sample_prot = convert_to_protein(sample_seq) + query_prot = convert_to_protein(query_seq) + minimun_length = min(len(sample_prot), len(query_prot)) + for i in range(minimun_length): + if sample_prot[i] == query_prot[i] : + aligment.append('|') + else: + aligment.append(' ') + protein_alignment = [['sample', sample_prot],['match', ''.join(aligment)], ['schema', query_prot]] + + return protein_alignment + + +def get_alignment_for_indels (blast_db_name, qseq) : ### Sustituido por get_alignment + #match_alignment =[] + cline = NcbiblastnCommandline(db=blast_db_name, evalue=0.001, perc_identity = 80, outfmt= 5, max_target_seqs=10, max_hsps=10,num_threads=1) + out, err = cline(stdin = qseq) + psiblast_xml = StringIO(out) + blast_records = NCBIXML.parse(psiblast_xml) + for blast_record in blast_records: + for alignment in blast_record.alignments: + for match in alignment.hsps: + match_alignment = [['sample', match.sbjct],['match', match.match], ['schema',match.query]] + return match_alignment + + +def get_alignment_for_deletions (sample_seq, query_seq): ### Sustituido por get_alignment + index_found = False + alignments = pairwise2.align.globalxx(sample_seq, query_seq) + for index in range(len(alignments)) : + if alignments[index][4] == len(query_seq) : + index_found = True + break + if not index_found : + index = 0 + values = format_alignment(*alignments[index]).split('\n') + match_alignment = [['sample', values[0]],['match', values[1]], ['schema',values[2]]] + return match_alignment + + +# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # +# Get DNA and protein alignment between the final sequence found in the sample and the matching allele # +# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # + +def get_alignment (sample_seq, query_seq, reward, penalty, gapopen, gapextend, seq_type = "dna"): + + ## If sequences alignment type desired is "protein" convert dna sequences to protein + if seq_type == "protein": + sample_seq = convert_to_protein(sample_seq) + query_seq = convert_to_protein(query_seq) + + ## Get dna/protein alignment between final sequence found and matching allele + # arguments pairwise2.align.globalms: match, mismatch, gap opening, gap extending + alignments = pairwise2.align.localms(sample_seq, query_seq, reward, penalty, -gapopen, -gapextend) + values = format_alignment(*alignments[0]).split('\n') + match_alignment = [['sample', values[0]],['match', values[1]], ['schema',values[2]]] + + return match_alignment + + +# · * · * · * · * · * · * · * · * # +# Tag LNF cases and keep LNF info # +# · * · * · * · * · * · * · * · * # + +def lnf_tpr_tag(core_name, sample_name, alleles_in_locus_dict, samples_matrix_dict, lnf_tpr_dict, schema_statistics, locus_alleles_path, qseqid, pident, s_length, new_sequence_length, perc_identity_ref, coverage, schema_quality, annotation_core_dict, count_dict, logger): + + gene_annot, product_annot = annotation_core_dict[core_name] + + if qseqid == '-': + samples_matrix_dict[sample_name].append('LNF') + tag_report = 'LNF' + matching_allele_length = '-' + + else: + if new_sequence_length == '-': + samples_matrix_dict[sample_name].append('LNF_' + str(qseqid)) + tag_report = 'LNF' + else: + samples_matrix_dict[sample_name].append('TPR_' + str(qseqid)) + tag_report = 'TPR' + + matching_allele_seq = alleles_in_locus_dict[core_name][qseqid] + matching_allele_length = len(matching_allele_seq) + + #alleles_in_locus = list (SeqIO.parse(locus_alleles_path, "fasta")) ## parse + #for allele in alleles_in_locus : ## parse + #if allele.id == qseqid : ## parse + #break ## parse + #matching_allele_seq = str(allele.seq) ## parse + #matching_allele_length = len(matching_allele_seq) ## parse + + if pident == '-': + # (los dos BLAST sin resultado) + coverage_blast = '-' + coverage_new_sequence = '-' + add_info = 'Locus not found' + logger.info('Locus not found at sample %s, for gene %s', sample_name, core_name) + + # Get allele quality + allele_quality = '-' + + # (recuento tags para plot) + count_dict[sample_name]['not_found'] += 1 + count_dict[sample_name]['total'] += 1 + + elif 90 > float(pident): + # (BLAST 90 sin resultado y BLAST 70 con resultado) + coverage_blast = '-' + coverage_new_sequence = '-' + add_info = 'BLAST sequence ID under threshold: {}%'.format(perc_identity_ref) + logger.info('BLAST sequence ID %s under threshold at sample %s, for gene %s', pident, sample_name, core_name) + + # Get allele quality + allele_quality = '-' + + # (recuento tags para plot) + count_dict[sample_name]['low_id'] += 1 + count_dict[sample_name]['total'] += 1 + + elif 90 <= float(pident) and new_sequence_length == '-': + # (BLAST 90 con resultado, bajo coverage BLAST) + locus_mean = int(schema_statistics[core_name][0]) + coverage_blast = int(s_length) / locus_mean + #coverage_blast = int(s_length) / matching_allele_length + coverage_new_sequence = '-' + if coverage_blast < 1: + add_info = 'BLAST sequence coverage under threshold: {}%'.format(coverage) + else: + add_info = 'BLAST sequence coverage above threshold: {}%'.format(coverage) + logger.info('BLAST sequence coverage %s under threshold at sample %s, for gene %s', coverage_blast, sample_name, core_name) + + # Get allele quality + allele_quality = '-' + + # (recuento tags para plot) + count_dict[sample_name]['low_coverage'] += 1 + count_dict[sample_name]['total'] += 1 + + elif 90 <= float(pident) and new_sequence_length != '-': + # (BLAST 90 con resultado, buen coverage BLAST, bajo coverage new_sseq) + locus_mean = int(schema_statistics[core_name][0]) + coverage_blast = int(s_length) / locus_mean * 100 + #coverage_blast = int(s_length) / matching_allele_length + coverage_new_sequence = new_sequence_length / matching_allele_length * 100 + if coverage_new_sequence < 1: + add_info = 'New sequence coverage under threshold: {}%'.format(coverage) + else: + add_info = 'New sequence coverage above threshold: {}%'.format(coverage) + logger.info('New sequence coverage %s under threshold at sample %s, for gene %s', coverage_new_sequence, sample_name, core_name) + + # Get allele quality + allele_quality = schema_quality[core_name][qseqid] + + # (recuento tags para plot) + count_dict[sample_name]['total'] += 1 + for count_class in count_dict[sample_name]: + if count_class in allele_quality: + count_dict[sample_name][count_class] += 1 + #if "bad_quality" in allele_quality: + # count_dict[sample_name]['bad_quality'] += 1 + + ## Keeping LNF and TPR report info + if not core_name in lnf_tpr_dict: + lnf_tpr_dict[core_name] = {} + if not sample_name in lnf_tpr_dict[core_name]: + lnf_tpr_dict[core_name][sample_name] = [] + + lnf_tpr_dict[core_name][sample_name].append([gene_annot, product_annot, tag_report, qseqid, allele_quality, pident, str(coverage_blast), str(coverage_new_sequence), str(matching_allele_length), str(s_length), str(new_sequence_length), add_info]) ### Meter secuencias alelo, blast y new_sseq (si las hay)? + + return True + + +# · * · * · * · * · * · * · * · * · * · * · * · * # +# Tag paralog and exact match cases and keep info # +# · * · * · * · * · * · * · * · * · * · * · * · * # + +def paralog_exact_tag(sample_name, core_name, tag, schema_quality, matching_genes_dict, samples_matrix_dict, allele_found, tag_dict, prodigal_report, prodigal_directory, blast_parameters, annotation_core_dict, count_dict, logger): + + logger.info('Found %s at sample %s for core gene %s ', tag, sample_name, core_name) + + paralog_quality_count = [] # (lista para contabilizar parálogos debido a bad o good quality) + + gene_annot, product_annot = annotation_core_dict[core_name] + + if not sample_name in tag_dict : + tag_dict[sample_name] = {} + if not core_name in tag_dict[sample_name] : + tag_dict[sample_name][core_name]= [] + + if tag == 'EXACT': + allele = list(allele_found.keys())[0] + qseqid = allele_found[allele][0] + tag = qseqid + + samples_matrix_dict[sample_name].append(tag) + + for sequence in allele_found: + qseqid, sseqid, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart, send, qstart, qend, sseq, qseq = allele_found[sequence] + sseq = sseq.replace('-', '') + + # Get allele quality + allele_quality = schema_quality[core_name][qseqid] + + if len(allele_found) > 1: + paralog_quality_count.append(allele_quality) + + # Get prodigal gene prediction if allele quality is 'bad_quality' + if 'bad_quality' in allele_quality: + complete_predicted_seq, start_prodigal, end_prodigal = get_prodigal_sequence(sseq, sseqid, prodigal_directory, sample_name, blast_parameters, logger) + + ##### informe prodigal ##### + prodigal_report.append([core_name, sample_name, qseqid, tag, sstart, send, start_prodigal, end_prodigal, sseq, complete_predicted_seq]) + + else: + complete_predicted_seq = '-' + + if not sseqid in matching_genes_dict[sample_name] : + matching_genes_dict[sample_name][sseqid] = [] + if sstart > send : + #matching_genes_dict[sample_name][sseqid].append([core_name, sstart, send,'-', tag]) + matching_genes_dict[sample_name][sseqid].append([core_name, qseqid, sstart, send,'-', tag]) + else: + #matching_genes_dict[sample_name][sseqid].append([core_name, sstart, send,'+', tag]) + matching_genes_dict[sample_name][sseqid].append([core_name, qseqid, sstart, send,'+', tag]) + + ## Keeping paralog NIPH/NIPHEM report info + if tag == 'NIPH' or tag == 'NIPHEM': + tag_dict[sample_name][core_name].append([gene_annot, product_annot, tag, pident, qseqid, allele_quality, sseqid, bitscore, sstart, send, sseq, complete_predicted_seq]) + else: + tag_dict[sample_name][core_name] = [gene_annot, product_annot, qseqid, allele_quality, sseqid, s_length, sstart, send, sseq, complete_predicted_seq] + + # (recuento tags para plot) + count_dict[sample_name]['total'] += 1 + for count_class in count_dict[sample_name]: + if count_class in allele_quality: + if "no_start_stop" not in count_class and "no_start_stop" in allele_quality: + if count_class == "bad_quality": + count_dict[sample_name][count_class] += 1 + else: + count_dict[sample_name][count_class] += 1 + + # (recuento tags para plot (parálogos)) + if len(allele_found) > 0: + count = 0 + for paralog_quality in paralog_quality_count: + count += 1 + if "bad_quality" in paralog_quality: + count_dict[sample_name]['total'] += 1 + for count_class in count_dict[sample_name]: + if count_class in paralog_quality: + if "no_start_stop" not in count_class and "no_start_stop" in paralog_quality: + if count_class == "bad_quality": + count_dict[sample_name][count_class] += 1 + else: + next + else: + count_dict[sample_name][count_class] += 1 + break + + else: + if count == len(paralog_quality_count): + count_dict[sample_name]['total'] += 1 + count_dict[sample_name]['good_quality'] += 1 + + return True + + +# · * · * · * · * · * · * · * · * · * · * # +# Tag INF/ASM/ALM/PLOT cases and keep info # +# · * · * · * · * · * · * · * · * · * · * # + +def inf_asm_alm_tag(core_name, sample_name, tag, blast_values, allele_quality, new_sseq, matching_allele_length, tag_dict, list_tag, samples_matrix_dict, matching_genes_dict, prodigal_report, start_prodigal, end_prodigal, complete_predicted_seq, annotation_core_dict, count_dict, logger): + + gene_annot, product_annot = annotation_core_dict[core_name] + + qseqid, sseqid, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart, send, qstart, qend, sseq, qseq = blast_values + + sseq = sseq.replace('-', '') + s_length = len(sseq) + new_sequence_length = len(new_sseq) + + logger.info('Found %s at sample %s for core gene %s ', tag, sample_name, core_name) + + if tag == 'PLOT': + tag_allele = tag + '_' + str(qseqid) + else: + # Adding ASM/ALM/INF allele to the allele_matrix if it is not already include + if not core_name in tag_dict: + tag_dict[core_name] = [] + if not new_sseq in tag_dict[core_name] : + tag_dict[core_name].append(new_sseq) + # Find the index of ASM/ALM/INF to include it in the sample matrix dict + index_tag = tag_dict[core_name].index(new_sseq) + + tag_allele = tag + '_' + core_name + '_' + str(qseqid) + '_' + str(index_tag) + + samples_matrix_dict[sample_name].append(tag_allele) + + # Keeping INF/ASM/ALM/PLOT report info + if not core_name in list_tag : + list_tag[core_name] = {} + if not sample_name in list_tag[core_name] : + list_tag[core_name][sample_name] = {} + + if tag == 'INF': + list_tag[core_name][sample_name][tag_allele] = [gene_annot, product_annot, qseqid, allele_quality, sseqid, bitscore, str(matching_allele_length), str(s_length), str(new_sequence_length), mismatch , r_gapopen, sstart, send, new_sseq, complete_predicted_seq] + + # (recuento tags para plots) + count_dict[sample_name]['total'] += 1 + for count_class in count_dict[sample_name]: + if count_class in allele_quality: + count_dict[sample_name][count_class] += 1 + #if "bad_quality" in allele_quality: + # count_dict[sample_name]['bad_quality'] += 1 + + elif tag == 'PLOT': + list_tag[core_name][sample_name] = [gene_annot, product_annot, qseqid, allele_quality, sseqid, bitscore, sstart, send, sseq, new_sseq] + + # (recuento tags para plots) + count_dict[sample_name]['total'] += 1 + + else : + if tag == 'ASM': + newsseq_vs_blastseq = 'shorter' + elif tag == 'ALM': + newsseq_vs_blastseq = 'longer' + + if len(sseq) < matching_allele_length: + add_info = 'Global effect: DELETION. BLAST sequence length shorter than matching allele sequence length / Net result: ' + tag + '. Final gene sequence length ' + newsseq_vs_blastseq + ' than matching allele sequence length' + + elif len(sseq) == matching_allele_length: + add_info = 'Global effect: BASE SUBSTITUTION. BLAST sequence length equal to matching allele sequence length / Net result: ' + tag + '. Final gene sequence length ' + newsseq_vs_blastseq + ' than matching allele sequence length' + + elif len(sseq) > matching_allele_length: + add_info = 'Global effect: INSERTION. BLAST sequence length longer than matching allele sequence length / Net result: ' + tag + '. Final gene sequence length ' + newsseq_vs_blastseq + ' than matching allele sequence length' + + list_tag[core_name][sample_name][tag_allele] = [gene_annot, product_annot, qseqid, allele_quality, sseqid, bitscore, str(matching_allele_length), str(s_length), str(new_sequence_length), mismatch , r_gapopen, sstart, send, new_sseq, add_info, complete_predicted_seq] + + # (recuento tags para plots) + if tag == 'ASM': + count_dict[sample_name]['total'] += 1 + for mut_type in count_dict[sample_name]: + if mut_type in add_info.lower(): + count_dict[sample_name][mut_type] += 1 + + elif tag == 'ALM': + count_dict[sample_name]['total'] += 1 + for mut_type in count_dict[sample_name]: + if mut_type in add_info.lower(): + count_dict[sample_name][mut_type] += 1 + + if not sseqid in matching_genes_dict[sample_name] : + matching_genes_dict[sample_name][sseqid] = [] + if sstart > send : + #matching_genes_dict[sample_name][sseqid].append([core_name, str(int(sstart)-new_sequence_length -1), sstart,'-', tag_allele]) + matching_genes_dict[sample_name][sseqid].append([core_name, qseqid, str(int(sstart)-new_sequence_length -1), sstart,'-', tag_allele]) + else: + #matching_genes_dict[sample_name][sseqid].append([core_name, sstart, str(int(sstart)+ new_sequence_length),'+', tag_allele]) + matching_genes_dict[sample_name][sseqid].append([core_name, qseqid, sstart, str(int(sstart)+ new_sequence_length),'+', tag_allele]) + + ##### informe prodigal ##### + prodigal_report.append([core_name, sample_name, qseqid, tag_allele, sstart, send, start_prodigal, end_prodigal, sseq, complete_predicted_seq]) + + return True + + +# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # +# Keep best results info after BLAST using results from previous reference allele BLAST as database VS ALL alleles in locus as query in allele calling analysis # +# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # + +def get_blast_results (sample_name, values, contigs_in_sample_dict, allele_found, logger) : + + qseqid, sseqid, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart, send, qstart, qend, sseq, qseq = values + + ## Get contig ID and BLAST sequence + sseqid_blast = "_".join(sseqid.split('_')[1:]) + sseq_no_gaps = sseq.replace('-', '') + + + ## Get start and end positions in contig searching for BLAST sequence index in contig sequence + + # Get contig sequence + accession_sequence = contigs_in_sample_dict[sample_name][sseqid_blast] + + #for record in sample_contigs: ## parse + #if record.id == sseqid_blast : ## parse + #break ## parse + #accession_sequence = str(record.seq) ## parse + + # Try to get BLAST sequence index in contig. If index -> error because different contig sequence and BLAST sequence + # direction, obtain reverse complement BLAST sequence and try again. + try: + sseq_index_1 = int(accession_sequence.index(sseq_no_gaps)) + 1 + + except: + sseq_no_gaps = str(Seq.Seq(sseq_no_gaps).reverse_complement()) + sseq_index_1 = int(accession_sequence.index(sseq_no_gaps)) + 1 + + sseq_index_2 = int(sseq_index_1) + len(sseq_no_gaps) - 1 + + # Assign found indexes to start and end possitions depending on BLAST sequence and allele sequence direction + if int(sstart) < int(send): + sstart_new = str(min(sseq_index_1, sseq_index_2)) + send_new = str(max(sseq_index_1, sseq_index_2)) + else: + sstart_new = str(max(sseq_index_1, sseq_index_2)) + send_new = str(min(sseq_index_1, sseq_index_2)) + + + ## Keep BLAST results info discarding subsets + allele_is_subset = False + + if len(allele_found) > 0 : + for allele_id in allele_found : + min_index = min(int(allele_found[allele_id][9]), int(allele_found[allele_id][10])) + max_index = max(int(allele_found[allele_id][9]), int(allele_found[allele_id][10])) + if int(sstart_new) in range(min_index, max_index + 1) or int(send_new) in range(min_index, max_index + 1): # if both genome locations overlap + if sseqid_blast == allele_found[allele_id][1]: # if both sequences are in the same contig + logger.info('Found allele %s that starts or ends at the same position as %s ' , qseqid, allele_id) + allele_is_subset = True + break + + if len(allele_found) == 0 or not allele_is_subset : + contig_id_start = str(sseqid_blast + '_'+ sstart_new) + + # Skip the allele found in the 100% identity and 100% alignment + if not contig_id_start in allele_found: + allele_found[contig_id_start] = [qseqid, sseqid_blast, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart_new, send_new, '-', '-', sseq, qseq] + + return True + + +# · * · * · * · * · * · * · * · * · * · # +# Get SNPs and ADN and protein alignment # +# · * · * · * · * · * · * · * · * · * · # + +def keep_snp_alignment_info(sseq, new_sseq, matching_allele_seq, qseqid, query_direction, core_name, sample_name, reward, penalty, gapopen, gapextend, snp_dict, match_alignment_dict, protein_dict, logger): + + ## Check allele sequence direction + if query_direction == 'reverse': + matching_allele_seq = str(Seq.Seq(matching_allele_seq).reverse_complement()) + else: + matching_allele_seq = str(matching_allele_seq) + + ## Get the SNP information + snp_information = get_snp(sseq, matching_allele_seq) + if len(snp_information) > 0 : + if not core_name in snp_dict : + snp_dict[core_name] = {} + if not sample_name in snp_dict[core_name] : + snp_dict[core_name][sample_name] = {} + snp_dict[core_name][sample_name][qseqid]= snp_information + + ## Get new sequence-allele sequence dna alignment + if not core_name in match_alignment_dict : + match_alignment_dict[core_name] = {} + if not sample_name in match_alignment_dict[core_name] : + match_alignment_dict[core_name][sample_name] = get_alignment (new_sseq, matching_allele_seq, reward, penalty, gapopen, gapextend) + + ## Get new sequence-allele sequence protein alignment + if not core_name in protein_dict : + protein_dict[core_name] = {} + if not sample_name in protein_dict[core_name] : + protein_dict[core_name][sample_name] = [] + protein_dict[core_name][sample_name] = get_alignment (new_sseq, matching_allele_seq, reward, penalty, gapopen, gapextend, "protein") + + return True + + +# · * · * · * · * · * · * · * · * · * · * · # +# Create allele tag summary for each sample # +# · * · * · * · * · * · * · * · * · * · * · # + +def create_summary (samples_matrix_dict, logger) : + + summary_dict = {} + summary_result_list = [] + summary_heading_list = ['Exact match', 'INF', 'ASM', 'ALM', 'LNF', 'TPR', 'NIPH', 'NIPHEM', 'PLOT', 'ERROR'] + summary_result_list.append('File\t' + '\t'.join(summary_heading_list)) + for key in sorted (samples_matrix_dict) : + + summary_dict[key] = {'Exact match':0, 'INF':0, 'ASM':0, 'ALM':0, 'LNF':0, 'TPR':0,'NIPH':0, 'NIPHEM':0, 'PLOT':0, 'ERROR':0} + for values in samples_matrix_dict[key] : + if 'INF_' in values : + summary_dict[key]['INF'] += 1 + elif 'ASM_' in values : + summary_dict[key]['ASM'] += 1 + elif 'ALM_' in values : + summary_dict[key]['ALM'] += 1 + elif 'LNF' in values : + summary_dict[key]['LNF'] += 1 + elif 'TPR' in values : + summary_dict[key]['TPR'] += 1 + elif 'NIPH' == values : + summary_dict[key]['NIPH'] += 1 + elif 'NIPHEM' == values : + summary_dict[key]['NIPHEM'] += 1 + elif 'PLOT' in values : + summary_dict[key]['PLOT'] += 1 + elif 'ERROR' in values : + summary_dict[key]['ERROR'] += 1 + else: + try: + number = int(values) + summary_dict[key]['Exact match'] +=1 + except: + if '_' in values : + tmp_value = values + try: + number = int(tmp_value[-1]) + summary_dict[key]['Exact match'] +=1 + except: + logger.debug('The value %s, was found when collecting summary information for the %s', values, summary_dict[key] ) + else: + logger.debug('The value %s, was found when collecting summary information for the %s', values, summary_dict[key] ) + summary_sample_list = [] + for item in summary_heading_list : + summary_sample_list.append(str(summary_dict[key][item])) + summary_result_list.append(key + '\t' +'\t'.join(summary_sample_list)) + return summary_result_list + + +# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # +# Get gene and product annotation for core gene using Prokka # +# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # + +### (tsv para algunos locus? Utils para analyze schema?) +def get_gene_annotation (annotation_file, annotation_dir, genus, species, usegenus, logger) : + + name_file = os.path.basename(annotation_file).split('.') + annotation_dir = os.path.join (annotation_dir, 'annotation', name_file[0]) + + if usegenus == 'true': + annotation_result = subprocess.run (['prokka', annotation_file, '--outdir', annotation_dir, + '--genus', genus, '--species', species, '--usegenus', + '--gcode', '11', '--prefix', name_file[0], '--quiet']) + + elif usegenus == 'false': + annotation_result = subprocess.run (['prokka', annotation_file, '--outdir', annotation_dir, + '--genus', genus, '--species', species, + '--gcode', '11', '--prefix', name_file[0], '--quiet']) + + annot_tsv = [] + tsv_path = os.path.join (annotation_dir, name_file[0] + '.tsv') + + try: + with open(tsv_path) as tsvfile: + tsvreader = csv.reader(tsvfile, delimiter="\t") + for line in tsvreader: + annot_tsv.append(line) + + if len(annot_tsv) > 1: + + gene_index = annot_tsv[0].index("gene") + product_index = annot_tsv[0].index("product") + + try: + if '_' in annot_tsv[1][2]: + gene_annot = annot_tsv[1][gene_index].split('_')[0] + else: + gene_annot = annot_tsv[1][gene_index] + except: + gene_annot = 'Not found by Prokka' + + try: + product_annot = annot_tsv[1][product_index] + except: + product_annot = 'Not found by Prokka' + else: + gene_annot = 'Not found by Prokka' + product_annot = 'Not found by Prokka' + except: + gene_annot = 'Not found by Prokka' + product_annot = 'Not found by Prokka' + + return gene_annot, product_annot + +""" +def get_gene_annotation (annotation_file, annotation_dir, logger) : + name_file = os.path.basename(annotation_file).split('.') + annotation_dir = os.path.join (annotation_dir, 'annotation', name_file[0]) + + annotation_result = subprocess.run (['prokka', annotation_file, '--outdir', annotation_dir, + '--prefix', name_file[0], '--quiet']) + + annot_tsv = [] + tsv_path = os.path.join (annotation_dir, name_file[0] + '.tsv') + + try: + with open(tsv_path) as tsvfile: + tsvreader = csv.reader(tsvfile, delimiter="\t") + for line in tsvreader: + annot_tsv.append(line) + + if len(annot_tsv) > 1: + try: + if '_' in annot_tsv[1][2]: + gene_annot = annot_tsv[1][2].split('_')[0] + else: + gene_annot = annot_tsv[1][2] + except: + gene_annot = 'Not found by Prokka' + + try: + product_annot = annot_tsv[1][4] + except: + product_annot = 'Not found by Prokka' + else: + gene_annot = 'Not found by Prokka' + product_annot = 'Not found by Prokka' + except: + gene_annot = 'Not found by Prokka' + product_annot = 'Not found by Prokka' + + return gene_annot, product_annot +""" + +""" +def get_gene_annotation (annotation_file, annotation_dir, logger) : + name_file = os.path.basename(annotation_file).split('.') + annotation_dir = os.path.join (annotation_dir, 'annotation', name_file[0]) + + annotation_result = subprocess.run (['prokka', annotation_file, '--outdir', annotation_dir, + '--prefix', name_file[0], '--quiet']) + + annot_tsv = [] + tsv_path = os.path.join (annotation_dir, name_file[0] + '.tsv') + with open(tsv_path) as tsvfile: + tsvreader = csv.reader(tsvfile, delimiter="\t") + for line in tsvreader: + annot_tsv.append(line) + + if len(annot_tsv) > 1: + try: + if '_' in annot_tsv[1][2]: + gene_annot = annot_tsv[1][2].split('_')[0] + else: + gene_annot = annot_tsv[1][2] + except: + gene_annot = 'Not found by Prokka' + + try: + product_annot = annot_tsv[1][4] + except: + product_annot = 'Not found by Prokka' + else: + gene_annot = 'Not found by Prokka' + product_annot = 'Not found by Prokka' + + return gene_annot, product_annot +""" + + +def analize_annotation_files (in_file, logger) : ## N + examiner = GFF.GFFExaminer() + file_fh = open(in_file) + datos = examiner.available_limits(in_file) + file_fh.close() + return True + + +def get_inferred_allele_number(core_dict, logger): ## N + #This function will look for the highest locus number and it will return a safe high value + # that will be added to the schema database + logger.debug('running get_inferred_allele_number function') + int_keys = [] + for key in core_dict.keys(): + int_keys.append(key) + max_value = max(int_keys) + digit_length = len(str(max_value)) + return True #str 1 ( #'1'+ '0'*digit_length + 2) + + +# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # +# Get ST profile for each samples based on allele calling results # +# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # + +def get_ST_profile(outputdir, profile_csv_path, exact_dict, inf_dict, core_gene_list_files, sample_list_files, logger): + ## logger + + csv_read = [] + ST_profiles_dict = {} + samples_profiles_dict = {} + analysis_profiles_dict = {} + inf_ST = {} + count_st = {} + + with open(profile_csv_path) as csvfile: + csvreader = csv.reader(csvfile, delimiter="\t") + for line in csvreader: + csv_read.append(line) + + profile_header = csv_read[0][1:len(core_gene_list_files) + 1] + + for ST_index in range(1, len(csv_read)): + ST_profiles_dict[csv_read[ST_index][0]] = {} + for core_index in range(len(profile_header)): + ST_profiles_dict[csv_read[ST_index][0]][profile_header[core_index]] = csv_read[ST_index][core_index + 1] + + for sample_file in sample_list_files: + sample_name = '.'.join(os.path.basename(sample_file).split('.')[:-1]) + + st_counter = 0 + for ST in ST_profiles_dict: + core_counter = 0 + + for core_name in profile_header: + allele_in_ST = ST_profiles_dict[ST][core_name] + exact_gene = True + + if sample_name in exact_dict: + if core_name in exact_dict[sample_name]: + allele_in_sample = exact_dict[sample_name][core_name][2] + + if not '_' in allele_in_ST: + if '_' in allele_in_sample: + allele_in_sample = allele_in_sample.split('_')[1] + + if st_counter == 0: + if sample_name not in analysis_profiles_dict: + analysis_profiles_dict[sample_name] = {} + analysis_profiles_dict[sample_name][core_name] = allele_in_sample + + if allele_in_sample == allele_in_ST: + core_counter += 1 + + else: + exact_gene = False + + else: + exact_gene = False + + if exact_gene == False: + if sample_name in inf_dict: + if core_name in inf_dict[sample_name]: + if st_counter == 0: + allele_in_sample = inf_dict[sample_name][core_name][2] + if sample_name not in analysis_profiles_dict: + analysis_profiles_dict[sample_name] = {} + analysis_profiles_dict[sample_name][core_name] = allele_in_sample + + else: + if st_counter == 0: + if sample_name not in analysis_profiles_dict: + analysis_profiles_dict[sample_name] = {} + + if allele_in_ST == 'N' and "allele_in_sample" not in locals(): + core_counter += 1 + + st_counter += 1 + if core_counter == len(profile_header): + samples_profiles_dict[sample_name] = ST + + if "_INF" in ST: + if "New" not in count_st: + count_st["New"] = {} + if ST not in count_st["New"]: + count_st["New"][ST] = 0 + count_st["New"][ST] += 1 + + else: + if "Known" not in count_st: + count_st["Known"] = {} + if ST not in count_st["Known"]: + count_st["Known"][ST] = 0 + count_st["Known"][ST] += 1 + + break + + if sample_name not in samples_profiles_dict: + if sample_name in analysis_profiles_dict: + if len(analysis_profiles_dict[sample_name]) == len(profile_header): + new_st_id = str(len(ST_profiles_dict) + 1) + ST_profiles_dict[new_st_id + "_INF"] = analysis_profile_dict[sample_name] + inf_ST[new_st_id] = analysis_profile_dict[sample_name] + + samples_profiles_dict[sample_name]=new_st_id + "_INF" + + if "New" not in count_st: + count_st["New"] = {} + if new_st_id not in count_st["New"]: + count_st["New"][new_st_id] = 0 + count_st["New"][new_st_id] += 1 + + else: + samples_profiles_dict[sample_name] = '-' + + if "Unknown" not in count_st: + count_st["Unknown"] = 0 + count_st["Unknown"] += 1 + else: + samples_profiles_dict[sample_name] = '-' + + if "Unknown" not in count_st: + count_st["Unknown"] = 0 + count_st["Unknown"] += 1 + + ## Create ST profile results report + save_st_profile_results (outputdir, samples_profiles_dict, logger) + + ## Obtain interactive piechart + logger.info('Creating interactive ST results piechart') + create_sunburst_plot_st (outputdir, count_st, logger) + + return True, inf_ST + + +# · * · * · * · * · * · * # +# Create ST results report # +# · * · * · * · * · * · * # + +def save_st_profile_results (outputdir, samples_profiles_dict, logger): + + header_stprofile = ['Sample Name', 'ST'] + + if samples_profiles_dict != '': + ## Saving ST profile to file + logger.info('Saving ST profile information to file..') + stprofile_file = os.path.join(outputdir, 'stprofile.tsv') + with open (stprofile_file , 'w') as st_fh : + st_fh.write('\t'.join(header_stprofile)+ '\n') + for sample in sorted(samples_profiles_dict): + st_fh.write(sample + '\t' + samples_profiles_dict[sample] + '\n') + + return True + + +def create_sunburst_plot_st (outputdir, count_st, logger): + ### logger? + counts = [] + st_ids = ["ST"] + st_labels = ["ST"] + st_parents = [""] + + total_samples = 0 + + for st_type in count_st: + + if type(count_st[st_type]) == dict: + total_st_type_count = sum(count_st[st_type].values()) + else: + total_st_type_count = count_st[st_type] + + counts.append(total_st_type_count) + st_ids.append(st_type) + st_labels.append(st_type) + st_parents.append("ST") + + total_samples += total_st_type_count + + if type(count_st[st_type]) == dict: + for st in count_st[st_type]: + counts.append(count_st[st_type][st]) + st_ids.append(st + " - " + st_type) + st_labels.append(st) + st_parents.append(st_type) + + counts.insert(0, total_samples) + + fig = go.Figure(go.Sunburst( + ids = st_ids, + labels = st_labels, + parents = st_parents, + values = counts, + branchvalues = "total", + )) + + fig.update_layout(margin = dict(t=0, l=0, r=0, b=0)) + + plotsdir = os.path.join(outputdir, 'plots', 'samples_st.html') + + fig.write_html(plotsdir) + + return True + + +# · * · * · * · * · * · * · * · * · * · * · # +# Update ST profile file adding new ST found # +# · * · * · * · * · * · * · * · * · * · * · # + +def update_st_profile (updateprofile, profile_csv_path, outputdir, inf_ST, core_gene_list_files, logger): + + ## Create a copy of ST profile file if updateprofile = 'new' + if updateprofile == 'new': + no_updated_profile_csv_path = profile_csv_path + profile_csv_path_name = os.path.basename(no_updated_profile_csv_path).split('.')[0] + profile_csv_path = os.path.join(outputdir, profile_csv_path_name + '_updated' + '.csv') + shutil.copyfile(no_updated_profile_csv_path, profile_csv_path) + logger.info('Copying ST profile file to update profiles') + + ## Update ST profile file + logger.info('Updating ST profile file adding new INF ST') + + with open (profile_csv_path, 'r') as csvfile: + csvreader = csv.reader(csvfile, delimiter="\t") + for line in csvreader: + profile_header = line[0][1:len(core_gene_list_files) + 1] + break + + with open (profile_csv_path, 'a') as profile_fh: + for ST in inf_ST: + locus_ST_list = [] + locus_ST_list.append(ST) + for locus in profile_header: + locus_ST_list.append(inf_ST[ST][locus]) + profile_fh.write ('\t'.join(locus_ST_list)+ '\n') + + return True + + +# · * · * · * · * · * · * · * · * · * · # +# Create allele calling results reports # +# · * · * · * · * · * · * · * · * · * · # + +def save_allele_call_results (outputdir, full_gene_list, samples_matrix_dict, exact_dict, paralog_dict, inf_dict, plot_dict, matching_genes_dict, list_asm, list_alm, lnf_tpr_dict, snp_dict, match_alignment_dict, protein_dict, prodigal_report, shorter_seq_coverage, longer_seq_coverage, equal_seq_coverage, shorter_blast_seq_coverage, longer_blast_seq_coverage, equal_blast_seq_coverage, logger): + header_matching_alleles_contig = ['Sample Name', 'Contig', 'Core Gene', 'Allele', 'Contig Start', 'Contig Stop', 'Direction', 'Codification'] + header_exact = ['Core Gene', 'Sample Name', 'Gene Annotation', 'Product Annotation', 'Allele', 'Allele Quality', 'Contig', 'Query length', 'Contig start', 'Contig end', 'Sequence', 'Predicted Sequence'] + header_paralogs = ['Core Gene','Sample Name', 'Gene Annotation', 'Product Annotation', 'Paralog Tag', 'ID %', 'Allele', 'Allele Quality', 'Contig', 'Bitscore', 'Contig start', 'Contig end', 'Sequence', 'Predicted Sequence'] + header_inferred = ['Core Gene','Sample Name', 'INF tag', 'Gene Annotation', 'Product Annotation', 'Allele', 'Allele Quality', 'Contig', 'Bitscore', 'Query length', 'Contig length', 'New sequence length' , 'Mismatch' , 'gaps', 'Contig start', 'Contig end', 'New sequence', 'Predicted Sequence'] + header_asm = ['Core Gene', 'Sample Name', 'ASM tag', 'Gene Annotation', 'Product Annotation', 'Allele', 'Allele Quality', 'Contig', 'Bitscore', 'Query length', 'Contig length', 'New sequence length' , 'Mismatch' , 'gaps', 'Contig start', 'Contig end', 'New sequence', 'Additional info', 'Predicted Sequence'] + header_alm = ['Core Gene', 'Sample Name', 'ALM tag', 'Gene Annotation', 'Product Annotation', 'Allele', 'Allele Quality', 'Contig', 'Bitscore', 'Query length', 'Contig length', 'New sequence length' , 'Mismatch' , 'gaps', 'Contig start', 'Contig end', 'New sequence', 'Additional info', 'Predicted Sequence'] + header_plot = ['Core Gene', 'Sample Name', 'Gene Annotation', 'Product Annotation', 'Allele', 'Allele Quality', 'Contig','Bitscore', 'Contig start', 'Contig end', 'Sequence', 'Predicted Sequence'] + header_lnf_tpr = ['Core Gene', 'Sample Name', 'Gene Annotation', 'Product Annotation', 'Tag', 'Allele', 'Allele Quality', 'ID %', 'Blast sequence coverage %', 'New sequence coverage %', 'Query length', 'Contig length', 'New sequence length', 'Additional info'] + header_snp = ['Core Gene', 'Sample Name', 'Allele', 'Position', 'Mutation Schema/Sample', 'Codon Schema/Sample','Amino acid in Schema/Sample', 'Mutation type','Annotation Schema/Sample'] + header_protein = ['Core Gene','Sample Name', 'Protein in ' , 'Protein sequence'] + header_match_alignment = ['Core Gene','Sample Name','Alignment', 'Sequence'] + header_stprofile = ['Sample Name', 'ST'] + + + # Añadido header_prodigal_report para report prodigal +# header_prodigal_report = ['Core gene', 'Sample Name', 'Allele', 'Sequence type', 'BLAST start', 'BLAST end', 'Prodigal start', 'Prodigal end', 'BLAST sequence', 'Prodigal sequence'] + # Añadido header_newsseq_coverage_report para determinar coverage threshold a imponer +# header_newsseq_coverage_report = ['Core gene', 'Sample Name', 'Query length', 'New sequence length', 'Locus mean', 'Coverage (new sequence/allele)', 'Coverage (new sequence/locus mean)'] + # Añadido header_blast_coverage_report para determinar coverage threshold a imponer +# header_blast_coverage_report = ['Core gene', 'Sample Name', 'Query length', 'Blast sequence length', 'Locus mean', 'Coverage (blast sequence/allele)', 'Coverage (blast sequence/locus mean)'] + + ## Saving the result information to file + print ('Saving results to files \n') + result_file = os.path.join ( outputdir, 'result.tsv') + logger.info('Saving result information to file..') + with open (result_file, 'w') as out_fh: + out_fh.write ('Sample Name\t'+'\t'.join( full_gene_list) + '\n') + for key in sorted (samples_matrix_dict): + out_fh.write (key + '\t' + '\t'.join(samples_matrix_dict[key])+ '\n') + + ## Saving exact matches to file + logger.info('Saving exact matches information to file..') + exact_file = os.path.join(outputdir, 'exact.tsv') + with open (exact_file , 'w') as exact_fh : + exact_fh.write('\t'.join(header_exact)+ '\n') + for sample in sorted(exact_dict): + for core in sorted(exact_dict[sample]): + exact_fh.write(core + '\t' + sample + '\t' + '\t'.join(exact_dict[sample][core]) + '\n') + + ## Saving paralog alleles to file + logger.info('Saving paralog information to file..') + paralog_file = os.path.join(outputdir, 'paralog.tsv') + with open (paralog_file , 'w') as paralog_fh : + paralog_fh.write('\t'.join(header_paralogs) + '\n') + for sample in sorted (paralog_dict) : + for core in sorted (paralog_dict[sample]): + for paralog in paralog_dict[sample][core] : + paralog_fh.write(core + '\t' + sample + '\t' + '\t'.join (paralog) + '\n') + + ## Saving inferred alleles to file + logger.info('Saving inferred alleles information to file..') + inferred_file = os.path.join(outputdir, 'inferred_alleles.tsv') + with open (inferred_file , 'w') as infer_fh : + infer_fh.write('\t'.join(header_inferred) + '\n') + for core in sorted (inf_dict) : + for sample in sorted (inf_dict[core]) : + for inferred in inf_dict[core][sample]: + # seq_in_inferred_allele = '\t'.join (inf_dict[sample]) + infer_fh.write(core + '\t' + sample + '\t' + inferred + '\t' + '\t'.join(inf_dict[core][sample][inferred]) + '\n') + + ## Saving PLOTs to file + logger.info('Saving PLOT information to file..') + plot_file = os.path.join(outputdir, 'plot.tsv') + with open (plot_file , 'w') as plot_fh : + plot_fh.write('\t'.join(header_plot) + '\n') + for core in sorted (plot_dict) : + for sample in sorted (plot_dict[core]): + plot_fh.write(core + '\t' + sample + '\t' + '\t'.join(plot_dict[core][sample]) + '\n') + + ## Saving matching contigs to file + logger.info('Saving matching information to file..') + matching_file = os.path.join(outputdir, 'matching_contigs.tsv') + with open (matching_file , 'w') as matching_fh : + matching_fh.write('\t'.join(header_matching_alleles_contig) + '\n') + for samples in sorted (matching_genes_dict) : + for contigs in matching_genes_dict[samples] : + for contig in matching_genes_dict[samples] [contigs]: + matching_alleles = '\t'.join (contig) + matching_fh.write(samples + '\t' + contigs +'\t' + matching_alleles + '\n') + + ## Saving ASMs to file + logger.info('Saving asm information to file..') + asm_file = os.path.join(outputdir, 'asm.tsv') + with open (asm_file , 'w') as asm_fh : + asm_fh.write('\t'.join(header_asm)+ '\n') + for core in list_asm : + for sample in list_asm[core] : + for asm in list_asm[core][sample]: + asm_fh.write(core + '\t' + sample + '\t' + asm + '\t' + '\t'.join(list_asm[core][sample][asm]) + '\n') + + ## Saving ALMs to file + logger.info('Saving alm information to file..') + alm_file = os.path.join(outputdir, 'alm.tsv') + with open (alm_file , 'w') as alm_fh : + alm_fh.write('\t'.join(header_alm)+ '\n') + for core in list_alm : + for sample in list_alm[core] : + for alm in list_alm[core][sample]: + alm_fh.write(core + '\t' + sample + '\t' + alm + '\t' + '\t'.join(list_alm[core][sample][alm]) + '\n') + + ## Saving LNFs to file + logger.info('Saving lnf information to file..') + lnf_file = os.path.join(outputdir, 'lnf_tpr.tsv') + with open (lnf_file , 'w') as lnf_fh : + lnf_fh.write('\t'.join(header_lnf_tpr)+ '\n') + for core in lnf_tpr_dict : + for sample in lnf_tpr_dict[core] : + for lnf in lnf_tpr_dict[core][sample] : + lnf_fh.write(core + '\t' + sample + '\t' + '\t'.join(lnf) + '\n') + + ## Saving SNPs information to file + logger.info('Saving SNPs information to file..') + snp_file = os.path.join(outputdir, 'snp.tsv') + with open (snp_file , 'w') as snp_fh : + snp_fh.write('\t'.join(header_snp) + '\n') + for core in sorted (snp_dict) : + for sample in sorted (snp_dict[core]): + for allele_id_snp in snp_dict[core][sample] : + for snp in snp_dict[core][sample][allele_id_snp] : + snp_fh.write(core + '\t' + sample + '\t' + allele_id_snp + '\t' + '\t'.join (snp) + '\n') + + ## Saving DNA sequences alignments to file + logger.info('Saving matching alignment information to files..') + alignment_dir = os.path.join(outputdir,'alignments') + if os.path.exists(alignment_dir) : + shutil.rmtree(alignment_dir) + logger.info('deleting the alignment files from previous execution') + os.makedirs(alignment_dir) + for core in sorted(match_alignment_dict) : + for sample in sorted (match_alignment_dict[core]) : + match_alignment_file = os.path.join(alignment_dir, str('match_alignment_' + core + '_' + sample + '.txt')) + with open(match_alignment_file, 'w') as match_alignment_fh : + match_alignment_fh.write( '\t'.join(header_match_alignment) + '\n') + for match_align in match_alignment_dict[core][sample] : + match_alignment_fh.write(core + '\t'+ sample +'\t'+ '\t'.join(match_align) + '\n') + + ## Saving protein sequences alignments to file + logger.info('Saving protein information to files..') + protein_dir = os.path.join(outputdir,'proteins') + if os.path.exists(protein_dir) : + shutil.rmtree(protein_dir) + logger.info('deleting the proteins files from previous execution') + os.makedirs(protein_dir) + for core in sorted(protein_dict) : + for sample in sorted (protein_dict[core]) : + protein_file = os.path.join(protein_dir, str('protein_' + core + '_' + sample + '.txt')) + with open(protein_file, 'w') as protein_fh : + protein_fh.write( '\t'.join(header_protein) + '\n') + for protein in protein_dict[core][sample] : + protein_fh.write(core + '\t'+ sample +'\t'+ '\t'.join(protein) + '\n') + + ## Saving summary information to file + logger.info('Saving summary information to file..') + summary_result = create_summary (samples_matrix_dict, logger) + summary_file = os.path.join( outputdir, 'summary_result.tsv') + with open (summary_file , 'w') as summ_fh: + for line in summary_result : + summ_fh.write(line + '\n') + + ## Modify the result file to remove the PLOT_ string for creating the file to use in the tree diagram +# logger.info('Saving result information for tree diagram') +# tree_diagram_file = os.path.join ( outputdir, 'result_for_tree_diagram.tsv') +# with open (result_file, 'r') as result_fh: +# with open(tree_diagram_file, 'w') as td_fh: +# for line in result_fh: +# tree_line = line.replace('PLOT_','') +# td_fh.write(tree_line) + + ########################################################################################### + # Guardando report de prodigal. Temporal +# prodigal_report_file = os.path.join (outputdir, 'prodigal_report.tsv') + # saving prodigal predictions to file +# with open (prodigal_report_file, 'w') as out_fh: +# out_fh.write ('\t'.join(header_prodigal_report)+ '\n') +# for prodigal_result in prodigal_report: +# out_fh.write ('\t'.join(prodigal_result)+ '\n') + + # Guardando coverage de new_sseq para estimar el threshold a establecer. Temporal +# newsseq_coverage_file = os.path.join (outputdir, 'newsseq_coverage_report.tsv') + # saving the coverage information to file +# with open (newsseq_coverage_file, 'w') as out_fh: +# out_fh.write ('\t' + '\t'.join(header_newsseq_coverage_report)+ '\n') +# for coverage in shorter_seq_coverage: +# out_fh.write ('Shorter new sequence' + '\t' + '\t'.join(coverage)+ '\n') +# for coverage in longer_seq_coverage: +# out_fh.write ('Longer new sequence' + '\t' + '\t'.join(coverage)+ '\n') +# for coverage in equal_seq_coverage: +# out_fh.write ('Same length new sequence' + '\t' + '\t'.join(coverage)+ '\n') + + # Guardando coverage de la sseq obtenida tras blast para estimar el threshold a establecer. Temporal +# blast_coverage_file = os.path.join (outputdir, 'blast_coverage_report.tsv') + # saving the result information to file +# with open (blast_coverage_file, 'w') as out_fh: +# out_fh.write ('\t' + '\t'.join(header_blast_coverage_report)+ '\n') +# for coverage in shorter_blast_seq_coverage: +# out_fh.write ('Shorter blast sequence' + '\t' + '\t'.join(coverage)+ '\n') +# for coverage in longer_blast_seq_coverage: +# out_fh.write ('Longer blast sequence' + '\t' + '\t'.join(coverage)+ '\n') +# for coverage in equal_blast_seq_coverage: +# out_fh.write ('Same length blast sequence' + '\t' + '\t'.join(coverage)+ '\n') + ########################################################################################### + + return True + + + +def save_allele_calling_plots (outputdir, sample_list_files, count_exact, count_inf, count_asm, count_alm, count_lnf, count_tpr, count_plot, count_niph, count_niphem, count_error, logger): + + ## Create result plots directory + plots_dir = os.path.join(outputdir,'plots') + try: + os.makedirs(plots_dir) + except: + logger.info('Deleting the results plots directory for a previous execution without cleaning up') + shutil.rmtree(os.path.join(outputdir, 'plots')) + try: + os.makedirs(plots_dir) + logger.info ('Results plots folder %s has been created again', plots_dir) + except: + logger.info('Unable to create again the results plots directory %s', plots_dir) + print('Cannot create Results plots directory on ', plots_dir) + exit(0) + + for sample_file in sample_list_files: + sample_name = '.'.join(os.path.basename(sample_file).split('.')[:-1]) + + ## Obtain interactive piechart + logger.info('Creating interactive results piecharts') + create_sunburst_allele_call (outputdir, sample_name, count_exact[sample_name], count_inf[sample_name], count_asm[sample_name], count_alm[sample_name], count_lnf[sample_name], count_tpr[sample_name], count_plot[sample_name], count_niph[sample_name], count_niphem[sample_name], count_error[sample_name]) + + return True + + + +def create_sunburst_allele_call (outputdir, sample_name, count_exact, count_inf, count_asm, count_alm, count_lnf, count_tpr, count_plot, count_niph, count_niphem, count_error): + ### logger + + total_locus = count_exact['total'] + count_inf['total'] + count_asm['total'] + count_alm['total'] + count_lnf['total'] + count_tpr['total'] + count_plot['total'] \ + + count_niph['total'] + count_niphem['total'] + count_error['total'] + + tag_counts = [total_locus, count_exact['total'], count_exact['good_quality'], count_exact['bad_quality'], count_exact['no_start'], count_exact['no_start_stop'], + count_exact['no_stop'], count_exact['multiple_stop'], count_inf['total'], count_inf['good_quality'], count_inf['bad_quality'], count_inf['no_start'], + count_inf['no_start_stop'], count_inf['no_stop'], count_inf['multiple_stop'], count_asm['total'], count_asm['insertion'], count_asm['deletion'], + count_asm['substitution'], count_alm['total'], count_alm['insertion'], count_alm['deletion'], count_alm['substitution'], count_plot['total'], + count_niph['total'], count_niph['good_quality'], count_niph['bad_quality'], count_niph['no_start'], count_niph['no_start_stop'], count_niph['no_stop'], + count_niph['multiple_stop'], count_niphem['total'], count_niphem['good_quality'], count_niphem['bad_quality'], count_niphem['no_start'], + count_niphem['no_start_stop'], count_niphem['no_stop'], count_niphem['multiple_stop'], count_lnf['total'], count_lnf['not_found'], count_lnf['low_id'], + count_lnf['low_coverage'], count_tpr['total'], count_tpr['good_quality'], count_tpr['bad_quality'], count_tpr['no_start'], count_tpr['no_start_stop'], + count_tpr['no_stop'], count_tpr['multiple_stop'], count_error['total'], count_error['good_quality'], count_error['bad_quality'], count_error['no_start'], + count_error['no_start_stop'], count_error['no_stop'], count_error['multiple_stop']] + + fig=go.Figure(go.Sunburst( + ids=[ + sample_name, "Exact Match", "Good Quality - Exact Match", "Bad Quality - Exact Match", + "No start - Bad Quality - Exact Match", "No start-stop - Bad Quality - Exact Match", + "No stop - Bad Quality - Exact Match", "Multiple stop - Bad Quality - Exact Match", + "INF", "Good Quality - INF", "Bad Quality - INF", "No start - Bad Quality - INF", + "No start-stop - Bad Quality - INF", "No stop - Bad Quality - INF", "Multiple stop - Bad Quality - INF", + "ASM", "Insertion - ASM", "Deletion - ASM", "Substitution - ASM", "ALM", "Insertion - ALM", + "Deletion - ALM", "Substitution - ALM", "PLOT", "NIPH", "Good Quality - NIPH", + "Bad Quality - NIPH", "No start - Bad Quality - NIPH", "No start-stop - Bad Quality - NIPH", + "No stop - Bad Quality - NIPH", "Multiple stop - Bad Quality - NIPH", "NIPHEM", + "Good Quality - NIPHEM", "Bad Quality - NIPHEM", "No start - Bad Quality - NIPHEM", + "No start-stop - Bad Quality - NIPHEM", "No stop - Bad Quality - NIPHEM", + "Multiple stop - Bad Quality - NIPHEM", "LNF", "Not found", + "Low ID", "Low coverage", "TPR", "Good Quality - TPR", "Bad Quality - TPR", + "No start - Bad Quality - TPR", "No start-stop - Bad Quality - TPR", "No stop - Bad Quality - TPR", + "Multiple stop - Bad Quality - TPR", "Error", "Good Quality - Error", "Bad Quality - Error", + "No start - Bad Quality - Error", "No start-stop - Bad Quality - Error", + "No stop - Bad Quality - Error", "Multiple stop - Bad Quality - Error" + ], + labels= [ + sample_name, "Exact
Match", "Good
Quality", "Bad
Quality", + "No
start", "No
start-stop", "No
stop", "Multiple
stop", + "INF", "Good
Quality", "Bad
Quality", "No
start", + "No
start-stop", "No
stop", "Multiple
stop", "ASM", "Insertion", + "Deletion", "Substitution", "ALM", "Insertion", "Deletion", + "Substitution", "PLOT", "NIPH", "Good
Quality", "Bad
Quality", + "No
start", "No
start-stop", "No
stop", "Multiple
stop", + "NIPHEM", "Good
Quality", "Bad
Quality", "No
start", + "No
start-stop", "No
stop", "Multiple
stop", "LNF", "Not
found", + "Low
ID", "Low
coverage", "TPR", "Good
Quality", "Bad
Quality", + "No
start", "No
start-stop", "No
stop", "Multiple
stop", + "Error", "Good
Quality", "Bad
Quality","No
start", + "No
start-stop", "No
stop", "Multiple
stop" + ], + parents=[ + "", sample_name, "Exact Match", "Exact Match", "Bad Quality - Exact Match", + "Bad Quality - Exact Match", "Bad Quality - Exact Match", "Bad Quality - Exact Match", + sample_name, "INF", "INF", "Bad Quality - INF", "Bad Quality - INF", "Bad Quality - INF", + "Bad Quality - INF", sample_name, "ASM", "ASM", "ASM", sample_name, "ALM", "ALM", "ALM", sample_name, + sample_name, "NIPH", "NIPH", "Bad Quality - NIPH", "Bad Quality - NIPH", "Bad Quality - NIPH", + "Bad Quality - NIPH", sample_name, "NIPHEM", "NIPHEM", "Bad Quality - NIPHEM", + "Bad Quality - NIPHEM", "Bad Quality - NIPHEM", "Bad Quality - NIPHEM", sample_name, "LNF", + "LNF", "LNF", sample_name, "TPR", "TPR", "Bad Quality - TPR", "Bad Quality - TPR", + "Bad Quality - TPR", "Bad Quality - TPR", sample_name, "Error", "Error", "Bad Quality - Error", + "Bad Quality - Error", "Bad Quality - Error", "Bad Quality - Error" + ], + values=tag_counts, + branchvalues="total", + )) + + fig.update_layout(margin = dict(t=0, l=0, r=0, b=0)) + + plotsdir = os.path.join(outputdir, 'plots', sample_name + '.html') + + fig.write_html(plotsdir) + + return True + + +# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # +# Update core genes schema adding new inferred alleles found for each locus in allele calling analysis # +# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # + +def update_schema (updateschema, schemadir, outputdir, core_gene_list_files, inferred_alleles_dict, alleles_in_locus_dict, logger): + + if len(inferred_alleles_dict) > 0: + ## Create a copy of core genes schema if updateschema = 'new' / 'New' + if updateschema == 'new': + no_updated_schemadir = schemadir + ##schemadir_name = os.path.dirname(no_updated_schemadir) ---> se puede usar si guardo finalmente el nuevo esquema en el mismo directorio que el antiguo esquema, pero para ello debo verificar si termina o no el path en / para eliminarlo o no del path antes de hacer el dirname + schemadir_name = no_updated_schemadir.split("/") + if no_updated_schemadir.endswith("/"): + schemadir_name = schemadir_name[-2] + else: + schemadir_name = schemadir_name[-1] + + schemadir = os.path.join(outputdir, schemadir_name + '_updated') + + try: + shutil.copytree(no_updated_schemadir, schemadir) + logger.info ('Schema copy %s has been created to update schema', schemadir) + except: + logger.info('Deleting preexisting directory') + shutil.rmtree(schemadir) + try: + shutil.copytree(no_updated_schemadir, schemadir) + logger.info ('Schema copy %s has been created to update schema', schemadir) + except: + logger.info('Unable to create schema copy %s', schemadir) + print('Cannot create schema copy on ', schemadir) + exit(0) + + ## Get INF alleles for each core gene and update each locus fasta file + for core_file in core_gene_list_files: + core_name = os.path.basename(core_file).split('.')[0] + if core_name in inferred_alleles_dict: + logger.info('Updating core gene file %s adding new INF alleles', core_file) + + inf_list = inferred_alleles_dict[core_name] + + try: + alleles_ids = [int(allele) for allele in alleles_in_locus_dict[core_name]] + allele_number = max(alleles_ids) + + locus_schema_file = os.path.join(schemadir, core_name + '.fasta') + with open (locus_schema_file, 'a') as core_fh: + for inf in inf_list: + allele_number += 1 + core_fh.write('\n' + '>' + str(allele_number) + ' # ' + 'INF by Taranis' + '\n' + inf + '\n') + except: + alleles_ids = [int(allele.split('_')[-1]) for allele in alleles_in_locus_dict[core_name]] + allele_number = max(alleles_ids) + + locus_schema_file = os.path.join(schemadir, core_name + '.fasta') + with open (locus_schema_file, 'a') as core_fh: + for inf in inf_list: + allele_number += 1 + complete_inf_id = core_name + '_' + str(allele_number) + core_fh.write('\n' + '>' + complete_inf_id + ' # ' + 'INF by Taranis' + '\n' + inf + '\n') + + return True + +""" +def update_schema (updateschema, schemadir, storedir, core_gene_list_files, inferred_alleles_dict, alleles_in_locus_dict, logger): + + ## Create a copy of core genes schema if updateschema = 'new' / 'New' + if updateschema == 'new' or updateschema == 'New': + no_updated_schemadir = schemadir + schemadir_name = os.path.basename(no_updated_schemadir) + schemadir = os.path.join(storedir, schemadir_name + '_updated') + shutil.copytree(no_updated_schemadir, schemadir) + logger.info('Copying core genes fasta files to update schema') + + ## Get INF alleles for each core gene and update each locus fasta file + for core_file in core_gene_list_files: + core_name = os.path.basename(core_file).split('.')[0] + if core_name in inferred_alleles_dict: + logger.info('Updating core gene file %s adding new INF alleles', core_file) + + inf_list = inferred_alleles_dict[core_name] + + alleles_ids = [int(allele) for allele in alleles_in_locus_dict[core_name]] + allele_number = max(alleles_ids) + + locus_schema_file = os.path.join(schemadir, core_name + '.fasta') + with open (locus_schema_file, 'a') as core_fh: + for inf in inf_list: + allele_number += 1 + core_fh.write('\n' + '>' + str(allele_number) + ' # ' + 'INF by Taranis' + '\n' + inf + '\n') + + return True +""" + + +# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # +# Allele calling analysis to find each core gene in schema and its variants in samples # +# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # + +def allele_call_nucleotides (core_gene_list_files, sample_list_files, alleles_in_locus_dict, contigs_in_sample_dict, query_directory, reference_alleles_directory, blast_db_directory, prodigal_directory, blast_results_seq_directory, blast_results_db_directory, inputdir, outputdir, cpus, percentlength, coverage, evalue, perc_identity_ref, perc_identity_loc, reward, penalty, gapopen, gapextend, max_target_seqs, max_hsps, num_threads, flankingnts, schema_variability, schema_statistics, schema_quality, annotation_core_dict, profile_csv_path, logger ): + + prodigal_report = [] # TEMPORAL. prodigal_report para checkear las secuencias obtenidas con prodigal vs blast y las posiciones sstart y send + # listas añadidas para calcular coverage medio de new_sseq con respecto a alelo para establecer coverage mínimo por debajo del cual considerar LNF + shorter_seq_coverage = [] # TEMPORAL + longer_seq_coverage = [] # TEMPORAL + equal_seq_coverage = [] # TEMPORAL + # listas añadidas para calcular coverage medio de sseq con respecto a alelo tras blast para establecer coverage mínimo por debajo del cual considerar LNF + shorter_blast_seq_coverage = [] # TEMPORAL + longer_blast_seq_coverage = [] # TEMPORAL + equal_blast_seq_coverage = [] # TEMPORAL + + full_gene_list = [] + samples_matrix_dict = {} # to keep allele number + matching_genes_dict = {} # to keep start and stop positions + exact_dict = {} # c/m: to keep exact matches found for each sample + inferred_alleles_dict = {} # to keep track of the new inferred alleles + inf_dict = {} # to keep inferred alleles found for each sample + paralog_dict = {} # to keep paralogs found for each sample + asm_dict = {} # c/m: to keep track of asm + alm_dict = {} # c/m: to keep track of alm + list_asm = {} # c/m: to keep asm found for each sample + list_alm = {} # c/m: to keep alm found for each sample + lnf_tpr_dict = {} # c/m: to keep locus not found for each sample + plot_dict = {} # c/m: to keep plots for each sample + snp_dict = {} # c/m: to keep snp information for each sample + protein_dict = {} + match_alignment_dict = {} + + # (recuento tags para plots) + count_exact = {} + count_inf = {} + count_asm = {} + count_alm = {} + count_lnf = {} + count_tpr = {} + count_plot = {} + count_niph = {} + count_niphem = {} + count_error = {} + + blast_parameters = '"6 , qseqid , sseqid , pident , qlen , length , mismatch , gapopen , evalue , bitscore , sstart , send , qstart , qend , sseq , qseq"' + + print('Allele calling starts') + pbar = ProgressBar () + + + ## # # # # # # # # # # # # # # # # # # # # # # # # ## + ## Processing the search for each schema core gene ## + ## # # # # # # # # # # # # # # # # # # # # # # # # ## + + for core_file in pbar(core_gene_list_files) : + core_name = os.path.basename(core_file).split('.')[0] + full_gene_list.append(core_name) + logger.info('Processing core gene file %s ', core_file) + + # Get path to this locus fasta file + locus_alleles_path = os.path.join(query_directory, str(core_name + '.fasta')) + + # Get path to reference allele fasta file for this locus + core_reference_allele_path = os.path.join(reference_alleles_directory, core_name + '.fasta') + + # Get length thresholds for INF, ASM and ALM classification + max_length_threshold, min_length_threshold = length_thresholds(core_name, schema_statistics, percentlength) + + # Get length thresholds for LNF, ASM and ALM classification + max_coverage_threshold, min_coverage_threshold = length_thresholds(core_name, schema_statistics, coverage) + + ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## + ## Processing the search for each schema core gene in each sample ## + ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## + + for sample_file in sample_list_files: + logger.info('Processing sample file %s ', sample_file) + + sample_name = '.'.join(os.path.basename(sample_file).split('.')[:-1]) + + # (recuento tags para plots) + if sample_name not in count_exact: + count_exact[sample_name] = {"good_quality" : 0, "bad_quality" : 0, "no_start" : 0, "no_start_stop" : 0, "no_stop" : 0, "multiple_stop" : 0, "total" : 0} + + if sample_name not in count_inf: + count_inf[sample_name] = {"good_quality" : 0, "bad_quality" : 0, "no_start" : 0, "no_start_stop" : 0, "no_stop" : 0, "multiple_stop" : 0, "total" : 0} + + if sample_name not in count_asm: + count_asm[sample_name] = {"insertion" : 0, "deletion" : 0, "substitution" : 0, "total" : 0} + + if sample_name not in count_alm: + count_alm[sample_name] = {"insertion" : 0, "deletion" : 0, "substitution" : 0, "total" : 0} + + if sample_name not in count_lnf: + count_lnf[sample_name] = {"not_found" : 0, "low_id" : 0, "low_coverage" : 0, "total" : 0} + + if sample_name not in count_tpr: + count_tpr[sample_name] = {"good_quality" : 0, "bad_quality" : 0, "no_start" : 0, "no_start_stop" : 0, "no_stop" : 0, "multiple_stop" : 0, "total" : 0} + + if sample_name not in count_plot: + count_plot[sample_name] = {"total" : 0} + + if sample_name not in count_niph: + count_niph[sample_name] = {"good_quality" : 0, "bad_quality" : 0, "no_start" : 0, "no_start_stop" : 0, "no_stop" : 0, "multiple_stop" : 0, "total" : 0} + + if sample_name not in count_niphem: + count_niphem[sample_name] = {"good_quality" : 0, "bad_quality" : 0, "no_start" : 0, "no_start_stop" : 0, "no_stop" : 0, "multiple_stop" : 0, "total" : 0} + + if sample_name not in count_error: + count_error[sample_name] = {"good_quality" : 0, "bad_quality" : 0, "no_start" : 0, "no_start_stop" : 0, "no_stop" : 0, "multiple_stop" : 0, "total" : 0} + + + # Initialize the sample list to add the number of alleles and the start, stop positions + if not sample_name in samples_matrix_dict: + samples_matrix_dict[sample_name] = [] + matching_genes_dict[sample_name] = {} + + # Path to this sample BLAST database created when processing samples + blast_db_name = os.path.join(blast_db_directory, sample_name, sample_name) + + # * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * # + # Sample contigs VS reference allele(s) BLAST for locus detection in sample # + # * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * # + + cline = NcbiblastnCommandline(db=blast_db_name, evalue=evalue, perc_identity=perc_identity_ref, reward=reward, penalty=penalty, gapopen=gapopen, gapextend=gapextend, outfmt=blast_parameters, max_target_seqs=max_target_seqs, max_hsps=max_hsps, num_threads=num_threads, query=core_reference_allele_path) + out, err = cline() + out_lines = out.splitlines() + + bigger_bitscore = 0 + + # ······························································ # + # LNF if there are no BLAST results for this gene in this sample # + # ······························································ # + if len (out_lines) == 0: + + # Trying to get the allele number to avoid that a bad quality assembly impact on the tree diagram + cline = NcbiblastnCommandline(db=blast_db_name, evalue=evalue, perc_identity = 70, reward=reward, penalty=penalty, gapopen=gapopen, gapextend=gapextend, outfmt=blast_parameters, max_target_seqs=1, max_hsps=1, num_threads=1, query=core_reference_allele_path) + out, err = cline() + out_lines = out.splitlines() + + if len (out_lines) > 0 : + + for line in out_lines : + values = line.split('\t') + if float(values[8]) > bigger_bitscore: + qseqid , sseqid , pident , qlen , s_length , mismatch , r_gapopen , r_evalue , bitscore , sstart , send , qstart , qend ,sseq , qseq = values + bigger_bitscore = float(bitscore) + + # Keep LNF info + lnf_tpr_tag(core_name, sample_name, alleles_in_locus_dict, samples_matrix_dict, lnf_tpr_dict, schema_statistics, locus_alleles_path, qseqid, pident, '-', '-', perc_identity_ref, '-', schema_quality, annotation_core_dict, count_lnf, logger) + + else: + # Keep LNF info + lnf_tpr_tag(core_name, sample_name, '-', samples_matrix_dict, lnf_tpr_dict, schema_statistics, locus_alleles_path, '-', '-', '-', '-', '-', '-', schema_quality, annotation_core_dict, count_lnf, logger) + + continue + + ## Continue classification process if the core gene has been detected in sample after BLAST search + if len (out_lines) > 0: + + # Parse contigs for this sample + #contig_file = os.path.join(inputdir, sample_name + ".fasta") ## parse + #records = list(SeqIO.parse(contig_file, "fasta")) ## parse + + ## Keep BLAST results after locus detection in sample using reference allele + + # Path to BLAST results fasta file + path_to_blast_seq = os.path.join(blast_results_seq_directory, sample_name, core_name + "_blast.fasta") + + with open (path_to_blast_seq, 'w') as outblast_fh: + seq_number = 1 + for line in out_lines : + values = line.split('\t') + qseqid = values[0] + if values[1] not in contigs_in_sample_dict[sample_name]: + sseqid = '|'.join(values[1].split('|')[1:-1]) + else: + sseqid = values[1] + sstart = values[9] + send = values[10] + + # Get flanked BLAST sequences from contig for correct allele tagging + + accession_sequence = contigs_in_sample_dict[sample_name][sseqid] + #for record in records: ## parse + #if record.id == sseqid : ## parse + #break ## parse + #accession_sequence = str(record.seq) ## parse + + if int(send) > int(sstart): + max_index = int(send) + min_index = int(sstart) + else: + max_index = int(sstart) + min_index = int(send) + + if (flankingnts + 1) <= min_index: + if flankingnts <= (len(accession_sequence) - max_index): + flanked_sseq = accession_sequence[ min_index -1 -flankingnts : max_index + flankingnts ] + else: + flanked_sseq = accession_sequence[ min_index -1 -flankingnts : ] + else: + flanked_sseq = accession_sequence[ : max_index + flankingnts ] + + seq_id = str(seq_number) + '_' + sseqid + outblast_fh.write('>' + seq_id + ' # ' + ' # '.join(values[0:13]) + '\n' + flanked_sseq + '\n' + '\n' ) + + seq_number += 1 + + ## Create local BLAST database for BLAST results after locus detection in sample using reference allele + db_name = os.path.join(blast_results_db_directory, sample_name) + if not create_blastdb(path_to_blast_seq, db_name, 'nucl', logger): + print('Error when creating the blastdb for blast results file for locus %s at sample %s. Check log file for more information. \n ', core_name, sample_name) + return False + + # Path to local BLAST database for BLAST results after locus detection in sample using reference allele + locus_blast_db_name = os.path.join(blast_results_db_directory, sample_name, os.path.basename(core_name) + '_blast', os.path.basename(core_name) + '_blast') + + + # * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * # + # BLAST result sequences VS ALL alleles in locus BLAST for allele identification detection in sample # + # * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * # + + cline = NcbiblastnCommandline(db=locus_blast_db_name, evalue=evalue, perc_identity=perc_identity_loc, reward=reward, penalty=penalty, gapopen=gapopen, gapextend=gapextend, outfmt = blast_parameters, max_target_seqs=max_target_seqs, max_hsps=max_hsps, num_threads=num_threads, query=locus_alleles_path) + + out, err = cline() + out_lines = out.splitlines() + + allele_found = {} # To keep filtered BLAST results + + ## Check if there is any BLAST result with ID = 100 ## + for line in out_lines: + + values = line.split('\t') + pident = values[2] + + if float(pident) == 100: + + qseqid, sseqid, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart, send, qstart, qend, sseq, qseq = values + + # Parse core gene fasta file to get matching allele sequence and length + #alleles_in_locus = list (SeqIO.parse(locus_alleles_path, "fasta")) ## parse + #for allele in alleles_in_locus : ## parse + #if allele.id == qseqid : ## parse + #break ## comentar parse + #matching_allele_seq = str(allele.seq) ## parse + #matching_allele_length = len(matching_allele_seq) ## parse + + matching_allele_seq = alleles_in_locus_dict[core_name][qseqid] + matching_allele_length = len(matching_allele_seq) + + # Keep BLAST results with ID = 100 and same length as matching allele + if int(s_length) == matching_allele_length: + #get_blast_results (values, records, allele_found, logger) + get_blast_results (sample_name, values, contigs_in_sample_dict, allele_found, logger) + + # ·································································································································· # + # NIPHEM (paralog) if there are multiple BLAST results with ID = 100 and same length as matching allele for this gene in this sample # + # ·································································································································· # + if len(allele_found) > 1: + + # Keep NIPHEM info + paralog_exact_tag(sample_name, core_name, 'NIPHEM', schema_quality, matching_genes_dict, samples_matrix_dict, allele_found, paralog_dict, prodigal_report, prodigal_directory, blast_parameters, annotation_core_dict, count_niphem, logger) + + continue + + ## Check for possible paralogs with ID < 100 if there is only one BLAST result with ID = 100 and same length as matching allele + elif len(allele_found) == 1 : + + for line in out_lines : + values = line.split('\t') + + sseq_no_gaps = values[13].replace('-', '') + s_length_no_gaps = len(sseq_no_gaps) + + # Keep BLAST result if its coverage is within min and max thresholds + if min_length_threshold <= s_length_no_gaps <= max_length_threshold: + #get_blast_results (values, records, allele_found, logger) + get_blast_results (sample_name, values, contigs_in_sample_dict, allele_found, logger) + + # ································································ # + # EXACT MATCH if there is any paralog for this gene in this sample # + # ································································ # + if len(allele_found) == 1 : + + paralog_exact_tag(sample_name, core_name, 'EXACT', schema_quality, matching_genes_dict, samples_matrix_dict, allele_found, exact_dict, prodigal_report, prodigal_directory, blast_parameters, annotation_core_dict, count_exact, logger) + + continue + + # ··········································································· # + # NIPH if there there are paralogs with ID < 100 for this gene in this sample # + # ··········································································· # + else: + + paralog_exact_tag(sample_name, core_name, 'NIPH', schema_quality, matching_genes_dict, samples_matrix_dict, allele_found, paralog_dict, prodigal_report, prodigal_directory, blast_parameters, annotation_core_dict, count_niph, logger) + + continue + + ## Look for the best BLAST result if there are no results with ID = 100 ## + elif len(allele_found) == 0: + + bigger_bitscore_seq_values = [] + + for line in out_lines : + values = line.split('\t') + + if float(values[8]) > bigger_bitscore: + s_length_no_gaps = len(values[13].replace('-', '')) + + # Keep BLAST result if its coverage is within min and max thresholds and its bitscore is bigger than the one previously kept + if min_coverage_threshold <= s_length_no_gaps <= max_coverage_threshold: + bigger_bitscore_seq_values = values + bigger_bitscore = float(bigger_bitscore_seq_values[8]) + + ## Check if best BLAST result out of coverage thresholds is a possible PLOT or LNF due to low coverage ## + #if len(allele_found) == 0: + if len(bigger_bitscore_seq_values) == 0: + + # Look for best bitscore BLAST result out of coverage thresholds to check possible PLOT or reporting LNF due to low coverage + + for line in out_lines : + values = line.split('\t') + + if float(values[8]) > bigger_bitscore: + qseqid, sseqid, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart, send, qstart, qend, sseq, qseq = values + bigger_bitscore_seq_values_out_cov = values ### + bigger_bitscore = float(bitscore) + + # Get BLAST values relatives to contig for bigger bitscore result + lnf_plot_found = {} ### + + get_blast_results (sample_name, bigger_bitscore_seq_values_out_cov, contigs_in_sample_dict, lnf_plot_found, logger) ### + + allele_id = str(list(lnf_plot_found.keys())[0]) ### + qseqid, sseqid, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart, send, qstart, qend, sseq, qseq = lnf_plot_found[allele_id] + + # Get contig sequence and length for best bitscore BLAST result ID + + #for record in records: ## parse + #if record.id == sseqid : ## parse + #break ## parse + #accession_sequence = record.seq ## parse + #length_sseqid = len(accession_sequence) ## parse + + accession_sequence = contigs_in_sample_dict[sample_name][sseqid] + length_sseqid = len(accession_sequence) + + # Check if best BLAST result out of coverage thresholds is a possible PLOT. If so, keep result info for later PLOT classification + if int(sstart) == length_sseqid or int(send) == length_sseqid or int(sstart) == 1 or int(send) == 1: + bigger_bitscore_seq_values = bigger_bitscore_seq_values_out_cov ### + + # ·············································································································································· # + # LNF if there are no BLAST results within coverage thresholds for this gene in this sample and best out threshold result is not a possible PLOT # + # ·············································································································································· # + else: + # Get sequence length + s_length_no_gaps = len(bigger_bitscore_seq_values_out_cov[13].replace('-', '')) + + # Keep LNF info + lnf_tpr_tag(core_name, sample_name, alleles_in_locus_dict, samples_matrix_dict, lnf_tpr_dict, schema_statistics, locus_alleles_path, qseqid, pident, s_length_no_gaps, '-', '-', coverage, schema_quality, annotation_core_dict, count_lnf, logger) + + ## Keep result with bigger bitscore in allele_found dict and look for possible paralogs ## + if len(bigger_bitscore_seq_values) > 0: + + qseqid, sseqid, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart, send, qstart, qend, sseq, qseq = bigger_bitscore_seq_values + + #get_blast_results (bigger_bitscore_seq_values, records, allele_found, logger) + get_blast_results (sample_name, bigger_bitscore_seq_values, contigs_in_sample_dict, allele_found, logger) + + # Possible paralogs search + for line in out_lines : + values = line.split('\t') + + qseqid, sseqid, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart, send, qstart, qend, sseq, qseq = values + sseq_no_gaps = sseq.replace('-', '') + s_length_no_gaps = len(sseq_no_gaps) + + if min_length_threshold <= s_length_no_gaps <= max_length_threshold: + + #get_blast_results (values, records, allele_found, logger) + get_blast_results (sample_name, values, contigs_in_sample_dict, allele_found, logger) + + # ····························································· # + # NIPH if there there are paralogs for this gene in this sample # + # ····························································· # + if len(allele_found) > 1 : + + paralog_exact_tag(sample_name, core_name, 'NIPH', schema_quality, matching_genes_dict, samples_matrix_dict, allele_found, paralog_dict, prodigal_report, prodigal_directory, blast_parameters, annotation_core_dict, count_niph, logger) + + continue + + ## Continue classification if there are no paralogs ## + elif len(allele_found) == 1 : + + allele_id = str(list(allele_found.keys())[0]) + qseqid, sseqid, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart, send, qstart, qend, sseq, qseq = allele_found[allele_id] + + sseq_no_gaps = sseq.replace('-', '') + s_length_no_gaps = len(sseq_no_gaps) + + # Get matching allele quality + allele_quality = schema_quality[core_name][qseqid] + + # Get matching allele sequence and length + + #alleles_in_locus = list (SeqIO.parse(locus_alleles_path, "fasta")) ## parse + #for allele in alleles_in_locus : ## parse + #if allele.id == qseqid : ## parse + #break ## parse + #matching_allele_seq = allele.seq ## parse + #matching_allele_length = len(matching_allele_seq) ## parse + + matching_allele_seq = alleles_in_locus_dict [core_name][qseqid] + matching_allele_length = len(matching_allele_seq) + + # Get contig sequence and length for ID found in BLAST + + #for record in records: ## parse + #if record.id == sseqid : ## parse + #break ## parse + #accession_sequence = record.seq ## parse + #length_sseqid = len(accession_sequence) ## parse + + accession_sequence = contigs_in_sample_dict[sample_name][sseqid] + length_sseqid = len(accession_sequence) + + # ········································································································· # + # PLOT if found sequence is shorter than matching allele and it is located on the edge of the sample contig # + # ········································································································· # + if int(sstart) == length_sseqid or int(send) == length_sseqid or int(sstart) == 1 or int(send) == 1: + if int(s_length) < matching_allele_length: + + ### sacar sec prodigal para PLOT? + # Get prodigal predicted sequence if matching allele quality is "bad quality" + if 'bad_quality' in allele_quality: + complete_predicted_seq, start_prodigal, end_prodigal = get_prodigal_sequence(sseq_no_gaps, sseqid, prodigal_directory, sample_name, blast_parameters, logger) + + # Keep info for prodigal report + prodigal_report.append([core_name, sample_name, qseqid, 'PLOT', sstart, send, start_prodigal, end_prodigal, sseq_no_gaps, complete_predicted_seq]) + + else: + complete_predicted_seq = '-' + start_prodigal = '-' + end_prodigal = '-' + + # Keep PLOT info + inf_asm_alm_tag(core_name, sample_name, 'PLOT', allele_found[allele_id], allele_quality, '-', matching_allele_length, '-', plot_dict, samples_matrix_dict, matching_genes_dict, prodigal_report, start_prodigal, end_prodigal, complete_predicted_seq, annotation_core_dict, count_plot, logger) + + continue + + # * * * * * * * * * * * * * * * * * * * * # + # Search for complete final new sequence # + # * * * * * * * * * * * * * * * * * * * * # + + ## Get Prodigal predicted sequence ## + complete_predicted_seq, start_prodigal, end_prodigal = get_prodigal_sequence(sseq_no_gaps, sseqid, prodigal_directory, sample_name, blast_parameters, logger) + + ## Search for new codon stop using contig sequence info ## + + # Check matching allele sequence direction + query_direction = check_sequence_order(matching_allele_seq, logger) + + # Get extended BLAST sequence for stop codon search + if query_direction == 'reverse': + if int(send) > int (sstart): + sample_gene_sequence = accession_sequence[ : int(send) ] + sample_gene_sequence = str(Seq.Seq(sample_gene_sequence).reverse_complement()) + else: + sample_gene_sequence = accession_sequence[ int(send) -1 : ] + + else: + if int(sstart) > int (send): + sample_gene_sequence = accession_sequence[ : int(sstart) ] + sample_gene_sequence = str(Seq.Seq(sample_gene_sequence).reverse_complement()) + else: + sample_gene_sequence = accession_sequence[ int(sstart) -1 : ] + + # Get new stop codon index + stop_index = get_stop_codon_index(sample_gene_sequence) + + ## Classification of final new sequence if it is found ## + if stop_index != False: + new_sequence_length = stop_index +3 + new_sseq = str(sample_gene_sequence[0:new_sequence_length]) + + ######################################################################################################################### + ### c/m: introducido para determinar qué umbral de coverage poner. TEMPORAL + new_sseq_coverage = new_sequence_length/matching_allele_length ### introduciendo coverage new_sseq /// debería ser con respecto a la media? + + if new_sseq_coverage < 1: + shorter_seq_coverage.append([core_name, sample_name, str(matching_allele_length), str(new_sequence_length), str(schema_statistics[core_name][0]), str(new_sseq_coverage), str(new_sequence_length/schema_statistics[core_name][0])]) + elif new_sseq_coverage > 1: + longer_seq_coverage.append([core_name, sample_name, str(matching_allele_length), str(new_sequence_length), str(schema_statistics[core_name][0]), str(new_sseq_coverage), str(new_sequence_length/schema_statistics[core_name][0])]) + elif new_sseq_coverage == 1: + equal_seq_coverage.append([core_name, sample_name, str(matching_allele_length), str(new_sequence_length), str(schema_statistics[core_name][0]), str(new_sseq_coverage), str(new_sequence_length/schema_statistics[core_name][0])]) + ######################################################################################################################### + + # Get and keep SNP and DNA and protein alignment + keep_snp_alignment_info(sseq, new_sseq, matching_allele_seq, qseqid, query_direction, core_name, sample_name, reward, penalty, gapopen, gapextend, snp_dict, match_alignment_dict, protein_dict, logger) + + # ····································································································· # + # INF if final new sequence length is within min and max length thresholds for this gene in this sample # + # ····································································································· # + if min_length_threshold <= new_sequence_length <= max_length_threshold: + + # Keep INF info + inf_asm_alm_tag(core_name, sample_name, 'INF', allele_found[allele_id], allele_quality, new_sseq, matching_allele_length, inferred_alleles_dict, inf_dict, samples_matrix_dict, matching_genes_dict, prodigal_report, start_prodigal, end_prodigal, complete_predicted_seq, annotation_core_dict, count_inf, logger) ### introducido start_prodigal, end_prodigal, complete_predicted_seq, prodigal_report como argumento a inf_asm_alm_tag para report prodigal, temporal + + # ············································································································································ # + # ASM if final new sequence length is under min length threshold but its coverage is above min coverage threshold for this gene in this sample # + # ············································································································································ # + elif min_coverage_threshold <= new_sequence_length < min_length_threshold: + + # Keep ASM info + inf_asm_alm_tag(core_name, sample_name, 'ASM', allele_found[allele_id], allele_quality, new_sseq, matching_allele_length, asm_dict, list_asm, samples_matrix_dict, matching_genes_dict, prodigal_report, start_prodigal, end_prodigal, complete_predicted_seq, annotation_core_dict, count_asm, logger) + + # ············································································································································ # + # ALM if final new sequence length is above max length threshold but its coverage is under max coverage threshold for this gene in this sample # + # ············································································································································ # + elif max_length_threshold < new_sequence_length <= max_coverage_threshold: + + # Keep ALM info + inf_asm_alm_tag(core_name, sample_name, 'ALM', allele_found[allele_id], allele_quality, new_sseq, matching_allele_length, alm_dict, list_alm, samples_matrix_dict, matching_genes_dict, prodigal_report, start_prodigal, end_prodigal, complete_predicted_seq, annotation_core_dict, count_alm, logger) ### introducido start_prodigal, end_prodigal, complete_predicted_seq, prodigal_report como argumento a inf_asm_alm_tag para report prodigal, temporal + + # ························································································· # + # TPR if final new sequence coverage is not within thresholds for this gene in this sample # + # ························································································· # + else: + + # Keep TPR info + lnf_tpr_tag(core_name, sample_name, alleles_in_locus_dict, samples_matrix_dict, lnf_tpr_dict, schema_statistics, locus_alleles_path, qseqid, pident, s_length_no_gaps, new_sequence_length, '-', coverage, schema_quality, annotation_core_dict, count_tpr, logger) + + # ········································ # + # ERROR if final new sequence is not found # + # ········································ # + else: + logger.error('ERROR : Stop codon was not found for the core %s and the sample %s', core_name, sample_name) + samples_matrix_dict[sample_name].append('ERROR not stop codon') + if not sseqid in matching_genes_dict[sample_name] : + matching_genes_dict[sample_name][sseqid] = [] + if sstart > send : + #matching_genes_dict[sample_name][sseqid].append([core_name, sstart, send,'-', 'ERROR']) + matching_genes_dict[sample_name][sseqid].append([core_name, qseqid, sstart, send,'-', 'ERROR']) + else: + #matching_genes_dict[sample_name][sseqid].append([core_name, sstart,send,'+', 'ERROR']) + matching_genes_dict[sample_name][sseqid].append([core_name, qseqid, sstart, send,'+', 'ERROR']) + + # (recuento tags para plot) + count_error[sample_name]['total'] += 1 + for count_class in count_error[sample_name]: + if count_class in allele_quality: + if "no_start_stop" not in count_class and "no_start_stop" in allele_quality: + if count_class == "bad_quality": + count_error[sample_name][count_class] += 1 + else: + count_error[sample_name][count_class] += 1 + + + ## Save results and create reports + + if not save_allele_call_results (outputdir, full_gene_list, samples_matrix_dict, exact_dict, paralog_dict, inf_dict, plot_dict, matching_genes_dict, list_asm, list_alm, lnf_tpr_dict, snp_dict, match_alignment_dict, protein_dict, prodigal_report, shorter_seq_coverage, longer_seq_coverage, equal_seq_coverage, shorter_blast_seq_coverage, longer_blast_seq_coverage, equal_blast_seq_coverage, logger): + print('There is an error while saving the allele calling results. Check the log file to get more information \n') + # exit(0) + + + ## Saving sample results plots + + if not save_allele_calling_plots (outputdir, sample_list_files, count_exact, count_inf, count_asm, count_alm, count_lnf, count_tpr, count_plot, count_niph, count_niphem, count_error, logger): + print('There is an error while saving the allele calling results plots. Check the log file to get more information \n') + + + return True, inferred_alleles_dict, inf_dict, exact_dict + + +# * * * * * * * * * * * * * * * * * * * # +# Processing gene by gene allele calling # +# * * * * * * * * * * * * * * * * * * * # + +def processing_allele_calling (arguments) : + ''' + Description: + This is the main function for allele calling. + With the support of additional functions it will create the output files + with the summary report. + Input: + arguments # Input arguments given on command line + Functions: + ???? + Variables: + ???? + Return: + ???? + ''' + + start_time = datetime.now() + print('Start the execution at :', start_time ) + + # Open log file + logger = open_log ('taranis_wgMLST.log') + #print('Checking the pre-requisites.') + + ############################################################ + ## Check additional programs are installed in your system ## + ############################################################ + #pre_requisites_list = [['blastp', '2.9'], ['makeblastdb', '2.9']] + #if not check_prerequisites (pre_requisites_list, logger): + # print ('your system does not fulfill the pre-requistes to run the script ') + # exit(0) + + ###################################################### + ## Check that given directories contain fasta files ## + ###################################################### + print('Validating schema fasta files in ' , arguments.coregenedir , '\n') + valid_core_gene_files = get_fasta_file_list(arguments.coregenedir, logger) + if not valid_core_gene_files : + print ('There are not valid fasta files in ', arguments.coregenedir , ' directory. Check log file for more information ') + exit(0) + + print('Validating reference alleles fasta files in ' , arguments.refalleles , '\n') + valid_reference_alleles_files = get_fasta_file_list(arguments.refalleles, logger) + if not valid_reference_alleles_files : + print ('There are not valid reference alleles fasta files in ', arguments.refalleles, ' directory. Check log file for more information ') + exit(0) + + print('Validating sample fasta files in ' , arguments.inputdir , '\n') + valid_sample_files = get_fasta_file_list(arguments.inputdir, logger) + if not valid_sample_files : + print ('There are not valid fasta files in ', arguments.inputdir , ' directory. Check log file for more information ') + exit(0) + + ################################# + ## Prepare the coreMLST schema ## + ################################# + tmp_core_gene_dir = os.path.join(arguments.outputdir,'tmp','cgMLST') + try: + os.makedirs(tmp_core_gene_dir) + except: + logger.info('Deleting the temporary directory for a previous execution without cleaning up') + shutil.rmtree(os.path.join(arguments.outputdir, 'tmp')) + try: + os.makedirs(tmp_core_gene_dir) + logger.info ('Temporary folder %s has been created again', tmp_core_gene_dir) + except: + logger.info('Unable to create again the temporary directory %s', tmp_core_gene_dir) + print('Cannot create temporary directory on ', tmp_core_gene_dir) + exit(0) + + alleles_in_locus_dict, annotation_core_dict, schema_variability, schema_statistics, schema_quality = prepare_core_gene (valid_core_gene_files, tmp_core_gene_dir, arguments.refalleles, arguments.genus, arguments.species, str(arguments.usegenus).lower(), logger) + #alleles_in_locus_dict, annotation_core_dict, schema_variability, schema_statistics, schema_quality = prepare_core_gene (valid_core_gene_files, tmp_core_gene_dir, arguments.refalleles, arguments.outputdir, logger) + if not alleles_in_locus_dict: + print('There is an error while processing the schema preparation phase. Check the log file to get more information \n') + logger.info('Deleting the temporary directory to clean up the temporary files created') + shutil.rmtree(os.path.join(arguments.outputdir, 'tmp')) + exit(0) + + ############################### + ## Prepare the samples files ## + ############################### + tmp_samples_dir = os.path.join(arguments.outputdir,'tmp','samples') + try: + os.makedirs(tmp_samples_dir) + except: + logger.info('Deleting the temporary directory for a previous execution without properly cleaning up') + shutil.rmtree(tmp_samples_dir) + try: + os.makedirs(tmp_samples_dir) + logger.info('Temporary folder %s has been created again', tmp_samples_dir) + except: + logger.info('Unable to create again the temporary directory %s', tmp_samples_dir) + shutil.rmtree(os.path.join(arguments.outputdir, 'tmp')) + logger.info('Cleaned up temporary directory ', ) + print('Cannot create temporary directory on ', tmp_samples_dir, 'Check the log file to get more information \n') + exit(0) + + contigs_in_sample_dict = prepare_samples(valid_sample_files, tmp_samples_dir, arguments.refgenome, logger) + if not contigs_in_sample_dict : + print('There is an error while processing the saving temporary files. Check the log file to get more information \n') + logger.info('Deleting the temporary directory to clean up the temporary files created') + shutil.rmtree(os.path.join(arguments.outputdir, 'tmp')) + exit(0) + + ################################## + ## Run allele callling analysis ## + ################################## + query_directory = arguments.coregenedir + reference_alleles_directory = arguments.refalleles + blast_db_directory = os.path.join(tmp_samples_dir,'blastdb') + prodigal_directory = os.path.join(tmp_samples_dir,'prodigal') + blast_results_seq_directory = os.path.join(tmp_samples_dir,'blast_results', 'blast_results_seq') ### path a directorio donde guardar secuencias encontradas tras blast con alelo de referencia + blast_results_db_directory = os.path.join(tmp_samples_dir,'blast_results', 'blast_results_db') ### path a directorio donde guardar db de secuencias encontradas tras blast con alelo de referencia + + complete_allele_call, inferred_alleles_dict, inf_dict, exact_dict = allele_call_nucleotides(valid_core_gene_files, valid_sample_files, alleles_in_locus_dict, contigs_in_sample_dict, query_directory, reference_alleles_directory, blast_db_directory, prodigal_directory, blast_results_seq_directory, blast_results_db_directory, arguments.inputdir, arguments.outputdir, int(arguments.cpus), arguments.percentlength, arguments.coverage, float(arguments.evalue), int(arguments.perc_identity_ref), int(arguments.perc_identity_loc), int(arguments.reward), int(arguments.penalty), int(arguments.gapopen), int(arguments.gapextend), int(arguments.max_target_seqs), int(arguments.max_hsps), int(arguments.num_threads), int(arguments.flankingnts), schema_variability, schema_statistics, schema_quality, annotation_core_dict, arguments.profile, logger) + if not complete_allele_call: + print('There is an error while processing the allele calling. Check the log file to get more information \n') + exit(0) + + ######################################################### + ## Update core gene schema adding new inferred alleles ## + ######################################################### + if inferred_alleles_dict: + if str(arguments.updateschema).lower() == 'true' or str(arguments.updateschema).lower() == 'new': + if not update_schema (str(arguments.updateschema).lower(), arguments.coregenedir, arguments.outputdir, valid_core_gene_files, inferred_alleles_dict, alleles_in_locus_dict, logger): + print('There is an error adding new inferred alleles found to the core genes schema. Check the log file to get more information \n') + exit(0) + + if str(arguments.profile).lower() != 'false': + + ############################ + ## Get ST for each sample ## + ############################ + complete_ST, inf_ST = get_ST_profile(arguments.outputdir, arguments.profile, exact_dict, inf_dict, valid_core_gene_files, valid_sample_files, logger) + + if not complete_ST: + print('There is an error while processing ST analysis. Check the log file to get more information \n') + exit(0) + + ########################################### + ## Update ST profile file adding new STs ## + ########################################### + if str(arguments.updateprofile).lower() == 'true' or str(arguments.updateprofile).lower() == 'new': + if len(inf_ST) > 0: + if not update_st_profile (str(arguments.updateprofile).lower(), arguments.profile, arguments.outputdir, inf_ST, valid_core_gene_files, logger): + print('There is an error adding new STs found to the ST profile file. Check the log file to get more information \n') + exit(0) + + shutil.rmtree(os.path.join(arguments.outputdir, 'tmp')) + + end_time = datetime.now() + print('completed execution at :', end_time ) + + return True + + + diff --git a/taranis/blast.py b/taranis/blast.py new file mode 100644 index 0000000..e4b17f0 --- /dev/null +++ b/taranis/blast.py @@ -0,0 +1,67 @@ +import logging +import os +import rich +import subprocess +import taranis.utils + +from pathlib import Path +from Bio.Blast.Applications import NcbiblastnCommandline +import pdb + +log = logging.getLogger(__name__) +stderr = rich.console.Console( + stderr=True, + style="dim", + highlight=False, + force_terminal=taranis.utils.rich_force_colors(), +) + + +class Blast(): + def __init__(self, db_type): + self.db_type = db_type + + def create_blastdb (self, file_name, blast_dir): + self.f_name = Path(file_name).stem + db_dir = os.path.join(blast_dir,self.f_name) + self.out_blast_dir = os.path.join(db_dir, self.f_name) + + blast_command = ["makeblastdb" , "-in" , file_name , "-parse_seqids", "-dbtype", self.db_type, "-out" , self.out_blast_dir] + try: + _ = subprocess.run(blast_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True) + except Exception as e: + log.error("Unable to create blast db for %s ", self.f_name) + log.error(e) + stderr.print(f"[red] Unable to create blast database for sample %s", self.f_name) + exit(1) + return + + def run_blast(self, query, evalue=0.001, perc_identity=90, reward=1, penalty=-2, gapopen=1, gapextend=1, max_target_seqs=10, max_hsps=10, num_threads=1): + """_summary_ + blastn -outfmt "6 , qseqid , sseqid , pident , qlen , length , mismatch , gapopen , evalue , bitscore , sstart , send , qstart , qend , sseq , qseq" -query /media/lchapado/Reference_data/proyectos_isciii/taranis/documentos_antiguos/pasteur_schema/lmo0002.fasta -db /media/lchapado/Reference_data/proyectos_isciii/taranis/test/blastdb/RA-L2073_R1/RA-L2073_R1 -evalue 0.001 -penalty -2 -reward 1 -gapopen 1 -gapextend 1 -perc_identity 100 > /media/lchapado/Reference_data/proyectos_isciii/taranis/test/blast_sample_locus002.txt + + Args: + query (_type_): _description_ + evalue (float, optional): _description_. Defaults to 0.001. + perc_identity (int, optional): _description_. Defaults to 90. + reward (int, optional): _description_. Defaults to 1. + penalty (int, optional): _description_. Defaults to -2. + gapopen (int, optional): _description_. Defaults to 1. + gapextend (int, optional): _description_. Defaults to 1. + max_target_seqs (int, optional): _description_. Defaults to 10. + max_hsps (int, optional): _description_. Defaults to 10. + num_threads (int, optional): _description_. Defaults to 1. + """ + blast_parameters = '"6 , qseqid , sseqid , pident , qlen , length , mismatch , gapopen , evalue , bitscore , sstart , send , qstart , qend , sseq , qseq"' + pdb.set_trace() + #db=self.blast_dir, evalue=evalue, perc_identity=perc_identity_ref, reward=reward, penalty=penalty, gapopen=gapopen, gapextend=gapextend, outfmt=blast_parameters, max_target_seqs=max_target_seqs, max_hsps=max_hsps, num_threads=num_threads, query=core_reference_allele_path) + cline = NcbiblastnCommandline(db=self.out_blast_dir, evalue=evalue, perc_identity=perc_identity, reward=reward, penalty=penalty, gapopen=gapopen, gapextend=gapextend, outfmt=blast_parameters, max_target_seqs=max_target_seqs, max_hsps=max_hsps, num_threads=num_threads, query=query) + try: + out, _ = cline() + except Exception as e: + log.error("Unable to run blast for %s ", self.out_blast_dir) + log.error(e) + stderr.print(f"[red] Unable to run blast for database %s", self.out_blast_dir) + exit(1) + return out.splitlines() + \ No newline at end of file diff --git a/taranis/prediction.py b/taranis/prediction.py new file mode 100644 index 0000000..1706853 --- /dev/null +++ b/taranis/prediction.py @@ -0,0 +1,65 @@ +import logging +import os +import rich +import subprocess +import taranis.utils + +from pathlib import Path + +log = logging.getLogger(__name__) +stderr = rich.console.Console( + stderr=True, + style="dim", + highlight=False, + force_terminal=taranis.utils.rich_force_colors(), +) + + +class Prediction(): + def __init__(self, genome_ref, sample_file, out_dir): + self.genome_ref = genome_ref + self.sample_file = sample_file + self.sample_name = Path(sample_file).stem + self.out_dir = out_dir + self.train = os.path.join(out_dir, self.sample_name + ".trn") + self.pred_protein = os.path.join(out_dir, self.sample_name + "_prot.faa") + self.pred_gene = os.path.join(out_dir, self.sample_name + "_dna.faa") + self.pred_coord = os.path.join(out_dir, self.sample_name + "_coord.gff") + + if not os.path.exists(self.out_dir): + try: + os.makedirs(self.out_dir, exist_ok=True) + log.debug("Created directory %s for prediction ", self.out_dir) + except OSError as e: + log.error("Cannot create %s directory", self.out_dir) + log.error(e) + stderr.print (f"[red] Unable to create {self.out_dir} folder") + exit(1) + + def training(self): + prodigal_command = ["prodigal" , "-i", self.genome_ref, "-t", self.train] + try: + _ = subprocess.run(prodigal_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True) + except Exception as e: + log.error("Unable to execute prodigal command for training") + log.error(e) + stderr.print (f"[red] Unable to run prodigal commmand. ERROR {e} ") + exit(1) + return + + + + def prediction(self): + + prodigal_command = ["prodigal" , "-i", self.sample_file , "-t", self.train, "-f", "gff", "-o", self.pred_coord, "-a", self.pred_protein, "-d", self.pred_gene] + try: + _ = subprocess.run(prodigal_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True) + except Exception as e: + log.error("Unable to execute prodigal command for training") + log.error(e) + stderr.print (f"[red] Unable to run prodigal commmand. ERROR {e} ") + exit(1) + return + + def get_sequence(self): + return \ No newline at end of file diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index 4a6ed91..db20a61 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -1,4 +1,5 @@ import logging +import numpy as np import os import re import rich.console @@ -7,7 +8,7 @@ # from Bio import SeqIO from Bio.Seq import Seq - +from Bio.Blast.Applications import NcbiblastnCommandline import taranis.utils import pdb @@ -22,34 +23,6 @@ class ReferenceAlleles: def __init__(self, fasta_file, output): - """ - self.schema_dir = schema_dir - self.out_dir = out_dir - if self.schema_dir is None: - self.schema_dir = taranis.utils.prompt_text("Write the path of the schema`s files") - if not taranis.utils.folder_exists(self.schema_dir): - log.error("schema folder %s does not exists", self.schema_dir) - stderr.print( - "[red] Schema folder does not exist. " + self.schema_dir + "!" - ) - sys.exit(1) - if out_dir is None: - self.out_dir = taranis.utils.prompt_text("Define the the directory to save results") - # Check if folder exists - if taranis.utils.folder_exists(self.out_dir): - q_question = "Folder " + self.out_dir + " already exists. Files will be overwritten. Do you want to continue?" - if "no" in taranis.utils.query_user_yes_no(q_question, "no"): - log.info("Aborting code by user request") - stderr.print("[red] Exiting code. ") - sys.exit(1) - else: - try: - os.makedirs(self.out_dir) - except OSError as e: - log.info("Unable to create folder at %s", self.out_dir) - stderr.print("[red] ERROR. Unable to create folder " + self.out_dir) - sys.exit(1) - """ self.fasta_file = fasta_file self.output = output self.records = None @@ -98,38 +71,198 @@ def check_locus_quality(self): return def create_matrix_distance(self): - f_name = os.path.basename(self.fasta_file).split('.')[0] - mash_folder = os.path.join(self.output, "mash", f_name ) - _ = taranis.utils.write_fasta_file(mash_folder, self.selected_locus, multiple_files=True, extension=False) + # f_name = os.path.basename(self.fasta_file).split('.')[0] + f_name = os.path.basename(self.fasta_file) + mash_folder = os.path.join(self.output, "mash" ) + # _ = taranis.utils.write_fasta_file(mash_folder, self.selected_locus, multiple_files=True, extension=False) # save directory to return after mash working_dir = os.getcwd() os.chdir(mash_folder) # run mash sketch command - sketch_path = "reference.msh" - mash_sketch_command = ["mash", "sketch", "-o", sketch_path] - mash_sketch_command += list(self.selected_locus.keys()) + sketch_file = "reference.msh" + mash_sketch_command = ["mash", "sketch", "-i", "-o", sketch_file, f_name] + # mash sketch -i -o prueba.msh lmo0003.fasta + # mash_sketch_command += list(self.selected_locus.keys()) mash_sketch_result = subprocess.run(mash_sketch_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) # Get pairwise allele sequences mash distances - mash_distance_command = ["mash", "dist", sketch_path, sketch_path] + # mash_distance_command = ["mash", "dist", sketch_path, sketch_path] + mash_distance_command = ["mash", "triangle", "-i", "reference.msh"] mash_distance_result = subprocess.Popen(mash_distance_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) - + # pdb.set_trace() out, err = mash_distance_result.communicate() with open ("matrix_distance.tsv", "w") as fo: + # adding alleles to create a heading + # the value are not required to be in order, just only any name and the right length + fo.write( "alleles\t" + "\t".join(list(self.selected_locus.keys())) + "\n") fo.write(out.decode("UTF-8")) import pandas as pd locus_num = len(self.selected_locus) - - matrix_df = pd.read_csv("matrix_distance.tsv", sep="\t", header=None) - list_alleles = matrix_df.iloc[0:locus_num,0] - values_np = matrix_df.iloc[:,2].to_numpy() - - matrix_np = values_np.reshape(locus_num, locus_num) + # pdb.set_trace() + matrix_df = pd.read_csv("matrix_distance.tsv", sep="\t").fillna(value=0) + # remove the first line of the matrix that contain only the number of alleles + matrix_df = matrix_df.drop(0) + locus_list = matrix_df.iloc[0:locus_num,0] + matrix_np = matrix_df.iloc[:,1:].to_numpy() + # convert the triangular matrix to mirror up triangular part + t_matrix_np = matrix_np.transpose() + matrix_np = t_matrix_np + matrix_np + # values_np = matrix_df.iloc[:,2].to_numpy() + + # matrix_np = values_np.reshape(locus_num, locus_num) # out = out.decode('UTF-8').split('\n') from sklearn.cluster import AgglomerativeClustering - clusterer = AgglomerativeClustering(n_clusters=3, metric="precomputed", linkage="average", distance_threshold=None) + clusterer = AgglomerativeClustering(n_clusters=7, metric="precomputed", linkage="average", distance_threshold=None) clusters = clusterer.fit_predict(matrix_np) # clustering = AgglomerativeClustering(affinity="precomputed").fit(matrix_np) + mean_distance =np.mean(matrix_np, 0) + std = np.std(matrix_np) + min_mean = min(mean_distance) + mean_all_alleles = np.mean(mean_distance) + max_mean= max(mean_distance) + # buscar el indice que tiene el minimo valor de media + min_mean_idx= np.where(mean_distance==float(min_mean))[0][0] + # create fasta file with the allele + min_allele = self.selected_locus[locus_list[min_mean_idx]] + + record_allele_folder = os.path.join(os.getcwd(), f_name.split(".")[0]) + min_allele_file = taranis.utils.write_fasta_file(record_allele_folder,min_allele, locus_list[min_mean_idx]) + # pdb.set_trace() + # busca el indice que tiene el valor de la media + mean_all_closser_value = taranis.utils.find_nearest_numpy_value(mean_distance, mean_all_alleles) + mean_all_alleles_idx= np.where(mean_distance==float(mean_all_closser_value))[0][0] + # create fasta file with the allele + mean_allele = self.selected_locus[locus_list[mean_all_alleles_idx]] + # record_allele_folder = os.path.join(mash_folder, f_name) + mean_allele_file = taranis.utils.write_fasta_file(record_allele_folder,mean_allele, locus_list[mean_all_alleles_idx]) + + # busca el indice con la mayor distancia + max_mean_idx= np.where(mean_distance==float(max_mean))[0][0] + # create fasta file with the allele + max_allele = self.selected_locus[locus_list[max_mean_idx]] + max_allele_file = taranis.utils.write_fasta_file(record_allele_folder,max_allele, locus_list[max_mean_idx]) + + + # Elijo un outlier lmo0002_185 para ver la distancia + outlier_allele = self.selected_locus[locus_list[184]] + outlier_allele_file = taranis.utils.write_fasta_file(record_allele_folder,outlier_allele, locus_list[184]) + + # elijo un segundo outlier lmo0002_95 que tiene como cluster =1 + outlier2_allele = self.selected_locus[locus_list[95]] + outlier2_allele_file = taranis.utils.write_fasta_file(record_allele_folder,outlier2_allele, locus_list[95]) + + # elijo un tercer outlier lmo0002_185 que tiene como cluster =4 + outlier3_allele = self.selected_locus[locus_list[185]] + outlier3_allele_file = taranis.utils.write_fasta_file(record_allele_folder,outlier3_allele, locus_list[185]) + + # saca una lista de cuantas veces se repite un valor + np.bincount(clusters) + blast_parameters = '"6 , qseqid , sseqid , pident , qlen , length , mismatch , gapopen , evalue , bitscore , sstart , send , qstart , qend , sseq , qseq"' + from Bio.Blast.Applications import NcbiblastnCommandline + # Create local BLAST database for all alleles in the locus + db_name = "/media/lchapado/Reference_data/proyectos_isciii/taranis/new_taranis_result_code/blast/locus_db" + # db_name = os.path.join("blast", 'locus_blastdb') + fasta_file = "/media/lchapado/Reference_data/proyectos_isciii/taranis/documentos_antiguos/datos_prueba/schema_1_locus/lmo0002.fasta" + # pdb.set_trace() + # blast_command = ['makeblastdb' , '-in' , fasta_file , '-parse_seqids', '-dbtype', "nucl", '-out' , db_name] + # blast_result = subprocess.run(blast_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) + # taranis.utils.create_blastdb(fasta_file, db_name, 'nucl', logger): + # locus_db_name = os.path.join(db_name, f_name[0], f_name[0]) + # query_data= self.selected_locus["lmo0002_1"] + # All alleles in locus VS reference allele chosen (centroid) BLAST + + # ref_query_file="/media/lchapado/Reference_data/proyectos_isciii/taranis/new_taranis_result_code/mash/lmo0002/query.fasta" + # cline = NcbiblastnCommandline(db=db_name, evalue=0.001, perc_identity=100, reward=1, penalty=-2, gapopen=1, gapextend=1, outfmt=blast_parameters, max_target_seqs=0, max_hsps=0, num_threads=4, query=ref_query_file) + + # minima distancia . + # min_dist_file="/media/lchapado/Reference_data/proyectos_isciii/taranis/new_taranis_result_code/mash/lmo0002/lmo0002_610" + # pdb.set_trace() + min_dist_file = os.path.join(record_allele_folder, min_allele_file) + cline = NcbiblastnCommandline(db=db_name, evalue=0.001, perc_identity=90, reward=1, penalty=-2, gapopen=1, gapextend=1, outfmt=blast_parameters, max_target_seqs=1100, max_hsps=1000, num_threads=4, query=min_dist_file) + out, err = cline() + min_dist_lines = out.splitlines() + min_dist_alleles = [] + for min_dist in min_dist_lines: + min_dist_alleles.append(min_dist.split("\t")[1]) + min_np = np.array(min_dist_alleles) + # pdb.set_trace() + print("matches con min distancia: ", len(min_dist_lines)) + print("Not coverage using as reference" , np.setdiff1d(locus_list, min_np)) + # distancia media. Sale 133 matches + # mean_dist_file="/media/lchapado/Reference_data/proyectos_isciii/taranis/new_taranis_result_code/mash/lmo0002/lmo0002_870" + mean_dist_file = os.path.join(record_allele_folder, mean_allele_file) + cline = NcbiblastnCommandline(db=db_name, evalue=0.001, perc_identity=90, reward=1, penalty=-2, gapopen=1, gapextend=1, outfmt=blast_parameters, max_target_seqs=1100, max_hsps=1000, num_threads=4, query=mean_dist_file) + out, err = cline() + mean_dist_lines = out.splitlines() + mean_dist_alleles = [] + for mean_dist in mean_dist_lines: + mean_dist_alleles.append(mean_dist.split("\t")[1]) + mean_np = np.array(mean_dist_alleles) + print("matches con distancia media: ", len(mean_dist_lines)) + print("Not coverage using as reference" , np.setdiff1d(locus_list, mean_np)) + + # maxima distancia, + # ref_query_file="/media/lchapado/Reference_data/proyectos_isciii/taranis/new_taranis_result_code/mash/lmo0002/lmo0002_216" + max_dist_file = os.path.join(record_allele_folder, max_allele_file) + cline = NcbiblastnCommandline(db=db_name, evalue=0.001, perc_identity=90, reward=1, penalty=-2, gapopen=1, gapextend=1, outfmt=blast_parameters, max_target_seqs=1100, max_hsps=1000, num_threads=4, query=max_dist_file) + out, err = cline() + max_dist_lines = out.splitlines() + max_dist_alleles = [] + for max_dist in max_dist_lines: + max_dist_alleles.append(max_dist.split("\t")[1]) + max_np = np.array(max_dist_alleles) + print("matches con max distancia: ", len(max_dist_lines)) + print("Not coverage using as reference" , np.setdiff1d(locus_list, max_np)) + + # eligiendo uno de los outliers , + # outlier_file="/media/lchapado/Reference_data/proyectos_isciii/taranis/new_taranis_result_code/mash/lmo0002/lmo0002_183" + outlier_file = os.path.join(record_allele_folder, outlier_allele_file) + cline = NcbiblastnCommandline(db=db_name, evalue=0.001, perc_identity=90, reward=1, penalty=-2, gapopen=1, gapextend=1, outfmt=blast_parameters, max_target_seqs=1100, max_hsps=1000, num_threads=4, query=outlier_file) + out, err = cline() + outlier_lines = out.splitlines() + outlier_alleles = [] + for outlier_line in outlier_lines: + outlier_alleles.append(outlier_line.split("\t")[1]) + outlier_np = np.array(outlier_alleles) + print("matches con outliers distancia: ", len(outlier_lines)) + + print("Alleles added using outlier as reference" , outlier_np) + new_ref_np = np.unique(np.concatenate((min_np, outlier_np), axis=0)) + print("\n", "remaining alleles ", np.setdiff1d(locus_list, new_ref_np)) + + # eligiendo el segundo de los outliers , + # outlier_file="/media/lchapado/Reference_data/proyectos_isciii/taranis/new_taranis_result_code/mash/lmo0002/lmo0002_183" + outlier2_file = os.path.join(record_allele_folder, outlier2_allele_file) + cline = NcbiblastnCommandline(db=db_name, evalue=0.001, perc_identity=90, reward=1, penalty=-2, gapopen=1, gapextend=1, outfmt=blast_parameters, max_target_seqs=1100, max_hsps=1000, num_threads=4, query=outlier2_file) + out, err = cline() + outlier2_lines = out.splitlines() + outlier2_alleles = [] + for outlier2_line in outlier2_lines: + outlier2_alleles.append(outlier2_line.split("\t")[1]) + outlier2_np = np.array(outlier2_alleles) + print("matches con second outliers distance: ", len(outlier2_lines)) + # print("Alleles added using second outlier as reference" , outlier2_np) + upd_new_ref_np = np.unique(np.concatenate((new_ref_np, outlier2_np), axis=0)) + print("\n", "remaining alleles after second outlier", np.setdiff1d(locus_list, upd_new_ref_np)) + + # eligiendo el tercero de los outliers , + # outlier_file="/media/lchapado/Reference_data/proyectos_isciii/taranis/new_taranis_result_code/mash/lmo0002/lmo0002_183" + outlier3_file = os.path.join(record_allele_folder, outlier3_allele_file) + cline = NcbiblastnCommandline(db=db_name, evalue=0.001, perc_identity=90, reward=1, penalty=-2, gapopen=1, gapextend=1, outfmt=blast_parameters, max_target_seqs=1100, max_hsps=1000, num_threads=4, query=outlier3_file) + out, err = cline() + outlier3_lines = out.splitlines() + outlier3_alleles = [] + for outlier3_line in outlier3_lines: + outlier3_alleles.append(outlier3_line.split("\t")[1]) + outlier3_np = np.array(outlier3_alleles) + print("matches con third outliers distance: ", len(outlier3_lines)) + # print("Alleles added using second outlier as reference" , outlier2_np) + upd2_new_ref_np = np.unique(np.concatenate((upd_new_ref_np, outlier3_np), axis=0)) + print("\n", "remaining alleles after second outlier", np.setdiff1d(locus_list, upd2_new_ref_np)) + + print("\n Still missing " ,len( np.setdiff1d(locus_list, upd2_new_ref_np))) + + pdb.set_trace() # from sklearn.cluster import AgglomerativeClustering @@ -141,7 +274,7 @@ def create_matrix_distance(self): def create_ref_alleles(self): self.records = taranis.utils.read_fasta_file(self.fasta_file) _ = self.check_locus_quality() - pdb.set_trace() + # pdb.set_trace() # Prepare data to use mash to create the distance matrix _ = self.create_matrix_distance() diff --git a/taranis/utils.py b/taranis/utils.py index 9ca894f..2767138 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -13,14 +13,21 @@ # from itertools import islice import os -from rich.console import Console +import rich.console +import numpy as np + import sys +from pathlib import Path from Bio import SeqIO + +import pdb log = logging.getLogger(__name__) + + def get_files_in_folder(folder, extension=None): """get the list of files, filtered by extension in the input folder. If extension is not set, then all files in folder are returned @@ -50,6 +57,11 @@ def file_exists(file_to_check): return True return False +def find_nearest_numpy_value(array, value): + array = np.asarray(array) + idx = (np.abs(array - value)).argmin() + return array[idx] + def folder_exists(folder_to_check): """Checks if input folder exists @@ -97,7 +109,7 @@ def query_user_yes_no(question, default): else: return "no" else: - sys.stdout.write("Please respond with 'yes' or 'no' " "(or 'y' or 'n').\n") + sys.stdout.write("Please respond with 'yes' or 'no' (or 'y' or 'n').\n") def read_fasta_file(fasta_file): return SeqIO.parse(fasta_file, "fasta") @@ -114,18 +126,34 @@ def rich_force_colors(): return True return None -def write_fasta_file(out_folder, seq_data, multiple_files=False, extension=True): + +def write_fasta_file(out_folder, seq_data, allele_name=None, f_name=None): try: os.makedirs(out_folder, exist_ok=True) except OSError as e: sys.exit(1) if isinstance(seq_data, dict): for key, seq in seq_data.items(): - if extension: - f_name = os.path.join(out_folder, key + ".fasta") - else: - f_name = os.path.join(out_folder, key) - with open (f_name, "w") as fo: + if f_name is None: + # use the fasta name as file name + f_name = key + ".fasta" + f_path_name = os.path.join(out_folder, f_name) + with open (f_path_name, "w") as fo: fo.write(">" + key + "\n") fo.write(seq) - \ No newline at end of file + else: + if f_name is None: + f_name = allele_name + f_path_name = os.path.join(out_folder, f_name) + with open (f_path_name, "w") as fo: + fo.write(">" + allele_name + "\n") + fo.write(seq_data) + return f_name + + +stderr = rich.console.Console( + stderr=True, + style="dim", + highlight=False, + force_terminal=rich_force_colors(), +) From dc10a7a685bd27bf6100afad57073c77420f4c2a Mon Sep 17 00:00:00 2001 From: luissian Date: Tue, 26 Dec 2023 19:10:01 +0100 Subject: [PATCH 003/214] Implemented analyze_schema in multiple cpus --- taranis/__main__.py | 218 ++++++++++++++++++++++++++++++++------ taranis/allele_calling.py | 51 ++++++--- taranis/analyze_schema.py | 165 +++++++++++++++++++++++++++++ taranis/utils.py | 146 ++++++++++++++++++++----- 4 files changed, 501 insertions(+), 79 deletions(-) create mode 100644 taranis/analyze_schema.py diff --git a/taranis/__main__.py b/taranis/__main__.py index 9affab9..bb507ac 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -1,6 +1,7 @@ import logging import click +import concurrent.futures import glob import os import rich.console @@ -10,6 +11,7 @@ import taranis.prediction import taranis.utils +import taranis.analyze_schema import taranis.reference_alleles import taranis.allele_calling @@ -27,11 +29,26 @@ def run_taranis(): # Print taranis header # stderr.print("\n[green]{},--.[grey39]/[green],-.".format(" " * 42), highlight=False) - stderr.print("[blue] ______ ___ ___ ", highlight=False, ) - stderr.print("[blue] \ |-[grey39]-| [blue] |__--__| /\ | \ /\ |\ | | | ", highlight=False,) - stderr.print("[blue] \ \ [grey39]/ [blue] || / \ |__ / / \ | \ | | |___ ", highlight=False,) - stderr.print("[blue] / [grey39] / [blue] \ || /____\ | \ /____\ | \ | | |", highlight=False, ) - stderr.print("[blue] / [grey39] |-[blue]-| || / \ | \ / \ | \| | ___| ", highlight=False,) + stderr.print( + "[blue] ______ ___ ___ ", + highlight=False, + ) + stderr.print( + "[blue] \ |-[grey39]-| [blue] |__--__| /\ | \ /\ |\ | | | ", + highlight=False, + ) + stderr.print( + "[blue] \ \ [grey39]/ [blue] || / \ |__ / / \ | \ | | |___ ", + highlight=False, + ) + stderr.print( + "[blue] / [grey39] / [blue] \ || /____\ | \ /____\ | \ | | |", + highlight=False, + ) + stderr.print( + "[blue] / [grey39] |-[blue]-| || / \ | \ / \ | \| | ___| ", + highlight=False, + ) # stderr.print("[green] `._,._,'\n", highlight=False) __version__ = "2.1.0" @@ -106,30 +123,127 @@ def taranis_cli(verbose, log_file): ) log.addHandler(log_fh) + +# Analyze schema +# taranis analyze-schema -i /media/lchapado/Reference_data/proyectos_isciii/taranis/documentos_antiguos/pasteur_schema -o /media/lchapado/Reference_data/proyectos_isciii/taranis/test/analyze_schema +# testing data for analyze schema +# taranis analyze-schema -i /media/lchapado/Reference_data/proyectos_isciii/taranis/taranis_testing_data/listeria_testing_schema -o /media/lchapado/Reference_data/proyectos_isciii/taranis/test/analyze_schema + +@taranis_cli.command(help_priority=1) +@click.option( + "-i", + "--inputdir", + required=True, + multiple=False, + type=click.Path(), + help="Directory where the schema with the core gene files are located. ", +) +@click.option( + "-o", + "--output", + required=True, + multiple=False, + type=click.Path(), + help="Output folder to save analyze schema", +) +@click.option( + "--remove-subset/--no-remove-subset", + required=False, + default=False, + help="Remove allele subsequences from the schema.", +) +@click.option( + "--remove-duplicated/--no-remove-duplicated", + required=False, + default=False, + help="Remove duplicated subsequences from the schema.", +) +@click.option( + "--remove-no-cds/--no-remove-no-cds", + required=False, + default=False, + help="Remove no CDS alleles from the schema.", +) +@click.option( + "--genus", + required=False, + default="Genus", + help="Genus name for Prokka schema genes annotation. Default is Genus.", +) +@click.option( + "--species", + required=False, + default="species", + help="Species name for Prokka schema genes annotation. Default is species", +) +@click.option( + "--usegenus", + required=False, + default="Genus", + help="Use genus-specific BLAST databases for Prokka schema genes annotation (needs --genus). Default is False.", +) +def analyze_schema( + inputdir, + output, + remove_subset, + remove_duplicated, + remove_no_cds, + genus, + species, + usegenus, +): + schema_files = taranis.utils.get_files_in_folder(inputdir, "fasta") + + + """ + schema_analyze = {} + for schema_file in schema_files: + schema_obj = taranis.analyze_schema.AnalyzeSchema(schema_file, output, remove_subset, remove_duplicated, remove_no_cds, genus, species, usegenus) + schema_analyze.update(schema_obj.analyze_allele_in_schema()) + + """ + # for schema_file in schema_files: + results = [] + with concurrent.futures.ProcessPoolExecutor() as executor: + futures = [executor.submit(taranis.analyze_schema.prueba_paralelizacion, schema_file, output, remove_subset, remove_duplicated, remove_no_cds, genus, species, usegenus) for schema_file in schema_files] + # Collect results as they complete + for future in concurrent.futures.as_completed(futures): + results.append(future.result()) + _ = taranis.analyze_schema.collect_statistics(results) + + # Reference alleles @taranis_cli.command(help_priority=2) -@click.option("-s", "--schema", required=True, multiple=False, type=click.Path(), help="Directory where the schema with the core gene files are located. ") -@click.option("-o", "--output", required=True, multiple=False, type=click.Path(), help="Output folder to save reference alleles") +@click.option( + "-s", + "--schema", + required=True, + multiple=False, + type=click.Path(), + help="Directory where the schema with the core gene files are located. ", +) +@click.option( + "-o", + "--output", + required=True, + multiple=False, + type=click.Path(), + help="Output folder to save reference alleles", +) def reference_alleles( schema, output, ): # taranis reference-alleles -s ../../documentos_antiguos/datos_prueba/schema_1_locus/ -o ../../new_taranis_result_code # taranis reference-alleles -s ../../documentos_antiguos/datos_prueba/schema_test/ -o ../../new_taranis_result_code - if not taranis.utils.folder_exists(schema): - log.error("schema folder %s does not exists", schema) - stderr.print( - "[red] Schema folder does not exist. " + schema + "!" - ) - sys.exit(1) schema_files = taranis.utils.get_files_in_folder(schema, "fasta") - if len(schema_files) == 0: - log.error("Schema folder %s does not have any fasta file", schema) - stderr.print("[red] Schema folder does not have any fasta file") - sys.exit(1) # Check if output folder exists if taranis.utils.folder_exists(output): - q_question = "Folder " + output + " already exists. Files will be overwritten. Do you want to continue?" + q_question = ( + "Folder " + + output + + " already exists. Files will be overwritten. Do you want to continue?" + ) if "no" in taranis.utils.query_user_yes_no(q_question, "no"): log.info("Aborting code by user request") stderr.print("[red] Exiting code. ") @@ -152,12 +266,48 @@ def reference_alleles( # taranis allele-calling -s ../../documentos_antiguos/datos_prueba/schema_test/ -r ../../documentos_antiguos/datos_prueba/reference_alleles/ -g ../../taranis_data/listeria_genoma_referencia/listeria.fasta -a ../../taranis_data/listeria_sampled/RA-L2073_R1.fasta -o ../../test/ # taranis allele-calling -s ../../documentos_antiguos/datos_prueba/schema_test/ -r ../../documentos_antiguos/datos_prueba/reference_alleles/ -g ../../taranis_data/listeria_genoma_referencia/listeria.fasta -a ../../taranis_data/muestras_listeria_servicio_fasta/3789/assembly.fasta -o ../../test/ + @taranis_cli.command(help_priority=3) -@click.option("-s", "--schema", required=True, multiple=False, type=click.Path(), help="Directory where the schema with the core gene files are located. ") -@click.option("-r", "--reference", required=True, multiple=False, type=click.Path(), help="Directory where the schema reference allele files are located. ") -@click.option("-g", "--genome", required=True, multiple=False, type=click.Path(), help="Genome reference file") -@click.option("-a", "--sample", required=True, multiple=False, type=click.Path(), help="Sample location file in fasta format. ") -@click.option("-o", "--output", required=True, multiple=False, type=click.Path(), help="Output folder to save reference alleles") +@click.option( + "-s", + "--schema", + required=True, + multiple=False, + type=click.Path(), + help="Directory where the schema with the core gene files are located. ", +) +@click.option( + "-r", + "--reference", + required=True, + multiple=False, + type=click.Path(), + help="Directory where the schema reference allele files are located. ", +) +@click.option( + "-g", + "--genome", + required=True, + multiple=False, + type=click.Path(), + help="Genome reference file", +) +@click.option( + "-a", + "--sample", + required=True, + multiple=False, + type=click.Path(), + help="Sample location file in fasta format. ", +) +@click.option( + "-o", + "--output", + required=True, + multiple=False, + type=click.Path(), + help="Output folder to save reference alleles", +) def allele_calling( schema, reference, @@ -169,25 +319,25 @@ def allele_calling( for folder in folder_to_check: if not taranis.utils.folder_exists(folder): log.error("folder %s does not exists", folder) - stderr.print( - "[red] Folder does not exist. " + folder + "!" - ) + stderr.print("[red] Folder does not exist. " + folder + "!") sys.exit(1) if not taranis.utils.file_exists(sample): log.error("file %s does not exists", sample) - stderr.print( - "[red] File does not exist. " + sample + "!" - ) + stderr.print("[red] File does not exist. " + sample + "!") sys.exit(1) schema_files = taranis.utils.get_files_in_folder(schema, "fasta") if len(schema_files) == 0: log.error("Schema folder %s does not have any fasta file", schema) stderr.print("[red] Schema folder does not have any fasta file") sys.exit(1) - + # Check if output folder exists if taranis.utils.folder_exists(output): - q_question = "Folder " + output + " already exists. Files will be overwritten. Do you want to continue?" + q_question = ( + "Folder " + + output + + " already exists. Files will be overwritten. Do you want to continue?" + ) if "no" in taranis.utils.query_user_yes_no(q_question, "no"): log.info("Aborting code by user request") stderr.print("[red] Exiting code. ") @@ -202,12 +352,14 @@ def allele_calling( # Filter fasta files from reference folder ref_alleles = glob.glob(os.path.join(reference, "*.fasta")) # Create predictions - pred_out = os.path.join(output, "prediction" ) + pred_out = os.path.join(output, "prediction") pred_sample = taranis.prediction.Prediction(genome, sample, pred_out) pred_sample.training() pred_sample.prediction() """Analyze the sample file against schema to identify outbreakers """ - sample_allele = taranis.allele_calling.Sample(pred_sample, sample, schema, ref_alleles ,output) + sample_allele = taranis.allele_calling.AlleleCalling( + pred_sample, sample, schema, ref_alleles, output + ) sample_allele.analyze_sample() diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 1759c48..20ef08c 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -5,7 +5,8 @@ import taranis.utils import taranis.blast -import numpy +# import numpy +import pandas as pd from pathlib import Path @@ -18,7 +19,7 @@ force_terminal=taranis.utils.rich_force_colors(), ) -class Sample: +class AlleleCalling: def __init__(self, prediction, sample_file, schema, reference_alleles, out_folder): self.prediction = prediction self.sample_file = sample_file @@ -28,8 +29,17 @@ def __init__(self, prediction, sample_file, schema, reference_alleles, out_folde self.s_name = Path(sample_file).stem self.blast_dir = os.path.join(out_folder,"blastdb") self.blast_sample = os.path.join(self.blast_dir, self.s_name) + self.blast_heading = ["qseqid", "sseqid", "pident", "qlen", "length", "mismatch", "gapopen", "evalue", "bitscore", "sstart", "send", "qstart", "qend", "sseq", "qseq"] - def assign_abbreviation(self, query_seq, allele_name, sample_contig, schema_gene): + def assign_allele_type(self, query_seq, allele_name, sample_contig, schema_gene): + """_summary_ + + Args: + query_seq (_type_): _description_ + allele_name (_type_): _description_ + sample_contig (_type_): _description_ + schema_gene (_type_): _description_ + """ s_alleles_blast = taranis.blast.Blast("nucl") ref_allele_blast_dir = os.path.join(self.blast_dir, "ref_alleles") query_path = os.path.join(self.out_folder, "tmp", allele_name) @@ -40,34 +50,41 @@ def assign_abbreviation(self, query_seq, allele_name, sample_contig, schema_gene # Blast with sample sequence to find the allele in the schema seq_blast_match = s_alleles_blast.run_blast(query_file, perc_identity=100) pdb.set_trace() - if len(seq_blast_match) == 1: + if len(seq_blast_match) >= 1: + # allele is named as NIPHEM # Hacer un blast con la query esta secuencia y la database del alelo # Create blast db with sample file pass + elif len(seq_blast_match) == 1: + pass + else: + pass - def catalog_alleles (self, ref_allele): + def search_alleles (self, ref_allele): allele_name = Path(ref_allele).stem schema_gene = os.path.join(self.schema, allele_name + ".fasta") allele_name = Path(ref_allele).stem # run blast with sample as db and reference allele as query sample_blast_match = self.sample_blast.run_blast(ref_allele) if len(sample_blast_match) > 0 : - s_lines = [] - for out_line in sample_blast_match: - s_lines.append(out_line.split("\t")) - np_lines = numpy.array(s_lines) + pd_lines = pd.DataFrame([item.split("\t") for item in sample_blast_match]) + pd_lines.columns = self.blast_heading + pd_lines["pident"] = pd_lines["pident"].apply(pd.to_numeric) + sel_max = pd_lines.loc[pd_lines["pident"].idxmax()] + query_seq = sel_max["qseq"] + # np_lines = numpy.array(s_lines) # convert to float the perc_identity to find out the max value - max_val = numpy.max(np_lines[:,2].astype(float)) - mask = np_lines[:, 2] ==str(max_val) + # max_val = numpy.max(np_lines[:,2].astype(float)) + # mask = np_lines[:, 2] ==str(max_val) # Select rows that match the percent identity. Index 2 in blast results - sel_row = np_lines[mask, :] = np_lines[mask, :] - query_seq = sel_row[0,14] - sample_contig = sel_row[0,1] - abbr = self.assign_abbreviation(query_seq, allele_name, sample_contig, schema_gene) + # sel_row = np_lines[mask, :] = np_lines[mask, :] + # query_seq = sel_row[0,14] + sample_contig = sel_max["sseqid"] + abbr = self.assign_allele_type(query_seq, allele_name, sample_contig, schema_gene) else: # Sample does not have a reference allele to be matched # Keep LNF info @@ -82,11 +99,11 @@ def analyze_sample(self): self.sample_blast = taranis.blast.Blast("nucl") _ = self.sample_blast.create_blastdb(self.sample_file, self.blast_dir) result = {} - pdb.set_trace() + # pdb.set_trace() for ref_allele in self.ref_alleles: # schema_alleles = os.path.join(self.schema, ref_allele) # parallel in all CPUs in cluster node - result[ref_allele] = self.catalog_alleles(ref_allele) + result[ref_allele] = self.search_alleles(ref_allele) pdb.set_trace() return diff --git a/taranis/analyze_schema.py b/taranis/analyze_schema.py new file mode 100644 index 0000000..c7b1376 --- /dev/null +++ b/taranis/analyze_schema.py @@ -0,0 +1,165 @@ +import logging +import pandas as pd +import os +import rich.console +import statistics +from pathlib import Path + +from Bio import SeqIO +from Bio.SeqRecord import SeqRecord + +import taranis.utils + +import pdb +log = logging.getLogger(__name__) +stderr = rich.console.Console( + stderr=True, + style="dim", + highlight=False, + force_terminal=taranis.utils.rich_force_colors(), +) + +class AnalyzeSchema: + def __init__( + self, + schema_allele, + output, + remove_subset, + remove_duplicated, + remove_no_cds, + genus, + species, + usegenus + ): + self.schema_allele = schema_allele + self.allele_name = Path(self.schema_allele).stem + self.output = output + self.remove_subset = remove_subset + self.remove_duplicated = remove_duplicated + self.remove_no_cds = remove_no_cds + self.genus = genus + self.species = species + self.usegenus = usegenus + + + def check_allele_quality (self): + a_quality = {} + allele_seq = {} + bad_quality_record = [] + with open(self.schema_allele) as fh: + for record in SeqIO.parse(self.schema_allele, "fasta"): + a_quality[record.id] = {"quality": "Good quality", "reason": "-" } + allele_seq[record.id] = str(record.seq) + a_quality[record.id]["length"] = len(str(record.seq)) + if len(record.seq) % 3 != 0: + a_quality[record.id]["quality"] = "Bad quality" + a_quality[record.id]["reason"] = "Can not be converted to protein" + a_quality[record.id]["order"] = "-" + continue + sequence_order = taranis.utils.check_sequence_order(str(record.seq)) + if sequence_order == "Error": + a_quality[record.id]["quality"] = "Bad quality" + a_quality[record.id]["reason"] = "Start or end codon not found" + a_quality[record.id]["order"] = "-" + continue + elif sequence_order == "reverse": + record_sequence = str(record.seq.reverse_complement()) + else: + record_sequence = str(record.seq) + a_quality[record.id]["order"] = sequence_order + if record_sequence[0:3] not in taranis.utils.START_CODON_FORWARD : + a_quality[record.id]["quality"] = "Bad quality" + a_quality[record.id]["reason"] = "Start codon not found" + continue + if record_sequence[-3:] not in taranis.utils.STOP_CODON_FORWARD : + a_quality[record.id]["quality"] = "Bad quality" + a_quality[record.id]["reason"] = "Stop codon not found" + continue + if taranis.utils.find_multiple_stop_codons(record_sequence): + a_quality[record.id]["quality"] = "Bad quality" + a_quality[record.id]["reason"] = "Multiple stop codons found" + continue + if self.remove_no_cds and a_quality[record.id]["quality"] == "Bad quality": + bad_quality_record.append(record.id) + + if self.remove_duplicated: + # get the unique sequences and compare the length with all sequences + unique_seq = list(set(list(allele_seq.values()))) + if len(unique_seq) < len(allele_seq): + tmp_dict = {} + for rec_id , seq_value in allele_seq.items(): + if seq_value not in tmp_dict: + tmp_dict[seq_value] = 0 + else: + bad_quality_record.append(rec_id) + if self.remove_subset: + unique_seq = list(set(list(allele_seq.values()))) + for rec_id, seq_value in allele_seq.items(): + unique_seq.remove(seq_value) + if seq_value in unique_seq: + bad_quality_record.append(rec_id) + new_schema_folder = os.path.join(self.output, "new_schema") + _ = taranis.utils.create_new_folder(new_schema_folder) + new_schema_file = os.path.join(new_schema_folder, self.allele_name + ".fasta") + with open(self.schema_allele, "r") as fh: + with open(new_schema_file, "w") as fo: + for record in SeqIO.parse(self.schema_allele, "fasta"): + if len(bad_quality_record) > 0: + if record.id not in bad_quality_record: + SeqIO.write(record, fo, "fasta") + else: + SeqIO.write(record, fo, "fasta") + # update the schema allele with the new file + self.schema_allele = new_schema_file + return a_quality + + def fetch_statistics_from_alleles(self, a_quality): + record_data = {} + bad_quality_reason = {} + a_length = [] + bad_quality_counter = 0 + for record_id in a_quality.keys(): + record_data["allele_name"] = self.allele_name + a_length.append(a_quality[record_id]["length"]) + if a_quality[record_id]["quality"] == "Bad quality": + bad_quality_counter += 1 + bad_quality_reason[a_quality[record_id]["reason"]] = bad_quality_reason.get(a_quality[record_id]["reason"], 0 ) +1 + total_alleles = len(a_length) + record_data["min_length"] = min(a_length) + record_data["max_length"] = max(a_length) + record_data["num_alleles"] = total_alleles + record_data["mean_length"] = round(statistics.mean(a_length),2) + record_data["good_percent"] = round(100*(total_alleles - bad_quality_counter) / total_alleles, 2) + return record_data + + + def analyze_allele_in_schema(self): + allele_data = {} + # Perform quality + a_quality = self.check_allele_quality() + # run annotations + prokka_folder = os.path.join(self.output, "prokka", self.allele_name) + anotation_files = taranis.utils.create_annotation_files(self.schema_allele, prokka_folder, self.allele_name) + allele_data["annotation_gene"] = taranis.utils.read_annotation_file(anotation_files+ ".tsv", self.allele_name).get(self.allele_name) + allele_data.update(self.fetch_statistics_from_alleles(a_quality)) + return allele_data + +def prueba_paralelizacion(schema_allele, + output, + remove_subset, + remove_duplicated, + remove_no_cds, + genus, + species, + usegenus + ): + schema_obj = AnalyzeSchema(schema_allele, output, remove_subset, remove_duplicated, remove_no_cds, genus, species, usegenus) + return schema_obj.analyze_allele_in_schema() + + +def collect_statistics(stat_data): + + + stats_df = pd.DataFrame(stat_data) + print(stats_df) + \ No newline at end of file diff --git a/taranis/utils.py b/taranis/utils.py index 2767138..7946721 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -4,30 +4,86 @@ """ import glob -# import hashlib + import logging +import numpy as np import questionary -# import json -# import openpyxl -# import yaml -# from itertools import islice - import os import rich.console -import numpy as np +import shutil +import subprocess + import sys from pathlib import Path from Bio import SeqIO - +from Bio.SeqRecord import SeqRecord import pdb log = logging.getLogger(__name__) +def rich_force_colors(): + """ + Check if any environment variables are set to force Rich to use coloured output + """ + if ( + os.getenv("GITHUB_ACTIONS") + or os.getenv("FORCE_COLOR") + or os.getenv("PY_COLORS") + ): + return True + return None +stderr = rich.console.Console( + stderr=True, + style="dim", + highlight=False, + force_terminal=rich_force_colors(), +) + +START_CODON_FORWARD= ['ATG','ATA','ATT','GTG', 'TTG'] +start_codon_reverse= ['CAT', 'TAT','AAT','CAC','CAA'] +STOP_CODON_FORWARD = ['TAA', 'TAG','TGA'] +stop_codon_reverse = ['TTA', 'CTA','TCA'] +def check_sequence_order(allele_sequence): + # check direction + if allele_sequence[0:3] in START_CODON_FORWARD or allele_sequence[-3:] in STOP_CODON_FORWARD: + return 'forward' + if allele_sequence[-3:] in start_codon_reverse or allele_sequence[0:3] in stop_codon_reverse: + return 'reverse' + return "Error" +def create_annotation_files(fasta_file, annotation_dir, prefix, genus="Genus", species="species", usegenus=False): + try: + _ = subprocess.run (['prokka', fasta_file, '--force', annotation_dir, '--genus', genus, '--species', species, '--usegenus', str(usegenus), '--gcode', '11', '--prefix', prefix, '--quiet']) + except Exception as e: + log.error("Unable to run prokka. Error message: %s ", e ) + stderr.print("[red] Unable to run prokka. Given error; " + e) + sys.exit(1) + # Check that prokka store files in the requested folder + # if prokka results are not found in the requested folder then move from the + # running directory to the right one + if not folder_exists(annotation_dir): + try: + shutil.move(prefix, annotation_dir) + except Exception as e: + log.error("Unable to move prokka result folder to %s ", e ) + stderr.print("[red] Unable to move result prokka folder. Error; " + e) + sys.exit(1) + return os.path.join(annotation_dir, prefix) + + +def create_new_folder(folder_name): + try: + os.makedirs(folder_name, exist_ok=True) + except Exception as e: + log.error("Folder %s can not be created %s", folder_name, e) + stderr.print("[red] Folder does not have any file which match your request") + sys.exit(1) + return + def get_files_in_folder(folder, extension=None): """get the list of files, filtered by extension in the input folder. If extension is not set, then all files in folder are returned @@ -37,12 +93,22 @@ def get_files_in_folder(folder, extension=None): extension (string, optional): extension for filtering. Defaults to None. Returns: - list: list of files + list: list of files which match the condition """ + if not folder_exists(folder): + log.error("Folder %s does not exists", folder) + stderr.print("[red] Schema folder does not exist. " + folder + "!") + sys.exit(1) if extension is None: extension = "*" - return glob.glob(folder + "*." + extension) - + folder_files = os.path.join(folder , "*." + extension) + files_in_folder = glob.glob(folder_files) + if len(files_in_folder) == 0: + log.error("Folder %s does not have any file which the extension %s", folder, extension) + stderr.print("[red] Folder does not have any file which match your request") + sys.exit(1) + return files_in_folder + def file_exists(file_to_check): """Checks if input file exists @@ -57,6 +123,18 @@ def file_exists(file_to_check): return True return False +def find_multiple_stop_codons(seq) : + stop_codons = ['TAA', 'TAG','TGA'] + c_index = [] + for idx in range (0, len(seq) -2, 3) : + c_seq = seq[idx : idx + 3] + if c_seq in stop_codons : + c_index.append(idx) + if len(c_index) == 1: + return False + return True + + def find_nearest_numpy_value(array, value): array = np.asarray(array) idx = (np.abs(array - value)).argmin() @@ -111,21 +189,36 @@ def query_user_yes_no(question, default): else: sys.stdout.write("Please respond with 'yes' or 'no' (or 'y' or 'n').\n") +def read_annotation_file(ann_file, allele_name, only_first_line=True): + + """ example of annotation file + locus_tag ftype length_bp gene EC_number COG product + IEKBEMEO_00001 CDS 1344 yeeO_1 COG0534 putative FMN/FAD exporter YeeO + IEKBEMEO_00002 CDS 1344 yeeO_2 COG0534 putative FMN/FAD exporter YeeO + + """ + ann_data = {} + with open (ann_file, "r") as fh: + lines = fh.readlines() + heading = lines[0].split("\t") + locus_tag_idx = heading.index("locus_tag") + gene_idx = heading.index("gene") + if only_first_line: + first_line = lines[1].split("\t") + ann_data[allele_name] = first_line[gene_idx] if first_line[gene_idx] != "" else "Not found by Prokka'" + else: + # Return all annotation lines + for line in lines[1:]: + s_line = line.strip().split("\t") + allele_key = allele_name + "_" + s_line[locus_tag_idx].split("_")[1] + ann_data[allele_key] = s_line[gene_idx] if s_line[gene_idx] != "" else "Not found by Prokka'" + return ann_data + + + def read_fasta_file(fasta_file): return SeqIO.parse(fasta_file, "fasta") -def rich_force_colors(): - """ - Check if any environment variables are set to force Rich to use coloured output - """ - if ( - os.getenv("GITHUB_ACTIONS") - or os.getenv("FORCE_COLOR") - or os.getenv("PY_COLORS") - ): - return True - return None - def write_fasta_file(out_folder, seq_data, allele_name=None, f_name=None): try: @@ -151,9 +244,4 @@ def write_fasta_file(out_folder, seq_data, allele_name=None, f_name=None): return f_name -stderr = rich.console.Console( - stderr=True, - style="dim", - highlight=False, - force_terminal=rich_force_colors(), -) + From dacde9975f3709ca16f18164e74d2f751d8f9e7a Mon Sep 17 00:00:00 2001 From: luissian Date: Tue, 26 Dec 2023 19:11:41 +0100 Subject: [PATCH 004/214] Liting analyze_schema --- taranis/__main__.py | 19 +++++-- taranis/analyze_schema.py | 105 ++++++++++++++++++++++---------------- 2 files changed, 78 insertions(+), 46 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index bb507ac..343d87e 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -129,6 +129,7 @@ def taranis_cli(verbose, log_file): # testing data for analyze schema # taranis analyze-schema -i /media/lchapado/Reference_data/proyectos_isciii/taranis/taranis_testing_data/listeria_testing_schema -o /media/lchapado/Reference_data/proyectos_isciii/taranis/test/analyze_schema + @taranis_cli.command(help_priority=1) @click.option( "-i", @@ -193,8 +194,7 @@ def analyze_schema( usegenus, ): schema_files = taranis.utils.get_files_in_folder(inputdir, "fasta") - - + """ schema_analyze = {} for schema_file in schema_files: @@ -205,7 +205,20 @@ def analyze_schema( # for schema_file in schema_files: results = [] with concurrent.futures.ProcessPoolExecutor() as executor: - futures = [executor.submit(taranis.analyze_schema.prueba_paralelizacion, schema_file, output, remove_subset, remove_duplicated, remove_no_cds, genus, species, usegenus) for schema_file in schema_files] + futures = [ + executor.submit( + taranis.analyze_schema.prueba_paralelizacion, + schema_file, + output, + remove_subset, + remove_duplicated, + remove_no_cds, + genus, + species, + usegenus, + ) + for schema_file in schema_files + ] # Collect results as they complete for future in concurrent.futures.as_completed(futures): results.append(future.result()) diff --git a/taranis/analyze_schema.py b/taranis/analyze_schema.py index c7b1376..c0c2a94 100644 --- a/taranis/analyze_schema.py +++ b/taranis/analyze_schema.py @@ -11,6 +11,7 @@ import taranis.utils import pdb + log = logging.getLogger(__name__) stderr = rich.console.Console( stderr=True, @@ -19,36 +20,36 @@ force_terminal=taranis.utils.rich_force_colors(), ) + class AnalyzeSchema: def __init__( - self, - schema_allele, - output, - remove_subset, - remove_duplicated, - remove_no_cds, - genus, - species, - usegenus - ): - self.schema_allele = schema_allele - self.allele_name = Path(self.schema_allele).stem - self.output = output - self.remove_subset = remove_subset - self.remove_duplicated = remove_duplicated - self.remove_no_cds = remove_no_cds - self.genus = genus - self.species = species - self.usegenus = usegenus - + self, + schema_allele, + output, + remove_subset, + remove_duplicated, + remove_no_cds, + genus, + species, + usegenus, + ): + self.schema_allele = schema_allele + self.allele_name = Path(self.schema_allele).stem + self.output = output + self.remove_subset = remove_subset + self.remove_duplicated = remove_duplicated + self.remove_no_cds = remove_no_cds + self.genus = genus + self.species = species + self.usegenus = usegenus - def check_allele_quality (self): + def check_allele_quality(self): a_quality = {} allele_seq = {} bad_quality_record = [] with open(self.schema_allele) as fh: for record in SeqIO.parse(self.schema_allele, "fasta"): - a_quality[record.id] = {"quality": "Good quality", "reason": "-" } + a_quality[record.id] = {"quality": "Good quality", "reason": "-"} allele_seq[record.id] = str(record.seq) a_quality[record.id]["length"] = len(str(record.seq)) if len(record.seq) % 3 != 0: @@ -67,11 +68,11 @@ def check_allele_quality (self): else: record_sequence = str(record.seq) a_quality[record.id]["order"] = sequence_order - if record_sequence[0:3] not in taranis.utils.START_CODON_FORWARD : + if record_sequence[0:3] not in taranis.utils.START_CODON_FORWARD: a_quality[record.id]["quality"] = "Bad quality" a_quality[record.id]["reason"] = "Start codon not found" continue - if record_sequence[-3:] not in taranis.utils.STOP_CODON_FORWARD : + if record_sequence[-3:] not in taranis.utils.STOP_CODON_FORWARD: a_quality[record.id]["quality"] = "Bad quality" a_quality[record.id]["reason"] = "Stop codon not found" continue @@ -79,15 +80,18 @@ def check_allele_quality (self): a_quality[record.id]["quality"] = "Bad quality" a_quality[record.id]["reason"] = "Multiple stop codons found" continue - if self.remove_no_cds and a_quality[record.id]["quality"] == "Bad quality": - bad_quality_record.append(record.id) - + if ( + self.remove_no_cds + and a_quality[record.id]["quality"] == "Bad quality" + ): + bad_quality_record.append(record.id) + if self.remove_duplicated: # get the unique sequences and compare the length with all sequences unique_seq = list(set(list(allele_seq.values()))) if len(unique_seq) < len(allele_seq): tmp_dict = {} - for rec_id , seq_value in allele_seq.items(): + for rec_id, seq_value in allele_seq.items(): if seq_value not in tmp_dict: tmp_dict[seq_value] = 0 else: @@ -116,50 +120,65 @@ def check_allele_quality (self): def fetch_statistics_from_alleles(self, a_quality): record_data = {} bad_quality_reason = {} - a_length = [] + a_length = [] bad_quality_counter = 0 for record_id in a_quality.keys(): record_data["allele_name"] = self.allele_name a_length.append(a_quality[record_id]["length"]) if a_quality[record_id]["quality"] == "Bad quality": bad_quality_counter += 1 - bad_quality_reason[a_quality[record_id]["reason"]] = bad_quality_reason.get(a_quality[record_id]["reason"], 0 ) +1 + bad_quality_reason[a_quality[record_id]["reason"]] = ( + bad_quality_reason.get(a_quality[record_id]["reason"], 0) + 1 + ) total_alleles = len(a_length) record_data["min_length"] = min(a_length) record_data["max_length"] = max(a_length) record_data["num_alleles"] = total_alleles - record_data["mean_length"] = round(statistics.mean(a_length),2) - record_data["good_percent"] = round(100*(total_alleles - bad_quality_counter) / total_alleles, 2) + record_data["mean_length"] = round(statistics.mean(a_length), 2) + record_data["good_percent"] = round( + 100 * (total_alleles - bad_quality_counter) / total_alleles, 2 + ) return record_data - def analyze_allele_in_schema(self): allele_data = {} # Perform quality a_quality = self.check_allele_quality() # run annotations prokka_folder = os.path.join(self.output, "prokka", self.allele_name) - anotation_files = taranis.utils.create_annotation_files(self.schema_allele, prokka_folder, self.allele_name) - allele_data["annotation_gene"] = taranis.utils.read_annotation_file(anotation_files+ ".tsv", self.allele_name).get(self.allele_name) + anotation_files = taranis.utils.create_annotation_files( + self.schema_allele, prokka_folder, self.allele_name + ) + allele_data["annotation_gene"] = taranis.utils.read_annotation_file( + anotation_files + ".tsv", self.allele_name + ).get(self.allele_name) allele_data.update(self.fetch_statistics_from_alleles(a_quality)) return allele_data - -def prueba_paralelizacion(schema_allele, + + +def prueba_paralelizacion( + schema_allele, + output, + remove_subset, + remove_duplicated, + remove_no_cds, + genus, + species, + usegenus, +): + schema_obj = AnalyzeSchema( + schema_allele, output, remove_subset, remove_duplicated, remove_no_cds, genus, species, - usegenus - ): - schema_obj = AnalyzeSchema(schema_allele, output, remove_subset, remove_duplicated, remove_no_cds, genus, species, usegenus) + usegenus, + ) return schema_obj.analyze_allele_in_schema() def collect_statistics(stat_data): - - stats_df = pd.DataFrame(stat_data) print(stats_df) - \ No newline at end of file From beb0413750d35903075a30bc1fc336dfa6cdb838 Mon Sep 17 00:00:00 2001 From: luissian Date: Fri, 29 Dec 2023 00:02:15 +0100 Subject: [PATCH 005/214] Added code to test alfaclust results --- taranis/pruebas.py | 63 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 63 insertions(+) create mode 100644 taranis/pruebas.py diff --git a/taranis/pruebas.py b/taranis/pruebas.py new file mode 100644 index 0000000..ff3fff4 --- /dev/null +++ b/taranis/pruebas.py @@ -0,0 +1,63 @@ +from Bio.Seq import Seq + +from Bio import SeqIO +from Bio.Blast.Applications import NcbiblastnCommandline +import subprocess +import taranis.utils +import pdb +import random + +# read result of alfatclust + +alfa_clust_file = "/media/lchapado/Reference_data/proyectos_isciii/taranis/test/resultado_alfatclust-090" +with open(alfa_clust_file, "r") as fh: + lines = fh.readlines() +alleles_found = False +locus_list = [] +for line in lines: + line = line.strip() + if line == "#Cluster 9" : + if alleles_found == False: + alleles_found = True + continue + if alleles_found: + if "#Cluster" in line: + break + locus_list.append(line) + +# import pdb; pdb.set_trace() +rand_locus = random.choice(locus_list) +schema_file = "/media/lchapado/Reference_data/proyectos_isciii/taranis/taranis_testing_data/listeria_testing_schema/lmo0002.fasta" +new_schema_file = "/media/lchapado/Reference_data/proyectos_isciii/taranis/test/cluster_lmo0002.fasta" +q_file = "/media/lchapado/Reference_data/proyectos_isciii/taranis/test/q_file.fasta" +with open(schema_file) as fh: + with open(new_schema_file, "w") as fo: + for record in SeqIO.parse(schema_file, "fasta"): + if record.id in locus_list: + SeqIO.write(record, fo, "fasta") + +# choose a random locus for testing +with open(new_schema_file) as fh: + with open(q_file, "w") as fo: + for record in SeqIO.parse(new_schema_file, "fasta"): + if record.id == rand_locus: + SeqIO.write(record, fo, "fasta") + break +print ("Selected locus: " , rand_locus) +db_name ="/media/lchapado/Reference_data/proyectos_isciii/taranis/test/testing_clster/lmo0002" +blast_command = ['makeblastdb' , '-in' , new_schema_file , '-parse_seqids', '-dbtype', "nucl", '-out' , db_name] +blast_result = subprocess.run(blast_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) + +blast_parameters = '"6 , qseqid , sseqid , pident , qlen , length , mismatch , gapopen , evalue , bitscore , sstart , send , qstart , qend , sseq , qseq"' +# pdb.set_trace() +#db=self.blast_dir, evalue=evalue, perc_identity=perc_identity_ref, reward=reward, penalty=penalty, gapopen=gapopen, gapextend=gapextend, outfmt=blast_parameters, max_target_seqs=max_target_seqs, max_hsps=max_hsps, num_threads=num_threads, query=core_reference_allele_path) +cline = NcbiblastnCommandline(db=db_name, evalue=0.001, perc_identity=90, reward=1, penalty=-2, gapopen=1, gapextend=1, outfmt=blast_parameters, max_target_seqs=1100, max_hsps=1000, num_threads=4, query=q_file) + +try: + out, _ = cline() +except Exception as e: + pdb.set_trace() +b_lines = out.splitlines() +print("longitud del cluster = ", len(locus_list)) +print("numero de matches = ", len(b_lines)) +# pdb.set_trace() \ No newline at end of file From a25365c9167de78ddf89d67be6b8f23c830e5fe9 Mon Sep 17 00:00:00 2001 From: luissian Date: Thu, 4 Jan 2024 17:47:21 +0100 Subject: [PATCH 006/214] Implemented Analyze schema --- environment.yml | 1 + requirements.txt | 3 +- taranis/__main__.py | 9 ++-- taranis/analyze_schema.py | 92 ++++++++++++++++++++++++++++----------- taranis/pruebas.py | 11 ++++- taranis/utils.py | 28 ++++++++++-- 6 files changed, 110 insertions(+), 34 deletions(-) diff --git a/environment.yml b/environment.yml index d50b6a5..d5ca5b5 100644 --- a/environment.yml +++ b/environment.yml @@ -11,6 +11,7 @@ dependencies: - conda-forge::plotly==5.0.0 - conda-forge::numpy==1.20.3 - conda-forge::rich==13.7.0 + - conda-forge::python-kaleido - bioconda::prokka>=1.14 - bioconda::blast>=2.9 - bioconda::mash>=2 diff --git a/requirements.txt b/requirements.txt index ea5b59f..cffcf88 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,5 @@ click questionary bio -scikit-learn \ No newline at end of file +scikit-learn +plotly \ No newline at end of file diff --git a/taranis/__main__.py b/taranis/__main__.py index 343d87e..d08cb47 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -8,6 +8,7 @@ import rich.logging import rich.traceback import sys +import time import taranis.prediction import taranis.utils @@ -204,10 +205,11 @@ def analyze_schema( """ # for schema_file in schema_files: results = [] + start = time.perf_counter() with concurrent.futures.ProcessPoolExecutor() as executor: futures = [ executor.submit( - taranis.analyze_schema.prueba_paralelizacion, + taranis.analyze_schema.parallel_execution, schema_file, output, remove_subset, @@ -222,8 +224,9 @@ def analyze_schema( # Collect results as they complete for future in concurrent.futures.as_completed(futures): results.append(future.result()) - _ = taranis.analyze_schema.collect_statistics(results) - + _ = taranis.analyze_schema.collect_statistics(results, output) + finish = time.perf_counter() + print(f"Schema analyze finish in {round((finish-start)/60, 2)} minutes") # Reference alleles @taranis_cli.command(help_priority=2) diff --git a/taranis/analyze_schema.py b/taranis/analyze_schema.py index c0c2a94..cf218bf 100644 --- a/taranis/analyze_schema.py +++ b/taranis/analyze_schema.py @@ -56,30 +56,27 @@ def check_allele_quality(self): a_quality[record.id]["quality"] = "Bad quality" a_quality[record.id]["reason"] = "Can not be converted to protein" a_quality[record.id]["order"] = "-" - continue - sequence_order = taranis.utils.check_sequence_order(str(record.seq)) - if sequence_order == "Error": - a_quality[record.id]["quality"] = "Bad quality" - a_quality[record.id]["reason"] = "Start or end codon not found" - a_quality[record.id]["order"] = "-" - continue - elif sequence_order == "reverse": - record_sequence = str(record.seq.reverse_complement()) else: - record_sequence = str(record.seq) - a_quality[record.id]["order"] = sequence_order - if record_sequence[0:3] not in taranis.utils.START_CODON_FORWARD: - a_quality[record.id]["quality"] = "Bad quality" - a_quality[record.id]["reason"] = "Start codon not found" - continue - if record_sequence[-3:] not in taranis.utils.STOP_CODON_FORWARD: - a_quality[record.id]["quality"] = "Bad quality" - a_quality[record.id]["reason"] = "Stop codon not found" - continue - if taranis.utils.find_multiple_stop_codons(record_sequence): - a_quality[record.id]["quality"] = "Bad quality" - a_quality[record.id]["reason"] = "Multiple stop codons found" - continue + sequence_order = taranis.utils.check_sequence_order(str(record.seq)) + if sequence_order == "Error": + a_quality[record.id]["quality"] = "Bad quality" + a_quality[record.id]["reason"] = "Start or end codon not found" + a_quality[record.id]["order"] = "-" + elif sequence_order == "reverse": + record_sequence = str(record.seq.reverse_complement()) + else: + record_sequence = str(record.seq) + a_quality[record.id]["order"] = sequence_order + if record_sequence[0:3] not in taranis.utils.START_CODON_FORWARD: + a_quality[record.id]["quality"] = "Bad quality" + a_quality[record.id]["reason"] = "Start codon not found" + elif record_sequence[-3:] not in taranis.utils.STOP_CODON_FORWARD: + a_quality[record.id]["quality"] = "Bad quality" + a_quality[record.id]["reason"] = "Stop codon not found" + + elif taranis.utils.find_multiple_stop_codons(record_sequence): + a_quality[record.id]["quality"] = "Bad quality" + a_quality[record.id]["reason"] = "Multiple stop codons found" if ( self.remove_no_cds and a_quality[record.id]["quality"] == "Bad quality" @@ -96,12 +93,16 @@ def check_allele_quality(self): tmp_dict[seq_value] = 0 else: bad_quality_record.append(rec_id) + a_quality[rec_id]["quality"] ="Bad quality" + a_quality[rec_id]["reason"] ="Duplicate allele" if self.remove_subset: unique_seq = list(set(list(allele_seq.values()))) for rec_id, seq_value in allele_seq.items(): unique_seq.remove(seq_value) if seq_value in unique_seq: bad_quality_record.append(rec_id) + a_quality[rec_id]["quality"] ="Bad quality" + a_quality[rec_id]["reason"] ="Sub set allele" new_schema_folder = os.path.join(self.output, "new_schema") _ = taranis.utils.create_new_folder(new_schema_folder) new_schema_file = os.path.join(new_schema_folder, self.allele_name + ".fasta") @@ -118,6 +119,7 @@ def check_allele_quality(self): return a_quality def fetch_statistics_from_alleles(self, a_quality): + possible_bad_quality = ["Can not be converted to protein", "Start codon not found", "Stop codon not found", "Multiple stop codons found" ,"Duplicate allele", "Sub set allele"] record_data = {} bad_quality_reason = {} a_length = [] @@ -138,6 +140,9 @@ def fetch_statistics_from_alleles(self, a_quality): record_data["good_percent"] = round( 100 * (total_alleles - bad_quality_counter) / total_alleles, 2 ) + for item in possible_bad_quality: + record_data[item] = bad_quality_reason[item] if item in bad_quality_reason else 0 + # record_data["bad_quality_reason"] = bad_quality_reason return record_data def analyze_allele_in_schema(self): @@ -156,7 +161,7 @@ def analyze_allele_in_schema(self): return allele_data -def prueba_paralelizacion( +def parallel_execution( schema_allele, output, remove_subset, @@ -179,6 +184,43 @@ def prueba_paralelizacion( return schema_obj.analyze_allele_in_schema() -def collect_statistics(stat_data): + +def collect_statistics(stat_data, out_folder): + def stats_graphics(stats_folder): + print(out_folder) + graphic_folder = os.path.join(stats_folder, "graphics") + _ = taranis.utils.create_new_folder(graphic_folder) + # create graphic for alleles/number of genes + genes_alleles_df = stats_df["num_alleles"].value_counts().rename_axis("alleles").sort_index().reset_index(name="genes") + _ = taranis.utils.create_graphic(graphic_folder, "num_genes_per_allele.png", "lines", genes_alleles_df["alleles"].to_list(), genes_alleles_df["genes"].to_list(), ["Allele", "number of genes"],"title") + # create pie graph for good quality + + good_percent = [round(stats_df["good_percent"].mean(),2)] + good_percent.append(100 - good_percent[0]) + labels = ["Good quality", "Bad quality"] + # pdb.set_trace() + _ = taranis.utils.create_graphic(graphic_folder, "quality_of_locus.png", "pie", good_percent, "", labels, "Quality of locus") + # create pie graph for bad quality reason. This is optional if there are + # bad quality alleles + labels = [] + values = [] + for item in taranis.utils.POSIBLE_BAD_QUALITY: + labels.append(item) + values.append(stats_df[item].sum()) + if sum(values) > 0: + _ = taranis.utils.create_graphic(graphic_folder, "bad_quality_reason.png", "pie", values, "", labels, "Bad quality reason") + # create pie graph for not found gene name + # pdb.set_trace() + times_not_found_gene = len(stats_df[stats_df["annotation_gene"] == "Not found by Prokka"]) + if times_not_found_gene > 0: + gene_not_found = [times_not_found_gene, len(stat_data)] + labels = ["Not found gene name", "Number of alleles"] + _ = taranis.utils.create_graphic(graphic_folder, "gene_not_found.png", "pie", gene_not_found, "", labels, "Quality of locus") + stats_df = pd.DataFrame(stat_data) + stats_folder = os.path.join(out_folder, "statistics") + _ = taranis.utils.create_new_folder(stats_folder) + _ = taranis.utils.write_data_to_file(stats_folder, "statistics.csv", stats_df) + stats_graphics(stats_folder) + print(stats_df) diff --git a/taranis/pruebas.py b/taranis/pruebas.py index ff3fff4..4481b72 100644 --- a/taranis/pruebas.py +++ b/taranis/pruebas.py @@ -7,6 +7,15 @@ import pdb import random +""" + Para hacer las pruebas con alfaclust activo el entorno de conda alfatclust_env + despues me voy a la carpeta donde me he descargado, de git, alfatclust y + ejecuto : + ./alfatclust.py -i /media/lchapado/Reference_data/proyectos_isciii/taranis/taranis_testing_data/listeria_testing_schema/lmo0003.fasta -o /media/lchapado/Reference_data/proyectos_isciii/taranis/test/alfacluster_test/resultado_alfaclust_lmo003 -l 0.9 + despues ejecuto este programa de prueba cambiando los ficheros de resultados + +""" + # read result of alfatclust alfa_clust_file = "/media/lchapado/Reference_data/proyectos_isciii/taranis/test/resultado_alfatclust-090" @@ -16,7 +25,7 @@ locus_list = [] for line in lines: line = line.strip() - if line == "#Cluster 9" : + if line == "#Cluster 5" : if alleles_found == False: alleles_found = True continue diff --git a/taranis/utils.py b/taranis/utils.py index 7946721..f41f297 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -9,6 +9,7 @@ import numpy as np import questionary import os +import plotly.graph_objects as go import rich.console import shutil import subprocess @@ -47,6 +48,8 @@ def rich_force_colors(): STOP_CODON_FORWARD = ['TAA', 'TAG','TGA'] stop_codon_reverse = ['TTA', 'CTA','TCA'] +POSIBLE_BAD_QUALITY = ["Can not be converted to protein", "Start codon not found", "Stop codon not found", "Multiple stop codons found" ,"Duplicate allele", "Sub set allele"] + def check_sequence_order(allele_sequence): # check direction if allele_sequence[0:3] in START_CODON_FORWARD or allele_sequence[-3:] in STOP_CODON_FORWARD: @@ -57,7 +60,7 @@ def check_sequence_order(allele_sequence): def create_annotation_files(fasta_file, annotation_dir, prefix, genus="Genus", species="species", usegenus=False): try: - _ = subprocess.run (['prokka', fasta_file, '--force', annotation_dir, '--genus', genus, '--species', species, '--usegenus', str(usegenus), '--gcode', '11', '--prefix', prefix, '--quiet']) + _ = subprocess.run (['prokka', fasta_file, '--force', '--outdir', annotation_dir, '--genus', genus, '--species', species, '--usegenus', str(usegenus), '--gcode', '11', '--prefix', prefix, '--quiet']) except Exception as e: log.error("Unable to run prokka. Error message: %s ", e ) stderr.print("[red] Unable to run prokka. Given error; " + e) @@ -83,7 +86,19 @@ def create_new_folder(folder_name): stderr.print("[red] Folder does not have any file which match your request") sys.exit(1) return - + + +def create_graphic(out_folder, f_name, mode, x_data, y_data, labels, title ): + fig = go.Figure() + # pdb.set_trace() + if mode == "lines": + fig.add_trace(go.Scatter(x=x_data, y=y_data, mode=mode, name=title)) + elif mode == "pie": + fig.add_trace(go.Pie(labels=labels, values=x_data)) + fig.update_layout(title_text= title) + fig.write_image(os.path.join(out_folder, f_name)) + + def get_files_in_folder(folder, extension=None): """get the list of files, filtered by extension in the input folder. If extension is not set, then all files in folder are returned @@ -205,13 +220,13 @@ def read_annotation_file(ann_file, allele_name, only_first_line=True): gene_idx = heading.index("gene") if only_first_line: first_line = lines[1].split("\t") - ann_data[allele_name] = first_line[gene_idx] if first_line[gene_idx] != "" else "Not found by Prokka'" + ann_data[allele_name] = first_line[gene_idx] if first_line[gene_idx] != "" else "Not found by Prokka" else: # Return all annotation lines for line in lines[1:]: s_line = line.strip().split("\t") allele_key = allele_name + "_" + s_line[locus_tag_idx].split("_")[1] - ann_data[allele_key] = s_line[gene_idx] if s_line[gene_idx] != "" else "Not found by Prokka'" + ann_data[allele_key] = s_line[gene_idx] if s_line[gene_idx] != "" else "Not found by Prokka" return ann_data @@ -243,5 +258,10 @@ def write_fasta_file(out_folder, seq_data, allele_name=None, f_name=None): fo.write(seq_data) return f_name +def write_data_to_file(out_folder, f_name, data, include_header=True, data_type="pandas", extension="csv"): + f_path_name = os.path.join(out_folder,f_name) + if data_type == "pandas": + data.to_csv(f_path_name, sep=",",header=include_header) + return From 087456941f43090ea7d7fbe3334cf0a8889dfaff Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 8 Jan 2024 19:42:24 +0100 Subject: [PATCH 007/214] Update analyze schema with Comments in previous PR. Added liting workflow --- .github/workflows/dockerhub_push_release.yml | 25 - .github/workflows/python_lint.yml | 35 + .github/workflows/tests.yml | 38 - setup.py | 2 +- taranis/__main__.py | 33 +- taranis/allele_calling.py | 49 +- taranis/allele_calling_old.py | 3853 +++++++++++++----- taranis/analyze_schema.py | 277 +- taranis/blast.py | 68 +- taranis/prediction.py | 45 +- taranis/pruebas.py | 44 +- taranis/reference_alleles.py | 241 +- taranis/utils.py | 214 +- 13 files changed, 3628 insertions(+), 1296 deletions(-) delete mode 100644 .github/workflows/dockerhub_push_release.yml create mode 100644 .github/workflows/python_lint.yml delete mode 100644 .github/workflows/tests.yml diff --git a/.github/workflows/dockerhub_push_release.yml b/.github/workflows/dockerhub_push_release.yml deleted file mode 100644 index e8b6638..0000000 --- a/.github/workflows/dockerhub_push_release.yml +++ /dev/null @@ -1,25 +0,0 @@ -name: deploy release -# This builds the docker image and pushes it to DockerHub -on: - release: - types: [published] -jobs: - push_dockerhub: - name: Push new Docker image to Docker Hub (release) - runs-on: ubuntu-latest - # Only run for the official repo, for releases and merged PRs - if: ${{ github.repository == 'BU-ISCIII/taranis' }} - env: - DOCKERHUB_USERNAME: ${{ secrets.DOCKERHUB_USERNAME }} - DOCKERHUB_PASS: ${{ secrets.DOCKERHUB_PASSWORD }} - steps: - - name: Check out pipeline code - uses: actions/checkout@v2 - - - name: Build new docker image - run: docker build --no-cache . -t buisciii/taranis:${{ github.event.release.tag_name }} - - - name: Push Docker image to DockerHub (develop) - run: | - echo "$DOCKERHUB_PASS" | docker login -u "$DOCKERHUB_USERNAME" --password-stdin - docker push buisciii/taranis:${{ github.event.release.tag_name }} diff --git a/.github/workflows/python_lint.yml b/.github/workflows/python_lint.yml new file mode 100644 index 0000000..9d043bb --- /dev/null +++ b/.github/workflows/python_lint.yml @@ -0,0 +1,35 @@ +name: python_lint + +on: + push: + paths: + - '**.py' + pull_request: + paths: + - '**.py' + +jobs: + flake8_py3: + runs-on: ubuntu-latest + steps: + - name: Setup Python + uses: actions/setup-python@v1 + with: + python-version: 3.9.x + architecture: x64 + - name: Checkout PyTorch + uses: actions/checkout@master + - name: Install flake8 + run: pip install flake8 + - name: Run flake8 + run: flake8 --ignore E501,W503,E203,W605 + + black_lint: + runs-on: ubuntu-latest + steps: + - name: Setup + uses: actions/checkout@v2 + - name: Install black in jupyter + run: pip install black[jupyter] + - name: Check code lints with Black + uses: psf/black@stable diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml deleted file mode 100644 index ed66541..0000000 --- a/.github/workflows/tests.yml +++ /dev/null @@ -1,38 +0,0 @@ -name: tests ci -# This workflow runs the pipeline with the minimal test dataset to check that it completes any errors -on: - push: - branches: [develop] - pull_request_target: - branches: [develop] - release: - types: [published] - -jobs: - push_dockerhub: - name: Push new Docker image to Docker Hub (dev) - runs-on: ubuntu-latest - # Only run for the official repo, for releases and merged PRs - if: ${{ github.repository == 'BU-ISCIII/taranis' }} - env: - DOCKERHUB_USERNAME: ${{ secrets.DOCKERHUB_USERNAME }} - DOCKERHUB_PASS: ${{ secrets.DOCKERHUB_PASSWORD }} - steps: - - name: Check out pipeline code - uses: actions/checkout@v2 - - - name: Build new docker image - run: docker build --no-cache . -t buisciii/taranis:dev - - - name: Push Docker image to DockerHub (develop) - run: | - echo "$DOCKERHUB_PASS" | docker login -u "$DOCKERHUB_USERNAME" --password-stdin - docker push buisciii/taranis:dev - run-tests: - name: Run tests - needs: push_dockerhub - runs-on: ubuntu-latest - steps: - - name: Run pipeline with test data - run: | - docker run buisciii/taranis:dev bash -c /opt/taranis/test/test.sh diff --git a/setup.py b/setup.py index 4b6fad4..eda9691 100644 --- a/setup.py +++ b/setup.py @@ -2,7 +2,7 @@ from setuptools import setup, find_packages -version = "2.2.0" +version = "3.0.0" with open("README.md") as f: readme = f.read() diff --git a/taranis/__main__.py b/taranis/__main__.py index d08cb47..a281777 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -52,7 +52,7 @@ def run_taranis(): ) # stderr.print("[green] `._,._,'\n", highlight=False) - __version__ = "2.1.0" + __version__ = "3.0.0" stderr.print( "\n" "[grey39] Taranis version {}".format(__version__), highlight=False ) @@ -166,6 +166,12 @@ def taranis_cli(verbose, log_file): default=False, help="Remove no CDS alleles from the schema.", ) +@click.option( + "--output-allele-annot/--no-output-allele-annot", + required=False, + default=True, + help="get extension annotation for all alleles in locus", +) @click.option( "--genus", required=False, @@ -184,29 +190,41 @@ def taranis_cli(verbose, log_file): default="Genus", help="Use genus-specific BLAST databases for Prokka schema genes annotation (needs --genus). Default is False.", ) +@click.option( + "--cpus", + required=False, + multiple=False, + type=int, + default=1, + help="Number of cpus used for execution", +) def analyze_schema( inputdir, output, remove_subset, remove_duplicated, remove_no_cds, + output_allele_annot, genus, species, usegenus, + cpus, ): schema_files = taranis.utils.get_files_in_folder(inputdir, "fasta") """ - schema_analyze = {} + schema_analyze = [] for schema_file in schema_files: schema_obj = taranis.analyze_schema.AnalyzeSchema(schema_file, output, remove_subset, remove_duplicated, remove_no_cds, genus, species, usegenus) - schema_analyze.update(schema_obj.analyze_allele_in_schema()) - - """ + schema_analyze.append(schema_obj.analyze_allele_in_schema()) + import pdb; pdb.set_trace() + _ = taranis.analyze_schema.collect_statistics(schema_analyze, output, output_allele_annot) + sys.exit(0) # for schema_file in schema_files: + """ results = [] start = time.perf_counter() - with concurrent.futures.ProcessPoolExecutor() as executor: + with concurrent.futures.ProcessPoolExecutor(max_workers=cpus) as executor: futures = [ executor.submit( taranis.analyze_schema.parallel_execution, @@ -224,10 +242,11 @@ def analyze_schema( # Collect results as they complete for future in concurrent.futures.as_completed(futures): results.append(future.result()) - _ = taranis.analyze_schema.collect_statistics(results, output) + _ = taranis.analyze_schema.collect_statistics(results, output, output_allele_annot) finish = time.perf_counter() print(f"Schema analyze finish in {round((finish-start)/60, 2)} minutes") + # Reference alleles @taranis_cli.command(help_priority=2) @click.option( diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 20ef08c..d9a1e99 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -5,12 +5,14 @@ import taranis.utils import taranis.blast + # import numpy import pandas as pd from pathlib import Path import pdb + log = logging.getLogger(__name__) stderr = rich.console.Console( stderr=True, @@ -19,6 +21,7 @@ force_terminal=taranis.utils.rich_force_colors(), ) + class AlleleCalling: def __init__(self, prediction, sample_file, schema, reference_alleles, out_folder): self.prediction = prediction @@ -27,9 +30,25 @@ def __init__(self, prediction, sample_file, schema, reference_alleles, out_folde self.ref_alleles = reference_alleles self.out_folder = out_folder self.s_name = Path(sample_file).stem - self.blast_dir = os.path.join(out_folder,"blastdb") + self.blast_dir = os.path.join(out_folder, "blastdb") self.blast_sample = os.path.join(self.blast_dir, self.s_name) - self.blast_heading = ["qseqid", "sseqid", "pident", "qlen", "length", "mismatch", "gapopen", "evalue", "bitscore", "sstart", "send", "qstart", "qend", "sseq", "qseq"] + self.blast_heading = [ + "qseqid", + "sseqid", + "pident", + "qlen", + "length", + "mismatch", + "gapopen", + "evalue", + "bitscore", + "sstart", + "send", + "qstart", + "qend", + "sseq", + "qseq", + ] def assign_allele_type(self, query_seq, allele_name, sample_contig, schema_gene): """_summary_ @@ -39,23 +58,22 @@ def assign_allele_type(self, query_seq, allele_name, sample_contig, schema_gene) allele_name (_type_): _description_ sample_contig (_type_): _description_ schema_gene (_type_): _description_ - """ + """ s_alleles_blast = taranis.blast.Blast("nucl") ref_allele_blast_dir = os.path.join(self.blast_dir, "ref_alleles") query_path = os.path.join(self.out_folder, "tmp", allele_name) - # Write to file the sequence to find out the loci name that fully match + # Write to file the sequence to find out the loci name that fully match f_name = taranis.utils.write_fasta_file(query_path, query_seq, allele_name) query_file = os.path.join(query_path, f_name) _ = s_alleles_blast.create_blastdb(schema_gene, ref_allele_blast_dir) - # Blast with sample sequence to find the allele in the schema + # Blast with sample sequence to find the allele in the schema seq_blast_match = s_alleles_blast.run_blast(query_file, perc_identity=100) pdb.set_trace() if len(seq_blast_match) >= 1: - # allele is named as NIPHEM - + # allele is named as NIPHEM + # Hacer un blast con la query esta secuencia y la database del alelo # Create blast db with sample file - pass elif len(seq_blast_match) == 1: @@ -63,14 +81,13 @@ def assign_allele_type(self, query_seq, allele_name, sample_contig, schema_gene) else: pass - - def search_alleles (self, ref_allele): + def search_alleles(self, ref_allele): allele_name = Path(ref_allele).stem - schema_gene = os.path.join(self.schema, allele_name + ".fasta") + schema_gene = os.path.join(self.schema, allele_name + ".fasta") allele_name = Path(ref_allele).stem # run blast with sample as db and reference allele as query sample_blast_match = self.sample_blast.run_blast(ref_allele) - if len(sample_blast_match) > 0 : + if len(sample_blast_match) > 0: pd_lines = pd.DataFrame([item.split("\t") for item in sample_blast_match]) pd_lines.columns = self.blast_heading pd_lines["pident"] = pd_lines["pident"].apply(pd.to_numeric) @@ -84,16 +101,17 @@ def search_alleles (self, ref_allele): # sel_row = np_lines[mask, :] = np_lines[mask, :] # query_seq = sel_row[0,14] sample_contig = sel_max["sseqid"] - abbr = self.assign_allele_type(query_seq, allele_name, sample_contig, schema_gene) + abbr = self.assign_allele_type( + query_seq, allele_name, sample_contig, schema_gene + ) else: # Sample does not have a reference allele to be matched # Keep LNF info # ver el codigo de espe - #lnf_tpr_tag() + # lnf_tpr_tag() pass pdb.set_trace() - def analyze_sample(self): # Create blast db with sample file self.sample_blast = taranis.blast.Blast("nucl") @@ -107,4 +125,3 @@ def analyze_sample(self): pdb.set_trace() return - diff --git a/taranis/allele_calling_old.py b/taranis/allele_calling_old.py index 72d3294..e8be72f 100644 --- a/taranis/allele_calling_old.py +++ b/taranis/allele_calling_old.py @@ -28,16 +28,24 @@ import plotly.graph_objects as go -def check_blast (reference_allele, sample_files, db_name, logger) : ## N +def check_blast(reference_allele, sample_files, db_name, logger): ## N for s_file in sample_files: - f_name = os.path.basename(s_file).split('.') + f_name = os.path.basename(s_file).split(".") dir_name = os.path.dirname(s_file) - blast_dir = os.path.join(dir_name, db_name,f_name[0]) - blast_db = os.path.join(blast_dir,f_name[0]) - if not os.path.exists(blast_dir) : - logger.error('Blast db folder for sample %s does not exist', f_name) + blast_dir = os.path.join(dir_name, db_name, f_name[0]) + blast_db = os.path.join(blast_dir, f_name[0]) + if not os.path.exists(blast_dir): + logger.error("Blast db folder for sample %s does not exist", f_name) return False - cline = NcbiblastnCommandline(db=blast_db, evalue=0.001, outfmt=5, max_target_seqs=10, max_hsps=10,num_threads=1, query=reference_allele) + cline = NcbiblastnCommandline( + db=blast_db, + evalue=0.001, + outfmt=5, + max_target_seqs=10, + max_hsps=10, + num_threads=1, + query=reference_allele, + ) out, err = cline() psiblast_xml = StringIO(out) @@ -51,16 +59,18 @@ def check_blast (reference_allele, sample_files, db_name, logger) : ## N alleleMatchid = int((blast_record.query_id.split("_"))[-1]) return True + # · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # # Parse samples and core genes schema fasta files to dictionary # # · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # -def parsing_fasta_file_to_dict (fasta_file, logger): + +def parsing_fasta_file_to_dict(fasta_file, logger): fasta_dict = {} fasta_dict_ordered = {} for contig in SeqIO.parse(fasta_file, "fasta"): fasta_dict[str(contig.id)] = str(contig.seq.upper()) - logger.debug('file %s parsed to dictionary', fasta_file) + logger.debug("file %s parsed to dictionary", fasta_file) for key in sorted(list(fasta_dict.keys())): fasta_dict_ordered[key] = fasta_dict[key] @@ -71,9 +81,11 @@ def parsing_fasta_file_to_dict (fasta_file, logger): # Get core genes schema info before allele calling analysis # # · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # -#def prepare_core_gene (core_gene_file_list, store_dir, ref_alleles_dir, logger): -def prepare_core_gene (core_gene_file_list, store_dir, ref_alleles_dir, genus, species, usegenus, logger): +# def prepare_core_gene (core_gene_file_list, store_dir, ref_alleles_dir, logger): +def prepare_core_gene( + core_gene_file_list, store_dir, ref_alleles_dir, genus, species, usegenus, logger +): ## Initialize dict for keeping id-allele, quality, length variability, length statistics and annotation info for each schema core gene alleles_in_locus_dict = {} schema_quality = {} @@ -81,13 +93,11 @@ def prepare_core_gene (core_gene_file_list, store_dir, ref_alleles_dir, genus, s schema_variability = {} schema_statistics = {} - ## Process each schema core gene - blast_dir = os.path.join(store_dir,'blastdb') - logger.info('start preparation of core genes files') + blast_dir = os.path.join(store_dir, "blastdb") + logger.info("start preparation of core genes files") for fasta_file in core_gene_file_list: - - f_name = os.path.basename(fasta_file).split('.') + f_name = os.path.basename(fasta_file).split(".") # Parse core gene fasta file and keep id-sequence info in dictionary fasta_file_parsed_dict = parsing_fasta_file_to_dict(fasta_file, logger) @@ -96,8 +106,8 @@ def prepare_core_gene (core_gene_file_list, store_dir, ref_alleles_dir, genus, s alleles_in_locus_dict[f_name[0]] = fasta_file_parsed_dict # dump fasta file into pickle file - #with open (file_list[-1],'wb') as f: - # pickle.dump(fasta_file_parsed_dict, f) + # with open (file_list[-1],'wb') as f: + # pickle.dump(fasta_file_parsed_dict, f) # Get core gene alleles quality locus_quality = check_core_gene_quality(fasta_file, logger) @@ -106,63 +116,90 @@ def prepare_core_gene (core_gene_file_list, store_dir, ref_alleles_dir, genus, s schema_quality[f_name[0]] = locus_quality # Get gene and product annotation for core gene using reference allele(s) - ref_allele = os.path.join(ref_alleles_dir, f_name[0] + '.fasta') + ref_allele = os.path.join(ref_alleles_dir, f_name[0] + ".fasta") - gene_annot, product_annot = get_gene_annotation (ref_allele, store_dir, genus, species, usegenus, logger) - #gene_annot, product_annot = get_gene_annotation (ref_allele, store_dir, logger) + gene_annot, product_annot = get_gene_annotation( + ref_allele, store_dir, genus, species, usegenus, logger + ) + # gene_annot, product_annot = get_gene_annotation (ref_allele, store_dir, logger) if f_name[0] not in annotation_core_dict.keys(): annotation_core_dict[f_name[0]] = {} annotation_core_dict[f_name[0]] = [gene_annot, product_annot] # Get core gene alleles length to keep length variability and statistics info alleles_len = [] - for allele in fasta_file_parsed_dict : + for allele in fasta_file_parsed_dict: alleles_len.append(len(fasta_file_parsed_dict[allele])) - #alleles_in_locus = list (SeqIO.parse(fasta_file, "fasta")) ## parse - #for allele in alleles_in_locus : ## parse - #alleles_len.append(len(str(allele.seq))) ## parse + # alleles_in_locus = list (SeqIO.parse(fasta_file, "fasta")) ## parse + # for allele in alleles_in_locus : ## parse + # alleles_len.append(len(str(allele.seq))) ## parse - schema_variability[f_name[0]]=list(set(alleles_len)) + schema_variability[f_name[0]] = list(set(alleles_len)) if len(alleles_len) == 1: stdev = 0 else: stdev = statistics.stdev(alleles_len) - schema_statistics[f_name[0]]=[statistics.mean(alleles_len), stdev, min(alleles_len), max(alleles_len)] - - return alleles_in_locus_dict, annotation_core_dict, schema_variability, schema_statistics, schema_quality + schema_statistics[f_name[0]] = [ + statistics.mean(alleles_len), + stdev, + min(alleles_len), + max(alleles_len), + ] + + return ( + alleles_in_locus_dict, + annotation_core_dict, + schema_variability, + schema_statistics, + schema_quality, + ) # · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # # Get Prodigal training file from reference genome for samples gene prediction # # · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # -def prodigal_training(reference_genome_file, prodigal_dir, logger): - f_name = os.path.basename(reference_genome_file).split('.')[0] - prodigal_train_dir = os.path.join(prodigal_dir, 'training') +def prodigal_training(reference_genome_file, prodigal_dir, logger): + f_name = os.path.basename(reference_genome_file).split(".")[0] + prodigal_train_dir = os.path.join(prodigal_dir, "training") - output_prodigal_train_dir = os.path.join(prodigal_train_dir, f_name + '.trn') + output_prodigal_train_dir = os.path.join(prodigal_train_dir, f_name + ".trn") if not os.path.exists(prodigal_train_dir): try: os.makedirs(prodigal_train_dir) - logger.debug('Created prodigal directory for training file %s', f_name) + logger.debug("Created prodigal directory for training file %s", f_name) except: - logger.info('Cannot create prodigal directory for training file %s', f_name) - print ('Error when creating the directory %s for training file', prodigal_train_dir) + logger.info("Cannot create prodigal directory for training file %s", f_name) + print( + "Error when creating the directory %s for training file", + prodigal_train_dir, + ) exit(0) - prodigal_command = ['prodigal' , '-i', reference_genome_file, '-t', output_prodigal_train_dir] - prodigal_result = subprocess.run(prodigal_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) - - # if prodigal_result.stderr: - # logger.error('cannot create training file for %s', f_name) - # logger.error('prodigal returning error code %s', prodigal_result.stderr) - # return False + prodigal_command = [ + "prodigal", + "-i", + reference_genome_file, + "-t", + output_prodigal_train_dir, + ] + prodigal_result = subprocess.run( + prodigal_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE + ) + + # if prodigal_result.stderr: + # logger.error('cannot create training file for %s', f_name) + # logger.error('prodigal returning error code %s', prodigal_result.stderr) + # return False else: - logger.info('Skeeping prodigal training file creation for %s, as it has already been created', f_name) + logger.info( + "Skeeping prodigal training file creation for %s, as it has already been created", + f_name, + ) return output_prodigal_train_dir @@ -171,33 +208,59 @@ def prodigal_training(reference_genome_file, prodigal_dir, logger): # Get Prodigal sample gene prediction # # · * · * · * · * · * · * · * · * · * # -def prodigal_prediction(file_name, prodigal_dir, prodigal_train_dir, logger): - f_name = '.'.join(os.path.basename(file_name).split('.')[:-1]) - prodigal_dir_sample = os.path.join(prodigal_dir,f_name) +def prodigal_prediction(file_name, prodigal_dir, prodigal_train_dir, logger): + f_name = ".".join(os.path.basename(file_name).split(".")[:-1]) + prodigal_dir_sample = os.path.join(prodigal_dir, f_name) - output_prodigal_coord = os.path.join(prodigal_dir_sample, f_name + '_coord.gff') ## no - output_prodigal_prot = os.path.join(prodigal_dir_sample, f_name + '_prot.faa') ## no - output_prodigal_dna = os.path.join(prodigal_dir_sample, f_name + '_dna.faa') + output_prodigal_coord = os.path.join( + prodigal_dir_sample, f_name + "_coord.gff" + ) ## no + output_prodigal_prot = os.path.join( + prodigal_dir_sample, f_name + "_prot.faa" + ) ## no + output_prodigal_dna = os.path.join(prodigal_dir_sample, f_name + "_dna.faa") if not os.path.exists(prodigal_dir_sample): try: os.makedirs(prodigal_dir_sample) - logger.debug('Created prodigal directory for Core Gene %s', f_name) + logger.debug("Created prodigal directory for Core Gene %s", f_name) except: - logger.info('Cannot create prodigal directory for Core Gene %s' , f_name) - print ('Error when creating the directory %s for prodigal genes prediction', prodigal_dir_sample) + logger.info("Cannot create prodigal directory for Core Gene %s", f_name) + print( + "Error when creating the directory %s for prodigal genes prediction", + prodigal_dir_sample, + ) exit(0) - prodigal_command = ['prodigal' , '-i', file_name , '-t', prodigal_train_dir, '-f', 'gff', '-o', output_prodigal_coord, '-a', output_prodigal_prot, '-d', output_prodigal_dna] - prodigal_result = subprocess.run(prodigal_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) + prodigal_command = [ + "prodigal", + "-i", + file_name, + "-t", + prodigal_train_dir, + "-f", + "gff", + "-o", + output_prodigal_coord, + "-a", + output_prodigal_prot, + "-d", + output_prodigal_dna, + ] + prodigal_result = subprocess.run( + prodigal_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE + ) # if prodigal_result.stderr: - # logger.error('cannot predict genes for %s ', f_name) - # logger.error('prodigal returning error code %s', prodigal_result.stderr) - #return False + # logger.error('cannot predict genes for %s ', f_name) + # logger.error('prodigal returning error code %s', prodigal_result.stderr) + # return False else: - logger.info('Skeeping prodigal genes prediction for %s, as it has already been made', f_name) + logger.info( + "Skeeping prodigal genes prediction for %s, as it has already been made", + f_name, + ) return True @@ -206,64 +269,111 @@ def prodigal_prediction(file_name, prodigal_dir, prodigal_train_dir, logger): # Get Prodigal predicted gene sequence equivalent to BLAST result matching bad quality allele or to no Exact Match BLAST result in allele calling analysis # # · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # -def get_prodigal_sequence(blast_sseq, contig_blast_id, prodigal_directory, sample_name, blast_parameters, logger): +def get_prodigal_sequence( + blast_sseq, + contig_blast_id, + prodigal_directory, + sample_name, + blast_parameters, + logger, +): prodigal_directory_sample = os.path.join(prodigal_directory, sample_name) - genes_file = os.path.join(prodigal_directory_sample, sample_name + '_dna.faa') + genes_file = os.path.join(prodigal_directory_sample, sample_name + "_dna.faa") ## Create directory for storing prodigal genes prediction per contig BLAST databases - blastdb_per_contig_directory = 'blastdb_per_contig' - full_path_blastdb_per_contig = os.path.join(prodigal_directory_sample, blastdb_per_contig_directory) + blastdb_per_contig_directory = "blastdb_per_contig" + full_path_blastdb_per_contig = os.path.join( + prodigal_directory_sample, blastdb_per_contig_directory + ) if not os.path.exists(full_path_blastdb_per_contig): try: os.makedirs(full_path_blastdb_per_contig) - logger.info('Directory %s has been created', full_path_blastdb_per_contig) + logger.info("Directory %s has been created", full_path_blastdb_per_contig) except: - print ('Cannot create the directory ', full_path_blastdb_per_contig) - logger.info('Directory %s cannot be created', full_path_blastdb_per_contig) - exit (0) + print("Cannot create the directory ", full_path_blastdb_per_contig) + logger.info("Directory %s cannot be created", full_path_blastdb_per_contig) + exit(0) ## Create directory for storing prodigal genes prediction sequences per contig - prodigal_genes_per_contig_directory = 'prodigal_genes_per_contig' - full_path_prodigal_genes_per_contig = os.path.join(prodigal_directory_sample, prodigal_genes_per_contig_directory) + prodigal_genes_per_contig_directory = "prodigal_genes_per_contig" + full_path_prodigal_genes_per_contig = os.path.join( + prodigal_directory_sample, prodigal_genes_per_contig_directory + ) if not os.path.exists(full_path_prodigal_genes_per_contig): try: os.makedirs(full_path_prodigal_genes_per_contig) - logger.info('Directory %s has been created', full_path_prodigal_genes_per_contig) + logger.info( + "Directory %s has been created", full_path_prodigal_genes_per_contig + ) except: - print ('Cannot create the directory ', full_path_prodigal_genes_per_contig) - logger.info('Directory %s cannot be created', full_path_prodigal_genes_per_contig) - exit (0) + print("Cannot create the directory ", full_path_prodigal_genes_per_contig) + logger.info( + "Directory %s cannot be created", full_path_prodigal_genes_per_contig + ) + exit(0) ## Parse prodigal genes prediction fasta file predicted_genes = SeqIO.parse(genes_file, "fasta") ## Create fasta file containing Prodigal predicted genes sequences for X contig in sample - contig_genes_path = os.path.join(full_path_prodigal_genes_per_contig, contig_blast_id + '.fasta') - with open (contig_genes_path, 'w') as out_fh: + contig_genes_path = os.path.join( + full_path_prodigal_genes_per_contig, contig_blast_id + ".fasta" + ) + with open(contig_genes_path, "w") as out_fh: for rec in predicted_genes: - contig_prodigal_id = '_'.join((rec.id).split("_")[:-1]) + contig_prodigal_id = "_".join((rec.id).split("_")[:-1]) if contig_prodigal_id == contig_blast_id: - out_fh.write ('>' + str(rec.description) + '\n' + str(rec.seq) + '\n') + out_fh.write(">" + str(rec.description) + "\n" + str(rec.seq) + "\n") ## Create local BLAST database for Prodigal predicted genes sequences for X contig in sample - if not create_blastdb(contig_genes_path, full_path_blastdb_per_contig, 'nucl', logger): - print('Error when creating the blastdb for samples files. Check log file for more information. \n ') + if not create_blastdb( + contig_genes_path, full_path_blastdb_per_contig, "nucl", logger + ): + print( + "Error when creating the blastdb for samples files. Check log file for more information. \n " + ) return False ## Local BLAST Prodigal predicted genes sequences database VS BLAST sequence obtained in sample in allele calling analysis - blast_db_name = os.path.join(full_path_blastdb_per_contig, contig_blast_id, contig_blast_id) - - cline = NcbiblastnCommandline(db=blast_db_name, evalue=0.001, perc_identity = 90, outfmt= blast_parameters, max_target_seqs=10, max_hsps=10, num_threads=1) - out, err = cline(stdin = blast_sseq) + blast_db_name = os.path.join( + full_path_blastdb_per_contig, contig_blast_id, contig_blast_id + ) + + cline = NcbiblastnCommandline( + db=blast_db_name, + evalue=0.001, + perc_identity=90, + outfmt=blast_parameters, + max_target_seqs=10, + max_hsps=10, + num_threads=1, + ) + out, err = cline(stdin=blast_sseq) out_lines = out.splitlines() bigger_bitscore = 0 - if len (out_lines) > 0 : - for line in out_lines : - values = line.split('\t') - if float(values[8]) > bigger_bitscore: - qseqid , sseqid , pident , qlen , s_length , mismatch , r_gapopen , r_evalue , bitscore , sstart , send , qstart , qend ,sseq , qseq = values + if len(out_lines) > 0: + for line in out_lines: + values = line.split("\t") + if float(values[8]) > bigger_bitscore: + ( + qseqid, + sseqid, + pident, + qlen, + s_length, + mismatch, + r_gapopen, + r_evalue, + bitscore, + sstart, + send, + qstart, + qend, + sseq, + qseq, + ) = values bigger_bitscore = float(bitscore) ## Get complete Prodigal sequence matching allele calling BLAST sequence using ID @@ -272,41 +382,51 @@ def get_prodigal_sequence(blast_sseq, contig_blast_id, prodigal_directory, sampl for rec in predicted_genes_in_contig: if rec.id == sseqid: predicted_gene_sequence = str(rec.seq) - start_prodigal = str(rec.description.split( '#')[1]) - end_prodigal = str(rec.description.split('#')[2]) + start_prodigal = str(rec.description.split("#")[1]) + end_prodigal = str(rec.description.split("#")[2]) break ## Sequence not found by Prodigal when there are no BLAST results matching allele calling BLAST sequence - if len (out_lines) == 0: - predicted_gene_sequence = 'Sequence not found by Prodigal' - start_prodigal = '-' - end_prodigal = '-' + if len(out_lines) == 0: + predicted_gene_sequence = "Sequence not found by Prodigal" + start_prodigal = "-" + end_prodigal = "-" - return predicted_gene_sequence, start_prodigal, end_prodigal ### start_prodigal y end_prodigal para report prodigal + return ( + predicted_gene_sequence, + start_prodigal, + end_prodigal, + ) ### start_prodigal y end_prodigal para report prodigal # · * · * · * · * · * · * · * · * · * · * · * · * # # Get samples info before allele calling analysis # # · * · * · * · * · * · * · * · * · * · * · * · * # -def prepare_samples(sample_file_list, store_dir, reference_genome_file, logger): +def prepare_samples(sample_file_list, store_dir, reference_genome_file, logger): ## Initialize dictionary for keeping id-contig contigs_in_sample_dict = {} ## Paths for samples blastdb, Prodigal genes prediction and BLAST results - blast_dir = os.path.join(store_dir,'blastdb') - prodigal_dir = os.path.join(store_dir,'prodigal') - blast_results_seq_dir = os.path.join(store_dir,'blast_results', 'blast_results_seq') + blast_dir = os.path.join(store_dir, "blastdb") + prodigal_dir = os.path.join(store_dir, "prodigal") + blast_results_seq_dir = os.path.join( + store_dir, "blast_results", "blast_results_seq" + ) ## Get training file for Prodigal genes prediction - output_prodigal_train_dir = prodigal_training(reference_genome_file, prodigal_dir, logger) + output_prodigal_train_dir = prodigal_training( + reference_genome_file, prodigal_dir, logger + ) if not output_prodigal_train_dir: - print('Error when creating training file for genes prediction. Check log file for more information. \n ') + print( + "Error when creating training file for genes prediction. Check log file for more information. \n " + ) return False for fasta_file in sample_file_list: - f_name = '.'.join(os.path.basename(fasta_file).split('.')[:-1]) + f_name = ".".join(os.path.basename(fasta_file).split(".")[:-1]) # Get samples id-contig dictionary fasta_file_parsed_dict = parsing_fasta_file_to_dict(fasta_file, logger) @@ -315,8 +435,8 @@ def prepare_samples(sample_file_list, store_dir, reference_genome_file, logger): contigs_in_sample_dict[f_name] = fasta_file_parsed_dict # dump fasta file into pickle file - #with open (file_list[-1],'wb') as f: # generación de diccionarios de contigs para cada muestra - # pickle.dump(fasta_file_parsed_dict, f) + # with open (file_list[-1],'wb') as f: # generación de diccionarios de contigs para cada muestra + # pickle.dump(fasta_file_parsed_dict, f) # Create directory for storing BLAST results using reference allele(s) blast_results_seq_per_sample_dir = os.path.join(blast_results_seq_dir, f_name) @@ -324,35 +444,51 @@ def prepare_samples(sample_file_list, store_dir, reference_genome_file, logger): if not os.path.exists(blast_results_seq_per_sample_dir): try: os.makedirs(blast_results_seq_per_sample_dir) - logger.debug('Created blast results directory for sample %s', f_name) + logger.debug("Created blast results directory for sample %s", f_name) except: - logger.info('Cannot create blast results directory for sample %s', f_name) - print ('Error when creating the directory for blast results', blast_results_seq_per_sample_dir) + logger.info( + "Cannot create blast results directory for sample %s", f_name + ) + print( + "Error when creating the directory for blast results", + blast_results_seq_per_sample_dir, + ) exit(0) # Prodigal genes prediction for each sample - if not prodigal_prediction(fasta_file, prodigal_dir, output_prodigal_train_dir, logger): - print('Error when predicting genes for samples files. Check log file for more information. \n ') + if not prodigal_prediction( + fasta_file, prodigal_dir, output_prodigal_train_dir, logger + ): + print( + "Error when predicting genes for samples files. Check log file for more information. \n " + ) return False # Create local BLAST db for each sample fasta file - if not create_blastdb(fasta_file, blast_dir, 'nucl', logger): - print('Error when creating the blastdb for samples files. Check log file for more information. \n ') + if not create_blastdb(fasta_file, blast_dir, "nucl", logger): + print( + "Error when creating the blastdb for samples files. Check log file for more information. \n " + ) return False return contigs_in_sample_dict + # · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # # Get established length thresholds for allele tagging in allele calling analysis # # · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # -def length_thresholds(core_name, schema_statistics, percent): ### logger +def length_thresholds(core_name, schema_statistics, percent): ### logger locus_mean = int(schema_statistics[core_name][0]) if percent != "SD": - max_length_threshold = math.ceil(locus_mean + ((locus_mean * float(percent)) / 100)) - min_length_threshold = math.floor(locus_mean - ((locus_mean * float(percent)) / 100)) + max_length_threshold = math.ceil( + locus_mean + ((locus_mean * float(percent)) / 100) + ) + min_length_threshold = math.floor( + locus_mean - ((locus_mean * float(percent)) / 100) + ) else: percent = float(schema_statistics[core_name][1]) @@ -366,103 +502,156 @@ def length_thresholds(core_name, schema_statistics, percent): ### logger # Convert dna sequence to protein sequence # # · * · * · * · * · * · * · * · * · * · * · # -def convert_to_protein (sequence) : +def convert_to_protein(sequence): seq = Seq.Seq(sequence) protein = str(seq.translate()) return protein + # · * · * · * · * · * · * · * · * · * · * · * · * · * # # Get SNPs between BLAST sequence and matching allele # # · * · * · * · * · * · * · * · * · * · * · * · * · * # -def get_snp (sample, query) : - prot_annotation = {'S': 'polar' ,'T': 'polar' ,'Y': 'polar' ,'Q': 'polar' ,'N': 'polar' ,'C': 'polar' ,'S': 'polar' , - 'F': 'nonpolar' ,'L': 'nonpolar','I': 'nonpolar','M': 'nonpolar','P': 'nonpolar','V': 'nonpolar','A': 'nonpolar','W': 'nonpolar','G': 'nonpolar', - 'D' : 'acidic', 'E' :'acidic', - 'H': 'basic' , 'K': 'basic' , 'R' : 'basic', - '-': '-----', '*' : 'Stop codon'} +def get_snp(sample, query): + prot_annotation = { + "S": "polar", + "T": "polar", + "Y": "polar", + "Q": "polar", + "N": "polar", + "C": "polar", + "S": "polar", + "F": "nonpolar", + "L": "nonpolar", + "I": "nonpolar", + "M": "nonpolar", + "P": "nonpolar", + "V": "nonpolar", + "A": "nonpolar", + "W": "nonpolar", + "G": "nonpolar", + "D": "acidic", + "E": "acidic", + "H": "basic", + "K": "basic", + "R": "basic", + "-": "-----", + "*": "Stop codon", + } snp_list = [] - sample = sample.replace('-','') - #length = max(len(sample), len(query)) + sample = sample.replace("-", "") + # length = max(len(sample), len(query)) length = len(query) # normalize the length of the sample for the iteration - if len(sample) < length : + if len(sample) < length: need_to_add = length - len(sample) - sample = sample + need_to_add * '-' + sample = sample + need_to_add * "-" # convert to Seq class to translate to protein seq_sample = Seq.Seq(sample) seq_query = Seq.Seq(query) for index in range(length): - if seq_query[index] != seq_sample[index] : + if seq_query[index] != seq_sample[index]: triple_index = index - (index % 3) codon_seq = seq_sample[triple_index : triple_index + 3] codon_que = seq_query[triple_index : triple_index + 3] - if not '-' in str(codon_seq) : + if not "-" in str(codon_seq): prot_seq = str(codon_seq.translate()) prot_que = str(codon_que.translate()) else: - prot_seq = '-' - prot_que = str(seq_query[triple_index: ].translate()) - if prot_annotation[prot_que[0]] == prot_annotation[prot_seq[0]] : - missense_synonym = 'Synonymous' - elif prot_seq == '*' : - missense_synonym = 'Nonsense' + prot_seq = "-" + prot_que = str(seq_query[triple_index:].translate()) + if prot_annotation[prot_que[0]] == prot_annotation[prot_seq[0]]: + missense_synonym = "Synonymous" + elif prot_seq == "*": + missense_synonym = "Nonsense" else: - missense_synonym = 'Missense' - #snp_list.append([str(index+1),str(seq_sample[index]) + '/' + str(seq_query[index]), str(codon_seq) + '/'+ str(codon_que), - snp_list.append([str(index+1),str(seq_query[index]) + '/' + str(seq_sample[index]), str(codon_que) + '/'+ str(codon_seq), - # when one of the sequence ends but not the other we will translate the remain sequence to proteins - # in that case we will only annotate the first protein. Using [0] as key of the dictionary annotation - prot_que + '/' + prot_seq, missense_synonym, prot_annotation[prot_que[0]] + ' / ' + prot_annotation[prot_seq[0]]]) - if '-' in str(codon_seq) : + missense_synonym = "Missense" + # snp_list.append([str(index+1),str(seq_sample[index]) + '/' + str(seq_query[index]), str(codon_seq) + '/'+ str(codon_que), + snp_list.append( + [ + str(index + 1), + str(seq_query[index]) + "/" + str(seq_sample[index]), + str(codon_que) + "/" + str(codon_seq), + # when one of the sequence ends but not the other we will translate the remain sequence to proteins + # in that case we will only annotate the first protein. Using [0] as key of the dictionary annotation + prot_que + "/" + prot_seq, + missense_synonym, + prot_annotation[prot_que[0]] + " / " + prot_annotation[prot_seq[0]], + ] + ) + if "-" in str(codon_seq): break return snp_list -def nucleotide_to_protein_alignment (sample_seq, query_seq ) : ### Sustituido por get_alignment +def nucleotide_to_protein_alignment( + sample_seq, query_seq +): ### Sustituido por get_alignment aligment = [] sample_prot = convert_to_protein(sample_seq) query_prot = convert_to_protein(query_seq) minimun_length = min(len(sample_prot), len(query_prot)) for i in range(minimun_length): - if sample_prot[i] == query_prot[i] : - aligment.append('|') + if sample_prot[i] == query_prot[i]: + aligment.append("|") else: - aligment.append(' ') - protein_alignment = [['sample', sample_prot],['match', ''.join(aligment)], ['schema', query_prot]] + aligment.append(" ") + protein_alignment = [ + ["sample", sample_prot], + ["match", "".join(aligment)], + ["schema", query_prot], + ] return protein_alignment -def get_alignment_for_indels (blast_db_name, qseq) : ### Sustituido por get_alignment - #match_alignment =[] - cline = NcbiblastnCommandline(db=blast_db_name, evalue=0.001, perc_identity = 80, outfmt= 5, max_target_seqs=10, max_hsps=10,num_threads=1) - out, err = cline(stdin = qseq) +def get_alignment_for_indels(blast_db_name, qseq): ### Sustituido por get_alignment + # match_alignment =[] + cline = NcbiblastnCommandline( + db=blast_db_name, + evalue=0.001, + perc_identity=80, + outfmt=5, + max_target_seqs=10, + max_hsps=10, + num_threads=1, + ) + out, err = cline(stdin=qseq) psiblast_xml = StringIO(out) blast_records = NCBIXML.parse(psiblast_xml) for blast_record in blast_records: for alignment in blast_record.alignments: for match in alignment.hsps: - match_alignment = [['sample', match.sbjct],['match', match.match], ['schema',match.query]] + match_alignment = [ + ["sample", match.sbjct], + ["match", match.match], + ["schema", match.query], + ] return match_alignment -def get_alignment_for_deletions (sample_seq, query_seq): ### Sustituido por get_alignment +def get_alignment_for_deletions( + sample_seq, query_seq +): ### Sustituido por get_alignment index_found = False alignments = pairwise2.align.globalxx(sample_seq, query_seq) - for index in range(len(alignments)) : - if alignments[index][4] == len(query_seq) : + for index in range(len(alignments)): + if alignments[index][4] == len(query_seq): index_found = True break - if not index_found : + if not index_found: index = 0 - values = format_alignment(*alignments[index]).split('\n') - match_alignment = [['sample', values[0]],['match', values[1]], ['schema',values[2]]] + values = format_alignment(*alignments[index]).split("\n") + match_alignment = [ + ["sample", values[0]], + ["match", values[1]], + ["schema", values[2]], + ] return match_alignment @@ -470,8 +659,10 @@ def get_alignment_for_deletions (sample_seq, query_seq): ### Sustituido por get_ # Get DNA and protein alignment between the final sequence found in the sample and the matching allele # # · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # -def get_alignment (sample_seq, query_seq, reward, penalty, gapopen, gapextend, seq_type = "dna"): +def get_alignment( + sample_seq, query_seq, reward, penalty, gapopen, gapextend, seq_type="dna" +): ## If sequences alignment type desired is "protein" convert dna sequences to protein if seq_type == "protein": sample_seq = convert_to_protein(sample_seq) @@ -479,9 +670,15 @@ def get_alignment (sample_seq, query_seq, reward, penalty, gapopen, gapextend, s ## Get dna/protein alignment between final sequence found and matching allele # arguments pairwise2.align.globalms: match, mismatch, gap opening, gap extending - alignments = pairwise2.align.localms(sample_seq, query_seq, reward, penalty, -gapopen, -gapextend) - values = format_alignment(*alignments[0]).split('\n') - match_alignment = [['sample', values[0]],['match', values[1]], ['schema',values[2]]] + alignments = pairwise2.align.localms( + sample_seq, query_seq, reward, penalty, -gapopen, -gapextend + ) + values = format_alignment(*alignments[0]).split("\n") + match_alignment = [ + ["sample", values[0]], + ["match", values[1]], + ["schema", values[2]], + ] return match_alignment @@ -490,102 +687,135 @@ def get_alignment (sample_seq, query_seq, reward, penalty, gapopen, gapextend, s # Tag LNF cases and keep LNF info # # · * · * · * · * · * · * · * · * # -def lnf_tpr_tag(core_name, sample_name, alleles_in_locus_dict, samples_matrix_dict, lnf_tpr_dict, schema_statistics, locus_alleles_path, qseqid, pident, s_length, new_sequence_length, perc_identity_ref, coverage, schema_quality, annotation_core_dict, count_dict, logger): +def lnf_tpr_tag( + core_name, + sample_name, + alleles_in_locus_dict, + samples_matrix_dict, + lnf_tpr_dict, + schema_statistics, + locus_alleles_path, + qseqid, + pident, + s_length, + new_sequence_length, + perc_identity_ref, + coverage, + schema_quality, + annotation_core_dict, + count_dict, + logger, +): gene_annot, product_annot = annotation_core_dict[core_name] - if qseqid == '-': - samples_matrix_dict[sample_name].append('LNF') - tag_report = 'LNF' - matching_allele_length = '-' + if qseqid == "-": + samples_matrix_dict[sample_name].append("LNF") + tag_report = "LNF" + matching_allele_length = "-" else: - if new_sequence_length == '-': - samples_matrix_dict[sample_name].append('LNF_' + str(qseqid)) - tag_report = 'LNF' + if new_sequence_length == "-": + samples_matrix_dict[sample_name].append("LNF_" + str(qseqid)) + tag_report = "LNF" else: - samples_matrix_dict[sample_name].append('TPR_' + str(qseqid)) - tag_report = 'TPR' + samples_matrix_dict[sample_name].append("TPR_" + str(qseqid)) + tag_report = "TPR" matching_allele_seq = alleles_in_locus_dict[core_name][qseqid] matching_allele_length = len(matching_allele_seq) - #alleles_in_locus = list (SeqIO.parse(locus_alleles_path, "fasta")) ## parse - #for allele in alleles_in_locus : ## parse - #if allele.id == qseqid : ## parse - #break ## parse - #matching_allele_seq = str(allele.seq) ## parse - #matching_allele_length = len(matching_allele_seq) ## parse + # alleles_in_locus = list (SeqIO.parse(locus_alleles_path, "fasta")) ## parse + # for allele in alleles_in_locus : ## parse + # if allele.id == qseqid : ## parse + # break ## parse + # matching_allele_seq = str(allele.seq) ## parse + # matching_allele_length = len(matching_allele_seq) ## parse - if pident == '-': + if pident == "-": # (los dos BLAST sin resultado) - coverage_blast = '-' - coverage_new_sequence = '-' - add_info = 'Locus not found' - logger.info('Locus not found at sample %s, for gene %s', sample_name, core_name) + coverage_blast = "-" + coverage_new_sequence = "-" + add_info = "Locus not found" + logger.info("Locus not found at sample %s, for gene %s", sample_name, core_name) # Get allele quality - allele_quality = '-' + allele_quality = "-" # (recuento tags para plot) - count_dict[sample_name]['not_found'] += 1 - count_dict[sample_name]['total'] += 1 + count_dict[sample_name]["not_found"] += 1 + count_dict[sample_name]["total"] += 1 elif 90 > float(pident): # (BLAST 90 sin resultado y BLAST 70 con resultado) - coverage_blast = '-' - coverage_new_sequence = '-' - add_info = 'BLAST sequence ID under threshold: {}%'.format(perc_identity_ref) - logger.info('BLAST sequence ID %s under threshold at sample %s, for gene %s', pident, sample_name, core_name) + coverage_blast = "-" + coverage_new_sequence = "-" + add_info = "BLAST sequence ID under threshold: {}%".format(perc_identity_ref) + logger.info( + "BLAST sequence ID %s under threshold at sample %s, for gene %s", + pident, + sample_name, + core_name, + ) # Get allele quality - allele_quality = '-' + allele_quality = "-" # (recuento tags para plot) - count_dict[sample_name]['low_id'] += 1 - count_dict[sample_name]['total'] += 1 + count_dict[sample_name]["low_id"] += 1 + count_dict[sample_name]["total"] += 1 - elif 90 <= float(pident) and new_sequence_length == '-': + elif 90 <= float(pident) and new_sequence_length == "-": # (BLAST 90 con resultado, bajo coverage BLAST) locus_mean = int(schema_statistics[core_name][0]) coverage_blast = int(s_length) / locus_mean - #coverage_blast = int(s_length) / matching_allele_length - coverage_new_sequence = '-' + # coverage_blast = int(s_length) / matching_allele_length + coverage_new_sequence = "-" if coverage_blast < 1: - add_info = 'BLAST sequence coverage under threshold: {}%'.format(coverage) + add_info = "BLAST sequence coverage under threshold: {}%".format(coverage) else: - add_info = 'BLAST sequence coverage above threshold: {}%'.format(coverage) - logger.info('BLAST sequence coverage %s under threshold at sample %s, for gene %s', coverage_blast, sample_name, core_name) + add_info = "BLAST sequence coverage above threshold: {}%".format(coverage) + logger.info( + "BLAST sequence coverage %s under threshold at sample %s, for gene %s", + coverage_blast, + sample_name, + core_name, + ) # Get allele quality - allele_quality = '-' + allele_quality = "-" # (recuento tags para plot) - count_dict[sample_name]['low_coverage'] += 1 - count_dict[sample_name]['total'] += 1 + count_dict[sample_name]["low_coverage"] += 1 + count_dict[sample_name]["total"] += 1 - elif 90 <= float(pident) and new_sequence_length != '-': + elif 90 <= float(pident) and new_sequence_length != "-": # (BLAST 90 con resultado, buen coverage BLAST, bajo coverage new_sseq) locus_mean = int(schema_statistics[core_name][0]) coverage_blast = int(s_length) / locus_mean * 100 - #coverage_blast = int(s_length) / matching_allele_length + # coverage_blast = int(s_length) / matching_allele_length coverage_new_sequence = new_sequence_length / matching_allele_length * 100 if coverage_new_sequence < 1: - add_info = 'New sequence coverage under threshold: {}%'.format(coverage) + add_info = "New sequence coverage under threshold: {}%".format(coverage) else: - add_info = 'New sequence coverage above threshold: {}%'.format(coverage) - logger.info('New sequence coverage %s under threshold at sample %s, for gene %s', coverage_new_sequence, sample_name, core_name) + add_info = "New sequence coverage above threshold: {}%".format(coverage) + logger.info( + "New sequence coverage %s under threshold at sample %s, for gene %s", + coverage_new_sequence, + sample_name, + core_name, + ) # Get allele quality allele_quality = schema_quality[core_name][qseqid] # (recuento tags para plot) - count_dict[sample_name]['total'] += 1 + count_dict[sample_name]["total"] += 1 for count_class in count_dict[sample_name]: if count_class in allele_quality: count_dict[sample_name][count_class] += 1 - #if "bad_quality" in allele_quality: - # count_dict[sample_name]['bad_quality'] += 1 + # if "bad_quality" in allele_quality: + # count_dict[sample_name]['bad_quality'] += 1 ## Keeping LNF and TPR report info if not core_name in lnf_tpr_dict: @@ -593,7 +823,22 @@ def lnf_tpr_tag(core_name, sample_name, alleles_in_locus_dict, samples_matrix_di if not sample_name in lnf_tpr_dict[core_name]: lnf_tpr_dict[core_name][sample_name] = [] - lnf_tpr_dict[core_name][sample_name].append([gene_annot, product_annot, tag_report, qseqid, allele_quality, pident, str(coverage_blast), str(coverage_new_sequence), str(matching_allele_length), str(s_length), str(new_sequence_length), add_info]) ### Meter secuencias alelo, blast y new_sseq (si las hay)? + lnf_tpr_dict[core_name][sample_name].append( + [ + gene_annot, + product_annot, + tag_report, + qseqid, + allele_quality, + pident, + str(coverage_blast), + str(coverage_new_sequence), + str(matching_allele_length), + str(s_length), + str(new_sequence_length), + add_info, + ] + ) ### Meter secuencias alelo, blast y new_sseq (si las hay)? return True @@ -602,20 +847,37 @@ def lnf_tpr_tag(core_name, sample_name, alleles_in_locus_dict, samples_matrix_di # Tag paralog and exact match cases and keep info # # · * · * · * · * · * · * · * · * · * · * · * · * # -def paralog_exact_tag(sample_name, core_name, tag, schema_quality, matching_genes_dict, samples_matrix_dict, allele_found, tag_dict, prodigal_report, prodigal_directory, blast_parameters, annotation_core_dict, count_dict, logger): - logger.info('Found %s at sample %s for core gene %s ', tag, sample_name, core_name) - - paralog_quality_count = [] # (lista para contabilizar parálogos debido a bad o good quality) +def paralog_exact_tag( + sample_name, + core_name, + tag, + schema_quality, + matching_genes_dict, + samples_matrix_dict, + allele_found, + tag_dict, + prodigal_report, + prodigal_directory, + blast_parameters, + annotation_core_dict, + count_dict, + logger, +): + logger.info("Found %s at sample %s for core gene %s ", tag, sample_name, core_name) + + paralog_quality_count = ( + [] + ) # (lista para contabilizar parálogos debido a bad o good quality) gene_annot, product_annot = annotation_core_dict[core_name] - if not sample_name in tag_dict : + if not sample_name in tag_dict: tag_dict[sample_name] = {} - if not core_name in tag_dict[sample_name] : - tag_dict[sample_name][core_name]= [] + if not core_name in tag_dict[sample_name]: + tag_dict[sample_name][core_name] = [] - if tag == 'EXACT': + if tag == "EXACT": allele = list(allele_found.keys())[0] qseqid = allele_found[allele][0] tag = qseqid @@ -623,8 +885,24 @@ def paralog_exact_tag(sample_name, core_name, tag, schema_quality, matching_gene samples_matrix_dict[sample_name].append(tag) for sequence in allele_found: - qseqid, sseqid, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart, send, qstart, qend, sseq, qseq = allele_found[sequence] - sseq = sseq.replace('-', '') + ( + qseqid, + sseqid, + pident, + qlen, + s_length, + mismatch, + r_gapopen, + r_evalue, + bitscore, + sstart, + send, + qstart, + qend, + sseq, + qseq, + ) = allele_found[sequence] + sseq = sseq.replace("-", "") # Get allele quality allele_quality = schema_quality[core_name][qseqid] @@ -633,35 +911,87 @@ def paralog_exact_tag(sample_name, core_name, tag, schema_quality, matching_gene paralog_quality_count.append(allele_quality) # Get prodigal gene prediction if allele quality is 'bad_quality' - if 'bad_quality' in allele_quality: - complete_predicted_seq, start_prodigal, end_prodigal = get_prodigal_sequence(sseq, sseqid, prodigal_directory, sample_name, blast_parameters, logger) + if "bad_quality" in allele_quality: + ( + complete_predicted_seq, + start_prodigal, + end_prodigal, + ) = get_prodigal_sequence( + sseq, sseqid, prodigal_directory, sample_name, blast_parameters, logger + ) ##### informe prodigal ##### - prodigal_report.append([core_name, sample_name, qseqid, tag, sstart, send, start_prodigal, end_prodigal, sseq, complete_predicted_seq]) + prodigal_report.append( + [ + core_name, + sample_name, + qseqid, + tag, + sstart, + send, + start_prodigal, + end_prodigal, + sseq, + complete_predicted_seq, + ] + ) else: - complete_predicted_seq = '-' + complete_predicted_seq = "-" - if not sseqid in matching_genes_dict[sample_name] : + if not sseqid in matching_genes_dict[sample_name]: matching_genes_dict[sample_name][sseqid] = [] - if sstart > send : - #matching_genes_dict[sample_name][sseqid].append([core_name, sstart, send,'-', tag]) - matching_genes_dict[sample_name][sseqid].append([core_name, qseqid, sstart, send,'-', tag]) + if sstart > send: + # matching_genes_dict[sample_name][sseqid].append([core_name, sstart, send,'-', tag]) + matching_genes_dict[sample_name][sseqid].append( + [core_name, qseqid, sstart, send, "-", tag] + ) else: - #matching_genes_dict[sample_name][sseqid].append([core_name, sstart, send,'+', tag]) - matching_genes_dict[sample_name][sseqid].append([core_name, qseqid, sstart, send,'+', tag]) + # matching_genes_dict[sample_name][sseqid].append([core_name, sstart, send,'+', tag]) + matching_genes_dict[sample_name][sseqid].append( + [core_name, qseqid, sstart, send, "+", tag] + ) ## Keeping paralog NIPH/NIPHEM report info - if tag == 'NIPH' or tag == 'NIPHEM': - tag_dict[sample_name][core_name].append([gene_annot, product_annot, tag, pident, qseqid, allele_quality, sseqid, bitscore, sstart, send, sseq, complete_predicted_seq]) + if tag == "NIPH" or tag == "NIPHEM": + tag_dict[sample_name][core_name].append( + [ + gene_annot, + product_annot, + tag, + pident, + qseqid, + allele_quality, + sseqid, + bitscore, + sstart, + send, + sseq, + complete_predicted_seq, + ] + ) else: - tag_dict[sample_name][core_name] = [gene_annot, product_annot, qseqid, allele_quality, sseqid, s_length, sstart, send, sseq, complete_predicted_seq] + tag_dict[sample_name][core_name] = [ + gene_annot, + product_annot, + qseqid, + allele_quality, + sseqid, + s_length, + sstart, + send, + sseq, + complete_predicted_seq, + ] # (recuento tags para plot) - count_dict[sample_name]['total'] += 1 + count_dict[sample_name]["total"] += 1 for count_class in count_dict[sample_name]: if count_class in allele_quality: - if "no_start_stop" not in count_class and "no_start_stop" in allele_quality: + if ( + "no_start_stop" not in count_class + and "no_start_stop" in allele_quality + ): if count_class == "bad_quality": count_dict[sample_name][count_class] += 1 else: @@ -673,10 +1003,13 @@ def paralog_exact_tag(sample_name, core_name, tag, schema_quality, matching_gene for paralog_quality in paralog_quality_count: count += 1 if "bad_quality" in paralog_quality: - count_dict[sample_name]['total'] += 1 + count_dict[sample_name]["total"] += 1 for count_class in count_dict[sample_name]: if count_class in paralog_quality: - if "no_start_stop" not in count_class and "no_start_stop" in paralog_quality: + if ( + "no_start_stop" not in count_class + and "no_start_stop" in paralog_quality + ): if count_class == "bad_quality": count_dict[sample_name][count_class] += 1 else: @@ -687,8 +1020,8 @@ def paralog_exact_tag(sample_name, core_name, tag, schema_quality, matching_gene else: if count == len(paralog_quality_count): - count_dict[sample_name]['total'] += 1 - count_dict[sample_name]['good_quality'] += 1 + count_dict[sample_name]["total"] += 1 + count_dict[sample_name]["good_quality"] += 1 return True @@ -697,97 +1030,225 @@ def paralog_exact_tag(sample_name, core_name, tag, schema_quality, matching_gene # Tag INF/ASM/ALM/PLOT cases and keep info # # · * · * · * · * · * · * · * · * · * · * # -def inf_asm_alm_tag(core_name, sample_name, tag, blast_values, allele_quality, new_sseq, matching_allele_length, tag_dict, list_tag, samples_matrix_dict, matching_genes_dict, prodigal_report, start_prodigal, end_prodigal, complete_predicted_seq, annotation_core_dict, count_dict, logger): +def inf_asm_alm_tag( + core_name, + sample_name, + tag, + blast_values, + allele_quality, + new_sseq, + matching_allele_length, + tag_dict, + list_tag, + samples_matrix_dict, + matching_genes_dict, + prodigal_report, + start_prodigal, + end_prodigal, + complete_predicted_seq, + annotation_core_dict, + count_dict, + logger, +): gene_annot, product_annot = annotation_core_dict[core_name] - qseqid, sseqid, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart, send, qstart, qend, sseq, qseq = blast_values - - sseq = sseq.replace('-', '') + ( + qseqid, + sseqid, + pident, + qlen, + s_length, + mismatch, + r_gapopen, + r_evalue, + bitscore, + sstart, + send, + qstart, + qend, + sseq, + qseq, + ) = blast_values + + sseq = sseq.replace("-", "") s_length = len(sseq) new_sequence_length = len(new_sseq) - logger.info('Found %s at sample %s for core gene %s ', tag, sample_name, core_name) + logger.info("Found %s at sample %s for core gene %s ", tag, sample_name, core_name) - if tag == 'PLOT': - tag_allele = tag + '_' + str(qseqid) + if tag == "PLOT": + tag_allele = tag + "_" + str(qseqid) else: # Adding ASM/ALM/INF allele to the allele_matrix if it is not already include if not core_name in tag_dict: tag_dict[core_name] = [] - if not new_sseq in tag_dict[core_name] : + if not new_sseq in tag_dict[core_name]: tag_dict[core_name].append(new_sseq) # Find the index of ASM/ALM/INF to include it in the sample matrix dict index_tag = tag_dict[core_name].index(new_sseq) - tag_allele = tag + '_' + core_name + '_' + str(qseqid) + '_' + str(index_tag) + tag_allele = tag + "_" + core_name + "_" + str(qseqid) + "_" + str(index_tag) samples_matrix_dict[sample_name].append(tag_allele) # Keeping INF/ASM/ALM/PLOT report info - if not core_name in list_tag : + if not core_name in list_tag: list_tag[core_name] = {} - if not sample_name in list_tag[core_name] : + if not sample_name in list_tag[core_name]: list_tag[core_name][sample_name] = {} - if tag == 'INF': - list_tag[core_name][sample_name][tag_allele] = [gene_annot, product_annot, qseqid, allele_quality, sseqid, bitscore, str(matching_allele_length), str(s_length), str(new_sequence_length), mismatch , r_gapopen, sstart, send, new_sseq, complete_predicted_seq] + if tag == "INF": + list_tag[core_name][sample_name][tag_allele] = [ + gene_annot, + product_annot, + qseqid, + allele_quality, + sseqid, + bitscore, + str(matching_allele_length), + str(s_length), + str(new_sequence_length), + mismatch, + r_gapopen, + sstart, + send, + new_sseq, + complete_predicted_seq, + ] # (recuento tags para plots) - count_dict[sample_name]['total'] += 1 + count_dict[sample_name]["total"] += 1 for count_class in count_dict[sample_name]: if count_class in allele_quality: count_dict[sample_name][count_class] += 1 - #if "bad_quality" in allele_quality: - # count_dict[sample_name]['bad_quality'] += 1 - - elif tag == 'PLOT': - list_tag[core_name][sample_name] = [gene_annot, product_annot, qseqid, allele_quality, sseqid, bitscore, sstart, send, sseq, new_sseq] + # if "bad_quality" in allele_quality: + # count_dict[sample_name]['bad_quality'] += 1 + + elif tag == "PLOT": + list_tag[core_name][sample_name] = [ + gene_annot, + product_annot, + qseqid, + allele_quality, + sseqid, + bitscore, + sstart, + send, + sseq, + new_sseq, + ] # (recuento tags para plots) - count_dict[sample_name]['total'] += 1 + count_dict[sample_name]["total"] += 1 - else : - if tag == 'ASM': - newsseq_vs_blastseq = 'shorter' - elif tag == 'ALM': - newsseq_vs_blastseq = 'longer' + else: + if tag == "ASM": + newsseq_vs_blastseq = "shorter" + elif tag == "ALM": + newsseq_vs_blastseq = "longer" if len(sseq) < matching_allele_length: - add_info = 'Global effect: DELETION. BLAST sequence length shorter than matching allele sequence length / Net result: ' + tag + '. Final gene sequence length ' + newsseq_vs_blastseq + ' than matching allele sequence length' + add_info = ( + "Global effect: DELETION. BLAST sequence length shorter than matching allele sequence length / Net result: " + + tag + + ". Final gene sequence length " + + newsseq_vs_blastseq + + " than matching allele sequence length" + ) elif len(sseq) == matching_allele_length: - add_info = 'Global effect: BASE SUBSTITUTION. BLAST sequence length equal to matching allele sequence length / Net result: ' + tag + '. Final gene sequence length ' + newsseq_vs_blastseq + ' than matching allele sequence length' + add_info = ( + "Global effect: BASE SUBSTITUTION. BLAST sequence length equal to matching allele sequence length / Net result: " + + tag + + ". Final gene sequence length " + + newsseq_vs_blastseq + + " than matching allele sequence length" + ) elif len(sseq) > matching_allele_length: - add_info = 'Global effect: INSERTION. BLAST sequence length longer than matching allele sequence length / Net result: ' + tag + '. Final gene sequence length ' + newsseq_vs_blastseq + ' than matching allele sequence length' - - list_tag[core_name][sample_name][tag_allele] = [gene_annot, product_annot, qseqid, allele_quality, sseqid, bitscore, str(matching_allele_length), str(s_length), str(new_sequence_length), mismatch , r_gapopen, sstart, send, new_sseq, add_info, complete_predicted_seq] + add_info = ( + "Global effect: INSERTION. BLAST sequence length longer than matching allele sequence length / Net result: " + + tag + + ". Final gene sequence length " + + newsseq_vs_blastseq + + " than matching allele sequence length" + ) + + list_tag[core_name][sample_name][tag_allele] = [ + gene_annot, + product_annot, + qseqid, + allele_quality, + sseqid, + bitscore, + str(matching_allele_length), + str(s_length), + str(new_sequence_length), + mismatch, + r_gapopen, + sstart, + send, + new_sseq, + add_info, + complete_predicted_seq, + ] # (recuento tags para plots) - if tag == 'ASM': - count_dict[sample_name]['total'] += 1 + if tag == "ASM": + count_dict[sample_name]["total"] += 1 for mut_type in count_dict[sample_name]: if mut_type in add_info.lower(): count_dict[sample_name][mut_type] += 1 - elif tag == 'ALM': - count_dict[sample_name]['total'] += 1 + elif tag == "ALM": + count_dict[sample_name]["total"] += 1 for mut_type in count_dict[sample_name]: if mut_type in add_info.lower(): count_dict[sample_name][mut_type] += 1 - if not sseqid in matching_genes_dict[sample_name] : + if not sseqid in matching_genes_dict[sample_name]: matching_genes_dict[sample_name][sseqid] = [] - if sstart > send : - #matching_genes_dict[sample_name][sseqid].append([core_name, str(int(sstart)-new_sequence_length -1), sstart,'-', tag_allele]) - matching_genes_dict[sample_name][sseqid].append([core_name, qseqid, str(int(sstart)-new_sequence_length -1), sstart,'-', tag_allele]) + if sstart > send: + # matching_genes_dict[sample_name][sseqid].append([core_name, str(int(sstart)-new_sequence_length -1), sstart,'-', tag_allele]) + matching_genes_dict[sample_name][sseqid].append( + [ + core_name, + qseqid, + str(int(sstart) - new_sequence_length - 1), + sstart, + "-", + tag_allele, + ] + ) else: - #matching_genes_dict[sample_name][sseqid].append([core_name, sstart, str(int(sstart)+ new_sequence_length),'+', tag_allele]) - matching_genes_dict[sample_name][sseqid].append([core_name, qseqid, sstart, str(int(sstart)+ new_sequence_length),'+', tag_allele]) + # matching_genes_dict[sample_name][sseqid].append([core_name, sstart, str(int(sstart)+ new_sequence_length),'+', tag_allele]) + matching_genes_dict[sample_name][sseqid].append( + [ + core_name, + qseqid, + sstart, + str(int(sstart) + new_sequence_length), + "+", + tag_allele, + ] + ) ##### informe prodigal ##### - prodigal_report.append([core_name, sample_name, qseqid, tag_allele, sstart, send, start_prodigal, end_prodigal, sseq, complete_predicted_seq]) + prodigal_report.append( + [ + core_name, + sample_name, + qseqid, + tag_allele, + sstart, + send, + start_prodigal, + end_prodigal, + sseq, + complete_predicted_seq, + ] + ) return True @@ -796,24 +1257,41 @@ def inf_asm_alm_tag(core_name, sample_name, tag, blast_values, allele_quality, n # Keep best results info after BLAST using results from previous reference allele BLAST as database VS ALL alleles in locus as query in allele calling analysis # # · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # -def get_blast_results (sample_name, values, contigs_in_sample_dict, allele_found, logger) : - qseqid, sseqid, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart, send, qstart, qend, sseq, qseq = values +def get_blast_results( + sample_name, values, contigs_in_sample_dict, allele_found, logger +): + ( + qseqid, + sseqid, + pident, + qlen, + s_length, + mismatch, + r_gapopen, + r_evalue, + bitscore, + sstart, + send, + qstart, + qend, + sseq, + qseq, + ) = values ## Get contig ID and BLAST sequence - sseqid_blast = "_".join(sseqid.split('_')[1:]) - sseq_no_gaps = sseq.replace('-', '') - + sseqid_blast = "_".join(sseqid.split("_")[1:]) + sseq_no_gaps = sseq.replace("-", "") ## Get start and end positions in contig searching for BLAST sequence index in contig sequence # Get contig sequence accession_sequence = contigs_in_sample_dict[sample_name][sseqid_blast] - #for record in sample_contigs: ## parse - #if record.id == sseqid_blast : ## parse - #break ## parse - #accession_sequence = str(record.seq) ## parse + # for record in sample_contigs: ## parse + # if record.id == sseqid_blast : ## parse + # break ## parse + # accession_sequence = str(record.seq) ## parse # Try to get BLAST sequence index in contig. If index -> error because different contig sequence and BLAST sequence # direction, obtain reverse complement BLAST sequence and try again. @@ -834,26 +1312,55 @@ def get_blast_results (sample_name, values, contigs_in_sample_dict, allele_found sstart_new = str(max(sseq_index_1, sseq_index_2)) send_new = str(min(sseq_index_1, sseq_index_2)) - ## Keep BLAST results info discarding subsets allele_is_subset = False - if len(allele_found) > 0 : - for allele_id in allele_found : - min_index = min(int(allele_found[allele_id][9]), int(allele_found[allele_id][10])) - max_index = max(int(allele_found[allele_id][9]), int(allele_found[allele_id][10])) - if int(sstart_new) in range(min_index, max_index + 1) or int(send_new) in range(min_index, max_index + 1): # if both genome locations overlap - if sseqid_blast == allele_found[allele_id][1]: # if both sequences are in the same contig - logger.info('Found allele %s that starts or ends at the same position as %s ' , qseqid, allele_id) + if len(allele_found) > 0: + for allele_id in allele_found: + min_index = min( + int(allele_found[allele_id][9]), int(allele_found[allele_id][10]) + ) + max_index = max( + int(allele_found[allele_id][9]), int(allele_found[allele_id][10]) + ) + if int(sstart_new) in range(min_index, max_index + 1) or int( + send_new + ) in range( + min_index, max_index + 1 + ): # if both genome locations overlap + if ( + sseqid_blast == allele_found[allele_id][1] + ): # if both sequences are in the same contig + logger.info( + "Found allele %s that starts or ends at the same position as %s ", + qseqid, + allele_id, + ) allele_is_subset = True break - if len(allele_found) == 0 or not allele_is_subset : - contig_id_start = str(sseqid_blast + '_'+ sstart_new) + if len(allele_found) == 0 or not allele_is_subset: + contig_id_start = str(sseqid_blast + "_" + sstart_new) # Skip the allele found in the 100% identity and 100% alignment if not contig_id_start in allele_found: - allele_found[contig_id_start] = [qseqid, sseqid_blast, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart_new, send_new, '-', '-', sseq, qseq] + allele_found[contig_id_start] = [ + qseqid, + sseqid_blast, + pident, + qlen, + s_length, + mismatch, + r_gapopen, + r_evalue, + bitscore, + sstart_new, + send_new, + "-", + "-", + sseq, + qseq, + ] return True @@ -862,35 +1369,55 @@ def get_blast_results (sample_name, values, contigs_in_sample_dict, allele_found # Get SNPs and ADN and protein alignment # # · * · * · * · * · * · * · * · * · * · # -def keep_snp_alignment_info(sseq, new_sseq, matching_allele_seq, qseqid, query_direction, core_name, sample_name, reward, penalty, gapopen, gapextend, snp_dict, match_alignment_dict, protein_dict, logger): +def keep_snp_alignment_info( + sseq, + new_sseq, + matching_allele_seq, + qseqid, + query_direction, + core_name, + sample_name, + reward, + penalty, + gapopen, + gapextend, + snp_dict, + match_alignment_dict, + protein_dict, + logger, +): ## Check allele sequence direction - if query_direction == 'reverse': + if query_direction == "reverse": matching_allele_seq = str(Seq.Seq(matching_allele_seq).reverse_complement()) else: matching_allele_seq = str(matching_allele_seq) ## Get the SNP information snp_information = get_snp(sseq, matching_allele_seq) - if len(snp_information) > 0 : - if not core_name in snp_dict : + if len(snp_information) > 0: + if not core_name in snp_dict: snp_dict[core_name] = {} - if not sample_name in snp_dict[core_name] : + if not sample_name in snp_dict[core_name]: snp_dict[core_name][sample_name] = {} - snp_dict[core_name][sample_name][qseqid]= snp_information + snp_dict[core_name][sample_name][qseqid] = snp_information ## Get new sequence-allele sequence dna alignment - if not core_name in match_alignment_dict : + if not core_name in match_alignment_dict: match_alignment_dict[core_name] = {} - if not sample_name in match_alignment_dict[core_name] : - match_alignment_dict[core_name][sample_name] = get_alignment (new_sseq, matching_allele_seq, reward, penalty, gapopen, gapextend) + if not sample_name in match_alignment_dict[core_name]: + match_alignment_dict[core_name][sample_name] = get_alignment( + new_sseq, matching_allele_seq, reward, penalty, gapopen, gapextend + ) ## Get new sequence-allele sequence protein alignment - if not core_name in protein_dict : + if not core_name in protein_dict: protein_dict[core_name] = {} - if not sample_name in protein_dict[core_name] : + if not sample_name in protein_dict[core_name]: protein_dict[core_name][sample_name] = [] - protein_dict[core_name][sample_name] = get_alignment (new_sseq, matching_allele_seq, reward, penalty, gapopen, gapextend, "protein") + protein_dict[core_name][sample_name] = get_alignment( + new_sseq, matching_allele_seq, reward, penalty, gapopen, gapextend, "protein" + ) return True @@ -899,52 +1426,81 @@ def keep_snp_alignment_info(sseq, new_sseq, matching_allele_seq, qseqid, query_d # Create allele tag summary for each sample # # · * · * · * · * · * · * · * · * · * · * · # -def create_summary (samples_matrix_dict, logger) : +def create_summary(samples_matrix_dict, logger): summary_dict = {} summary_result_list = [] - summary_heading_list = ['Exact match', 'INF', 'ASM', 'ALM', 'LNF', 'TPR', 'NIPH', 'NIPHEM', 'PLOT', 'ERROR'] - summary_result_list.append('File\t' + '\t'.join(summary_heading_list)) - for key in sorted (samples_matrix_dict) : - - summary_dict[key] = {'Exact match':0, 'INF':0, 'ASM':0, 'ALM':0, 'LNF':0, 'TPR':0,'NIPH':0, 'NIPHEM':0, 'PLOT':0, 'ERROR':0} - for values in samples_matrix_dict[key] : - if 'INF_' in values : - summary_dict[key]['INF'] += 1 - elif 'ASM_' in values : - summary_dict[key]['ASM'] += 1 - elif 'ALM_' in values : - summary_dict[key]['ALM'] += 1 - elif 'LNF' in values : - summary_dict[key]['LNF'] += 1 - elif 'TPR' in values : - summary_dict[key]['TPR'] += 1 - elif 'NIPH' == values : - summary_dict[key]['NIPH'] += 1 - elif 'NIPHEM' == values : - summary_dict[key]['NIPHEM'] += 1 - elif 'PLOT' in values : - summary_dict[key]['PLOT'] += 1 - elif 'ERROR' in values : - summary_dict[key]['ERROR'] += 1 + summary_heading_list = [ + "Exact match", + "INF", + "ASM", + "ALM", + "LNF", + "TPR", + "NIPH", + "NIPHEM", + "PLOT", + "ERROR", + ] + summary_result_list.append("File\t" + "\t".join(summary_heading_list)) + for key in sorted(samples_matrix_dict): + summary_dict[key] = { + "Exact match": 0, + "INF": 0, + "ASM": 0, + "ALM": 0, + "LNF": 0, + "TPR": 0, + "NIPH": 0, + "NIPHEM": 0, + "PLOT": 0, + "ERROR": 0, + } + for values in samples_matrix_dict[key]: + if "INF_" in values: + summary_dict[key]["INF"] += 1 + elif "ASM_" in values: + summary_dict[key]["ASM"] += 1 + elif "ALM_" in values: + summary_dict[key]["ALM"] += 1 + elif "LNF" in values: + summary_dict[key]["LNF"] += 1 + elif "TPR" in values: + summary_dict[key]["TPR"] += 1 + elif "NIPH" == values: + summary_dict[key]["NIPH"] += 1 + elif "NIPHEM" == values: + summary_dict[key]["NIPHEM"] += 1 + elif "PLOT" in values: + summary_dict[key]["PLOT"] += 1 + elif "ERROR" in values: + summary_dict[key]["ERROR"] += 1 else: try: number = int(values) - summary_dict[key]['Exact match'] +=1 + summary_dict[key]["Exact match"] += 1 except: - if '_' in values : + if "_" in values: tmp_value = values try: number = int(tmp_value[-1]) - summary_dict[key]['Exact match'] +=1 + summary_dict[key]["Exact match"] += 1 except: - logger.debug('The value %s, was found when collecting summary information for the %s', values, summary_dict[key] ) + logger.debug( + "The value %s, was found when collecting summary information for the %s", + values, + summary_dict[key], + ) else: - logger.debug('The value %s, was found when collecting summary information for the %s', values, summary_dict[key] ) + logger.debug( + "The value %s, was found when collecting summary information for the %s", + values, + summary_dict[key], + ) summary_sample_list = [] - for item in summary_heading_list : + for item in summary_heading_list: summary_sample_list.append(str(summary_dict[key][item])) - summary_result_list.append(key + '\t' +'\t'.join(summary_sample_list)) + summary_result_list.append(key + "\t" + "\t".join(summary_sample_list)) return summary_result_list @@ -952,24 +1508,55 @@ def create_summary (samples_matrix_dict, logger) : # Get gene and product annotation for core gene using Prokka # # · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # -### (tsv para algunos locus? Utils para analyze schema?) -def get_gene_annotation (annotation_file, annotation_dir, genus, species, usegenus, logger) : - name_file = os.path.basename(annotation_file).split('.') - annotation_dir = os.path.join (annotation_dir, 'annotation', name_file[0]) - - if usegenus == 'true': - annotation_result = subprocess.run (['prokka', annotation_file, '--outdir', annotation_dir, - '--genus', genus, '--species', species, '--usegenus', - '--gcode', '11', '--prefix', name_file[0], '--quiet']) - - elif usegenus == 'false': - annotation_result = subprocess.run (['prokka', annotation_file, '--outdir', annotation_dir, - '--genus', genus, '--species', species, - '--gcode', '11', '--prefix', name_file[0], '--quiet']) +### (tsv para algunos locus? Utils para analyze schema?) +def get_gene_annotation( + annotation_file, annotation_dir, genus, species, usegenus, logger +): + name_file = os.path.basename(annotation_file).split(".") + annotation_dir = os.path.join(annotation_dir, "annotation", name_file[0]) + + if usegenus == "true": + annotation_result = subprocess.run( + [ + "prokka", + annotation_file, + "--outdir", + annotation_dir, + "--genus", + genus, + "--species", + species, + "--usegenus", + "--gcode", + "11", + "--prefix", + name_file[0], + "--quiet", + ] + ) + + elif usegenus == "false": + annotation_result = subprocess.run( + [ + "prokka", + annotation_file, + "--outdir", + annotation_dir, + "--genus", + genus, + "--species", + species, + "--gcode", + "11", + "--prefix", + name_file[0], + "--quiet", + ] + ) annot_tsv = [] - tsv_path = os.path.join (annotation_dir, name_file[0] + '.tsv') + tsv_path = os.path.join(annotation_dir, name_file[0] + ".tsv") try: with open(tsv_path) as tsvfile: @@ -978,31 +1565,31 @@ def get_gene_annotation (annotation_file, annotation_dir, genus, species, usegen annot_tsv.append(line) if len(annot_tsv) > 1: - gene_index = annot_tsv[0].index("gene") product_index = annot_tsv[0].index("product") try: - if '_' in annot_tsv[1][2]: - gene_annot = annot_tsv[1][gene_index].split('_')[0] + if "_" in annot_tsv[1][2]: + gene_annot = annot_tsv[1][gene_index].split("_")[0] else: gene_annot = annot_tsv[1][gene_index] except: - gene_annot = 'Not found by Prokka' + gene_annot = "Not found by Prokka" try: product_annot = annot_tsv[1][product_index] except: - product_annot = 'Not found by Prokka' + product_annot = "Not found by Prokka" else: - gene_annot = 'Not found by Prokka' - product_annot = 'Not found by Prokka' + gene_annot = "Not found by Prokka" + product_annot = "Not found by Prokka" except: - gene_annot = 'Not found by Prokka' - product_annot = 'Not found by Prokka' + gene_annot = "Not found by Prokka" + product_annot = "Not found by Prokka" return gene_annot, product_annot + """ def get_gene_annotation (annotation_file, annotation_dir, logger) : name_file = os.path.basename(annotation_file).split('.') @@ -1079,7 +1666,7 @@ def get_gene_annotation (annotation_file, annotation_dir, logger) : """ -def analize_annotation_files (in_file, logger) : ## N +def analize_annotation_files(in_file, logger): ## N examiner = GFF.GFFExaminer() file_fh = open(in_file) datos = examiner.available_limits(in_file) @@ -1087,24 +1674,33 @@ def analize_annotation_files (in_file, logger) : ## N return True -def get_inferred_allele_number(core_dict, logger): ## N - #This function will look for the highest locus number and it will return a safe high value +def get_inferred_allele_number(core_dict, logger): ## N + # This function will look for the highest locus number and it will return a safe high value # that will be added to the schema database - logger.debug('running get_inferred_allele_number function') + logger.debug("running get_inferred_allele_number function") int_keys = [] for key in core_dict.keys(): int_keys.append(key) max_value = max(int_keys) digit_length = len(str(max_value)) - return True #str 1 ( #'1'+ '0'*digit_length + 2) + return True # str 1 ( #'1'+ '0'*digit_length + 2) # · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # # Get ST profile for each samples based on allele calling results # # · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # -def get_ST_profile(outputdir, profile_csv_path, exact_dict, inf_dict, core_gene_list_files, sample_list_files, logger): - ## logger + +def get_ST_profile( + outputdir, + profile_csv_path, + exact_dict, + inf_dict, + core_gene_list_files, + sample_list_files, + logger, +): + ## logger csv_read = [] ST_profiles_dict = {} @@ -1118,15 +1714,17 @@ def get_ST_profile(outputdir, profile_csv_path, exact_dict, inf_dict, core_gene_ for line in csvreader: csv_read.append(line) - profile_header = csv_read[0][1:len(core_gene_list_files) + 1] + profile_header = csv_read[0][1 : len(core_gene_list_files) + 1] for ST_index in range(1, len(csv_read)): ST_profiles_dict[csv_read[ST_index][0]] = {} for core_index in range(len(profile_header)): - ST_profiles_dict[csv_read[ST_index][0]][profile_header[core_index]] = csv_read[ST_index][core_index + 1] + ST_profiles_dict[csv_read[ST_index][0]][ + profile_header[core_index] + ] = csv_read[ST_index][core_index + 1] for sample_file in sample_list_files: - sample_name = '.'.join(os.path.basename(sample_file).split('.')[:-1]) + sample_name = ".".join(os.path.basename(sample_file).split(".")[:-1]) st_counter = 0 for ST in ST_profiles_dict: @@ -1140,14 +1738,16 @@ def get_ST_profile(outputdir, profile_csv_path, exact_dict, inf_dict, core_gene_ if core_name in exact_dict[sample_name]: allele_in_sample = exact_dict[sample_name][core_name][2] - if not '_' in allele_in_ST: - if '_' in allele_in_sample: - allele_in_sample = allele_in_sample.split('_')[1] + if not "_" in allele_in_ST: + if "_" in allele_in_sample: + allele_in_sample = allele_in_sample.split("_")[1] if st_counter == 0: if sample_name not in analysis_profiles_dict: analysis_profiles_dict[sample_name] = {} - analysis_profiles_dict[sample_name][core_name] = allele_in_sample + analysis_profiles_dict[sample_name][ + core_name + ] = allele_in_sample if allele_in_sample == allele_in_ST: core_counter += 1 @@ -1165,14 +1765,16 @@ def get_ST_profile(outputdir, profile_csv_path, exact_dict, inf_dict, core_gene_ allele_in_sample = inf_dict[sample_name][core_name][2] if sample_name not in analysis_profiles_dict: analysis_profiles_dict[sample_name] = {} - analysis_profiles_dict[sample_name][core_name] = allele_in_sample + analysis_profiles_dict[sample_name][ + core_name + ] = allele_in_sample else: if st_counter == 0: if sample_name not in analysis_profiles_dict: analysis_profiles_dict[sample_name] = {} - if allele_in_ST == 'N' and "allele_in_sample" not in locals(): + if allele_in_ST == "N" and "allele_in_sample" not in locals(): core_counter += 1 st_counter += 1 @@ -1199,10 +1801,12 @@ def get_ST_profile(outputdir, profile_csv_path, exact_dict, inf_dict, core_gene_ if sample_name in analysis_profiles_dict: if len(analysis_profiles_dict[sample_name]) == len(profile_header): new_st_id = str(len(ST_profiles_dict) + 1) - ST_profiles_dict[new_st_id + "_INF"] = analysis_profile_dict[sample_name] + ST_profiles_dict[new_st_id + "_INF"] = analysis_profile_dict[ + sample_name + ] inf_ST[new_st_id] = analysis_profile_dict[sample_name] - samples_profiles_dict[sample_name]=new_st_id + "_INF" + samples_profiles_dict[sample_name] = new_st_id + "_INF" if "New" not in count_st: count_st["New"] = {} @@ -1211,24 +1815,24 @@ def get_ST_profile(outputdir, profile_csv_path, exact_dict, inf_dict, core_gene_ count_st["New"][new_st_id] += 1 else: - samples_profiles_dict[sample_name] = '-' + samples_profiles_dict[sample_name] = "-" if "Unknown" not in count_st: count_st["Unknown"] = 0 count_st["Unknown"] += 1 else: - samples_profiles_dict[sample_name] = '-' + samples_profiles_dict[sample_name] = "-" if "Unknown" not in count_st: count_st["Unknown"] = 0 count_st["Unknown"] += 1 ## Create ST profile results report - save_st_profile_results (outputdir, samples_profiles_dict, logger) + save_st_profile_results(outputdir, samples_profiles_dict, logger) ## Obtain interactive piechart - logger.info('Creating interactive ST results piechart') - create_sunburst_plot_st (outputdir, count_st, logger) + logger.info("Creating interactive ST results piechart") + create_sunburst_plot_st(outputdir, count_st, logger) return True, inf_ST @@ -1237,24 +1841,24 @@ def get_ST_profile(outputdir, profile_csv_path, exact_dict, inf_dict, core_gene_ # Create ST results report # # · * · * · * · * · * · * # -def save_st_profile_results (outputdir, samples_profiles_dict, logger): - header_stprofile = ['Sample Name', 'ST'] +def save_st_profile_results(outputdir, samples_profiles_dict, logger): + header_stprofile = ["Sample Name", "ST"] - if samples_profiles_dict != '': + if samples_profiles_dict != "": ## Saving ST profile to file - logger.info('Saving ST profile information to file..') - stprofile_file = os.path.join(outputdir, 'stprofile.tsv') - with open (stprofile_file , 'w') as st_fh : - st_fh.write('\t'.join(header_stprofile)+ '\n') + logger.info("Saving ST profile information to file..") + stprofile_file = os.path.join(outputdir, "stprofile.tsv") + with open(stprofile_file, "w") as st_fh: + st_fh.write("\t".join(header_stprofile) + "\n") for sample in sorted(samples_profiles_dict): - st_fh.write(sample + '\t' + samples_profiles_dict[sample] + '\n') + st_fh.write(sample + "\t" + samples_profiles_dict[sample] + "\n") return True -def create_sunburst_plot_st (outputdir, count_st, logger): - ### logger? +def create_sunburst_plot_st(outputdir, count_st, logger): + ### logger? counts = [] st_ids = ["ST"] st_labels = ["ST"] @@ -1263,7 +1867,6 @@ def create_sunburst_plot_st (outputdir, count_st, logger): total_samples = 0 for st_type in count_st: - if type(count_st[st_type]) == dict: total_st_type_count = sum(count_st[st_type].values()) else: @@ -1285,17 +1888,19 @@ def create_sunburst_plot_st (outputdir, count_st, logger): counts.insert(0, total_samples) - fig = go.Figure(go.Sunburst( - ids = st_ids, - labels = st_labels, - parents = st_parents, - values = counts, - branchvalues = "total", - )) + fig = go.Figure( + go.Sunburst( + ids=st_ids, + labels=st_labels, + parents=st_parents, + values=counts, + branchvalues="total", + ) + ) - fig.update_layout(margin = dict(t=0, l=0, r=0, b=0)) + fig.update_layout(margin=dict(t=0, l=0, r=0, b=0)) - plotsdir = os.path.join(outputdir, 'plots', 'samples_st.html') + plotsdir = os.path.join(outputdir, "plots", "samples_st.html") fig.write_html(plotsdir) @@ -1306,32 +1911,38 @@ def create_sunburst_plot_st (outputdir, count_st, logger): # Update ST profile file adding new ST found # # · * · * · * · * · * · * · * · * · * · * · # -def update_st_profile (updateprofile, profile_csv_path, outputdir, inf_ST, core_gene_list_files, logger): +def update_st_profile( + updateprofile, profile_csv_path, outputdir, inf_ST, core_gene_list_files, logger +): ## Create a copy of ST profile file if updateprofile = 'new' - if updateprofile == 'new': + if updateprofile == "new": no_updated_profile_csv_path = profile_csv_path - profile_csv_path_name = os.path.basename(no_updated_profile_csv_path).split('.')[0] - profile_csv_path = os.path.join(outputdir, profile_csv_path_name + '_updated' + '.csv') + profile_csv_path_name = os.path.basename(no_updated_profile_csv_path).split( + "." + )[0] + profile_csv_path = os.path.join( + outputdir, profile_csv_path_name + "_updated" + ".csv" + ) shutil.copyfile(no_updated_profile_csv_path, profile_csv_path) - logger.info('Copying ST profile file to update profiles') + logger.info("Copying ST profile file to update profiles") ## Update ST profile file - logger.info('Updating ST profile file adding new INF ST') + logger.info("Updating ST profile file adding new INF ST") - with open (profile_csv_path, 'r') as csvfile: + with open(profile_csv_path, "r") as csvfile: csvreader = csv.reader(csvfile, delimiter="\t") for line in csvreader: - profile_header = line[0][1:len(core_gene_list_files) + 1] + profile_header = line[0][1 : len(core_gene_list_files) + 1] break - with open (profile_csv_path, 'a') as profile_fh: + with open(profile_csv_path, "a") as profile_fh: for ST in inf_ST: locus_ST_list = [] locus_ST_list.append(ST) for locus in profile_header: locus_ST_list.append(inf_ST[ST][locus]) - profile_fh.write ('\t'.join(locus_ST_list)+ '\n') + profile_fh.write("\t".join(locus_ST_list) + "\n") return True @@ -1340,312 +1951,760 @@ def update_st_profile (updateprofile, profile_csv_path, outputdir, inf_ST, core_ # Create allele calling results reports # # · * · * · * · * · * · * · * · * · * · # -def save_allele_call_results (outputdir, full_gene_list, samples_matrix_dict, exact_dict, paralog_dict, inf_dict, plot_dict, matching_genes_dict, list_asm, list_alm, lnf_tpr_dict, snp_dict, match_alignment_dict, protein_dict, prodigal_report, shorter_seq_coverage, longer_seq_coverage, equal_seq_coverage, shorter_blast_seq_coverage, longer_blast_seq_coverage, equal_blast_seq_coverage, logger): - header_matching_alleles_contig = ['Sample Name', 'Contig', 'Core Gene', 'Allele', 'Contig Start', 'Contig Stop', 'Direction', 'Codification'] - header_exact = ['Core Gene', 'Sample Name', 'Gene Annotation', 'Product Annotation', 'Allele', 'Allele Quality', 'Contig', 'Query length', 'Contig start', 'Contig end', 'Sequence', 'Predicted Sequence'] - header_paralogs = ['Core Gene','Sample Name', 'Gene Annotation', 'Product Annotation', 'Paralog Tag', 'ID %', 'Allele', 'Allele Quality', 'Contig', 'Bitscore', 'Contig start', 'Contig end', 'Sequence', 'Predicted Sequence'] - header_inferred = ['Core Gene','Sample Name', 'INF tag', 'Gene Annotation', 'Product Annotation', 'Allele', 'Allele Quality', 'Contig', 'Bitscore', 'Query length', 'Contig length', 'New sequence length' , 'Mismatch' , 'gaps', 'Contig start', 'Contig end', 'New sequence', 'Predicted Sequence'] - header_asm = ['Core Gene', 'Sample Name', 'ASM tag', 'Gene Annotation', 'Product Annotation', 'Allele', 'Allele Quality', 'Contig', 'Bitscore', 'Query length', 'Contig length', 'New sequence length' , 'Mismatch' , 'gaps', 'Contig start', 'Contig end', 'New sequence', 'Additional info', 'Predicted Sequence'] - header_alm = ['Core Gene', 'Sample Name', 'ALM tag', 'Gene Annotation', 'Product Annotation', 'Allele', 'Allele Quality', 'Contig', 'Bitscore', 'Query length', 'Contig length', 'New sequence length' , 'Mismatch' , 'gaps', 'Contig start', 'Contig end', 'New sequence', 'Additional info', 'Predicted Sequence'] - header_plot = ['Core Gene', 'Sample Name', 'Gene Annotation', 'Product Annotation', 'Allele', 'Allele Quality', 'Contig','Bitscore', 'Contig start', 'Contig end', 'Sequence', 'Predicted Sequence'] - header_lnf_tpr = ['Core Gene', 'Sample Name', 'Gene Annotation', 'Product Annotation', 'Tag', 'Allele', 'Allele Quality', 'ID %', 'Blast sequence coverage %', 'New sequence coverage %', 'Query length', 'Contig length', 'New sequence length', 'Additional info'] - header_snp = ['Core Gene', 'Sample Name', 'Allele', 'Position', 'Mutation Schema/Sample', 'Codon Schema/Sample','Amino acid in Schema/Sample', 'Mutation type','Annotation Schema/Sample'] - header_protein = ['Core Gene','Sample Name', 'Protein in ' , 'Protein sequence'] - header_match_alignment = ['Core Gene','Sample Name','Alignment', 'Sequence'] - header_stprofile = ['Sample Name', 'ST'] +def save_allele_call_results( + outputdir, + full_gene_list, + samples_matrix_dict, + exact_dict, + paralog_dict, + inf_dict, + plot_dict, + matching_genes_dict, + list_asm, + list_alm, + lnf_tpr_dict, + snp_dict, + match_alignment_dict, + protein_dict, + prodigal_report, + shorter_seq_coverage, + longer_seq_coverage, + equal_seq_coverage, + shorter_blast_seq_coverage, + longer_blast_seq_coverage, + equal_blast_seq_coverage, + logger, +): + header_matching_alleles_contig = [ + "Sample Name", + "Contig", + "Core Gene", + "Allele", + "Contig Start", + "Contig Stop", + "Direction", + "Codification", + ] + header_exact = [ + "Core Gene", + "Sample Name", + "Gene Annotation", + "Product Annotation", + "Allele", + "Allele Quality", + "Contig", + "Query length", + "Contig start", + "Contig end", + "Sequence", + "Predicted Sequence", + ] + header_paralogs = [ + "Core Gene", + "Sample Name", + "Gene Annotation", + "Product Annotation", + "Paralog Tag", + "ID %", + "Allele", + "Allele Quality", + "Contig", + "Bitscore", + "Contig start", + "Contig end", + "Sequence", + "Predicted Sequence", + ] + header_inferred = [ + "Core Gene", + "Sample Name", + "INF tag", + "Gene Annotation", + "Product Annotation", + "Allele", + "Allele Quality", + "Contig", + "Bitscore", + "Query length", + "Contig length", + "New sequence length", + "Mismatch", + "gaps", + "Contig start", + "Contig end", + "New sequence", + "Predicted Sequence", + ] + header_asm = [ + "Core Gene", + "Sample Name", + "ASM tag", + "Gene Annotation", + "Product Annotation", + "Allele", + "Allele Quality", + "Contig", + "Bitscore", + "Query length", + "Contig length", + "New sequence length", + "Mismatch", + "gaps", + "Contig start", + "Contig end", + "New sequence", + "Additional info", + "Predicted Sequence", + ] + header_alm = [ + "Core Gene", + "Sample Name", + "ALM tag", + "Gene Annotation", + "Product Annotation", + "Allele", + "Allele Quality", + "Contig", + "Bitscore", + "Query length", + "Contig length", + "New sequence length", + "Mismatch", + "gaps", + "Contig start", + "Contig end", + "New sequence", + "Additional info", + "Predicted Sequence", + ] + header_plot = [ + "Core Gene", + "Sample Name", + "Gene Annotation", + "Product Annotation", + "Allele", + "Allele Quality", + "Contig", + "Bitscore", + "Contig start", + "Contig end", + "Sequence", + "Predicted Sequence", + ] + header_lnf_tpr = [ + "Core Gene", + "Sample Name", + "Gene Annotation", + "Product Annotation", + "Tag", + "Allele", + "Allele Quality", + "ID %", + "Blast sequence coverage %", + "New sequence coverage %", + "Query length", + "Contig length", + "New sequence length", + "Additional info", + ] + header_snp = [ + "Core Gene", + "Sample Name", + "Allele", + "Position", + "Mutation Schema/Sample", + "Codon Schema/Sample", + "Amino acid in Schema/Sample", + "Mutation type", + "Annotation Schema/Sample", + ] + header_protein = ["Core Gene", "Sample Name", "Protein in ", "Protein sequence"] + header_match_alignment = ["Core Gene", "Sample Name", "Alignment", "Sequence"] + header_stprofile = ["Sample Name", "ST"] # Añadido header_prodigal_report para report prodigal -# header_prodigal_report = ['Core gene', 'Sample Name', 'Allele', 'Sequence type', 'BLAST start', 'BLAST end', 'Prodigal start', 'Prodigal end', 'BLAST sequence', 'Prodigal sequence'] + # header_prodigal_report = ['Core gene', 'Sample Name', 'Allele', 'Sequence type', 'BLAST start', 'BLAST end', 'Prodigal start', 'Prodigal end', 'BLAST sequence', 'Prodigal sequence'] # Añadido header_newsseq_coverage_report para determinar coverage threshold a imponer -# header_newsseq_coverage_report = ['Core gene', 'Sample Name', 'Query length', 'New sequence length', 'Locus mean', 'Coverage (new sequence/allele)', 'Coverage (new sequence/locus mean)'] + # header_newsseq_coverage_report = ['Core gene', 'Sample Name', 'Query length', 'New sequence length', 'Locus mean', 'Coverage (new sequence/allele)', 'Coverage (new sequence/locus mean)'] # Añadido header_blast_coverage_report para determinar coverage threshold a imponer -# header_blast_coverage_report = ['Core gene', 'Sample Name', 'Query length', 'Blast sequence length', 'Locus mean', 'Coverage (blast sequence/allele)', 'Coverage (blast sequence/locus mean)'] + # header_blast_coverage_report = ['Core gene', 'Sample Name', 'Query length', 'Blast sequence length', 'Locus mean', 'Coverage (blast sequence/allele)', 'Coverage (blast sequence/locus mean)'] ## Saving the result information to file - print ('Saving results to files \n') - result_file = os.path.join ( outputdir, 'result.tsv') - logger.info('Saving result information to file..') - with open (result_file, 'w') as out_fh: - out_fh.write ('Sample Name\t'+'\t'.join( full_gene_list) + '\n') - for key in sorted (samples_matrix_dict): - out_fh.write (key + '\t' + '\t'.join(samples_matrix_dict[key])+ '\n') + print("Saving results to files \n") + result_file = os.path.join(outputdir, "result.tsv") + logger.info("Saving result information to file..") + with open(result_file, "w") as out_fh: + out_fh.write("Sample Name\t" + "\t".join(full_gene_list) + "\n") + for key in sorted(samples_matrix_dict): + out_fh.write(key + "\t" + "\t".join(samples_matrix_dict[key]) + "\n") ## Saving exact matches to file - logger.info('Saving exact matches information to file..') - exact_file = os.path.join(outputdir, 'exact.tsv') - with open (exact_file , 'w') as exact_fh : - exact_fh.write('\t'.join(header_exact)+ '\n') + logger.info("Saving exact matches information to file..") + exact_file = os.path.join(outputdir, "exact.tsv") + with open(exact_file, "w") as exact_fh: + exact_fh.write("\t".join(header_exact) + "\n") for sample in sorted(exact_dict): for core in sorted(exact_dict[sample]): - exact_fh.write(core + '\t' + sample + '\t' + '\t'.join(exact_dict[sample][core]) + '\n') + exact_fh.write( + core + + "\t" + + sample + + "\t" + + "\t".join(exact_dict[sample][core]) + + "\n" + ) ## Saving paralog alleles to file - logger.info('Saving paralog information to file..') - paralog_file = os.path.join(outputdir, 'paralog.tsv') - with open (paralog_file , 'w') as paralog_fh : - paralog_fh.write('\t'.join(header_paralogs) + '\n') - for sample in sorted (paralog_dict) : - for core in sorted (paralog_dict[sample]): - for paralog in paralog_dict[sample][core] : - paralog_fh.write(core + '\t' + sample + '\t' + '\t'.join (paralog) + '\n') + logger.info("Saving paralog information to file..") + paralog_file = os.path.join(outputdir, "paralog.tsv") + with open(paralog_file, "w") as paralog_fh: + paralog_fh.write("\t".join(header_paralogs) + "\n") + for sample in sorted(paralog_dict): + for core in sorted(paralog_dict[sample]): + for paralog in paralog_dict[sample][core]: + paralog_fh.write( + core + "\t" + sample + "\t" + "\t".join(paralog) + "\n" + ) ## Saving inferred alleles to file - logger.info('Saving inferred alleles information to file..') - inferred_file = os.path.join(outputdir, 'inferred_alleles.tsv') - with open (inferred_file , 'w') as infer_fh : - infer_fh.write('\t'.join(header_inferred) + '\n') - for core in sorted (inf_dict) : - for sample in sorted (inf_dict[core]) : + logger.info("Saving inferred alleles information to file..") + inferred_file = os.path.join(outputdir, "inferred_alleles.tsv") + with open(inferred_file, "w") as infer_fh: + infer_fh.write("\t".join(header_inferred) + "\n") + for core in sorted(inf_dict): + for sample in sorted(inf_dict[core]): for inferred in inf_dict[core][sample]: # seq_in_inferred_allele = '\t'.join (inf_dict[sample]) - infer_fh.write(core + '\t' + sample + '\t' + inferred + '\t' + '\t'.join(inf_dict[core][sample][inferred]) + '\n') + infer_fh.write( + core + + "\t" + + sample + + "\t" + + inferred + + "\t" + + "\t".join(inf_dict[core][sample][inferred]) + + "\n" + ) ## Saving PLOTs to file - logger.info('Saving PLOT information to file..') - plot_file = os.path.join(outputdir, 'plot.tsv') - with open (plot_file , 'w') as plot_fh : - plot_fh.write('\t'.join(header_plot) + '\n') - for core in sorted (plot_dict) : - for sample in sorted (plot_dict[core]): - plot_fh.write(core + '\t' + sample + '\t' + '\t'.join(plot_dict[core][sample]) + '\n') + logger.info("Saving PLOT information to file..") + plot_file = os.path.join(outputdir, "plot.tsv") + with open(plot_file, "w") as plot_fh: + plot_fh.write("\t".join(header_plot) + "\n") + for core in sorted(plot_dict): + for sample in sorted(plot_dict[core]): + plot_fh.write( + core + + "\t" + + sample + + "\t" + + "\t".join(plot_dict[core][sample]) + + "\n" + ) ## Saving matching contigs to file - logger.info('Saving matching information to file..') - matching_file = os.path.join(outputdir, 'matching_contigs.tsv') - with open (matching_file , 'w') as matching_fh : - matching_fh.write('\t'.join(header_matching_alleles_contig) + '\n') - for samples in sorted (matching_genes_dict) : - for contigs in matching_genes_dict[samples] : - for contig in matching_genes_dict[samples] [contigs]: - matching_alleles = '\t'.join (contig) - matching_fh.write(samples + '\t' + contigs +'\t' + matching_alleles + '\n') + logger.info("Saving matching information to file..") + matching_file = os.path.join(outputdir, "matching_contigs.tsv") + with open(matching_file, "w") as matching_fh: + matching_fh.write("\t".join(header_matching_alleles_contig) + "\n") + for samples in sorted(matching_genes_dict): + for contigs in matching_genes_dict[samples]: + for contig in matching_genes_dict[samples][contigs]: + matching_alleles = "\t".join(contig) + matching_fh.write( + samples + "\t" + contigs + "\t" + matching_alleles + "\n" + ) ## Saving ASMs to file - logger.info('Saving asm information to file..') - asm_file = os.path.join(outputdir, 'asm.tsv') - with open (asm_file , 'w') as asm_fh : - asm_fh.write('\t'.join(header_asm)+ '\n') - for core in list_asm : - for sample in list_asm[core] : + logger.info("Saving asm information to file..") + asm_file = os.path.join(outputdir, "asm.tsv") + with open(asm_file, "w") as asm_fh: + asm_fh.write("\t".join(header_asm) + "\n") + for core in list_asm: + for sample in list_asm[core]: for asm in list_asm[core][sample]: - asm_fh.write(core + '\t' + sample + '\t' + asm + '\t' + '\t'.join(list_asm[core][sample][asm]) + '\n') + asm_fh.write( + core + + "\t" + + sample + + "\t" + + asm + + "\t" + + "\t".join(list_asm[core][sample][asm]) + + "\n" + ) ## Saving ALMs to file - logger.info('Saving alm information to file..') - alm_file = os.path.join(outputdir, 'alm.tsv') - with open (alm_file , 'w') as alm_fh : - alm_fh.write('\t'.join(header_alm)+ '\n') - for core in list_alm : - for sample in list_alm[core] : + logger.info("Saving alm information to file..") + alm_file = os.path.join(outputdir, "alm.tsv") + with open(alm_file, "w") as alm_fh: + alm_fh.write("\t".join(header_alm) + "\n") + for core in list_alm: + for sample in list_alm[core]: for alm in list_alm[core][sample]: - alm_fh.write(core + '\t' + sample + '\t' + alm + '\t' + '\t'.join(list_alm[core][sample][alm]) + '\n') + alm_fh.write( + core + + "\t" + + sample + + "\t" + + alm + + "\t" + + "\t".join(list_alm[core][sample][alm]) + + "\n" + ) ## Saving LNFs to file - logger.info('Saving lnf information to file..') - lnf_file = os.path.join(outputdir, 'lnf_tpr.tsv') - with open (lnf_file , 'w') as lnf_fh : - lnf_fh.write('\t'.join(header_lnf_tpr)+ '\n') - for core in lnf_tpr_dict : - for sample in lnf_tpr_dict[core] : - for lnf in lnf_tpr_dict[core][sample] : - lnf_fh.write(core + '\t' + sample + '\t' + '\t'.join(lnf) + '\n') + logger.info("Saving lnf information to file..") + lnf_file = os.path.join(outputdir, "lnf_tpr.tsv") + with open(lnf_file, "w") as lnf_fh: + lnf_fh.write("\t".join(header_lnf_tpr) + "\n") + for core in lnf_tpr_dict: + for sample in lnf_tpr_dict[core]: + for lnf in lnf_tpr_dict[core][sample]: + lnf_fh.write(core + "\t" + sample + "\t" + "\t".join(lnf) + "\n") ## Saving SNPs information to file - logger.info('Saving SNPs information to file..') - snp_file = os.path.join(outputdir, 'snp.tsv') - with open (snp_file , 'w') as snp_fh : - snp_fh.write('\t'.join(header_snp) + '\n') - for core in sorted (snp_dict) : - for sample in sorted (snp_dict[core]): - for allele_id_snp in snp_dict[core][sample] : - for snp in snp_dict[core][sample][allele_id_snp] : - snp_fh.write(core + '\t' + sample + '\t' + allele_id_snp + '\t' + '\t'.join (snp) + '\n') + logger.info("Saving SNPs information to file..") + snp_file = os.path.join(outputdir, "snp.tsv") + with open(snp_file, "w") as snp_fh: + snp_fh.write("\t".join(header_snp) + "\n") + for core in sorted(snp_dict): + for sample in sorted(snp_dict[core]): + for allele_id_snp in snp_dict[core][sample]: + for snp in snp_dict[core][sample][allele_id_snp]: + snp_fh.write( + core + + "\t" + + sample + + "\t" + + allele_id_snp + + "\t" + + "\t".join(snp) + + "\n" + ) ## Saving DNA sequences alignments to file - logger.info('Saving matching alignment information to files..') - alignment_dir = os.path.join(outputdir,'alignments') - if os.path.exists(alignment_dir) : + logger.info("Saving matching alignment information to files..") + alignment_dir = os.path.join(outputdir, "alignments") + if os.path.exists(alignment_dir): shutil.rmtree(alignment_dir) - logger.info('deleting the alignment files from previous execution') + logger.info("deleting the alignment files from previous execution") os.makedirs(alignment_dir) - for core in sorted(match_alignment_dict) : - for sample in sorted (match_alignment_dict[core]) : - match_alignment_file = os.path.join(alignment_dir, str('match_alignment_' + core + '_' + sample + '.txt')) - with open(match_alignment_file, 'w') as match_alignment_fh : - match_alignment_fh.write( '\t'.join(header_match_alignment) + '\n') - for match_align in match_alignment_dict[core][sample] : - match_alignment_fh.write(core + '\t'+ sample +'\t'+ '\t'.join(match_align) + '\n') + for core in sorted(match_alignment_dict): + for sample in sorted(match_alignment_dict[core]): + match_alignment_file = os.path.join( + alignment_dir, str("match_alignment_" + core + "_" + sample + ".txt") + ) + with open(match_alignment_file, "w") as match_alignment_fh: + match_alignment_fh.write("\t".join(header_match_alignment) + "\n") + for match_align in match_alignment_dict[core][sample]: + match_alignment_fh.write( + core + "\t" + sample + "\t" + "\t".join(match_align) + "\n" + ) ## Saving protein sequences alignments to file - logger.info('Saving protein information to files..') - protein_dir = os.path.join(outputdir,'proteins') - if os.path.exists(protein_dir) : + logger.info("Saving protein information to files..") + protein_dir = os.path.join(outputdir, "proteins") + if os.path.exists(protein_dir): shutil.rmtree(protein_dir) - logger.info('deleting the proteins files from previous execution') + logger.info("deleting the proteins files from previous execution") os.makedirs(protein_dir) - for core in sorted(protein_dict) : - for sample in sorted (protein_dict[core]) : - protein_file = os.path.join(protein_dir, str('protein_' + core + '_' + sample + '.txt')) - with open(protein_file, 'w') as protein_fh : - protein_fh.write( '\t'.join(header_protein) + '\n') - for protein in protein_dict[core][sample] : - protein_fh.write(core + '\t'+ sample +'\t'+ '\t'.join(protein) + '\n') + for core in sorted(protein_dict): + for sample in sorted(protein_dict[core]): + protein_file = os.path.join( + protein_dir, str("protein_" + core + "_" + sample + ".txt") + ) + with open(protein_file, "w") as protein_fh: + protein_fh.write("\t".join(header_protein) + "\n") + for protein in protein_dict[core][sample]: + protein_fh.write( + core + "\t" + sample + "\t" + "\t".join(protein) + "\n" + ) ## Saving summary information to file - logger.info('Saving summary information to file..') - summary_result = create_summary (samples_matrix_dict, logger) - summary_file = os.path.join( outputdir, 'summary_result.tsv') - with open (summary_file , 'w') as summ_fh: - for line in summary_result : - summ_fh.write(line + '\n') + logger.info("Saving summary information to file..") + summary_result = create_summary(samples_matrix_dict, logger) + summary_file = os.path.join(outputdir, "summary_result.tsv") + with open(summary_file, "w") as summ_fh: + for line in summary_result: + summ_fh.write(line + "\n") ## Modify the result file to remove the PLOT_ string for creating the file to use in the tree diagram -# logger.info('Saving result information for tree diagram') -# tree_diagram_file = os.path.join ( outputdir, 'result_for_tree_diagram.tsv') -# with open (result_file, 'r') as result_fh: -# with open(tree_diagram_file, 'w') as td_fh: -# for line in result_fh: -# tree_line = line.replace('PLOT_','') -# td_fh.write(tree_line) + # logger.info('Saving result information for tree diagram') + # tree_diagram_file = os.path.join ( outputdir, 'result_for_tree_diagram.tsv') + # with open (result_file, 'r') as result_fh: + # with open(tree_diagram_file, 'w') as td_fh: + # for line in result_fh: + # tree_line = line.replace('PLOT_','') + # td_fh.write(tree_line) ########################################################################################### # Guardando report de prodigal. Temporal -# prodigal_report_file = os.path.join (outputdir, 'prodigal_report.tsv') + # prodigal_report_file = os.path.join (outputdir, 'prodigal_report.tsv') # saving prodigal predictions to file -# with open (prodigal_report_file, 'w') as out_fh: -# out_fh.write ('\t'.join(header_prodigal_report)+ '\n') -# for prodigal_result in prodigal_report: -# out_fh.write ('\t'.join(prodigal_result)+ '\n') + # with open (prodigal_report_file, 'w') as out_fh: + # out_fh.write ('\t'.join(header_prodigal_report)+ '\n') + # for prodigal_result in prodigal_report: + # out_fh.write ('\t'.join(prodigal_result)+ '\n') # Guardando coverage de new_sseq para estimar el threshold a establecer. Temporal -# newsseq_coverage_file = os.path.join (outputdir, 'newsseq_coverage_report.tsv') + # newsseq_coverage_file = os.path.join (outputdir, 'newsseq_coverage_report.tsv') # saving the coverage information to file -# with open (newsseq_coverage_file, 'w') as out_fh: -# out_fh.write ('\t' + '\t'.join(header_newsseq_coverage_report)+ '\n') -# for coverage in shorter_seq_coverage: -# out_fh.write ('Shorter new sequence' + '\t' + '\t'.join(coverage)+ '\n') -# for coverage in longer_seq_coverage: -# out_fh.write ('Longer new sequence' + '\t' + '\t'.join(coverage)+ '\n') -# for coverage in equal_seq_coverage: -# out_fh.write ('Same length new sequence' + '\t' + '\t'.join(coverage)+ '\n') + # with open (newsseq_coverage_file, 'w') as out_fh: + # out_fh.write ('\t' + '\t'.join(header_newsseq_coverage_report)+ '\n') + # for coverage in shorter_seq_coverage: + # out_fh.write ('Shorter new sequence' + '\t' + '\t'.join(coverage)+ '\n') + # for coverage in longer_seq_coverage: + # out_fh.write ('Longer new sequence' + '\t' + '\t'.join(coverage)+ '\n') + # for coverage in equal_seq_coverage: + # out_fh.write ('Same length new sequence' + '\t' + '\t'.join(coverage)+ '\n') # Guardando coverage de la sseq obtenida tras blast para estimar el threshold a establecer. Temporal -# blast_coverage_file = os.path.join (outputdir, 'blast_coverage_report.tsv') + # blast_coverage_file = os.path.join (outputdir, 'blast_coverage_report.tsv') # saving the result information to file -# with open (blast_coverage_file, 'w') as out_fh: -# out_fh.write ('\t' + '\t'.join(header_blast_coverage_report)+ '\n') -# for coverage in shorter_blast_seq_coverage: -# out_fh.write ('Shorter blast sequence' + '\t' + '\t'.join(coverage)+ '\n') -# for coverage in longer_blast_seq_coverage: -# out_fh.write ('Longer blast sequence' + '\t' + '\t'.join(coverage)+ '\n') -# for coverage in equal_blast_seq_coverage: -# out_fh.write ('Same length blast sequence' + '\t' + '\t'.join(coverage)+ '\n') + # with open (blast_coverage_file, 'w') as out_fh: + # out_fh.write ('\t' + '\t'.join(header_blast_coverage_report)+ '\n') + # for coverage in shorter_blast_seq_coverage: + # out_fh.write ('Shorter blast sequence' + '\t' + '\t'.join(coverage)+ '\n') + # for coverage in longer_blast_seq_coverage: + # out_fh.write ('Longer blast sequence' + '\t' + '\t'.join(coverage)+ '\n') + # for coverage in equal_blast_seq_coverage: + # out_fh.write ('Same length blast sequence' + '\t' + '\t'.join(coverage)+ '\n') ########################################################################################### return True - -def save_allele_calling_plots (outputdir, sample_list_files, count_exact, count_inf, count_asm, count_alm, count_lnf, count_tpr, count_plot, count_niph, count_niphem, count_error, logger): - +def save_allele_calling_plots( + outputdir, + sample_list_files, + count_exact, + count_inf, + count_asm, + count_alm, + count_lnf, + count_tpr, + count_plot, + count_niph, + count_niphem, + count_error, + logger, +): ## Create result plots directory - plots_dir = os.path.join(outputdir,'plots') + plots_dir = os.path.join(outputdir, "plots") try: os.makedirs(plots_dir) except: - logger.info('Deleting the results plots directory for a previous execution without cleaning up') - shutil.rmtree(os.path.join(outputdir, 'plots')) + logger.info( + "Deleting the results plots directory for a previous execution without cleaning up" + ) + shutil.rmtree(os.path.join(outputdir, "plots")) try: os.makedirs(plots_dir) - logger.info ('Results plots folder %s has been created again', plots_dir) + logger.info("Results plots folder %s has been created again", plots_dir) except: - logger.info('Unable to create again the results plots directory %s', plots_dir) - print('Cannot create Results plots directory on ', plots_dir) + logger.info( + "Unable to create again the results plots directory %s", plots_dir + ) + print("Cannot create Results plots directory on ", plots_dir) exit(0) for sample_file in sample_list_files: - sample_name = '.'.join(os.path.basename(sample_file).split('.')[:-1]) + sample_name = ".".join(os.path.basename(sample_file).split(".")[:-1]) ## Obtain interactive piechart - logger.info('Creating interactive results piecharts') - create_sunburst_allele_call (outputdir, sample_name, count_exact[sample_name], count_inf[sample_name], count_asm[sample_name], count_alm[sample_name], count_lnf[sample_name], count_tpr[sample_name], count_plot[sample_name], count_niph[sample_name], count_niphem[sample_name], count_error[sample_name]) + logger.info("Creating interactive results piecharts") + create_sunburst_allele_call( + outputdir, + sample_name, + count_exact[sample_name], + count_inf[sample_name], + count_asm[sample_name], + count_alm[sample_name], + count_lnf[sample_name], + count_tpr[sample_name], + count_plot[sample_name], + count_niph[sample_name], + count_niphem[sample_name], + count_error[sample_name], + ) return True - -def create_sunburst_allele_call (outputdir, sample_name, count_exact, count_inf, count_asm, count_alm, count_lnf, count_tpr, count_plot, count_niph, count_niphem, count_error): - ### logger - - total_locus = count_exact['total'] + count_inf['total'] + count_asm['total'] + count_alm['total'] + count_lnf['total'] + count_tpr['total'] + count_plot['total'] \ - + count_niph['total'] + count_niphem['total'] + count_error['total'] - - tag_counts = [total_locus, count_exact['total'], count_exact['good_quality'], count_exact['bad_quality'], count_exact['no_start'], count_exact['no_start_stop'], - count_exact['no_stop'], count_exact['multiple_stop'], count_inf['total'], count_inf['good_quality'], count_inf['bad_quality'], count_inf['no_start'], - count_inf['no_start_stop'], count_inf['no_stop'], count_inf['multiple_stop'], count_asm['total'], count_asm['insertion'], count_asm['deletion'], - count_asm['substitution'], count_alm['total'], count_alm['insertion'], count_alm['deletion'], count_alm['substitution'], count_plot['total'], - count_niph['total'], count_niph['good_quality'], count_niph['bad_quality'], count_niph['no_start'], count_niph['no_start_stop'], count_niph['no_stop'], - count_niph['multiple_stop'], count_niphem['total'], count_niphem['good_quality'], count_niphem['bad_quality'], count_niphem['no_start'], - count_niphem['no_start_stop'], count_niphem['no_stop'], count_niphem['multiple_stop'], count_lnf['total'], count_lnf['not_found'], count_lnf['low_id'], - count_lnf['low_coverage'], count_tpr['total'], count_tpr['good_quality'], count_tpr['bad_quality'], count_tpr['no_start'], count_tpr['no_start_stop'], - count_tpr['no_stop'], count_tpr['multiple_stop'], count_error['total'], count_error['good_quality'], count_error['bad_quality'], count_error['no_start'], - count_error['no_start_stop'], count_error['no_stop'], count_error['multiple_stop']] - - fig=go.Figure(go.Sunburst( - ids=[ - sample_name, "Exact Match", "Good Quality - Exact Match", "Bad Quality - Exact Match", - "No start - Bad Quality - Exact Match", "No start-stop - Bad Quality - Exact Match", - "No stop - Bad Quality - Exact Match", "Multiple stop - Bad Quality - Exact Match", - "INF", "Good Quality - INF", "Bad Quality - INF", "No start - Bad Quality - INF", - "No start-stop - Bad Quality - INF", "No stop - Bad Quality - INF", "Multiple stop - Bad Quality - INF", - "ASM", "Insertion - ASM", "Deletion - ASM", "Substitution - ASM", "ALM", "Insertion - ALM", - "Deletion - ALM", "Substitution - ALM", "PLOT", "NIPH", "Good Quality - NIPH", - "Bad Quality - NIPH", "No start - Bad Quality - NIPH", "No start-stop - Bad Quality - NIPH", - "No stop - Bad Quality - NIPH", "Multiple stop - Bad Quality - NIPH", "NIPHEM", - "Good Quality - NIPHEM", "Bad Quality - NIPHEM", "No start - Bad Quality - NIPHEM", - "No start-stop - Bad Quality - NIPHEM", "No stop - Bad Quality - NIPHEM", - "Multiple stop - Bad Quality - NIPHEM", "LNF", "Not found", - "Low ID", "Low coverage", "TPR", "Good Quality - TPR", "Bad Quality - TPR", - "No start - Bad Quality - TPR", "No start-stop - Bad Quality - TPR", "No stop - Bad Quality - TPR", - "Multiple stop - Bad Quality - TPR", "Error", "Good Quality - Error", "Bad Quality - Error", - "No start - Bad Quality - Error", "No start-stop - Bad Quality - Error", - "No stop - Bad Quality - Error", "Multiple stop - Bad Quality - Error" - ], - labels= [ - sample_name, "Exact
Match", "Good
Quality", "Bad
Quality", - "No
start", "No
start-stop", "No
stop", "Multiple
stop", - "INF", "Good
Quality", "Bad
Quality", "No
start", - "No
start-stop", "No
stop", "Multiple
stop", "ASM", "Insertion", - "Deletion", "Substitution", "ALM", "Insertion", "Deletion", - "Substitution", "PLOT", "NIPH", "Good
Quality", "Bad
Quality", - "No
start", "No
start-stop", "No
stop", "Multiple
stop", - "NIPHEM", "Good
Quality", "Bad
Quality", "No
start", - "No
start-stop", "No
stop", "Multiple
stop", "LNF", "Not
found", - "Low
ID", "Low
coverage", "TPR", "Good
Quality", "Bad
Quality", - "No
start", "No
start-stop", "No
stop", "Multiple
stop", - "Error", "Good
Quality", "Bad
Quality","No
start", - "No
start-stop", "No
stop", "Multiple
stop" - ], - parents=[ - "", sample_name, "Exact Match", "Exact Match", "Bad Quality - Exact Match", - "Bad Quality - Exact Match", "Bad Quality - Exact Match", "Bad Quality - Exact Match", - sample_name, "INF", "INF", "Bad Quality - INF", "Bad Quality - INF", "Bad Quality - INF", - "Bad Quality - INF", sample_name, "ASM", "ASM", "ASM", sample_name, "ALM", "ALM", "ALM", sample_name, - sample_name, "NIPH", "NIPH", "Bad Quality - NIPH", "Bad Quality - NIPH", "Bad Quality - NIPH", - "Bad Quality - NIPH", sample_name, "NIPHEM", "NIPHEM", "Bad Quality - NIPHEM", - "Bad Quality - NIPHEM", "Bad Quality - NIPHEM", "Bad Quality - NIPHEM", sample_name, "LNF", - "LNF", "LNF", sample_name, "TPR", "TPR", "Bad Quality - TPR", "Bad Quality - TPR", - "Bad Quality - TPR", "Bad Quality - TPR", sample_name, "Error", "Error", "Bad Quality - Error", - "Bad Quality - Error", "Bad Quality - Error", "Bad Quality - Error" - ], - values=tag_counts, - branchvalues="total", - )) - - fig.update_layout(margin = dict(t=0, l=0, r=0, b=0)) - - plotsdir = os.path.join(outputdir, 'plots', sample_name + '.html') +def create_sunburst_allele_call( + outputdir, + sample_name, + count_exact, + count_inf, + count_asm, + count_alm, + count_lnf, + count_tpr, + count_plot, + count_niph, + count_niphem, + count_error, +): + ### logger + + total_locus = ( + count_exact["total"] + + count_inf["total"] + + count_asm["total"] + + count_alm["total"] + + count_lnf["total"] + + count_tpr["total"] + + count_plot["total"] + + count_niph["total"] + + count_niphem["total"] + + count_error["total"] + ) + + tag_counts = [ + total_locus, + count_exact["total"], + count_exact["good_quality"], + count_exact["bad_quality"], + count_exact["no_start"], + count_exact["no_start_stop"], + count_exact["no_stop"], + count_exact["multiple_stop"], + count_inf["total"], + count_inf["good_quality"], + count_inf["bad_quality"], + count_inf["no_start"], + count_inf["no_start_stop"], + count_inf["no_stop"], + count_inf["multiple_stop"], + count_asm["total"], + count_asm["insertion"], + count_asm["deletion"], + count_asm["substitution"], + count_alm["total"], + count_alm["insertion"], + count_alm["deletion"], + count_alm["substitution"], + count_plot["total"], + count_niph["total"], + count_niph["good_quality"], + count_niph["bad_quality"], + count_niph["no_start"], + count_niph["no_start_stop"], + count_niph["no_stop"], + count_niph["multiple_stop"], + count_niphem["total"], + count_niphem["good_quality"], + count_niphem["bad_quality"], + count_niphem["no_start"], + count_niphem["no_start_stop"], + count_niphem["no_stop"], + count_niphem["multiple_stop"], + count_lnf["total"], + count_lnf["not_found"], + count_lnf["low_id"], + count_lnf["low_coverage"], + count_tpr["total"], + count_tpr["good_quality"], + count_tpr["bad_quality"], + count_tpr["no_start"], + count_tpr["no_start_stop"], + count_tpr["no_stop"], + count_tpr["multiple_stop"], + count_error["total"], + count_error["good_quality"], + count_error["bad_quality"], + count_error["no_start"], + count_error["no_start_stop"], + count_error["no_stop"], + count_error["multiple_stop"], + ] + + fig = go.Figure( + go.Sunburst( + ids=[ + sample_name, + "Exact Match", + "Good Quality - Exact Match", + "Bad Quality - Exact Match", + "No start - Bad Quality - Exact Match", + "No start-stop - Bad Quality - Exact Match", + "No stop - Bad Quality - Exact Match", + "Multiple stop - Bad Quality - Exact Match", + "INF", + "Good Quality - INF", + "Bad Quality - INF", + "No start - Bad Quality - INF", + "No start-stop - Bad Quality - INF", + "No stop - Bad Quality - INF", + "Multiple stop - Bad Quality - INF", + "ASM", + "Insertion - ASM", + "Deletion - ASM", + "Substitution - ASM", + "ALM", + "Insertion - ALM", + "Deletion - ALM", + "Substitution - ALM", + "PLOT", + "NIPH", + "Good Quality - NIPH", + "Bad Quality - NIPH", + "No start - Bad Quality - NIPH", + "No start-stop - Bad Quality - NIPH", + "No stop - Bad Quality - NIPH", + "Multiple stop - Bad Quality - NIPH", + "NIPHEM", + "Good Quality - NIPHEM", + "Bad Quality - NIPHEM", + "No start - Bad Quality - NIPHEM", + "No start-stop - Bad Quality - NIPHEM", + "No stop - Bad Quality - NIPHEM", + "Multiple stop - Bad Quality - NIPHEM", + "LNF", + "Not found", + "Low ID", + "Low coverage", + "TPR", + "Good Quality - TPR", + "Bad Quality - TPR", + "No start - Bad Quality - TPR", + "No start-stop - Bad Quality - TPR", + "No stop - Bad Quality - TPR", + "Multiple stop - Bad Quality - TPR", + "Error", + "Good Quality - Error", + "Bad Quality - Error", + "No start - Bad Quality - Error", + "No start-stop - Bad Quality - Error", + "No stop - Bad Quality - Error", + "Multiple stop - Bad Quality - Error", + ], + labels=[ + sample_name, + "Exact
Match", + "Good
Quality", + "Bad
Quality", + "No
start", + "No
start-stop", + "No
stop", + "Multiple
stop", + "INF", + "Good
Quality", + "Bad
Quality", + "No
start", + "No
start-stop", + "No
stop", + "Multiple
stop", + "ASM", + "Insertion", + "Deletion", + "Substitution", + "ALM", + "Insertion", + "Deletion", + "Substitution", + "PLOT", + "NIPH", + "Good
Quality", + "Bad
Quality", + "No
start", + "No
start-stop", + "No
stop", + "Multiple
stop", + "NIPHEM", + "Good
Quality", + "Bad
Quality", + "No
start", + "No
start-stop", + "No
stop", + "Multiple
stop", + "LNF", + "Not
found", + "Low
ID", + "Low
coverage", + "TPR", + "Good
Quality", + "Bad
Quality", + "No
start", + "No
start-stop", + "No
stop", + "Multiple
stop", + "Error", + "Good
Quality", + "Bad
Quality", + "No
start", + "No
start-stop", + "No
stop", + "Multiple
stop", + ], + parents=[ + "", + sample_name, + "Exact Match", + "Exact Match", + "Bad Quality - Exact Match", + "Bad Quality - Exact Match", + "Bad Quality - Exact Match", + "Bad Quality - Exact Match", + sample_name, + "INF", + "INF", + "Bad Quality - INF", + "Bad Quality - INF", + "Bad Quality - INF", + "Bad Quality - INF", + sample_name, + "ASM", + "ASM", + "ASM", + sample_name, + "ALM", + "ALM", + "ALM", + sample_name, + sample_name, + "NIPH", + "NIPH", + "Bad Quality - NIPH", + "Bad Quality - NIPH", + "Bad Quality - NIPH", + "Bad Quality - NIPH", + sample_name, + "NIPHEM", + "NIPHEM", + "Bad Quality - NIPHEM", + "Bad Quality - NIPHEM", + "Bad Quality - NIPHEM", + "Bad Quality - NIPHEM", + sample_name, + "LNF", + "LNF", + "LNF", + sample_name, + "TPR", + "TPR", + "Bad Quality - TPR", + "Bad Quality - TPR", + "Bad Quality - TPR", + "Bad Quality - TPR", + sample_name, + "Error", + "Error", + "Bad Quality - Error", + "Bad Quality - Error", + "Bad Quality - Error", + "Bad Quality - Error", + ], + values=tag_counts, + branchvalues="total", + ) + ) + + fig.update_layout(margin=dict(t=0, l=0, r=0, b=0)) + + plotsdir = os.path.join(outputdir, "plots", sample_name + ".html") fig.write_html(plotsdir) @@ -1656,11 +2715,19 @@ def create_sunburst_allele_call (outputdir, sample_name, count_exact, count_inf, # Update core genes schema adding new inferred alleles found for each locus in allele calling analysis # # · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # -def update_schema (updateschema, schemadir, outputdir, core_gene_list_files, inferred_alleles_dict, alleles_in_locus_dict, logger): +def update_schema( + updateschema, + schemadir, + outputdir, + core_gene_list_files, + inferred_alleles_dict, + alleles_in_locus_dict, + logger, +): if len(inferred_alleles_dict) > 0: ## Create a copy of core genes schema if updateschema = 'new' / 'New' - if updateschema == 'new': + if updateschema == "new": no_updated_schemadir = schemadir ##schemadir_name = os.path.dirname(no_updated_schemadir) ---> se puede usar si guardo finalmente el nuevo esquema en el mismo directorio que el antiguo esquema, pero para ello debo verificar si termina o no el path en / para eliminarlo o no del path antes de hacer el dirname schemadir_name = no_updated_schemadir.split("/") @@ -1669,52 +2736,82 @@ def update_schema (updateschema, schemadir, outputdir, core_gene_list_files, inf else: schemadir_name = schemadir_name[-1] - schemadir = os.path.join(outputdir, schemadir_name + '_updated') + schemadir = os.path.join(outputdir, schemadir_name + "_updated") try: shutil.copytree(no_updated_schemadir, schemadir) - logger.info ('Schema copy %s has been created to update schema', schemadir) + logger.info( + "Schema copy %s has been created to update schema", schemadir + ) except: - logger.info('Deleting preexisting directory') + logger.info("Deleting preexisting directory") shutil.rmtree(schemadir) try: shutil.copytree(no_updated_schemadir, schemadir) - logger.info ('Schema copy %s has been created to update schema', schemadir) + logger.info( + "Schema copy %s has been created to update schema", schemadir + ) except: - logger.info('Unable to create schema copy %s', schemadir) - print('Cannot create schema copy on ', schemadir) + logger.info("Unable to create schema copy %s", schemadir) + print("Cannot create schema copy on ", schemadir) exit(0) ## Get INF alleles for each core gene and update each locus fasta file for core_file in core_gene_list_files: - core_name = os.path.basename(core_file).split('.')[0] + core_name = os.path.basename(core_file).split(".")[0] if core_name in inferred_alleles_dict: - logger.info('Updating core gene file %s adding new INF alleles', core_file) + logger.info( + "Updating core gene file %s adding new INF alleles", core_file + ) inf_list = inferred_alleles_dict[core_name] try: - alleles_ids = [int(allele) for allele in alleles_in_locus_dict[core_name]] + alleles_ids = [ + int(allele) for allele in alleles_in_locus_dict[core_name] + ] allele_number = max(alleles_ids) - locus_schema_file = os.path.join(schemadir, core_name + '.fasta') - with open (locus_schema_file, 'a') as core_fh: + locus_schema_file = os.path.join(schemadir, core_name + ".fasta") + with open(locus_schema_file, "a") as core_fh: for inf in inf_list: allele_number += 1 - core_fh.write('\n' + '>' + str(allele_number) + ' # ' + 'INF by Taranis' + '\n' + inf + '\n') + core_fh.write( + "\n" + + ">" + + str(allele_number) + + " # " + + "INF by Taranis" + + "\n" + + inf + + "\n" + ) except: - alleles_ids = [int(allele.split('_')[-1]) for allele in alleles_in_locus_dict[core_name]] + alleles_ids = [ + int(allele.split("_")[-1]) + for allele in alleles_in_locus_dict[core_name] + ] allele_number = max(alleles_ids) - locus_schema_file = os.path.join(schemadir, core_name + '.fasta') - with open (locus_schema_file, 'a') as core_fh: + locus_schema_file = os.path.join(schemadir, core_name + ".fasta") + with open(locus_schema_file, "a") as core_fh: for inf in inf_list: allele_number += 1 - complete_inf_id = core_name + '_' + str(allele_number) - core_fh.write('\n' + '>' + complete_inf_id + ' # ' + 'INF by Taranis' + '\n' + inf + '\n') + complete_inf_id = core_name + "_" + str(allele_number) + core_fh.write( + "\n" + + ">" + + complete_inf_id + + " # " + + "INF by Taranis" + + "\n" + + inf + + "\n" + ) return True + """ def update_schema (updateschema, schemadir, storedir, core_gene_list_files, inferred_alleles_dict, alleles_in_locus_dict, logger): @@ -1751,32 +2848,67 @@ def update_schema (updateschema, schemadir, storedir, core_gene_list_files, infe # Allele calling analysis to find each core gene in schema and its variants in samples # # · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # -def allele_call_nucleotides (core_gene_list_files, sample_list_files, alleles_in_locus_dict, contigs_in_sample_dict, query_directory, reference_alleles_directory, blast_db_directory, prodigal_directory, blast_results_seq_directory, blast_results_db_directory, inputdir, outputdir, cpus, percentlength, coverage, evalue, perc_identity_ref, perc_identity_loc, reward, penalty, gapopen, gapextend, max_target_seqs, max_hsps, num_threads, flankingnts, schema_variability, schema_statistics, schema_quality, annotation_core_dict, profile_csv_path, logger ): - prodigal_report = [] # TEMPORAL. prodigal_report para checkear las secuencias obtenidas con prodigal vs blast y las posiciones sstart y send +def allele_call_nucleotides( + core_gene_list_files, + sample_list_files, + alleles_in_locus_dict, + contigs_in_sample_dict, + query_directory, + reference_alleles_directory, + blast_db_directory, + prodigal_directory, + blast_results_seq_directory, + blast_results_db_directory, + inputdir, + outputdir, + cpus, + percentlength, + coverage, + evalue, + perc_identity_ref, + perc_identity_loc, + reward, + penalty, + gapopen, + gapextend, + max_target_seqs, + max_hsps, + num_threads, + flankingnts, + schema_variability, + schema_statistics, + schema_quality, + annotation_core_dict, + profile_csv_path, + logger, +): + prodigal_report = ( + [] + ) # TEMPORAL. prodigal_report para checkear las secuencias obtenidas con prodigal vs blast y las posiciones sstart y send # listas añadidas para calcular coverage medio de new_sseq con respecto a alelo para establecer coverage mínimo por debajo del cual considerar LNF - shorter_seq_coverage = [] # TEMPORAL - longer_seq_coverage = [] # TEMPORAL - equal_seq_coverage = [] # TEMPORAL + shorter_seq_coverage = [] # TEMPORAL + longer_seq_coverage = [] # TEMPORAL + equal_seq_coverage = [] # TEMPORAL # listas añadidas para calcular coverage medio de sseq con respecto a alelo tras blast para establecer coverage mínimo por debajo del cual considerar LNF - shorter_blast_seq_coverage = [] # TEMPORAL - longer_blast_seq_coverage = [] # TEMPORAL - equal_blast_seq_coverage = [] # TEMPORAL + shorter_blast_seq_coverage = [] # TEMPORAL + longer_blast_seq_coverage = [] # TEMPORAL + equal_blast_seq_coverage = [] # TEMPORAL full_gene_list = [] - samples_matrix_dict = {} # to keep allele number - matching_genes_dict = {} # to keep start and stop positions - exact_dict = {} # c/m: to keep exact matches found for each sample - inferred_alleles_dict = {} # to keep track of the new inferred alleles - inf_dict = {} # to keep inferred alleles found for each sample - paralog_dict = {} # to keep paralogs found for each sample - asm_dict = {} # c/m: to keep track of asm - alm_dict = {} # c/m: to keep track of alm - list_asm = {} # c/m: to keep asm found for each sample - list_alm = {} # c/m: to keep alm found for each sample - lnf_tpr_dict = {} # c/m: to keep locus not found for each sample - plot_dict = {} # c/m: to keep plots for each sample - snp_dict = {} # c/m: to keep snp information for each sample + samples_matrix_dict = {} # to keep allele number + matching_genes_dict = {} # to keep start and stop positions + exact_dict = {} # c/m: to keep exact matches found for each sample + inferred_alleles_dict = {} # to keep track of the new inferred alleles + inf_dict = {} # to keep inferred alleles found for each sample + paralog_dict = {} # to keep paralogs found for each sample + asm_dict = {} # c/m: to keep track of asm + alm_dict = {} # c/m: to keep track of alm + list_asm = {} # c/m: to keep asm found for each sample + list_alm = {} # c/m: to keep alm found for each sample + lnf_tpr_dict = {} # c/m: to keep locus not found for each sample + plot_dict = {} # c/m: to keep plots for each sample + snp_dict = {} # c/m: to keep snp information for each sample protein_dict = {} match_alignment_dict = {} @@ -1794,71 +2926,138 @@ def allele_call_nucleotides (core_gene_list_files, sample_list_files, alleles_in blast_parameters = '"6 , qseqid , sseqid , pident , qlen , length , mismatch , gapopen , evalue , bitscore , sstart , send , qstart , qend , sseq , qseq"' - print('Allele calling starts') - pbar = ProgressBar () - + print("Allele calling starts") + pbar = ProgressBar() ## # # # # # # # # # # # # # # # # # # # # # # # # ## ## Processing the search for each schema core gene ## ## # # # # # # # # # # # # # # # # # # # # # # # # ## - for core_file in pbar(core_gene_list_files) : - core_name = os.path.basename(core_file).split('.')[0] + for core_file in pbar(core_gene_list_files): + core_name = os.path.basename(core_file).split(".")[0] full_gene_list.append(core_name) - logger.info('Processing core gene file %s ', core_file) + logger.info("Processing core gene file %s ", core_file) # Get path to this locus fasta file - locus_alleles_path = os.path.join(query_directory, str(core_name + '.fasta')) + locus_alleles_path = os.path.join(query_directory, str(core_name + ".fasta")) # Get path to reference allele fasta file for this locus - core_reference_allele_path = os.path.join(reference_alleles_directory, core_name + '.fasta') + core_reference_allele_path = os.path.join( + reference_alleles_directory, core_name + ".fasta" + ) # Get length thresholds for INF, ASM and ALM classification - max_length_threshold, min_length_threshold = length_thresholds(core_name, schema_statistics, percentlength) + max_length_threshold, min_length_threshold = length_thresholds( + core_name, schema_statistics, percentlength + ) # Get length thresholds for LNF, ASM and ALM classification - max_coverage_threshold, min_coverage_threshold = length_thresholds(core_name, schema_statistics, coverage) + max_coverage_threshold, min_coverage_threshold = length_thresholds( + core_name, schema_statistics, coverage + ) ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## ## Processing the search for each schema core gene in each sample ## ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## for sample_file in sample_list_files: - logger.info('Processing sample file %s ', sample_file) + logger.info("Processing sample file %s ", sample_file) - sample_name = '.'.join(os.path.basename(sample_file).split('.')[:-1]) + sample_name = ".".join(os.path.basename(sample_file).split(".")[:-1]) # (recuento tags para plots) if sample_name not in count_exact: - count_exact[sample_name] = {"good_quality" : 0, "bad_quality" : 0, "no_start" : 0, "no_start_stop" : 0, "no_stop" : 0, "multiple_stop" : 0, "total" : 0} + count_exact[sample_name] = { + "good_quality": 0, + "bad_quality": 0, + "no_start": 0, + "no_start_stop": 0, + "no_stop": 0, + "multiple_stop": 0, + "total": 0, + } if sample_name not in count_inf: - count_inf[sample_name] = {"good_quality" : 0, "bad_quality" : 0, "no_start" : 0, "no_start_stop" : 0, "no_stop" : 0, "multiple_stop" : 0, "total" : 0} + count_inf[sample_name] = { + "good_quality": 0, + "bad_quality": 0, + "no_start": 0, + "no_start_stop": 0, + "no_stop": 0, + "multiple_stop": 0, + "total": 0, + } if sample_name not in count_asm: - count_asm[sample_name] = {"insertion" : 0, "deletion" : 0, "substitution" : 0, "total" : 0} + count_asm[sample_name] = { + "insertion": 0, + "deletion": 0, + "substitution": 0, + "total": 0, + } if sample_name not in count_alm: - count_alm[sample_name] = {"insertion" : 0, "deletion" : 0, "substitution" : 0, "total" : 0} + count_alm[sample_name] = { + "insertion": 0, + "deletion": 0, + "substitution": 0, + "total": 0, + } if sample_name not in count_lnf: - count_lnf[sample_name] = {"not_found" : 0, "low_id" : 0, "low_coverage" : 0, "total" : 0} + count_lnf[sample_name] = { + "not_found": 0, + "low_id": 0, + "low_coverage": 0, + "total": 0, + } if sample_name not in count_tpr: - count_tpr[sample_name] = {"good_quality" : 0, "bad_quality" : 0, "no_start" : 0, "no_start_stop" : 0, "no_stop" : 0, "multiple_stop" : 0, "total" : 0} + count_tpr[sample_name] = { + "good_quality": 0, + "bad_quality": 0, + "no_start": 0, + "no_start_stop": 0, + "no_stop": 0, + "multiple_stop": 0, + "total": 0, + } if sample_name not in count_plot: - count_plot[sample_name] = {"total" : 0} + count_plot[sample_name] = {"total": 0} if sample_name not in count_niph: - count_niph[sample_name] = {"good_quality" : 0, "bad_quality" : 0, "no_start" : 0, "no_start_stop" : 0, "no_stop" : 0, "multiple_stop" : 0, "total" : 0} + count_niph[sample_name] = { + "good_quality": 0, + "bad_quality": 0, + "no_start": 0, + "no_start_stop": 0, + "no_stop": 0, + "multiple_stop": 0, + "total": 0, + } if sample_name not in count_niphem: - count_niphem[sample_name] = {"good_quality" : 0, "bad_quality" : 0, "no_start" : 0, "no_start_stop" : 0, "no_stop" : 0, "multiple_stop" : 0, "total" : 0} + count_niphem[sample_name] = { + "good_quality": 0, + "bad_quality": 0, + "no_start": 0, + "no_start_stop": 0, + "no_stop": 0, + "multiple_stop": 0, + "total": 0, + } if sample_name not in count_error: - count_error[sample_name] = {"good_quality" : 0, "bad_quality" : 0, "no_start" : 0, "no_start_stop" : 0, "no_stop" : 0, "multiple_stop" : 0, "total" : 0} - + count_error[sample_name] = { + "good_quality": 0, + "bad_quality": 0, + "no_start": 0, + "no_start_stop": 0, + "no_stop": 0, + "multiple_stop": 0, + "total": 0, + } # Initialize the sample list to add the number of alleles and the start, stop positions if not sample_name in samples_matrix_dict: @@ -1872,7 +3071,20 @@ def allele_call_nucleotides (core_gene_list_files, sample_list_files, alleles_in # Sample contigs VS reference allele(s) BLAST for locus detection in sample # # * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * # - cline = NcbiblastnCommandline(db=blast_db_name, evalue=evalue, perc_identity=perc_identity_ref, reward=reward, penalty=penalty, gapopen=gapopen, gapextend=gapextend, outfmt=blast_parameters, max_target_seqs=max_target_seqs, max_hsps=max_hsps, num_threads=num_threads, query=core_reference_allele_path) + cline = NcbiblastnCommandline( + db=blast_db_name, + evalue=evalue, + perc_identity=perc_identity_ref, + reward=reward, + penalty=penalty, + gapopen=gapopen, + gapextend=gapextend, + outfmt=blast_parameters, + max_target_seqs=max_target_seqs, + max_hsps=max_hsps, + num_threads=num_threads, + query=core_reference_allele_path, + ) out, err = cline() out_lines = out.splitlines() @@ -1881,49 +3093,113 @@ def allele_call_nucleotides (core_gene_list_files, sample_list_files, alleles_in # ······························································ # # LNF if there are no BLAST results for this gene in this sample # # ······························································ # - if len (out_lines) == 0: - + if len(out_lines) == 0: # Trying to get the allele number to avoid that a bad quality assembly impact on the tree diagram - cline = NcbiblastnCommandline(db=blast_db_name, evalue=evalue, perc_identity = 70, reward=reward, penalty=penalty, gapopen=gapopen, gapextend=gapextend, outfmt=blast_parameters, max_target_seqs=1, max_hsps=1, num_threads=1, query=core_reference_allele_path) + cline = NcbiblastnCommandline( + db=blast_db_name, + evalue=evalue, + perc_identity=70, + reward=reward, + penalty=penalty, + gapopen=gapopen, + gapextend=gapextend, + outfmt=blast_parameters, + max_target_seqs=1, + max_hsps=1, + num_threads=1, + query=core_reference_allele_path, + ) out, err = cline() out_lines = out.splitlines() - if len (out_lines) > 0 : - - for line in out_lines : - values = line.split('\t') - if float(values[8]) > bigger_bitscore: - qseqid , sseqid , pident , qlen , s_length , mismatch , r_gapopen , r_evalue , bitscore , sstart , send , qstart , qend ,sseq , qseq = values + if len(out_lines) > 0: + for line in out_lines: + values = line.split("\t") + if float(values[8]) > bigger_bitscore: + ( + qseqid, + sseqid, + pident, + qlen, + s_length, + mismatch, + r_gapopen, + r_evalue, + bitscore, + sstart, + send, + qstart, + qend, + sseq, + qseq, + ) = values bigger_bitscore = float(bitscore) # Keep LNF info - lnf_tpr_tag(core_name, sample_name, alleles_in_locus_dict, samples_matrix_dict, lnf_tpr_dict, schema_statistics, locus_alleles_path, qseqid, pident, '-', '-', perc_identity_ref, '-', schema_quality, annotation_core_dict, count_lnf, logger) + lnf_tpr_tag( + core_name, + sample_name, + alleles_in_locus_dict, + samples_matrix_dict, + lnf_tpr_dict, + schema_statistics, + locus_alleles_path, + qseqid, + pident, + "-", + "-", + perc_identity_ref, + "-", + schema_quality, + annotation_core_dict, + count_lnf, + logger, + ) else: # Keep LNF info - lnf_tpr_tag(core_name, sample_name, '-', samples_matrix_dict, lnf_tpr_dict, schema_statistics, locus_alleles_path, '-', '-', '-', '-', '-', '-', schema_quality, annotation_core_dict, count_lnf, logger) + lnf_tpr_tag( + core_name, + sample_name, + "-", + samples_matrix_dict, + lnf_tpr_dict, + schema_statistics, + locus_alleles_path, + "-", + "-", + "-", + "-", + "-", + "-", + schema_quality, + annotation_core_dict, + count_lnf, + logger, + ) continue ## Continue classification process if the core gene has been detected in sample after BLAST search - if len (out_lines) > 0: - + if len(out_lines) > 0: # Parse contigs for this sample - #contig_file = os.path.join(inputdir, sample_name + ".fasta") ## parse - #records = list(SeqIO.parse(contig_file, "fasta")) ## parse + # contig_file = os.path.join(inputdir, sample_name + ".fasta") ## parse + # records = list(SeqIO.parse(contig_file, "fasta")) ## parse ## Keep BLAST results after locus detection in sample using reference allele # Path to BLAST results fasta file - path_to_blast_seq = os.path.join(blast_results_seq_directory, sample_name, core_name + "_blast.fasta") + path_to_blast_seq = os.path.join( + blast_results_seq_directory, sample_name, core_name + "_blast.fasta" + ) - with open (path_to_blast_seq, 'w') as outblast_fh: + with open(path_to_blast_seq, "w") as outblast_fh: seq_number = 1 - for line in out_lines : - values = line.split('\t') + for line in out_lines: + values = line.split("\t") qseqid = values[0] if values[1] not in contigs_in_sample_dict[sample_name]: - sseqid = '|'.join(values[1].split('|')[1:-1]) + sseqid = "|".join(values[1].split("|")[1:-1]) else: sseqid = values[1] sstart = values[9] @@ -1932,10 +3208,10 @@ def allele_call_nucleotides (core_gene_list_files, sample_list_files, alleles_in # Get flanked BLAST sequences from contig for correct allele tagging accession_sequence = contigs_in_sample_dict[sample_name][sseqid] - #for record in records: ## parse - #if record.id == sseqid : ## parse - #break ## parse - #accession_sequence = str(record.seq) ## parse + # for record in records: ## parse + # if record.id == sseqid : ## parse + # break ## parse + # accession_sequence = str(record.seq) ## parse if int(send) > int(sstart): max_index = int(send) @@ -1946,94 +3222,188 @@ def allele_call_nucleotides (core_gene_list_files, sample_list_files, alleles_in if (flankingnts + 1) <= min_index: if flankingnts <= (len(accession_sequence) - max_index): - flanked_sseq = accession_sequence[ min_index -1 -flankingnts : max_index + flankingnts ] + flanked_sseq = accession_sequence[ + min_index + - 1 + - flankingnts : max_index + + flankingnts + ] else: - flanked_sseq = accession_sequence[ min_index -1 -flankingnts : ] + flanked_sseq = accession_sequence[ + min_index - 1 - flankingnts : + ] else: - flanked_sseq = accession_sequence[ : max_index + flankingnts ] - - seq_id = str(seq_number) + '_' + sseqid - outblast_fh.write('>' + seq_id + ' # ' + ' # '.join(values[0:13]) + '\n' + flanked_sseq + '\n' + '\n' ) + flanked_sseq = accession_sequence[: max_index + flankingnts] + + seq_id = str(seq_number) + "_" + sseqid + outblast_fh.write( + ">" + + seq_id + + " # " + + " # ".join(values[0:13]) + + "\n" + + flanked_sseq + + "\n" + + "\n" + ) seq_number += 1 ## Create local BLAST database for BLAST results after locus detection in sample using reference allele db_name = os.path.join(blast_results_db_directory, sample_name) - if not create_blastdb(path_to_blast_seq, db_name, 'nucl', logger): - print('Error when creating the blastdb for blast results file for locus %s at sample %s. Check log file for more information. \n ', core_name, sample_name) + if not create_blastdb(path_to_blast_seq, db_name, "nucl", logger): + print( + "Error when creating the blastdb for blast results file for locus %s at sample %s. Check log file for more information. \n ", + core_name, + sample_name, + ) return False # Path to local BLAST database for BLAST results after locus detection in sample using reference allele - locus_blast_db_name = os.path.join(blast_results_db_directory, sample_name, os.path.basename(core_name) + '_blast', os.path.basename(core_name) + '_blast') - + locus_blast_db_name = os.path.join( + blast_results_db_directory, + sample_name, + os.path.basename(core_name) + "_blast", + os.path.basename(core_name) + "_blast", + ) # * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * # # BLAST result sequences VS ALL alleles in locus BLAST for allele identification detection in sample # # * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * # - cline = NcbiblastnCommandline(db=locus_blast_db_name, evalue=evalue, perc_identity=perc_identity_loc, reward=reward, penalty=penalty, gapopen=gapopen, gapextend=gapextend, outfmt = blast_parameters, max_target_seqs=max_target_seqs, max_hsps=max_hsps, num_threads=num_threads, query=locus_alleles_path) + cline = NcbiblastnCommandline( + db=locus_blast_db_name, + evalue=evalue, + perc_identity=perc_identity_loc, + reward=reward, + penalty=penalty, + gapopen=gapopen, + gapextend=gapextend, + outfmt=blast_parameters, + max_target_seqs=max_target_seqs, + max_hsps=max_hsps, + num_threads=num_threads, + query=locus_alleles_path, + ) out, err = cline() out_lines = out.splitlines() - allele_found = {} # To keep filtered BLAST results + allele_found = {} # To keep filtered BLAST results ## Check if there is any BLAST result with ID = 100 ## for line in out_lines: - - values = line.split('\t') + values = line.split("\t") pident = values[2] if float(pident) == 100: - - qseqid, sseqid, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart, send, qstart, qend, sseq, qseq = values + ( + qseqid, + sseqid, + pident, + qlen, + s_length, + mismatch, + r_gapopen, + r_evalue, + bitscore, + sstart, + send, + qstart, + qend, + sseq, + qseq, + ) = values # Parse core gene fasta file to get matching allele sequence and length - #alleles_in_locus = list (SeqIO.parse(locus_alleles_path, "fasta")) ## parse - #for allele in alleles_in_locus : ## parse - #if allele.id == qseqid : ## parse - #break ## comentar parse - #matching_allele_seq = str(allele.seq) ## parse - #matching_allele_length = len(matching_allele_seq) ## parse + # alleles_in_locus = list (SeqIO.parse(locus_alleles_path, "fasta")) ## parse + # for allele in alleles_in_locus : ## parse + # if allele.id == qseqid : ## parse + # break ## comentar parse + # matching_allele_seq = str(allele.seq) ## parse + # matching_allele_length = len(matching_allele_seq) ## parse matching_allele_seq = alleles_in_locus_dict[core_name][qseqid] matching_allele_length = len(matching_allele_seq) # Keep BLAST results with ID = 100 and same length as matching allele if int(s_length) == matching_allele_length: - #get_blast_results (values, records, allele_found, logger) - get_blast_results (sample_name, values, contigs_in_sample_dict, allele_found, logger) + # get_blast_results (values, records, allele_found, logger) + get_blast_results( + sample_name, + values, + contigs_in_sample_dict, + allele_found, + logger, + ) # ·································································································································· # # NIPHEM (paralog) if there are multiple BLAST results with ID = 100 and same length as matching allele for this gene in this sample # # ·································································································································· # if len(allele_found) > 1: - # Keep NIPHEM info - paralog_exact_tag(sample_name, core_name, 'NIPHEM', schema_quality, matching_genes_dict, samples_matrix_dict, allele_found, paralog_dict, prodigal_report, prodigal_directory, blast_parameters, annotation_core_dict, count_niphem, logger) + paralog_exact_tag( + sample_name, + core_name, + "NIPHEM", + schema_quality, + matching_genes_dict, + samples_matrix_dict, + allele_found, + paralog_dict, + prodigal_report, + prodigal_directory, + blast_parameters, + annotation_core_dict, + count_niphem, + logger, + ) continue ## Check for possible paralogs with ID < 100 if there is only one BLAST result with ID = 100 and same length as matching allele - elif len(allele_found) == 1 : + elif len(allele_found) == 1: + for line in out_lines: + values = line.split("\t") - for line in out_lines : - values = line.split('\t') - - sseq_no_gaps = values[13].replace('-', '') + sseq_no_gaps = values[13].replace("-", "") s_length_no_gaps = len(sseq_no_gaps) # Keep BLAST result if its coverage is within min and max thresholds - if min_length_threshold <= s_length_no_gaps <= max_length_threshold: - #get_blast_results (values, records, allele_found, logger) - get_blast_results (sample_name, values, contigs_in_sample_dict, allele_found, logger) + if ( + min_length_threshold + <= s_length_no_gaps + <= max_length_threshold + ): + # get_blast_results (values, records, allele_found, logger) + get_blast_results( + sample_name, + values, + contigs_in_sample_dict, + allele_found, + logger, + ) # ································································ # # EXACT MATCH if there is any paralog for this gene in this sample # # ································································ # - if len(allele_found) == 1 : - - paralog_exact_tag(sample_name, core_name, 'EXACT', schema_quality, matching_genes_dict, samples_matrix_dict, allele_found, exact_dict, prodigal_report, prodigal_directory, blast_parameters, annotation_core_dict, count_exact, logger) + if len(allele_found) == 1: + paralog_exact_tag( + sample_name, + core_name, + "EXACT", + schema_quality, + matching_genes_dict, + samples_matrix_dict, + allele_found, + exact_dict, + prodigal_report, + prodigal_directory, + blast_parameters, + annotation_core_dict, + count_exact, + logger, + ) continue @@ -2041,111 +3411,267 @@ def allele_call_nucleotides (core_gene_list_files, sample_list_files, alleles_in # NIPH if there there are paralogs with ID < 100 for this gene in this sample # # ··········································································· # else: - - paralog_exact_tag(sample_name, core_name, 'NIPH', schema_quality, matching_genes_dict, samples_matrix_dict, allele_found, paralog_dict, prodigal_report, prodigal_directory, blast_parameters, annotation_core_dict, count_niph, logger) + paralog_exact_tag( + sample_name, + core_name, + "NIPH", + schema_quality, + matching_genes_dict, + samples_matrix_dict, + allele_found, + paralog_dict, + prodigal_report, + prodigal_directory, + blast_parameters, + annotation_core_dict, + count_niph, + logger, + ) continue ## Look for the best BLAST result if there are no results with ID = 100 ## elif len(allele_found) == 0: - bigger_bitscore_seq_values = [] - for line in out_lines : - values = line.split('\t') + for line in out_lines: + values = line.split("\t") - if float(values[8]) > bigger_bitscore: - s_length_no_gaps = len(values[13].replace('-', '')) + if float(values[8]) > bigger_bitscore: + s_length_no_gaps = len(values[13].replace("-", "")) # Keep BLAST result if its coverage is within min and max thresholds and its bitscore is bigger than the one previously kept - if min_coverage_threshold <= s_length_no_gaps <= max_coverage_threshold: + if ( + min_coverage_threshold + <= s_length_no_gaps + <= max_coverage_threshold + ): bigger_bitscore_seq_values = values bigger_bitscore = float(bigger_bitscore_seq_values[8]) ## Check if best BLAST result out of coverage thresholds is a possible PLOT or LNF due to low coverage ## - #if len(allele_found) == 0: + # if len(allele_found) == 0: if len(bigger_bitscore_seq_values) == 0: - # Look for best bitscore BLAST result out of coverage thresholds to check possible PLOT or reporting LNF due to low coverage - for line in out_lines : - values = line.split('\t') - - if float(values[8]) > bigger_bitscore: - qseqid, sseqid, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart, send, qstart, qend, sseq, qseq = values - bigger_bitscore_seq_values_out_cov = values ### + for line in out_lines: + values = line.split("\t") + + if float(values[8]) > bigger_bitscore: + ( + qseqid, + sseqid, + pident, + qlen, + s_length, + mismatch, + r_gapopen, + r_evalue, + bitscore, + sstart, + send, + qstart, + qend, + sseq, + qseq, + ) = values + bigger_bitscore_seq_values_out_cov = values ### bigger_bitscore = float(bitscore) # Get BLAST values relatives to contig for bigger bitscore result - lnf_plot_found = {} ### - - get_blast_results (sample_name, bigger_bitscore_seq_values_out_cov, contigs_in_sample_dict, lnf_plot_found, logger) ### - - allele_id = str(list(lnf_plot_found.keys())[0]) ### - qseqid, sseqid, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart, send, qstart, qend, sseq, qseq = lnf_plot_found[allele_id] + lnf_plot_found = {} ### + + get_blast_results( + sample_name, + bigger_bitscore_seq_values_out_cov, + contigs_in_sample_dict, + lnf_plot_found, + logger, + ) ### + + allele_id = str(list(lnf_plot_found.keys())[0]) ### + ( + qseqid, + sseqid, + pident, + qlen, + s_length, + mismatch, + r_gapopen, + r_evalue, + bitscore, + sstart, + send, + qstart, + qend, + sseq, + qseq, + ) = lnf_plot_found[allele_id] # Get contig sequence and length for best bitscore BLAST result ID - #for record in records: ## parse - #if record.id == sseqid : ## parse - #break ## parse - #accession_sequence = record.seq ## parse - #length_sseqid = len(accession_sequence) ## parse + # for record in records: ## parse + # if record.id == sseqid : ## parse + # break ## parse + # accession_sequence = record.seq ## parse + # length_sseqid = len(accession_sequence) ## parse accession_sequence = contigs_in_sample_dict[sample_name][sseqid] length_sseqid = len(accession_sequence) # Check if best BLAST result out of coverage thresholds is a possible PLOT. If so, keep result info for later PLOT classification - if int(sstart) == length_sseqid or int(send) == length_sseqid or int(sstart) == 1 or int(send) == 1: - bigger_bitscore_seq_values = bigger_bitscore_seq_values_out_cov ### + if ( + int(sstart) == length_sseqid + or int(send) == length_sseqid + or int(sstart) == 1 + or int(send) == 1 + ): + bigger_bitscore_seq_values = ( + bigger_bitscore_seq_values_out_cov ### + ) # ·············································································································································· # # LNF if there are no BLAST results within coverage thresholds for this gene in this sample and best out threshold result is not a possible PLOT # # ·············································································································································· # else: # Get sequence length - s_length_no_gaps = len(bigger_bitscore_seq_values_out_cov[13].replace('-', '')) + s_length_no_gaps = len( + bigger_bitscore_seq_values_out_cov[13].replace("-", "") + ) # Keep LNF info - lnf_tpr_tag(core_name, sample_name, alleles_in_locus_dict, samples_matrix_dict, lnf_tpr_dict, schema_statistics, locus_alleles_path, qseqid, pident, s_length_no_gaps, '-', '-', coverage, schema_quality, annotation_core_dict, count_lnf, logger) + lnf_tpr_tag( + core_name, + sample_name, + alleles_in_locus_dict, + samples_matrix_dict, + lnf_tpr_dict, + schema_statistics, + locus_alleles_path, + qseqid, + pident, + s_length_no_gaps, + "-", + "-", + coverage, + schema_quality, + annotation_core_dict, + count_lnf, + logger, + ) ## Keep result with bigger bitscore in allele_found dict and look for possible paralogs ## if len(bigger_bitscore_seq_values) > 0: - - qseqid, sseqid, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart, send, qstart, qend, sseq, qseq = bigger_bitscore_seq_values - - #get_blast_results (bigger_bitscore_seq_values, records, allele_found, logger) - get_blast_results (sample_name, bigger_bitscore_seq_values, contigs_in_sample_dict, allele_found, logger) + ( + qseqid, + sseqid, + pident, + qlen, + s_length, + mismatch, + r_gapopen, + r_evalue, + bitscore, + sstart, + send, + qstart, + qend, + sseq, + qseq, + ) = bigger_bitscore_seq_values + + # get_blast_results (bigger_bitscore_seq_values, records, allele_found, logger) + get_blast_results( + sample_name, + bigger_bitscore_seq_values, + contigs_in_sample_dict, + allele_found, + logger, + ) # Possible paralogs search - for line in out_lines : - values = line.split('\t') - - qseqid, sseqid, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart, send, qstart, qend, sseq, qseq = values - sseq_no_gaps = sseq.replace('-', '') + for line in out_lines: + values = line.split("\t") + + ( + qseqid, + sseqid, + pident, + qlen, + s_length, + mismatch, + r_gapopen, + r_evalue, + bitscore, + sstart, + send, + qstart, + qend, + sseq, + qseq, + ) = values + sseq_no_gaps = sseq.replace("-", "") s_length_no_gaps = len(sseq_no_gaps) - if min_length_threshold <= s_length_no_gaps <= max_length_threshold: - - #get_blast_results (values, records, allele_found, logger) - get_blast_results (sample_name, values, contigs_in_sample_dict, allele_found, logger) + if ( + min_length_threshold + <= s_length_no_gaps + <= max_length_threshold + ): + # get_blast_results (values, records, allele_found, logger) + get_blast_results( + sample_name, + values, + contigs_in_sample_dict, + allele_found, + logger, + ) # ····························································· # # NIPH if there there are paralogs for this gene in this sample # # ····························································· # - if len(allele_found) > 1 : - - paralog_exact_tag(sample_name, core_name, 'NIPH', schema_quality, matching_genes_dict, samples_matrix_dict, allele_found, paralog_dict, prodigal_report, prodigal_directory, blast_parameters, annotation_core_dict, count_niph, logger) + if len(allele_found) > 1: + paralog_exact_tag( + sample_name, + core_name, + "NIPH", + schema_quality, + matching_genes_dict, + samples_matrix_dict, + allele_found, + paralog_dict, + prodigal_report, + prodigal_directory, + blast_parameters, + annotation_core_dict, + count_niph, + logger, + ) continue ## Continue classification if there are no paralogs ## - elif len(allele_found) == 1 : - + elif len(allele_found) == 1: allele_id = str(list(allele_found.keys())[0]) - qseqid, sseqid, pident, qlen, s_length, mismatch, r_gapopen, r_evalue, bitscore, sstart, send, qstart, qend, sseq, qseq = allele_found[allele_id] - - sseq_no_gaps = sseq.replace('-', '') + ( + qseqid, + sseqid, + pident, + qlen, + s_length, + mismatch, + r_gapopen, + r_evalue, + bitscore, + sstart, + send, + qstart, + qend, + sseq, + qseq, + ) = allele_found[allele_id] + + sseq_no_gaps = sseq.replace("-", "") s_length_no_gaps = len(sseq_no_gaps) # Get matching allele quality @@ -2153,48 +3679,99 @@ def allele_call_nucleotides (core_gene_list_files, sample_list_files, alleles_in # Get matching allele sequence and length - #alleles_in_locus = list (SeqIO.parse(locus_alleles_path, "fasta")) ## parse - #for allele in alleles_in_locus : ## parse - #if allele.id == qseqid : ## parse - #break ## parse - #matching_allele_seq = allele.seq ## parse - #matching_allele_length = len(matching_allele_seq) ## parse + # alleles_in_locus = list (SeqIO.parse(locus_alleles_path, "fasta")) ## parse + # for allele in alleles_in_locus : ## parse + # if allele.id == qseqid : ## parse + # break ## parse + # matching_allele_seq = allele.seq ## parse + # matching_allele_length = len(matching_allele_seq) ## parse - matching_allele_seq = alleles_in_locus_dict [core_name][qseqid] + matching_allele_seq = alleles_in_locus_dict[core_name][ + qseqid + ] matching_allele_length = len(matching_allele_seq) # Get contig sequence and length for ID found in BLAST - #for record in records: ## parse - #if record.id == sseqid : ## parse - #break ## parse - #accession_sequence = record.seq ## parse - #length_sseqid = len(accession_sequence) ## parse + # for record in records: ## parse + # if record.id == sseqid : ## parse + # break ## parse + # accession_sequence = record.seq ## parse + # length_sseqid = len(accession_sequence) ## parse - accession_sequence = contigs_in_sample_dict[sample_name][sseqid] + accession_sequence = contigs_in_sample_dict[sample_name][ + sseqid + ] length_sseqid = len(accession_sequence) # ········································································································· # # PLOT if found sequence is shorter than matching allele and it is located on the edge of the sample contig # # ········································································································· # - if int(sstart) == length_sseqid or int(send) == length_sseqid or int(sstart) == 1 or int(send) == 1: + if ( + int(sstart) == length_sseqid + or int(send) == length_sseqid + or int(sstart) == 1 + or int(send) == 1 + ): if int(s_length) < matching_allele_length: - ### sacar sec prodigal para PLOT? # Get prodigal predicted sequence if matching allele quality is "bad quality" - if 'bad_quality' in allele_quality: - complete_predicted_seq, start_prodigal, end_prodigal = get_prodigal_sequence(sseq_no_gaps, sseqid, prodigal_directory, sample_name, blast_parameters, logger) + if "bad_quality" in allele_quality: + ( + complete_predicted_seq, + start_prodigal, + end_prodigal, + ) = get_prodigal_sequence( + sseq_no_gaps, + sseqid, + prodigal_directory, + sample_name, + blast_parameters, + logger, + ) # Keep info for prodigal report - prodigal_report.append([core_name, sample_name, qseqid, 'PLOT', sstart, send, start_prodigal, end_prodigal, sseq_no_gaps, complete_predicted_seq]) + prodigal_report.append( + [ + core_name, + sample_name, + qseqid, + "PLOT", + sstart, + send, + start_prodigal, + end_prodigal, + sseq_no_gaps, + complete_predicted_seq, + ] + ) else: - complete_predicted_seq = '-' - start_prodigal = '-' - end_prodigal = '-' + complete_predicted_seq = "-" + start_prodigal = "-" + end_prodigal = "-" # Keep PLOT info - inf_asm_alm_tag(core_name, sample_name, 'PLOT', allele_found[allele_id], allele_quality, '-', matching_allele_length, '-', plot_dict, samples_matrix_dict, matching_genes_dict, prodigal_report, start_prodigal, end_prodigal, complete_predicted_seq, annotation_core_dict, count_plot, logger) + inf_asm_alm_tag( + core_name, + sample_name, + "PLOT", + allele_found[allele_id], + allele_quality, + "-", + matching_allele_length, + "-", + plot_dict, + samples_matrix_dict, + matching_genes_dict, + prodigal_report, + start_prodigal, + end_prodigal, + complete_predicted_seq, + annotation_core_dict, + count_plot, + logger, + ) continue @@ -2203,121 +3780,345 @@ def allele_call_nucleotides (core_gene_list_files, sample_list_files, alleles_in # * * * * * * * * * * * * * * * * * * * * # ## Get Prodigal predicted sequence ## - complete_predicted_seq, start_prodigal, end_prodigal = get_prodigal_sequence(sseq_no_gaps, sseqid, prodigal_directory, sample_name, blast_parameters, logger) + ( + complete_predicted_seq, + start_prodigal, + end_prodigal, + ) = get_prodigal_sequence( + sseq_no_gaps, + sseqid, + prodigal_directory, + sample_name, + blast_parameters, + logger, + ) ## Search for new codon stop using contig sequence info ## # Check matching allele sequence direction - query_direction = check_sequence_order(matching_allele_seq, logger) + query_direction = check_sequence_order( + matching_allele_seq, logger + ) # Get extended BLAST sequence for stop codon search - if query_direction == 'reverse': - if int(send) > int (sstart): - sample_gene_sequence = accession_sequence[ : int(send) ] - sample_gene_sequence = str(Seq.Seq(sample_gene_sequence).reverse_complement()) + if query_direction == "reverse": + if int(send) > int(sstart): + sample_gene_sequence = accession_sequence[ + : int(send) + ] + sample_gene_sequence = str( + Seq.Seq( + sample_gene_sequence + ).reverse_complement() + ) else: - sample_gene_sequence = accession_sequence[ int(send) -1 : ] + sample_gene_sequence = accession_sequence[ + int(send) - 1 : + ] else: - if int(sstart) > int (send): - sample_gene_sequence = accession_sequence[ : int(sstart) ] - sample_gene_sequence = str(Seq.Seq(sample_gene_sequence).reverse_complement()) + if int(sstart) > int(send): + sample_gene_sequence = accession_sequence[ + : int(sstart) + ] + sample_gene_sequence = str( + Seq.Seq( + sample_gene_sequence + ).reverse_complement() + ) else: - sample_gene_sequence = accession_sequence[ int(sstart) -1 : ] + sample_gene_sequence = accession_sequence[ + int(sstart) - 1 : + ] # Get new stop codon index stop_index = get_stop_codon_index(sample_gene_sequence) ## Classification of final new sequence if it is found ## if stop_index != False: - new_sequence_length = stop_index +3 - new_sseq = str(sample_gene_sequence[0:new_sequence_length]) + new_sequence_length = stop_index + 3 + new_sseq = str( + sample_gene_sequence[0:new_sequence_length] + ) ######################################################################################################################### ### c/m: introducido para determinar qué umbral de coverage poner. TEMPORAL - new_sseq_coverage = new_sequence_length/matching_allele_length ### introduciendo coverage new_sseq /// debería ser con respecto a la media? + new_sseq_coverage = ( + new_sequence_length / matching_allele_length + ) ### introduciendo coverage new_sseq /// debería ser con respecto a la media? if new_sseq_coverage < 1: - shorter_seq_coverage.append([core_name, sample_name, str(matching_allele_length), str(new_sequence_length), str(schema_statistics[core_name][0]), str(new_sseq_coverage), str(new_sequence_length/schema_statistics[core_name][0])]) + shorter_seq_coverage.append( + [ + core_name, + sample_name, + str(matching_allele_length), + str(new_sequence_length), + str(schema_statistics[core_name][0]), + str(new_sseq_coverage), + str( + new_sequence_length + / schema_statistics[core_name][0] + ), + ] + ) elif new_sseq_coverage > 1: - longer_seq_coverage.append([core_name, sample_name, str(matching_allele_length), str(new_sequence_length), str(schema_statistics[core_name][0]), str(new_sseq_coverage), str(new_sequence_length/schema_statistics[core_name][0])]) + longer_seq_coverage.append( + [ + core_name, + sample_name, + str(matching_allele_length), + str(new_sequence_length), + str(schema_statistics[core_name][0]), + str(new_sseq_coverage), + str( + new_sequence_length + / schema_statistics[core_name][0] + ), + ] + ) elif new_sseq_coverage == 1: - equal_seq_coverage.append([core_name, sample_name, str(matching_allele_length), str(new_sequence_length), str(schema_statistics[core_name][0]), str(new_sseq_coverage), str(new_sequence_length/schema_statistics[core_name][0])]) + equal_seq_coverage.append( + [ + core_name, + sample_name, + str(matching_allele_length), + str(new_sequence_length), + str(schema_statistics[core_name][0]), + str(new_sseq_coverage), + str( + new_sequence_length + / schema_statistics[core_name][0] + ), + ] + ) ######################################################################################################################### # Get and keep SNP and DNA and protein alignment - keep_snp_alignment_info(sseq, new_sseq, matching_allele_seq, qseqid, query_direction, core_name, sample_name, reward, penalty, gapopen, gapextend, snp_dict, match_alignment_dict, protein_dict, logger) + keep_snp_alignment_info( + sseq, + new_sseq, + matching_allele_seq, + qseqid, + query_direction, + core_name, + sample_name, + reward, + penalty, + gapopen, + gapextend, + snp_dict, + match_alignment_dict, + protein_dict, + logger, + ) # ····································································································· # # INF if final new sequence length is within min and max length thresholds for this gene in this sample # # ····································································································· # - if min_length_threshold <= new_sequence_length <= max_length_threshold: - + if ( + min_length_threshold + <= new_sequence_length + <= max_length_threshold + ): # Keep INF info - inf_asm_alm_tag(core_name, sample_name, 'INF', allele_found[allele_id], allele_quality, new_sseq, matching_allele_length, inferred_alleles_dict, inf_dict, samples_matrix_dict, matching_genes_dict, prodigal_report, start_prodigal, end_prodigal, complete_predicted_seq, annotation_core_dict, count_inf, logger) ### introducido start_prodigal, end_prodigal, complete_predicted_seq, prodigal_report como argumento a inf_asm_alm_tag para report prodigal, temporal + inf_asm_alm_tag( + core_name, + sample_name, + "INF", + allele_found[allele_id], + allele_quality, + new_sseq, + matching_allele_length, + inferred_alleles_dict, + inf_dict, + samples_matrix_dict, + matching_genes_dict, + prodigal_report, + start_prodigal, + end_prodigal, + complete_predicted_seq, + annotation_core_dict, + count_inf, + logger, + ) ### introducido start_prodigal, end_prodigal, complete_predicted_seq, prodigal_report como argumento a inf_asm_alm_tag para report prodigal, temporal # ············································································································································ # # ASM if final new sequence length is under min length threshold but its coverage is above min coverage threshold for this gene in this sample # # ············································································································································ # - elif min_coverage_threshold <= new_sequence_length < min_length_threshold: - + elif ( + min_coverage_threshold + <= new_sequence_length + < min_length_threshold + ): # Keep ASM info - inf_asm_alm_tag(core_name, sample_name, 'ASM', allele_found[allele_id], allele_quality, new_sseq, matching_allele_length, asm_dict, list_asm, samples_matrix_dict, matching_genes_dict, prodigal_report, start_prodigal, end_prodigal, complete_predicted_seq, annotation_core_dict, count_asm, logger) + inf_asm_alm_tag( + core_name, + sample_name, + "ASM", + allele_found[allele_id], + allele_quality, + new_sseq, + matching_allele_length, + asm_dict, + list_asm, + samples_matrix_dict, + matching_genes_dict, + prodigal_report, + start_prodigal, + end_prodigal, + complete_predicted_seq, + annotation_core_dict, + count_asm, + logger, + ) # ············································································································································ # # ALM if final new sequence length is above max length threshold but its coverage is under max coverage threshold for this gene in this sample # # ············································································································································ # - elif max_length_threshold < new_sequence_length <= max_coverage_threshold: - + elif ( + max_length_threshold + < new_sequence_length + <= max_coverage_threshold + ): # Keep ALM info - inf_asm_alm_tag(core_name, sample_name, 'ALM', allele_found[allele_id], allele_quality, new_sseq, matching_allele_length, alm_dict, list_alm, samples_matrix_dict, matching_genes_dict, prodigal_report, start_prodigal, end_prodigal, complete_predicted_seq, annotation_core_dict, count_alm, logger) ### introducido start_prodigal, end_prodigal, complete_predicted_seq, prodigal_report como argumento a inf_asm_alm_tag para report prodigal, temporal + inf_asm_alm_tag( + core_name, + sample_name, + "ALM", + allele_found[allele_id], + allele_quality, + new_sseq, + matching_allele_length, + alm_dict, + list_alm, + samples_matrix_dict, + matching_genes_dict, + prodigal_report, + start_prodigal, + end_prodigal, + complete_predicted_seq, + annotation_core_dict, + count_alm, + logger, + ) ### introducido start_prodigal, end_prodigal, complete_predicted_seq, prodigal_report como argumento a inf_asm_alm_tag para report prodigal, temporal # ························································································· # # TPR if final new sequence coverage is not within thresholds for this gene in this sample # # ························································································· # else: - # Keep TPR info - lnf_tpr_tag(core_name, sample_name, alleles_in_locus_dict, samples_matrix_dict, lnf_tpr_dict, schema_statistics, locus_alleles_path, qseqid, pident, s_length_no_gaps, new_sequence_length, '-', coverage, schema_quality, annotation_core_dict, count_tpr, logger) + lnf_tpr_tag( + core_name, + sample_name, + alleles_in_locus_dict, + samples_matrix_dict, + lnf_tpr_dict, + schema_statistics, + locus_alleles_path, + qseqid, + pident, + s_length_no_gaps, + new_sequence_length, + "-", + coverage, + schema_quality, + annotation_core_dict, + count_tpr, + logger, + ) # ········································ # # ERROR if final new sequence is not found # # ········································ # else: - logger.error('ERROR : Stop codon was not found for the core %s and the sample %s', core_name, sample_name) - samples_matrix_dict[sample_name].append('ERROR not stop codon') - if not sseqid in matching_genes_dict[sample_name] : + logger.error( + "ERROR : Stop codon was not found for the core %s and the sample %s", + core_name, + sample_name, + ) + samples_matrix_dict[sample_name].append( + "ERROR not stop codon" + ) + if not sseqid in matching_genes_dict[sample_name]: matching_genes_dict[sample_name][sseqid] = [] - if sstart > send : - #matching_genes_dict[sample_name][sseqid].append([core_name, sstart, send,'-', 'ERROR']) - matching_genes_dict[sample_name][sseqid].append([core_name, qseqid, sstart, send,'-', 'ERROR']) + if sstart > send: + # matching_genes_dict[sample_name][sseqid].append([core_name, sstart, send,'-', 'ERROR']) + matching_genes_dict[sample_name][sseqid].append( + [core_name, qseqid, sstart, send, "-", "ERROR"] + ) else: - #matching_genes_dict[sample_name][sseqid].append([core_name, sstart,send,'+', 'ERROR']) - matching_genes_dict[sample_name][sseqid].append([core_name, qseqid, sstart, send,'+', 'ERROR']) + # matching_genes_dict[sample_name][sseqid].append([core_name, sstart,send,'+', 'ERROR']) + matching_genes_dict[sample_name][sseqid].append( + [core_name, qseqid, sstart, send, "+", "ERROR"] + ) # (recuento tags para plot) - count_error[sample_name]['total'] += 1 + count_error[sample_name]["total"] += 1 for count_class in count_error[sample_name]: if count_class in allele_quality: - if "no_start_stop" not in count_class and "no_start_stop" in allele_quality: + if ( + "no_start_stop" not in count_class + and "no_start_stop" in allele_quality + ): if count_class == "bad_quality": - count_error[sample_name][count_class] += 1 + count_error[sample_name][ + count_class + ] += 1 else: count_error[sample_name][count_class] += 1 - ## Save results and create reports - if not save_allele_call_results (outputdir, full_gene_list, samples_matrix_dict, exact_dict, paralog_dict, inf_dict, plot_dict, matching_genes_dict, list_asm, list_alm, lnf_tpr_dict, snp_dict, match_alignment_dict, protein_dict, prodigal_report, shorter_seq_coverage, longer_seq_coverage, equal_seq_coverage, shorter_blast_seq_coverage, longer_blast_seq_coverage, equal_blast_seq_coverage, logger): - print('There is an error while saving the allele calling results. Check the log file to get more information \n') - # exit(0) - + if not save_allele_call_results( + outputdir, + full_gene_list, + samples_matrix_dict, + exact_dict, + paralog_dict, + inf_dict, + plot_dict, + matching_genes_dict, + list_asm, + list_alm, + lnf_tpr_dict, + snp_dict, + match_alignment_dict, + protein_dict, + prodigal_report, + shorter_seq_coverage, + longer_seq_coverage, + equal_seq_coverage, + shorter_blast_seq_coverage, + longer_blast_seq_coverage, + equal_blast_seq_coverage, + logger, + ): + print( + "There is an error while saving the allele calling results. Check the log file to get more information \n" + ) + # exit(0) ## Saving sample results plots - if not save_allele_calling_plots (outputdir, sample_list_files, count_exact, count_inf, count_asm, count_alm, count_lnf, count_tpr, count_plot, count_niph, count_niphem, count_error, logger): - print('There is an error while saving the allele calling results plots. Check the log file to get more information \n') - + if not save_allele_calling_plots( + outputdir, + sample_list_files, + count_exact, + count_inf, + count_asm, + count_alm, + count_lnf, + count_tpr, + count_plot, + count_niph, + count_niphem, + count_error, + logger, + ): + print( + "There is an error while saving the allele calling results plots. Check the log file to get more information \n" + ) return True, inferred_alleles_dict, inf_dict, exact_dict @@ -2326,8 +4127,9 @@ def allele_call_nucleotides (core_gene_list_files, sample_list_files, alleles_in # Processing gene by gene allele calling # # * * * * * * * * * * * * * * * * * * * # -def processing_allele_calling (arguments) : - ''' + +def processing_allele_calling(arguments): + """ Description: This is the main function for allele calling. With the support of additional functions it will create the output files @@ -2340,93 +4142,145 @@ def processing_allele_calling (arguments) : ???? Return: ???? - ''' + """ start_time = datetime.now() - print('Start the execution at :', start_time ) + print("Start the execution at :", start_time) # Open log file - logger = open_log ('taranis_wgMLST.log') - #print('Checking the pre-requisites.') + logger = open_log("taranis_wgMLST.log") + # print('Checking the pre-requisites.') ############################################################ ## Check additional programs are installed in your system ## ############################################################ - #pre_requisites_list = [['blastp', '2.9'], ['makeblastdb', '2.9']] - #if not check_prerequisites (pre_requisites_list, logger): + # pre_requisites_list = [['blastp', '2.9'], ['makeblastdb', '2.9']] + # if not check_prerequisites (pre_requisites_list, logger): # print ('your system does not fulfill the pre-requistes to run the script ') # exit(0) ###################################################### ## Check that given directories contain fasta files ## ###################################################### - print('Validating schema fasta files in ' , arguments.coregenedir , '\n') + print("Validating schema fasta files in ", arguments.coregenedir, "\n") valid_core_gene_files = get_fasta_file_list(arguments.coregenedir, logger) - if not valid_core_gene_files : - print ('There are not valid fasta files in ', arguments.coregenedir , ' directory. Check log file for more information ') + if not valid_core_gene_files: + print( + "There are not valid fasta files in ", + arguments.coregenedir, + " directory. Check log file for more information ", + ) exit(0) - print('Validating reference alleles fasta files in ' , arguments.refalleles , '\n') + print("Validating reference alleles fasta files in ", arguments.refalleles, "\n") valid_reference_alleles_files = get_fasta_file_list(arguments.refalleles, logger) - if not valid_reference_alleles_files : - print ('There are not valid reference alleles fasta files in ', arguments.refalleles, ' directory. Check log file for more information ') + if not valid_reference_alleles_files: + print( + "There are not valid reference alleles fasta files in ", + arguments.refalleles, + " directory. Check log file for more information ", + ) exit(0) - print('Validating sample fasta files in ' , arguments.inputdir , '\n') + print("Validating sample fasta files in ", arguments.inputdir, "\n") valid_sample_files = get_fasta_file_list(arguments.inputdir, logger) - if not valid_sample_files : - print ('There are not valid fasta files in ', arguments.inputdir , ' directory. Check log file for more information ') + if not valid_sample_files: + print( + "There are not valid fasta files in ", + arguments.inputdir, + " directory. Check log file for more information ", + ) exit(0) ################################# ## Prepare the coreMLST schema ## ################################# - tmp_core_gene_dir = os.path.join(arguments.outputdir,'tmp','cgMLST') + tmp_core_gene_dir = os.path.join(arguments.outputdir, "tmp", "cgMLST") try: os.makedirs(tmp_core_gene_dir) except: - logger.info('Deleting the temporary directory for a previous execution without cleaning up') - shutil.rmtree(os.path.join(arguments.outputdir, 'tmp')) + logger.info( + "Deleting the temporary directory for a previous execution without cleaning up" + ) + shutil.rmtree(os.path.join(arguments.outputdir, "tmp")) try: os.makedirs(tmp_core_gene_dir) - logger.info ('Temporary folder %s has been created again', tmp_core_gene_dir) + logger.info( + "Temporary folder %s has been created again", tmp_core_gene_dir + ) except: - logger.info('Unable to create again the temporary directory %s', tmp_core_gene_dir) - print('Cannot create temporary directory on ', tmp_core_gene_dir) + logger.info( + "Unable to create again the temporary directory %s", tmp_core_gene_dir + ) + print("Cannot create temporary directory on ", tmp_core_gene_dir) exit(0) - alleles_in_locus_dict, annotation_core_dict, schema_variability, schema_statistics, schema_quality = prepare_core_gene (valid_core_gene_files, tmp_core_gene_dir, arguments.refalleles, arguments.genus, arguments.species, str(arguments.usegenus).lower(), logger) - #alleles_in_locus_dict, annotation_core_dict, schema_variability, schema_statistics, schema_quality = prepare_core_gene (valid_core_gene_files, tmp_core_gene_dir, arguments.refalleles, arguments.outputdir, logger) + ( + alleles_in_locus_dict, + annotation_core_dict, + schema_variability, + schema_statistics, + schema_quality, + ) = prepare_core_gene( + valid_core_gene_files, + tmp_core_gene_dir, + arguments.refalleles, + arguments.genus, + arguments.species, + str(arguments.usegenus).lower(), + logger, + ) + # alleles_in_locus_dict, annotation_core_dict, schema_variability, schema_statistics, schema_quality = prepare_core_gene (valid_core_gene_files, tmp_core_gene_dir, arguments.refalleles, arguments.outputdir, logger) if not alleles_in_locus_dict: - print('There is an error while processing the schema preparation phase. Check the log file to get more information \n') - logger.info('Deleting the temporary directory to clean up the temporary files created') - shutil.rmtree(os.path.join(arguments.outputdir, 'tmp')) + print( + "There is an error while processing the schema preparation phase. Check the log file to get more information \n" + ) + logger.info( + "Deleting the temporary directory to clean up the temporary files created" + ) + shutil.rmtree(os.path.join(arguments.outputdir, "tmp")) exit(0) ############################### ## Prepare the samples files ## ############################### - tmp_samples_dir = os.path.join(arguments.outputdir,'tmp','samples') + tmp_samples_dir = os.path.join(arguments.outputdir, "tmp", "samples") try: os.makedirs(tmp_samples_dir) except: - logger.info('Deleting the temporary directory for a previous execution without properly cleaning up') + logger.info( + "Deleting the temporary directory for a previous execution without properly cleaning up" + ) shutil.rmtree(tmp_samples_dir) try: os.makedirs(tmp_samples_dir) - logger.info('Temporary folder %s has been created again', tmp_samples_dir) + logger.info("Temporary folder %s has been created again", tmp_samples_dir) except: - logger.info('Unable to create again the temporary directory %s', tmp_samples_dir) - shutil.rmtree(os.path.join(arguments.outputdir, 'tmp')) - logger.info('Cleaned up temporary directory ', ) - print('Cannot create temporary directory on ', tmp_samples_dir, 'Check the log file to get more information \n') + logger.info( + "Unable to create again the temporary directory %s", tmp_samples_dir + ) + shutil.rmtree(os.path.join(arguments.outputdir, "tmp")) + logger.info( + "Cleaned up temporary directory ", + ) + print( + "Cannot create temporary directory on ", + tmp_samples_dir, + "Check the log file to get more information \n", + ) exit(0) - contigs_in_sample_dict = prepare_samples(valid_sample_files, tmp_samples_dir, arguments.refgenome, logger) - if not contigs_in_sample_dict : - print('There is an error while processing the saving temporary files. Check the log file to get more information \n') - logger.info('Deleting the temporary directory to clean up the temporary files created') - shutil.rmtree(os.path.join(arguments.outputdir, 'tmp')) + contigs_in_sample_dict = prepare_samples( + valid_sample_files, tmp_samples_dir, arguments.refgenome, logger + ) + if not contigs_in_sample_dict: + print( + "There is an error while processing the saving temporary files. Check the log file to get more information \n" + ) + logger.info( + "Deleting the temporary directory to clean up the temporary files created" + ) + shutil.rmtree(os.path.join(arguments.outputdir, "tmp")) exit(0) ################################## @@ -2434,51 +4288,126 @@ def processing_allele_calling (arguments) : ################################## query_directory = arguments.coregenedir reference_alleles_directory = arguments.refalleles - blast_db_directory = os.path.join(tmp_samples_dir,'blastdb') - prodigal_directory = os.path.join(tmp_samples_dir,'prodigal') - blast_results_seq_directory = os.path.join(tmp_samples_dir,'blast_results', 'blast_results_seq') ### path a directorio donde guardar secuencias encontradas tras blast con alelo de referencia - blast_results_db_directory = os.path.join(tmp_samples_dir,'blast_results', 'blast_results_db') ### path a directorio donde guardar db de secuencias encontradas tras blast con alelo de referencia - - complete_allele_call, inferred_alleles_dict, inf_dict, exact_dict = allele_call_nucleotides(valid_core_gene_files, valid_sample_files, alleles_in_locus_dict, contigs_in_sample_dict, query_directory, reference_alleles_directory, blast_db_directory, prodigal_directory, blast_results_seq_directory, blast_results_db_directory, arguments.inputdir, arguments.outputdir, int(arguments.cpus), arguments.percentlength, arguments.coverage, float(arguments.evalue), int(arguments.perc_identity_ref), int(arguments.perc_identity_loc), int(arguments.reward), int(arguments.penalty), int(arguments.gapopen), int(arguments.gapextend), int(arguments.max_target_seqs), int(arguments.max_hsps), int(arguments.num_threads), int(arguments.flankingnts), schema_variability, schema_statistics, schema_quality, annotation_core_dict, arguments.profile, logger) + blast_db_directory = os.path.join(tmp_samples_dir, "blastdb") + prodigal_directory = os.path.join(tmp_samples_dir, "prodigal") + blast_results_seq_directory = os.path.join( + tmp_samples_dir, "blast_results", "blast_results_seq" + ) ### path a directorio donde guardar secuencias encontradas tras blast con alelo de referencia + blast_results_db_directory = os.path.join( + tmp_samples_dir, "blast_results", "blast_results_db" + ) ### path a directorio donde guardar db de secuencias encontradas tras blast con alelo de referencia + + ( + complete_allele_call, + inferred_alleles_dict, + inf_dict, + exact_dict, + ) = allele_call_nucleotides( + valid_core_gene_files, + valid_sample_files, + alleles_in_locus_dict, + contigs_in_sample_dict, + query_directory, + reference_alleles_directory, + blast_db_directory, + prodigal_directory, + blast_results_seq_directory, + blast_results_db_directory, + arguments.inputdir, + arguments.outputdir, + int(arguments.cpus), + arguments.percentlength, + arguments.coverage, + float(arguments.evalue), + int(arguments.perc_identity_ref), + int(arguments.perc_identity_loc), + int(arguments.reward), + int(arguments.penalty), + int(arguments.gapopen), + int(arguments.gapextend), + int(arguments.max_target_seqs), + int(arguments.max_hsps), + int(arguments.num_threads), + int(arguments.flankingnts), + schema_variability, + schema_statistics, + schema_quality, + annotation_core_dict, + arguments.profile, + logger, + ) if not complete_allele_call: - print('There is an error while processing the allele calling. Check the log file to get more information \n') + print( + "There is an error while processing the allele calling. Check the log file to get more information \n" + ) exit(0) ######################################################### ## Update core gene schema adding new inferred alleles ## ######################################################### if inferred_alleles_dict: - if str(arguments.updateschema).lower() == 'true' or str(arguments.updateschema).lower() == 'new': - if not update_schema (str(arguments.updateschema).lower(), arguments.coregenedir, arguments.outputdir, valid_core_gene_files, inferred_alleles_dict, alleles_in_locus_dict, logger): - print('There is an error adding new inferred alleles found to the core genes schema. Check the log file to get more information \n') + if ( + str(arguments.updateschema).lower() == "true" + or str(arguments.updateschema).lower() == "new" + ): + if not update_schema( + str(arguments.updateschema).lower(), + arguments.coregenedir, + arguments.outputdir, + valid_core_gene_files, + inferred_alleles_dict, + alleles_in_locus_dict, + logger, + ): + print( + "There is an error adding new inferred alleles found to the core genes schema. Check the log file to get more information \n" + ) exit(0) - if str(arguments.profile).lower() != 'false': - + if str(arguments.profile).lower() != "false": ############################ ## Get ST for each sample ## ############################ - complete_ST, inf_ST = get_ST_profile(arguments.outputdir, arguments.profile, exact_dict, inf_dict, valid_core_gene_files, valid_sample_files, logger) + complete_ST, inf_ST = get_ST_profile( + arguments.outputdir, + arguments.profile, + exact_dict, + inf_dict, + valid_core_gene_files, + valid_sample_files, + logger, + ) if not complete_ST: - print('There is an error while processing ST analysis. Check the log file to get more information \n') + print( + "There is an error while processing ST analysis. Check the log file to get more information \n" + ) exit(0) ########################################### ## Update ST profile file adding new STs ## ########################################### - if str(arguments.updateprofile).lower() == 'true' or str(arguments.updateprofile).lower() == 'new': + if ( + str(arguments.updateprofile).lower() == "true" + or str(arguments.updateprofile).lower() == "new" + ): if len(inf_ST) > 0: - if not update_st_profile (str(arguments.updateprofile).lower(), arguments.profile, arguments.outputdir, inf_ST, valid_core_gene_files, logger): - print('There is an error adding new STs found to the ST profile file. Check the log file to get more information \n') + if not update_st_profile( + str(arguments.updateprofile).lower(), + arguments.profile, + arguments.outputdir, + inf_ST, + valid_core_gene_files, + logger, + ): + print( + "There is an error adding new STs found to the ST profile file. Check the log file to get more information \n" + ) exit(0) - shutil.rmtree(os.path.join(arguments.outputdir, 'tmp')) + shutil.rmtree(os.path.join(arguments.outputdir, "tmp")) end_time = datetime.now() - print('completed execution at :', end_time ) + print("completed execution at :", end_time) return True - - - diff --git a/taranis/analyze_schema.py b/taranis/analyze_schema.py index cf218bf..c17fd99 100644 --- a/taranis/analyze_schema.py +++ b/taranis/analyze_schema.py @@ -4,9 +4,12 @@ import rich.console import statistics from pathlib import Path +import Bio.Data.CodonTable from Bio import SeqIO -from Bio.SeqRecord import SeqRecord + +# from Bio.SeqRecord import SeqRecord +from collections import OrderedDict import taranis.utils @@ -43,66 +46,89 @@ def __init__( self.species = species self.usegenus = usegenus - def check_allele_quality(self): - a_quality = {} + def check_allele_quality(self, prokka_annotation): + a_quality = OrderedDict() allele_seq = {} bad_quality_record = [] with open(self.schema_allele) as fh: for record in SeqIO.parse(self.schema_allele, "fasta"): - a_quality[record.id] = {"quality": "Good quality", "reason": "-"} + try: + prokka_ann = prokka_annotation[record.id] + except: + prokka_ann = "Not found in prokka" + a_quality[record.id] = { + "allele_name": self.allele_name, + "quality": "Good quality", + "reason": "-", + "direction": "forward", + "start_codon_alt": "standard", + "protein_seq": "", + "cds_coding": prokka_ann, + } allele_seq[record.id] = str(record.seq) - a_quality[record.id]["length"] = len(str(record.seq)) - if len(record.seq) % 3 != 0: - a_quality[record.id]["quality"] = "Bad quality" - a_quality[record.id]["reason"] = "Can not be converted to protein" - a_quality[record.id]["order"] = "-" - else: - sequence_order = taranis.utils.check_sequence_order(str(record.seq)) - if sequence_order == "Error": - a_quality[record.id]["quality"] = "Bad quality" - a_quality[record.id]["reason"] = "Start or end codon not found" - a_quality[record.id]["order"] = "-" - elif sequence_order == "reverse": - record_sequence = str(record.seq.reverse_complement()) + a_quality[record.id]["length"] = str(len(str(record.seq))) + a_quality[record.id]["dna_seq"] = str(record.seq) + sequence_direction = taranis.utils.get_seq_direction(str(record.seq)) + + if sequence_direction == "reverse": + record.seq = record.seq.reverse_complement() + a_quality[record.id]["direction"] = sequence_direction + elif sequence_direction == "Error": + a_quality[record.id]["direction"] = "-" + try: + a_quality[record.id]["protein_seq"] = str( + record.seq.translate(table=1, cds=True) + ) + + except Bio.Data.CodonTable.TranslationError as e: + if "not a start codon" in str(e): + try: + # Check if sequence has an alternative start codon + # for protein coding + a_quality[record.id]["protein_seq"] = str( + record.seq.translate(table=2, cds=True) + ) + a_quality[record.id]["start_codon_alt"] = "alternative" + except Bio.Data.CodonTable.TranslationError as e_2: + if "stop" in str(e_2): + a_quality[record.id]["reason"] = str(e_2).replace( + "'", "" + ) + else: + a_quality[record.id]["reason"] = str(e).replace("'", "") + a_quality[record.id]["quality"] = "Bad quality" else: - record_sequence = str(record.seq) - a_quality[record.id]["order"] = sequence_order - if record_sequence[0:3] not in taranis.utils.START_CODON_FORWARD: a_quality[record.id]["quality"] = "Bad quality" - a_quality[record.id]["reason"] = "Start codon not found" - elif record_sequence[-3:] not in taranis.utils.STOP_CODON_FORWARD: - a_quality[record.id]["quality"] = "Bad quality" - a_quality[record.id]["reason"] = "Stop codon not found" - - elif taranis.utils.find_multiple_stop_codons(record_sequence): - a_quality[record.id]["quality"] = "Bad quality" - a_quality[record.id]["reason"] = "Multiple stop codons found" + a_quality[record.id]["reason"] = str(e).replace("'", "") + if ( self.remove_no_cds and a_quality[record.id]["quality"] == "Bad quality" ): bad_quality_record.append(record.id) - if self.remove_duplicated: - # get the unique sequences and compare the length with all sequences - unique_seq = list(set(list(allele_seq.values()))) - if len(unique_seq) < len(allele_seq): - tmp_dict = {} - for rec_id, seq_value in allele_seq.items(): - if seq_value not in tmp_dict: - tmp_dict[seq_value] = 0 - else: - bad_quality_record.append(rec_id) - a_quality[rec_id]["quality"] ="Bad quality" - a_quality[rec_id]["reason"] ="Duplicate allele" - if self.remove_subset: - unique_seq = list(set(list(allele_seq.values()))) + # check if there are duplicated alleles + # get the unique sequences and compare the length with all sequences + unique_seq = list(set(list(allele_seq.values()))) + if len(unique_seq) < len(allele_seq): + tmp_dict = {} for rec_id, seq_value in allele_seq.items(): - unique_seq.remove(seq_value) - if seq_value in unique_seq: + if seq_value not in tmp_dict: + tmp_dict[seq_value] = 0 + else: + a_quality[rec_id]["quality"] = "Bad quality" + a_quality[rec_id]["reason"] = "Duplicate allele" + if self.remove_duplicated: + bad_quality_record.append(rec_id) + + for rec_id, seq_value in allele_seq.items(): + unique_seq.remove(seq_value) + if seq_value in unique_seq: + a_quality[rec_id]["quality"] = "Bad quality" + a_quality[rec_id]["reason"] = "Sub set allele" + if self.remove_subset: bad_quality_record.append(rec_id) - a_quality[rec_id]["quality"] ="Bad quality" - a_quality[rec_id]["reason"] ="Sub set allele" + new_schema_folder = os.path.join(self.output, "new_schema") _ = taranis.utils.create_new_folder(new_schema_folder) new_schema_file = os.path.join(new_schema_folder, self.allele_name + ".fasta") @@ -116,22 +142,38 @@ def check_allele_quality(self): SeqIO.write(record, fo, "fasta") # update the schema allele with the new file self.schema_allele = new_schema_file + + """ + if self.output_allele_annot: + # dump allele annotation to file + ann_heading = ["gene", "allele", "allele direction","nucleotide sequence", "protein sequence", "nucleotide sequence length", "star codon", "CDS coding", "allele quality", "bad quality reason" ] + ann_fields = ["direction", "dna_seq", "protein_seq", "length", "start_codon_alt","cds_coding", "quality", "reason"] + f_name = os.path.join(self.output, self.allele_name +"_allele_annotation.csv") + with open (f_name, "w") as fo: + fo.write(",".join(ann_heading) + "\n") + for allele in a_quality.keys(): + data_field = [a_quality[allele][field] for field in ann_fields] + fo.write(self.allele_name + "," + allele + "," + ",".join(data_field) + "\n") + """ + return a_quality def fetch_statistics_from_alleles(self, a_quality): - possible_bad_quality = ["Can not be converted to protein", "Start codon not found", "Stop codon not found", "Multiple stop codons found" ,"Duplicate allele", "Sub set allele"] + # POSIBLE_BAD_QUALITY = ["not a start codon", "not a stop codon", "Extra in frame stop codon", "is not a multiple of three", "Duplicate allele", "Sub set allele"] record_data = {} bad_quality_reason = {} a_length = [] bad_quality_counter = 0 for record_id in a_quality.keys(): record_data["allele_name"] = self.allele_name - a_length.append(a_quality[record_id]["length"]) + a_length.append(int(a_quality[record_id]["length"])) if a_quality[record_id]["quality"] == "Bad quality": bad_quality_counter += 1 - bad_quality_reason[a_quality[record_id]["reason"]] = ( - bad_quality_reason.get(a_quality[record_id]["reason"], 0) + 1 - ) + for reason in taranis.utils.POSIBLE_BAD_QUALITY: + if reason in a_quality[record_id]["reason"]: + bad_quality_reason[reason] = ( + bad_quality_reason.get(reason, 0) + 1 + ) total_alleles = len(a_length) record_data["min_length"] = min(a_length) record_data["max_length"] = max(a_length) @@ -140,25 +182,26 @@ def fetch_statistics_from_alleles(self, a_quality): record_data["good_percent"] = round( 100 * (total_alleles - bad_quality_counter) / total_alleles, 2 ) - for item in possible_bad_quality: - record_data[item] = bad_quality_reason[item] if item in bad_quality_reason else 0 - # record_data["bad_quality_reason"] = bad_quality_reason + for item in taranis.utils.POSIBLE_BAD_QUALITY: + record_data[item] = ( + bad_quality_reason[item] if item in bad_quality_reason else 0 + ) + return record_data def analyze_allele_in_schema(self): allele_data = {} - # Perform quality - a_quality = self.check_allele_quality() # run annotations prokka_folder = os.path.join(self.output, "prokka", self.allele_name) anotation_files = taranis.utils.create_annotation_files( self.schema_allele, prokka_folder, self.allele_name ) - allele_data["annotation_gene"] = taranis.utils.read_annotation_file( - anotation_files + ".tsv", self.allele_name - ).get(self.allele_name) - allele_data.update(self.fetch_statistics_from_alleles(a_quality)) - return allele_data + prokka_annotation = taranis.utils.read_annotation_file(anotation_files + ".gff") + + # Perform quality + a_quality = self.check_allele_quality(prokka_annotation) + allele_data = self.fetch_statistics_from_alleles(a_quality) + return [allele_data, a_quality] def parallel_execution( @@ -184,17 +227,30 @@ def parallel_execution( return schema_obj.analyze_allele_in_schema() - -def collect_statistics(stat_data, out_folder): +def collect_statistics(data, out_folder, output_allele_annot): def stats_graphics(stats_folder): - print(out_folder) + allele_range = [0, 300, 600, 1000, 1500] + graphic_folder = os.path.join(stats_folder, "graphics") _ = taranis.utils.create_new_folder(graphic_folder) # create graphic for alleles/number of genes - genes_alleles_df = stats_df["num_alleles"].value_counts().rename_axis("alleles").sort_index().reset_index(name="genes") - _ = taranis.utils.create_graphic(graphic_folder, "num_genes_per_allele.png", "lines", genes_alleles_df["alleles"].to_list(), genes_alleles_df["genes"].to_list(), ["Allele", "number of genes"],"title") + # genes_alleles_df = stats_df["num_alleles"].value_counts().rename_axis("alleles").sort_index().reset_index(name="genes") + group_alleles_df = stats_df.groupby( + pd.cut(stats_df["num_alleles"], allele_range) + ).count() + _ = taranis.utils.create_graphic( + graphic_folder, + "num_genes_per_allele.png", + "bar", + allele_range[1:], + group_alleles_df["num_alleles"].to_list(), + ["Allele", "number of genes"], + "title", + ) + # _ = taranis.utils.create_graphic(graphic_folder, "num_genes_per_allele.png", "lines", genes_alleles_df["alleles"].to_list(), genes_alleles_df["genes"].to_list(), ["Allele", "number of genes"],"title") # create pie graph for good quality - + + """ good_percent = [round(stats_df["good_percent"].mean(),2)] good_percent.append(100 - good_percent[0]) labels = ["Good quality", "Bad quality"] @@ -202,25 +258,90 @@ def stats_graphics(stats_folder): _ = taranis.utils.create_graphic(graphic_folder, "quality_of_locus.png", "pie", good_percent, "", labels, "Quality of locus") # create pie graph for bad quality reason. This is optional if there are # bad quality alleles + """ + sum_all_alleles = stats_df["num_alleles"].sum() + labels = [] values = [] for item in taranis.utils.POSIBLE_BAD_QUALITY: labels.append(item) values.append(stats_df[item].sum()) - if sum(values) > 0: - _ = taranis.utils.create_graphic(graphic_folder, "bad_quality_reason.png", "pie", values, "", labels, "Bad quality reason") - # create pie graph for not found gene name + labels.append("Good quality") + values.append(sum_all_alleles - sum(values)) + _ = taranis.utils.create_graphic( + graphic_folder, + "quality_percent.png", + "pie", + values, + "", + labels, + "Quality percent", + ) + # create box plot for allele length variability + _ = taranis.utils.create_graphic( + graphic_folder, + "allele_variability.png", + "box", + "", + stats_df["mean_length"].to_list(), + "", + "Allele variability", + ) + + summary_data = [] + a_quality = [] + for idx in range(len(data)): # pdb.set_trace() - times_not_found_gene = len(stats_df[stats_df["annotation_gene"] == "Not found by Prokka"]) - if times_not_found_gene > 0: - gene_not_found = [times_not_found_gene, len(stat_data)] - labels = ["Not found gene name", "Number of alleles"] - _ = taranis.utils.create_graphic(graphic_folder, "gene_not_found.png", "pie", gene_not_found, "", labels, "Quality of locus") - - stats_df = pd.DataFrame(stat_data) + summary_data.append(data[idx][0]) + a_quality.append(data[idx][1]) + + stats_df = pd.DataFrame(summary_data) + # a_quality = data[1] stats_folder = os.path.join(out_folder, "statistics") _ = taranis.utils.create_new_folder(stats_folder) _ = taranis.utils.write_data_to_file(stats_folder, "statistics.csv", stats_df) + # pdb.set_trace() stats_graphics(stats_folder) - print(stats_df) + if output_allele_annot: + # dump allele annotation to file + ann_heading = [ + "gene", + "allele", + "allele direction", + "nucleotide sequence", + "protein sequence", + "nucleotide sequence length", + "star codon", + "CDS coding", + "allele quality", + "bad quality reason", + ] + ann_fields = [ + "direction", + "dna_seq", + "protein_seq", + "length", + "start_codon_alt", + "cds_coding", + "quality", + "reason", + ] + # f_name = os.path.join(self.output, self.allele_name +"_allele_annotation.csv") + ann_data = ",".join(ann_heading) + "\n" + for gene in a_quality: + for allele in gene.keys(): + data_field = [gene[allele][field] for field in ann_fields] + ann_data += ( + gene[allele]["allele_name"] + + "," + + allele + + "," + + ",".join(data_field) + + "\n" + ) + + _ = taranis.utils.write_data_to_compress_filed( + out_folder, "allele_annotation.csv", ann_data + ) + return diff --git a/taranis/blast.py b/taranis/blast.py index e4b17f0..923b640 100644 --- a/taranis/blast.py +++ b/taranis/blast.py @@ -17,29 +17,57 @@ ) -class Blast(): +class Blast: def __init__(self, db_type): self.db_type = db_type - def create_blastdb (self, file_name, blast_dir): + def create_blastdb(self, file_name, blast_dir): self.f_name = Path(file_name).stem - db_dir = os.path.join(blast_dir,self.f_name) + db_dir = os.path.join(blast_dir, self.f_name) self.out_blast_dir = os.path.join(db_dir, self.f_name) - blast_command = ["makeblastdb" , "-in" , file_name , "-parse_seqids", "-dbtype", self.db_type, "-out" , self.out_blast_dir] + blast_command = [ + "makeblastdb", + "-in", + file_name, + "-parse_seqids", + "-dbtype", + self.db_type, + "-out", + self.out_blast_dir, + ] try: - _ = subprocess.run(blast_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True) + _ = subprocess.run( + blast_command, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + check=True, + ) except Exception as e: log.error("Unable to create blast db for %s ", self.f_name) log.error(e) - stderr.print(f"[red] Unable to create blast database for sample %s", self.f_name) + stderr.print( + f"[red] Unable to create blast database for sample %s", self.f_name + ) exit(1) return - - def run_blast(self, query, evalue=0.001, perc_identity=90, reward=1, penalty=-2, gapopen=1, gapextend=1, max_target_seqs=10, max_hsps=10, num_threads=1): + + def run_blast( + self, + query, + evalue=0.001, + perc_identity=90, + reward=1, + penalty=-2, + gapopen=1, + gapextend=1, + max_target_seqs=10, + max_hsps=10, + num_threads=1, + ): """_summary_ blastn -outfmt "6 , qseqid , sseqid , pident , qlen , length , mismatch , gapopen , evalue , bitscore , sstart , send , qstart , qend , sseq , qseq" -query /media/lchapado/Reference_data/proyectos_isciii/taranis/documentos_antiguos/pasteur_schema/lmo0002.fasta -db /media/lchapado/Reference_data/proyectos_isciii/taranis/test/blastdb/RA-L2073_R1/RA-L2073_R1 -evalue 0.001 -penalty -2 -reward 1 -gapopen 1 -gapextend 1 -perc_identity 100 > /media/lchapado/Reference_data/proyectos_isciii/taranis/test/blast_sample_locus002.txt - + Args: query (_type_): _description_ evalue (float, optional): _description_. Defaults to 0.001. @@ -54,14 +82,28 @@ def run_blast(self, query, evalue=0.001, perc_identity=90, reward=1, penalty=-2, """ blast_parameters = '"6 , qseqid , sseqid , pident , qlen , length , mismatch , gapopen , evalue , bitscore , sstart , send , qstart , qend , sseq , qseq"' pdb.set_trace() - #db=self.blast_dir, evalue=evalue, perc_identity=perc_identity_ref, reward=reward, penalty=penalty, gapopen=gapopen, gapextend=gapextend, outfmt=blast_parameters, max_target_seqs=max_target_seqs, max_hsps=max_hsps, num_threads=num_threads, query=core_reference_allele_path) - cline = NcbiblastnCommandline(db=self.out_blast_dir, evalue=evalue, perc_identity=perc_identity, reward=reward, penalty=penalty, gapopen=gapopen, gapextend=gapextend, outfmt=blast_parameters, max_target_seqs=max_target_seqs, max_hsps=max_hsps, num_threads=num_threads, query=query) + # db=self.blast_dir, evalue=evalue, perc_identity=perc_identity_ref, reward=reward, penalty=penalty, gapopen=gapopen, gapextend=gapextend, outfmt=blast_parameters, max_target_seqs=max_target_seqs, max_hsps=max_hsps, num_threads=num_threads, query=core_reference_allele_path) + cline = NcbiblastnCommandline( + db=self.out_blast_dir, + evalue=evalue, + perc_identity=perc_identity, + reward=reward, + penalty=penalty, + gapopen=gapopen, + gapextend=gapextend, + outfmt=blast_parameters, + max_target_seqs=max_target_seqs, + max_hsps=max_hsps, + num_threads=num_threads, + query=query, + ) try: out, _ = cline() except Exception as e: log.error("Unable to run blast for %s ", self.out_blast_dir) log.error(e) - stderr.print(f"[red] Unable to run blast for database %s", self.out_blast_dir) + stderr.print( + f"[red] Unable to run blast for database %s", self.out_blast_dir + ) exit(1) return out.splitlines() - \ No newline at end of file diff --git a/taranis/prediction.py b/taranis/prediction.py index 1706853..da2a395 100644 --- a/taranis/prediction.py +++ b/taranis/prediction.py @@ -15,7 +15,7 @@ ) -class Prediction(): +class Prediction: def __init__(self, genome_ref, sample_file, out_dir): self.genome_ref = genome_ref self.sample_file = sample_file @@ -33,33 +33,54 @@ def __init__(self, genome_ref, sample_file, out_dir): except OSError as e: log.error("Cannot create %s directory", self.out_dir) log.error(e) - stderr.print (f"[red] Unable to create {self.out_dir} folder") + stderr.print(f"[red] Unable to create {self.out_dir} folder") exit(1) def training(self): - prodigal_command = ["prodigal" , "-i", self.genome_ref, "-t", self.train] + prodigal_command = ["prodigal", "-i", self.genome_ref, "-t", self.train] try: - _ = subprocess.run(prodigal_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True) + _ = subprocess.run( + prodigal_command, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + check=True, + ) except Exception as e: log.error("Unable to execute prodigal command for training") log.error(e) - stderr.print (f"[red] Unable to run prodigal commmand. ERROR {e} ") + stderr.print(f"[red] Unable to run prodigal commmand. ERROR {e} ") exit(1) return - - def prediction(self): - - prodigal_command = ["prodigal" , "-i", self.sample_file , "-t", self.train, "-f", "gff", "-o", self.pred_coord, "-a", self.pred_protein, "-d", self.pred_gene] + prodigal_command = [ + "prodigal", + "-i", + self.sample_file, + "-t", + self.train, + "-f", + "gff", + "-o", + self.pred_coord, + "-a", + self.pred_protein, + "-d", + self.pred_gene, + ] try: - _ = subprocess.run(prodigal_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True) + _ = subprocess.run( + prodigal_command, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + check=True, + ) except Exception as e: log.error("Unable to execute prodigal command for training") log.error(e) - stderr.print (f"[red] Unable to run prodigal commmand. ERROR {e} ") + stderr.print(f"[red] Unable to run prodigal commmand. ERROR {e} ") exit(1) return def get_sequence(self): - return \ No newline at end of file + return diff --git a/taranis/pruebas.py b/taranis/pruebas.py index 4481b72..0303bf2 100644 --- a/taranis/pruebas.py +++ b/taranis/pruebas.py @@ -25,7 +25,7 @@ locus_list = [] for line in lines: line = line.strip() - if line == "#Cluster 5" : + if line == "#Cluster 5": if alleles_found == False: alleles_found = True continue @@ -37,7 +37,9 @@ # import pdb; pdb.set_trace() rand_locus = random.choice(locus_list) schema_file = "/media/lchapado/Reference_data/proyectos_isciii/taranis/taranis_testing_data/listeria_testing_schema/lmo0002.fasta" -new_schema_file = "/media/lchapado/Reference_data/proyectos_isciii/taranis/test/cluster_lmo0002.fasta" +new_schema_file = ( + "/media/lchapado/Reference_data/proyectos_isciii/taranis/test/cluster_lmo0002.fasta" +) q_file = "/media/lchapado/Reference_data/proyectos_isciii/taranis/test/q_file.fasta" with open(schema_file) as fh: with open(new_schema_file, "w") as fo: @@ -52,15 +54,39 @@ if record.id == rand_locus: SeqIO.write(record, fo, "fasta") break -print ("Selected locus: " , rand_locus) -db_name ="/media/lchapado/Reference_data/proyectos_isciii/taranis/test/testing_clster/lmo0002" -blast_command = ['makeblastdb' , '-in' , new_schema_file , '-parse_seqids', '-dbtype', "nucl", '-out' , db_name] -blast_result = subprocess.run(blast_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) +print("Selected locus: ", rand_locus) +db_name = "/media/lchapado/Reference_data/proyectos_isciii/taranis/test/testing_clster/lmo0002" +blast_command = [ + "makeblastdb", + "-in", + new_schema_file, + "-parse_seqids", + "-dbtype", + "nucl", + "-out", + db_name, +] +blast_result = subprocess.run( + blast_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE +) blast_parameters = '"6 , qseqid , sseqid , pident , qlen , length , mismatch , gapopen , evalue , bitscore , sstart , send , qstart , qend , sseq , qseq"' # pdb.set_trace() -#db=self.blast_dir, evalue=evalue, perc_identity=perc_identity_ref, reward=reward, penalty=penalty, gapopen=gapopen, gapextend=gapextend, outfmt=blast_parameters, max_target_seqs=max_target_seqs, max_hsps=max_hsps, num_threads=num_threads, query=core_reference_allele_path) -cline = NcbiblastnCommandline(db=db_name, evalue=0.001, perc_identity=90, reward=1, penalty=-2, gapopen=1, gapextend=1, outfmt=blast_parameters, max_target_seqs=1100, max_hsps=1000, num_threads=4, query=q_file) +# db=self.blast_dir, evalue=evalue, perc_identity=perc_identity_ref, reward=reward, penalty=penalty, gapopen=gapopen, gapextend=gapextend, outfmt=blast_parameters, max_target_seqs=max_target_seqs, max_hsps=max_hsps, num_threads=num_threads, query=core_reference_allele_path) +cline = NcbiblastnCommandline( + db=db_name, + evalue=0.001, + perc_identity=90, + reward=1, + penalty=-2, + gapopen=1, + gapextend=1, + outfmt=blast_parameters, + max_target_seqs=1100, + max_hsps=1000, + num_threads=4, + query=q_file, +) try: out, _ = cline() @@ -69,4 +95,4 @@ b_lines = out.splitlines() print("longitud del cluster = ", len(locus_list)) print("numero de matches = ", len(b_lines)) -# pdb.set_trace() \ No newline at end of file +# pdb.set_trace() diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index db20a61..13819af 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -21,6 +21,7 @@ force_terminal=taranis.utils.rich_force_colors(), ) + class ReferenceAlleles: def __init__(self, fasta_file, output): self.fasta_file = fasta_file @@ -45,7 +46,7 @@ def check_locus_quality(self): # Check if multiple stop codon by translating to protein and # comparing length locus_prot = Seq(record.seq).translate() - if len(locus_prot) == int(len(seq)/3): + if len(locus_prot) == int(len(seq) / 3): self.locus_quality[record.id] = "good quality" self.selected_locus[record.id] = seq else: @@ -59,7 +60,7 @@ def check_locus_quality(self): # Matched reverse start codon if s_codon_f.group(1) in STOP_CODONS_REVERSE: locus_prot = Seq(record.seq).reverse_complement().translate() - if len(locus_prot) == int(len(record.seq)/3): + if len(locus_prot) == int(len(record.seq) / 3): self.locus_quality[record.id] = "good quality" self.selected_locus[record.id] = seq else: @@ -73,92 +74,119 @@ def check_locus_quality(self): def create_matrix_distance(self): # f_name = os.path.basename(self.fasta_file).split('.')[0] f_name = os.path.basename(self.fasta_file) - mash_folder = os.path.join(self.output, "mash" ) + mash_folder = os.path.join(self.output, "mash") # _ = taranis.utils.write_fasta_file(mash_folder, self.selected_locus, multiple_files=True, extension=False) # save directory to return after mash working_dir = os.getcwd() os.chdir(mash_folder) # run mash sketch command - sketch_file = "reference.msh" + sketch_file = "reference.msh" mash_sketch_command = ["mash", "sketch", "-i", "-o", sketch_file, f_name] # mash sketch -i -o prueba.msh lmo0003.fasta # mash_sketch_command += list(self.selected_locus.keys()) - - mash_sketch_result = subprocess.run(mash_sketch_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) + + mash_sketch_result = subprocess.run( + mash_sketch_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE + ) # Get pairwise allele sequences mash distances # mash_distance_command = ["mash", "dist", sketch_path, sketch_path] - mash_distance_command = ["mash", "triangle", "-i", "reference.msh"] - mash_distance_result = subprocess.Popen(mash_distance_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) + mash_distance_command = ["mash", "triangle", "-i", "reference.msh"] + mash_distance_result = subprocess.Popen( + mash_distance_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE + ) # pdb.set_trace() out, err = mash_distance_result.communicate() - with open ("matrix_distance.tsv", "w") as fo: + with open("matrix_distance.tsv", "w") as fo: # adding alleles to create a heading # the value are not required to be in order, just only any name and the right length - fo.write( "alleles\t" + "\t".join(list(self.selected_locus.keys())) + "\n") + fo.write("alleles\t" + "\t".join(list(self.selected_locus.keys())) + "\n") fo.write(out.decode("UTF-8")) import pandas as pd + locus_num = len(self.selected_locus) # pdb.set_trace() matrix_df = pd.read_csv("matrix_distance.tsv", sep="\t").fillna(value=0) # remove the first line of the matrix that contain only the number of alleles matrix_df = matrix_df.drop(0) - locus_list = matrix_df.iloc[0:locus_num,0] - matrix_np = matrix_df.iloc[:,1:].to_numpy() + locus_list = matrix_df.iloc[0:locus_num, 0] + matrix_np = matrix_df.iloc[:, 1:].to_numpy() # convert the triangular matrix to mirror up triangular part t_matrix_np = matrix_np.transpose() - matrix_np = t_matrix_np + matrix_np + matrix_np = t_matrix_np + matrix_np # values_np = matrix_df.iloc[:,2].to_numpy() - + # matrix_np = values_np.reshape(locus_num, locus_num) # out = out.decode('UTF-8').split('\n') from sklearn.cluster import AgglomerativeClustering - clusterer = AgglomerativeClustering(n_clusters=7, metric="precomputed", linkage="average", distance_threshold=None) + + clusterer = AgglomerativeClustering( + n_clusters=7, + metric="precomputed", + linkage="average", + distance_threshold=None, + ) clusters = clusterer.fit_predict(matrix_np) # clustering = AgglomerativeClustering(affinity="precomputed").fit(matrix_np) - mean_distance =np.mean(matrix_np, 0) + mean_distance = np.mean(matrix_np, 0) std = np.std(matrix_np) min_mean = min(mean_distance) mean_all_alleles = np.mean(mean_distance) - max_mean= max(mean_distance) + max_mean = max(mean_distance) # buscar el indice que tiene el minimo valor de media - min_mean_idx= np.where(mean_distance==float(min_mean))[0][0] + min_mean_idx = np.where(mean_distance == float(min_mean))[0][0] # create fasta file with the allele min_allele = self.selected_locus[locus_list[min_mean_idx]] record_allele_folder = os.path.join(os.getcwd(), f_name.split(".")[0]) - min_allele_file = taranis.utils.write_fasta_file(record_allele_folder,min_allele, locus_list[min_mean_idx]) + min_allele_file = taranis.utils.write_fasta_file( + record_allele_folder, min_allele, locus_list[min_mean_idx] + ) # pdb.set_trace() # busca el indice que tiene el valor de la media - mean_all_closser_value = taranis.utils.find_nearest_numpy_value(mean_distance, mean_all_alleles) - mean_all_alleles_idx= np.where(mean_distance==float(mean_all_closser_value))[0][0] + mean_all_closser_value = taranis.utils.find_nearest_numpy_value( + mean_distance, mean_all_alleles + ) + mean_all_alleles_idx = np.where(mean_distance == float(mean_all_closser_value))[ + 0 + ][0] # create fasta file with the allele mean_allele = self.selected_locus[locus_list[mean_all_alleles_idx]] # record_allele_folder = os.path.join(mash_folder, f_name) - mean_allele_file = taranis.utils.write_fasta_file(record_allele_folder,mean_allele, locus_list[mean_all_alleles_idx]) - + mean_allele_file = taranis.utils.write_fasta_file( + record_allele_folder, mean_allele, locus_list[mean_all_alleles_idx] + ) + # busca el indice con la mayor distancia - max_mean_idx= np.where(mean_distance==float(max_mean))[0][0] + max_mean_idx = np.where(mean_distance == float(max_mean))[0][0] # create fasta file with the allele max_allele = self.selected_locus[locus_list[max_mean_idx]] - max_allele_file = taranis.utils.write_fasta_file(record_allele_folder,max_allele, locus_list[max_mean_idx]) - + max_allele_file = taranis.utils.write_fasta_file( + record_allele_folder, max_allele, locus_list[max_mean_idx] + ) # Elijo un outlier lmo0002_185 para ver la distancia outlier_allele = self.selected_locus[locus_list[184]] - outlier_allele_file = taranis.utils.write_fasta_file(record_allele_folder,outlier_allele, locus_list[184]) + outlier_allele_file = taranis.utils.write_fasta_file( + record_allele_folder, outlier_allele, locus_list[184] + ) - # elijo un segundo outlier lmo0002_95 que tiene como cluster =1 + # elijo un segundo outlier lmo0002_95 que tiene como cluster =1 outlier2_allele = self.selected_locus[locus_list[95]] - outlier2_allele_file = taranis.utils.write_fasta_file(record_allele_folder,outlier2_allele, locus_list[95]) - - # elijo un tercer outlier lmo0002_185 que tiene como cluster =4 + outlier2_allele_file = taranis.utils.write_fasta_file( + record_allele_folder, outlier2_allele, locus_list[95] + ) + + # elijo un tercer outlier lmo0002_185 que tiene como cluster =4 outlier3_allele = self.selected_locus[locus_list[185]] - outlier3_allele_file = taranis.utils.write_fasta_file(record_allele_folder,outlier3_allele, locus_list[185]) + outlier3_allele_file = taranis.utils.write_fasta_file( + record_allele_folder, outlier3_allele, locus_list[185] + ) # saca una lista de cuantas veces se repite un valor np.bincount(clusters) blast_parameters = '"6 , qseqid , sseqid , pident , qlen , length , mismatch , gapopen , evalue , bitscore , sstart , send , qstart , qend , sseq , qseq"' from Bio.Blast.Applications import NcbiblastnCommandline + # Create local BLAST database for all alleles in the locus db_name = "/media/lchapado/Reference_data/proyectos_isciii/taranis/new_taranis_result_code/blast/locus_db" # db_name = os.path.join("blast", 'locus_blastdb') @@ -169,16 +197,29 @@ def create_matrix_distance(self): # taranis.utils.create_blastdb(fasta_file, db_name, 'nucl', logger): # locus_db_name = os.path.join(db_name, f_name[0], f_name[0]) # query_data= self.selected_locus["lmo0002_1"] - # All alleles in locus VS reference allele chosen (centroid) BLAST - + # All alleles in locus VS reference allele chosen (centroid) BLAST + # ref_query_file="/media/lchapado/Reference_data/proyectos_isciii/taranis/new_taranis_result_code/mash/lmo0002/query.fasta" # cline = NcbiblastnCommandline(db=db_name, evalue=0.001, perc_identity=100, reward=1, penalty=-2, gapopen=1, gapextend=1, outfmt=blast_parameters, max_target_seqs=0, max_hsps=0, num_threads=4, query=ref_query_file) - # minima distancia . + # minima distancia . # min_dist_file="/media/lchapado/Reference_data/proyectos_isciii/taranis/new_taranis_result_code/mash/lmo0002/lmo0002_610" # pdb.set_trace() min_dist_file = os.path.join(record_allele_folder, min_allele_file) - cline = NcbiblastnCommandline(db=db_name, evalue=0.001, perc_identity=90, reward=1, penalty=-2, gapopen=1, gapextend=1, outfmt=blast_parameters, max_target_seqs=1100, max_hsps=1000, num_threads=4, query=min_dist_file) + cline = NcbiblastnCommandline( + db=db_name, + evalue=0.001, + perc_identity=90, + reward=1, + penalty=-2, + gapopen=1, + gapextend=1, + outfmt=blast_parameters, + max_target_seqs=1100, + max_hsps=1000, + num_threads=4, + query=min_dist_file, + ) out, err = cline() min_dist_lines = out.splitlines() min_dist_alleles = [] @@ -187,11 +228,24 @@ def create_matrix_distance(self): min_np = np.array(min_dist_alleles) # pdb.set_trace() print("matches con min distancia: ", len(min_dist_lines)) - print("Not coverage using as reference" , np.setdiff1d(locus_list, min_np)) + print("Not coverage using as reference", np.setdiff1d(locus_list, min_np)) # distancia media. Sale 133 matches # mean_dist_file="/media/lchapado/Reference_data/proyectos_isciii/taranis/new_taranis_result_code/mash/lmo0002/lmo0002_870" - mean_dist_file = os.path.join(record_allele_folder, mean_allele_file) - cline = NcbiblastnCommandline(db=db_name, evalue=0.001, perc_identity=90, reward=1, penalty=-2, gapopen=1, gapextend=1, outfmt=blast_parameters, max_target_seqs=1100, max_hsps=1000, num_threads=4, query=mean_dist_file) + mean_dist_file = os.path.join(record_allele_folder, mean_allele_file) + cline = NcbiblastnCommandline( + db=db_name, + evalue=0.001, + perc_identity=90, + reward=1, + penalty=-2, + gapopen=1, + gapextend=1, + outfmt=blast_parameters, + max_target_seqs=1100, + max_hsps=1000, + num_threads=4, + query=mean_dist_file, + ) out, err = cline() mean_dist_lines = out.splitlines() mean_dist_alleles = [] @@ -199,12 +253,25 @@ def create_matrix_distance(self): mean_dist_alleles.append(mean_dist.split("\t")[1]) mean_np = np.array(mean_dist_alleles) print("matches con distancia media: ", len(mean_dist_lines)) - print("Not coverage using as reference" , np.setdiff1d(locus_list, mean_np)) - - # maxima distancia, + print("Not coverage using as reference", np.setdiff1d(locus_list, mean_np)) + + # maxima distancia, # ref_query_file="/media/lchapado/Reference_data/proyectos_isciii/taranis/new_taranis_result_code/mash/lmo0002/lmo0002_216" - max_dist_file = os.path.join(record_allele_folder, max_allele_file) - cline = NcbiblastnCommandline(db=db_name, evalue=0.001, perc_identity=90, reward=1, penalty=-2, gapopen=1, gapextend=1, outfmt=blast_parameters, max_target_seqs=1100, max_hsps=1000, num_threads=4, query=max_dist_file) + max_dist_file = os.path.join(record_allele_folder, max_allele_file) + cline = NcbiblastnCommandline( + db=db_name, + evalue=0.001, + perc_identity=90, + reward=1, + penalty=-2, + gapopen=1, + gapextend=1, + outfmt=blast_parameters, + max_target_seqs=1100, + max_hsps=1000, + num_threads=4, + query=max_dist_file, + ) out, err = cline() max_dist_lines = out.splitlines() max_dist_alleles = [] @@ -212,12 +279,25 @@ def create_matrix_distance(self): max_dist_alleles.append(max_dist.split("\t")[1]) max_np = np.array(max_dist_alleles) print("matches con max distancia: ", len(max_dist_lines)) - print("Not coverage using as reference" , np.setdiff1d(locus_list, max_np)) - - # eligiendo uno de los outliers , + print("Not coverage using as reference", np.setdiff1d(locus_list, max_np)) + + # eligiendo uno de los outliers , # outlier_file="/media/lchapado/Reference_data/proyectos_isciii/taranis/new_taranis_result_code/mash/lmo0002/lmo0002_183" - outlier_file = os.path.join(record_allele_folder, outlier_allele_file) - cline = NcbiblastnCommandline(db=db_name, evalue=0.001, perc_identity=90, reward=1, penalty=-2, gapopen=1, gapextend=1, outfmt=blast_parameters, max_target_seqs=1100, max_hsps=1000, num_threads=4, query=outlier_file) + outlier_file = os.path.join(record_allele_folder, outlier_allele_file) + cline = NcbiblastnCommandline( + db=db_name, + evalue=0.001, + perc_identity=90, + reward=1, + penalty=-2, + gapopen=1, + gapextend=1, + outfmt=blast_parameters, + max_target_seqs=1100, + max_hsps=1000, + num_threads=4, + query=outlier_file, + ) out, err = cline() outlier_lines = out.splitlines() outlier_alleles = [] @@ -226,14 +306,27 @@ def create_matrix_distance(self): outlier_np = np.array(outlier_alleles) print("matches con outliers distancia: ", len(outlier_lines)) - print("Alleles added using outlier as reference" , outlier_np) + print("Alleles added using outlier as reference", outlier_np) new_ref_np = np.unique(np.concatenate((min_np, outlier_np), axis=0)) print("\n", "remaining alleles ", np.setdiff1d(locus_list, new_ref_np)) - # eligiendo el segundo de los outliers , + # eligiendo el segundo de los outliers , # outlier_file="/media/lchapado/Reference_data/proyectos_isciii/taranis/new_taranis_result_code/mash/lmo0002/lmo0002_183" - outlier2_file = os.path.join(record_allele_folder, outlier2_allele_file) - cline = NcbiblastnCommandline(db=db_name, evalue=0.001, perc_identity=90, reward=1, penalty=-2, gapopen=1, gapextend=1, outfmt=blast_parameters, max_target_seqs=1100, max_hsps=1000, num_threads=4, query=outlier2_file) + outlier2_file = os.path.join(record_allele_folder, outlier2_allele_file) + cline = NcbiblastnCommandline( + db=db_name, + evalue=0.001, + perc_identity=90, + reward=1, + penalty=-2, + gapopen=1, + gapextend=1, + outfmt=blast_parameters, + max_target_seqs=1100, + max_hsps=1000, + num_threads=4, + query=outlier2_file, + ) out, err = cline() outlier2_lines = out.splitlines() outlier2_alleles = [] @@ -243,12 +336,29 @@ def create_matrix_distance(self): print("matches con second outliers distance: ", len(outlier2_lines)) # print("Alleles added using second outlier as reference" , outlier2_np) upd_new_ref_np = np.unique(np.concatenate((new_ref_np, outlier2_np), axis=0)) - print("\n", "remaining alleles after second outlier", np.setdiff1d(locus_list, upd_new_ref_np)) + print( + "\n", + "remaining alleles after second outlier", + np.setdiff1d(locus_list, upd_new_ref_np), + ) - # eligiendo el tercero de los outliers , + # eligiendo el tercero de los outliers , # outlier_file="/media/lchapado/Reference_data/proyectos_isciii/taranis/new_taranis_result_code/mash/lmo0002/lmo0002_183" - outlier3_file = os.path.join(record_allele_folder, outlier3_allele_file) - cline = NcbiblastnCommandline(db=db_name, evalue=0.001, perc_identity=90, reward=1, penalty=-2, gapopen=1, gapextend=1, outfmt=blast_parameters, max_target_seqs=1100, max_hsps=1000, num_threads=4, query=outlier3_file) + outlier3_file = os.path.join(record_allele_folder, outlier3_allele_file) + cline = NcbiblastnCommandline( + db=db_name, + evalue=0.001, + perc_identity=90, + reward=1, + penalty=-2, + gapopen=1, + gapextend=1, + outfmt=blast_parameters, + max_target_seqs=1100, + max_hsps=1000, + num_threads=4, + query=outlier3_file, + ) out, err = cline() outlier3_lines = out.splitlines() outlier3_alleles = [] @@ -257,11 +367,16 @@ def create_matrix_distance(self): outlier3_np = np.array(outlier3_alleles) print("matches con third outliers distance: ", len(outlier3_lines)) # print("Alleles added using second outlier as reference" , outlier2_np) - upd2_new_ref_np = np.unique(np.concatenate((upd_new_ref_np, outlier3_np), axis=0)) - print("\n", "remaining alleles after second outlier", np.setdiff1d(locus_list, upd2_new_ref_np)) - - print("\n Still missing " ,len( np.setdiff1d(locus_list, upd2_new_ref_np))) + upd2_new_ref_np = np.unique( + np.concatenate((upd_new_ref_np, outlier3_np), axis=0) + ) + print( + "\n", + "remaining alleles after second outlier", + np.setdiff1d(locus_list, upd2_new_ref_np), + ) + print("\n Still missing ", len(np.setdiff1d(locus_list, upd2_new_ref_np))) pdb.set_trace() @@ -270,13 +385,11 @@ def create_matrix_distance(self): # X = np.array([[0, 2, 3], [2, 0, 3], [3, 3, 0]]) # clustering = AgglomerativeClustering(affinity="precomputed").fit(X) - def create_ref_alleles(self): self.records = taranis.utils.read_fasta_file(self.fasta_file) _ = self.check_locus_quality() # pdb.set_trace() # Prepare data to use mash to create the distance matrix _ = self.create_matrix_distance() - - pass \ No newline at end of file + pass diff --git a/taranis/utils.py b/taranis/utils.py index f41f297..bd1b09a 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -4,16 +4,17 @@ """ import glob - +import io import logging import numpy as np import questionary import os import plotly.graph_objects as go +import re import rich.console import shutil import subprocess - +import tarfile import sys @@ -22,8 +23,10 @@ from Bio.SeqRecord import SeqRecord import pdb + log = logging.getLogger(__name__) + def rich_force_colors(): """ Check if any environment variables are set to force Rich to use coloured output @@ -35,6 +38,8 @@ def rich_force_colors(): ): return True return None + + stderr = rich.console.Console( stderr=True, style="dim", @@ -42,39 +47,73 @@ def rich_force_colors(): force_terminal=rich_force_colors(), ) -START_CODON_FORWARD= ['ATG','ATA','ATT','GTG', 'TTG'] -start_codon_reverse= ['CAT', 'TAT','AAT','CAC','CAA'] +START_CODON_FORWARD = ["ATG", "ATA", "ATT", "GTG", "TTG"] +START_CODON_REVERSE = ["CAT", "TAT", "AAT", "CAC", "CAA"] + +STOP_CODON_FORWARD = ["TAA", "TAG", "TGA"] +STOP_CODON_REVERSE = ["TTA", "CTA", "TCA"] -STOP_CODON_FORWARD = ['TAA', 'TAG','TGA'] -stop_codon_reverse = ['TTA', 'CTA','TCA'] +POSIBLE_BAD_QUALITY = [ + "not a start codon", + "not a stop codon", + "Extra in frame stop codon", + "is not a multiple of three", + "Duplicate allele", + "Sub set allele", +] -POSIBLE_BAD_QUALITY = ["Can not be converted to protein", "Start codon not found", "Stop codon not found", "Multiple stop codons found" ,"Duplicate allele", "Sub set allele"] -def check_sequence_order(allele_sequence): +def get_seq_direction(allele_sequence): # check direction - if allele_sequence[0:3] in START_CODON_FORWARD or allele_sequence[-3:] in STOP_CODON_FORWARD: - return 'forward' - if allele_sequence[-3:] in start_codon_reverse or allele_sequence[0:3] in stop_codon_reverse: - return 'reverse' + if ( + allele_sequence[0:3] in START_CODON_FORWARD + or allele_sequence[-3:] in STOP_CODON_FORWARD + ): + return "forward" + if ( + allele_sequence[-3:] in START_CODON_REVERSE + or allele_sequence[0:3] in STOP_CODON_REVERSE + ): + return "reverse" return "Error" -def create_annotation_files(fasta_file, annotation_dir, prefix, genus="Genus", species="species", usegenus=False): + +def create_annotation_files( + fasta_file, + annotation_dir, + prefix, + genus="Genus", + species="species", + usegenus=False, + cpus=3, +): try: - _ = subprocess.run (['prokka', fasta_file, '--force', '--outdir', annotation_dir, '--genus', genus, '--species', species, '--usegenus', str(usegenus), '--gcode', '11', '--prefix', prefix, '--quiet']) + _ = subprocess.run( + [ + "prokka", + fasta_file, + "--force", + "--outdir", + annotation_dir, + "--genus", + genus, + "--species", + species, + "--usegenus", + str(usegenus), + "--gcode", + "11", + "--prefix", + prefix, + "--cpus", + str(cpus), + "--quiet", + ] + ) except Exception as e: - log.error("Unable to run prokka. Error message: %s ", e ) + log.error("Unable to run prokka. Error message: %s ", e) stderr.print("[red] Unable to run prokka. Given error; " + e) sys.exit(1) - # Check that prokka store files in the requested folder - # if prokka results are not found in the requested folder then move from the - # running directory to the right one - if not folder_exists(annotation_dir): - try: - shutil.move(prefix, annotation_dir) - except Exception as e: - log.error("Unable to move prokka result folder to %s ", e ) - stderr.print("[red] Unable to move result prokka folder. Error; " + e) - sys.exit(1) return os.path.join(annotation_dir, prefix) @@ -88,14 +127,19 @@ def create_new_folder(folder_name): return -def create_graphic(out_folder, f_name, mode, x_data, y_data, labels, title ): +def create_graphic(out_folder, f_name, mode, x_data, y_data, labels, title): fig = go.Figure() # pdb.set_trace() if mode == "lines": fig.add_trace(go.Scatter(x=x_data, y=y_data, mode=mode, name=title)) elif mode == "pie": fig.add_trace(go.Pie(labels=labels, values=x_data)) - fig.update_layout(title_text= title) + elif mode == "bar": + fig.add_trace(go.Bar(x=x_data, y=y_data)) + elif mode == "box": + fig.add_trace(go.Box(y=y_data)) + + fig.update_layout(title_text=title) fig.write_image(os.path.join(out_folder, f_name)) @@ -106,7 +150,7 @@ def get_files_in_folder(folder, extension=None): Args: folder (string): folder path extension (string, optional): extension for filtering. Defaults to None. - + Returns: list: list of files which match the condition """ @@ -116,14 +160,16 @@ def get_files_in_folder(folder, extension=None): sys.exit(1) if extension is None: extension = "*" - folder_files = os.path.join(folder , "*." + extension) + folder_files = os.path.join(folder, "*." + extension) files_in_folder = glob.glob(folder_files) if len(files_in_folder) == 0: - log.error("Folder %s does not have any file which the extension %s", folder, extension) + log.error( + "Folder %s does not have any file which the extension %s", folder, extension + ) stderr.print("[red] Folder does not have any file which match your request") sys.exit(1) return files_in_folder - + def file_exists(file_to_check): """Checks if input file exists @@ -133,28 +179,18 @@ def file_exists(file_to_check): Returns: boolean: True if exists - """ + """ if os.path.isfile(file_to_check): return True return False -def find_multiple_stop_codons(seq) : - stop_codons = ['TAA', 'TAG','TGA'] - c_index = [] - for idx in range (0, len(seq) -2, 3) : - c_seq = seq[idx : idx + 3] - if c_seq in stop_codons : - c_index.append(idx) - if len(c_index) == 1: - return False - return True - def find_nearest_numpy_value(array, value): array = np.asarray(array) idx = (np.abs(array - value)).argmin() return array[idx] + def folder_exists(folder_to_check): """Checks if input folder exists @@ -163,15 +199,17 @@ def folder_exists(folder_to_check): Returns: boolean: True if exists - """ + """ if os.path.isdir(folder_to_check): return True return False + def prompt_text(msg): source = questionary.text(msg).unsafe_ask() return source + def query_user_yes_no(question, default): """Query the user to choose yes or no for the query question @@ -180,8 +218,8 @@ def query_user_yes_no(question, default): default (string): default option to be used: yes or no Returns: - user select: True continue with code - """ + user select: True continue with code + """ valid = {"yes": True, "y": True, "ye": True, "no": False, "n": False} if default is None: prompt = " [y/n] " @@ -204,36 +242,37 @@ def query_user_yes_no(question, default): else: sys.stdout.write("Please respond with 'yes' or 'no' (or 'y' or 'n').\n") -def read_annotation_file(ann_file, allele_name, only_first_line=True): - """ example of annotation file - locus_tag ftype length_bp gene EC_number COG product - IEKBEMEO_00001 CDS 1344 yeeO_1 COG0534 putative FMN/FAD exporter YeeO - IEKBEMEO_00002 CDS 1344 yeeO_2 COG0534 putative FMN/FAD exporter YeeO +def read_annotation_file(ann_file): + """example of annotation file + + lmo0002_782 Prodigal:002006 CDS 1 1146 . + 0 ID=OJGEGONH_00782;Name=dnaN_782;db_xref=COG:COG0592;gene=dnaN_782;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:P05649;locus_tag=OJGEGONH_00782;product=Beta sliding clamp + lmo0002_783 Prodigal:002006 CDS 1 1146 . + 0 ID=OJGEGONH_00783;Name=dnaN_783;db_xref=COG:COG0592;gene=dnaN_783;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:P05649;locus_tag=OJGEGONH_00783;product=Beta sliding clamp + lmo0049_3 Prodigal:002006 CDS 1 162 . + 0 ID=CODOCEEL_00001;inference=ab initio prediction:Prodigal:002006;locus_tag=CODOCEEL_00001;product=hypothetical protein + lmo0049_6 Prodigal:002006 CDS 1 162 . + 0 ID=CODOCEEL_00002;inference=ab initio prediction:Prodigal:002006;locus_tag=CODOCEEL_00002;product=hypothetical protein """ ann_data = {} - with open (ann_file, "r") as fh: + with open(ann_file, "r") as fh: lines = fh.readlines() - heading = lines[0].split("\t") - locus_tag_idx = heading.index("locus_tag") - gene_idx = heading.index("gene") - if only_first_line: - first_line = lines[1].split("\t") - ann_data[allele_name] = first_line[gene_idx] if first_line[gene_idx] != "" else "Not found by Prokka" - else: - # Return all annotation lines - for line in lines[1:]: - s_line = line.strip().split("\t") - allele_key = allele_name + "_" + s_line[locus_tag_idx].split("_")[1] - ann_data[allele_key] = s_line[gene_idx] if s_line[gene_idx] != "" else "Not found by Prokka" - return ann_data + for line in lines: + if "Prodigal" in line: + gene_match = re.search(r"(.*)[\t]Prodigal.*gene=(\w+)_.*", line) + if gene_match: + ann_data[gene_match.group(1)] = gene_match.group(2) + else: + pred_match = re.search(r"(.*)[\t]Prodigal.*product=(\w+)_.*", line) + if pred_match: + ann_data[pred_match.group(1)] = pred_match.group(2).strip() + if "fasta" in line: + break + return ann_data def read_fasta_file(fasta_file): return SeqIO.parse(fasta_file, "fasta") - + def write_fasta_file(out_folder, seq_data, allele_name=None, f_name=None): try: @@ -246,22 +285,55 @@ def write_fasta_file(out_folder, seq_data, allele_name=None, f_name=None): # use the fasta name as file name f_name = key + ".fasta" f_path_name = os.path.join(out_folder, f_name) - with open (f_path_name, "w") as fo: + with open(f_path_name, "w") as fo: fo.write(">" + key + "\n") fo.write(seq) else: if f_name is None: f_name = allele_name f_path_name = os.path.join(out_folder, f_name) - with open (f_path_name, "w") as fo: + with open(f_path_name, "w") as fo: fo.write(">" + allele_name + "\n") fo.write(seq_data) return f_name -def write_data_to_file(out_folder, f_name, data, include_header=True, data_type="pandas", extension="csv"): - f_path_name = os.path.join(out_folder,f_name) + +def write_data_to_compress_filed(out_folder, f_name, dump_data): + with io.BytesIO() as buffer: + with tarfile.open(fileobj=buffer, mode="w:gz") as tar: + # Add data to the tar archive + tarinfo = tarfile.TarInfo(f_name) + # Example: Write a string to the tar.gz file (replace this with your data) + data_bytes = dump_data.encode("utf-8") + tarinfo.size = len(data_bytes) + tar.addfile(tarinfo, io.BytesIO(data_bytes)) + + # Get the content of the in-memory tar.gz file + buffer.seek(0) + tar_data = buffer.read() + file_path_name = os.path.join(out_folder, Path(f_name).stem + ".tar.gz") + with open(file_path_name, "wb") as fo: + fo.write(tar_data) + + +def write_data_to_file( + out_folder, f_name, data, include_header=True, data_type="pandas", extension="csv" +): + f_path_name = os.path.join(out_folder, f_name) if data_type == "pandas": - data.to_csv(f_path_name, sep=",",header=include_header) + data.to_csv(f_path_name, sep=",", header=include_header) return +""" +def find_multiple_stop_codons(seq) : + stop_codons = ['TAA', 'TAG','TGA'] + c_index = [] + for idx in range (0, len(seq) -2, 3) : + c_seq = seq[idx : idx + 3] + if c_seq in stop_codons : + c_index.append(idx) + if len(c_index) == 1: + return False + return True +""" From a3b02f5048f8c096b1efc87ffa234a44018760bd Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 8 Jan 2024 20:33:34 +0100 Subject: [PATCH 008/214] fixiing some liting --- taranis/pruebas.py | 12 ++++++------ taranis/reference_alleles.py | 13 +++++++------ taranis/utils.py | 7 +------ 3 files changed, 14 insertions(+), 18 deletions(-) diff --git a/taranis/pruebas.py b/taranis/pruebas.py index 0303bf2..84eb84b 100644 --- a/taranis/pruebas.py +++ b/taranis/pruebas.py @@ -1,18 +1,18 @@ -from Bio.Seq import Seq +# from Bio.Seq import Seq from Bio import SeqIO from Bio.Blast.Applications import NcbiblastnCommandline import subprocess -import taranis.utils +# import taranis.utils import pdb import random """ Para hacer las pruebas con alfaclust activo el entorno de conda alfatclust_env - despues me voy a la carpeta donde me he descargado, de git, alfatclust y + despues me voy a la carpeta donde me he descargado, de git, alfatclust y ejecuto : - ./alfatclust.py -i /media/lchapado/Reference_data/proyectos_isciii/taranis/taranis_testing_data/listeria_testing_schema/lmo0003.fasta -o /media/lchapado/Reference_data/proyectos_isciii/taranis/test/alfacluster_test/resultado_alfaclust_lmo003 -l 0.9 - despues ejecuto este programa de prueba cambiando los ficheros de resultados + ./alfatclust.py -i /media/lchapado/Reference_data/proyectos_isciii/taranis/taranis_testing_data/listeria_testing_schema/lmo0003.fasta -o /media/lchapado/Reference_data/proyectos_isciii/taranis/test/alfacluster_test/resultado_alfaclust_lmo003 -l 0.9 + despues ejecuto este programa de prueba cambiando los ficheros de resultados """ @@ -26,7 +26,7 @@ for line in lines: line = line.strip() if line == "#Cluster 5": - if alleles_found == False: + if alleles_found is False: alleles_found = True continue if alleles_found: diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index 13819af..ace0ec1 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -3,7 +3,7 @@ import os import re import rich.console -import sys +# import sys import subprocess # from Bio import SeqIO @@ -77,17 +77,18 @@ def create_matrix_distance(self): mash_folder = os.path.join(self.output, "mash") # _ = taranis.utils.write_fasta_file(mash_folder, self.selected_locus, multiple_files=True, extension=False) # save directory to return after mash - working_dir = os.getcwd() + # working_dir = os.getcwd() os.chdir(mash_folder) # run mash sketch command sketch_file = "reference.msh" mash_sketch_command = ["mash", "sketch", "-i", "-o", sketch_file, f_name] # mash sketch -i -o prueba.msh lmo0003.fasta # mash_sketch_command += list(self.selected_locus.keys()) - + """ mash_sketch_result = subprocess.run( mash_sketch_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE ) + """ # Get pairwise allele sequences mash distances # mash_distance_command = ["mash", "dist", sketch_path, sketch_path] mash_distance_command = ["mash", "triangle", "-i", "reference.msh"] @@ -128,7 +129,7 @@ def create_matrix_distance(self): clusters = clusterer.fit_predict(matrix_np) # clustering = AgglomerativeClustering(affinity="precomputed").fit(matrix_np) mean_distance = np.mean(matrix_np, 0) - std = np.std(matrix_np) + # std = np.std(matrix_np) min_mean = min(mean_distance) mean_all_alleles = np.mean(mean_distance) max_mean = max(mean_distance) @@ -185,12 +186,12 @@ def create_matrix_distance(self): # saca una lista de cuantas veces se repite un valor np.bincount(clusters) blast_parameters = '"6 , qseqid , sseqid , pident , qlen , length , mismatch , gapopen , evalue , bitscore , sstart , send , qstart , qend , sseq , qseq"' - from Bio.Blast.Applications import NcbiblastnCommandline + # Create local BLAST database for all alleles in the locus db_name = "/media/lchapado/Reference_data/proyectos_isciii/taranis/new_taranis_result_code/blast/locus_db" # db_name = os.path.join("blast", 'locus_blastdb') - fasta_file = "/media/lchapado/Reference_data/proyectos_isciii/taranis/documentos_antiguos/datos_prueba/schema_1_locus/lmo0002.fasta" + # fasta_file = "/media/lchapado/Reference_data/proyectos_isciii/taranis/documentos_antiguos/datos_prueba/schema_1_locus/lmo0002.fasta" # pdb.set_trace() # blast_command = ['makeblastdb' , '-in' , fasta_file , '-parse_seqids', '-dbtype', "nucl", '-out' , db_name] # blast_result = subprocess.run(blast_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) diff --git a/taranis/utils.py b/taranis/utils.py index bd1b09a..eaf6efe 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -12,7 +12,7 @@ import plotly.graph_objects as go import re import rich.console -import shutil + import subprocess import tarfile @@ -20,13 +20,9 @@ from pathlib import Path from Bio import SeqIO -from Bio.SeqRecord import SeqRecord - -import pdb log = logging.getLogger(__name__) - def rich_force_colors(): """ Check if any environment variables are set to force Rich to use coloured output @@ -39,7 +35,6 @@ def rich_force_colors(): return True return None - stderr = rich.console.Console( stderr=True, style="dim", From 1335166dd89eec20e224d743ea4a9e8514f80530 Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 8 Jan 2024 20:49:02 +0100 Subject: [PATCH 009/214] fixiing more liting errors --- taranis/{allele_calling_old.py => allele_calling.old_py} | 0 taranis/analyze_schema.py | 7 +++---- taranis/blast.py | 4 ++-- taranis/pruebas.py | 3 ++- taranis/reference_alleles.py | 6 +++--- taranis/utils.py | 3 +++ utils/{taranis_utils.py => taranis_utils.old_py} | 0 7 files changed, 13 insertions(+), 10 deletions(-) rename taranis/{allele_calling_old.py => allele_calling.old_py} (100%) rename utils/{taranis_utils.py => taranis_utils.old_py} (100%) diff --git a/taranis/allele_calling_old.py b/taranis/allele_calling.old_py similarity index 100% rename from taranis/allele_calling_old.py rename to taranis/allele_calling.old_py diff --git a/taranis/analyze_schema.py b/taranis/analyze_schema.py index c17fd99..c948820 100644 --- a/taranis/analyze_schema.py +++ b/taranis/analyze_schema.py @@ -13,7 +13,6 @@ import taranis.utils -import pdb log = logging.getLogger(__name__) stderr = rich.console.Console( @@ -54,7 +53,7 @@ def check_allele_quality(self, prokka_annotation): for record in SeqIO.parse(self.schema_allele, "fasta"): try: prokka_ann = prokka_annotation[record.id] - except: + except Exception: prokka_ann = "Not found in prokka" a_quality[record.id] = { "allele_name": self.allele_name, @@ -132,7 +131,7 @@ def check_allele_quality(self, prokka_annotation): new_schema_folder = os.path.join(self.output, "new_schema") _ = taranis.utils.create_new_folder(new_schema_folder) new_schema_file = os.path.join(new_schema_folder, self.allele_name + ".fasta") - with open(self.schema_allele, "r") as fh: + with open(self.schema_allele, "r") as _: with open(new_schema_file, "w") as fo: for record in SeqIO.parse(self.schema_allele, "fasta"): if len(bad_quality_record) > 0: @@ -145,7 +144,7 @@ def check_allele_quality(self, prokka_annotation): """ if self.output_allele_annot: - # dump allele annotation to file + # dump allele annotation to file ann_heading = ["gene", "allele", "allele direction","nucleotide sequence", "protein sequence", "nucleotide sequence length", "star codon", "CDS coding", "allele quality", "bad quality reason" ] ann_fields = ["direction", "dna_seq", "protein_seq", "length", "start_codon_alt","cds_coding", "quality", "reason"] f_name = os.path.join(self.output, self.allele_name +"_allele_annotation.csv") diff --git a/taranis/blast.py b/taranis/blast.py index 923b640..1932a9e 100644 --- a/taranis/blast.py +++ b/taranis/blast.py @@ -47,7 +47,7 @@ def create_blastdb(self, file_name, blast_dir): log.error("Unable to create blast db for %s ", self.f_name) log.error(e) stderr.print( - f"[red] Unable to create blast database for sample %s", self.f_name + f"[red] Unable to create blast database for sample {self.f_name}" ) exit(1) return @@ -103,7 +103,7 @@ def run_blast( log.error("Unable to run blast for %s ", self.out_blast_dir) log.error(e) stderr.print( - f"[red] Unable to run blast for database %s", self.out_blast_dir + f"[red] Unable to run blast for database {self.out_blast_dir}" ) exit(1) return out.splitlines() diff --git a/taranis/pruebas.py b/taranis/pruebas.py index 84eb84b..ccd4023 100644 --- a/taranis/pruebas.py +++ b/taranis/pruebas.py @@ -13,7 +13,7 @@ ejecuto : ./alfatclust.py -i /media/lchapado/Reference_data/proyectos_isciii/taranis/taranis_testing_data/listeria_testing_schema/lmo0003.fasta -o /media/lchapado/Reference_data/proyectos_isciii/taranis/test/alfacluster_test/resultado_alfaclust_lmo003 -l 0.9 despues ejecuto este programa de prueba cambiando los ficheros de resultados - + """ # read result of alfatclust @@ -91,6 +91,7 @@ try: out, _ = cline() except Exception as e: + print(e) pdb.set_trace() b_lines = out.splitlines() print("longitud del cluster = ", len(locus_list)) diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index ace0ec1..9c55eac 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -84,11 +84,11 @@ def create_matrix_distance(self): mash_sketch_command = ["mash", "sketch", "-i", "-o", sketch_file, f_name] # mash sketch -i -o prueba.msh lmo0003.fasta # mash_sketch_command += list(self.selected_locus.keys()) - """ - mash_sketch_result = subprocess.run( + + _ = subprocess.run( mash_sketch_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE ) - """ + # Get pairwise allele sequences mash distances # mash_distance_command = ["mash", "dist", sketch_path, sketch_path] mash_distance_command = ["mash", "triangle", "-i", "reference.msh"] diff --git a/taranis/utils.py b/taranis/utils.py index eaf6efe..8575b97 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -23,6 +23,7 @@ log = logging.getLogger(__name__) + def rich_force_colors(): """ Check if any environment variables are set to force Rich to use coloured output @@ -35,6 +36,7 @@ def rich_force_colors(): return True return None + stderr = rich.console.Console( stderr=True, style="dim", @@ -273,6 +275,7 @@ def write_fasta_file(out_folder, seq_data, allele_name=None, f_name=None): try: os.makedirs(out_folder, exist_ok=True) except OSError as e: + print(e) sys.exit(1) if isinstance(seq_data, dict): for key, seq in seq_data.items(): diff --git a/utils/taranis_utils.py b/utils/taranis_utils.old_py similarity index 100% rename from utils/taranis_utils.py rename to utils/taranis_utils.old_py From 4a21e140cc2be68860f00607c4c7923986a12777 Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 8 Jan 2024 21:06:39 +0100 Subject: [PATCH 010/214] changed file extension for old python files --- setup.py | 4 +--- taranis/__init__.py | 2 +- taranis/__main__.py | 2 +- taranis/allele_calling.py | 2 +- taranis/analyze_schema.py | 2 +- taranis/blast.py | 4 +--- taranis/pruebas.py | 1 + taranis/reference_alleles.py | 7 ++----- 8 files changed, 9 insertions(+), 15 deletions(-) diff --git a/setup.py b/setup.py index eda9691..14646b6 100644 --- a/setup.py +++ b/setup.py @@ -28,9 +28,7 @@ author_email="smonzon@isciii.es", url="https://github.com/BU-ISCIII/taranis", license="GNU GENERAL PUBLIC LICENSE v.3", - entry_points={ - "console_scripts": ["taranis=taranis.__main__:run_taranis"] - }, + entry_points={"console_scripts": ["taranis=taranis.__main__:run_taranis"]}, install_requires=required, packages=find_packages(exclude=("docs")), include_package_data=True, diff --git a/taranis/__init__.py b/taranis/__init__.py index c968626..caed400 100644 --- a/taranis/__init__.py +++ b/taranis/__init__.py @@ -1,3 +1,3 @@ import pkg_resources -__version__ = pkg_resources.get_distribution("taranis").version \ No newline at end of file +__version__ = pkg_resources.get_distribution("taranis").version diff --git a/taranis/__main__.py b/taranis/__main__.py index a281777..c3a4112 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -287,7 +287,7 @@ def reference_alleles( try: os.makedirs(output) except OSError as e: - log.info("Unable to create folder at %s", output) + log.info("Unable to create folder at %s with error %s", output, e) stderr.print("[red] ERROR. Unable to create folder " + output) sys.exit(1) """Create the reference alleles from the schema """ diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index d9a1e99..cee068a 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -101,7 +101,7 @@ def search_alleles(self, ref_allele): # sel_row = np_lines[mask, :] = np_lines[mask, :] # query_seq = sel_row[0,14] sample_contig = sel_max["sseqid"] - abbr = self.assign_allele_type( + _ = self.assign_allele_type( query_seq, allele_name, sample_contig, schema_gene ) else: diff --git a/taranis/analyze_schema.py b/taranis/analyze_schema.py index c948820..5186a76 100644 --- a/taranis/analyze_schema.py +++ b/taranis/analyze_schema.py @@ -49,7 +49,7 @@ def check_allele_quality(self, prokka_annotation): a_quality = OrderedDict() allele_seq = {} bad_quality_record = [] - with open(self.schema_allele) as fh: + with open(self.schema_allele) as _: for record in SeqIO.parse(self.schema_allele, "fasta"): try: prokka_ann = prokka_annotation[record.id] diff --git a/taranis/blast.py b/taranis/blast.py index 1932a9e..e8b13af 100644 --- a/taranis/blast.py +++ b/taranis/blast.py @@ -46,9 +46,7 @@ def create_blastdb(self, file_name, blast_dir): except Exception as e: log.error("Unable to create blast db for %s ", self.f_name) log.error(e) - stderr.print( - f"[red] Unable to create blast database for sample {self.f_name}" - ) + stderr.print(f"[red] Unable to create blast database for sample {self.f_name}") exit(1) return diff --git a/taranis/pruebas.py b/taranis/pruebas.py index ccd4023..39331c5 100644 --- a/taranis/pruebas.py +++ b/taranis/pruebas.py @@ -3,6 +3,7 @@ from Bio import SeqIO from Bio.Blast.Applications import NcbiblastnCommandline import subprocess + # import taranis.utils import pdb import random diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index 9c55eac..90829bd 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -84,11 +84,9 @@ def create_matrix_distance(self): mash_sketch_command = ["mash", "sketch", "-i", "-o", sketch_file, f_name] # mash sketch -i -o prueba.msh lmo0003.fasta # mash_sketch_command += list(self.selected_locus.keys()) - _ = subprocess.run( mash_sketch_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE ) - # Get pairwise allele sequences mash distances # mash_distance_command = ["mash", "dist", sketch_path, sketch_path] mash_distance_command = ["mash", "triangle", "-i", "reference.msh"] @@ -187,7 +185,6 @@ def create_matrix_distance(self): np.bincount(clusters) blast_parameters = '"6 , qseqid , sseqid , pident , qlen , length , mismatch , gapopen , evalue , bitscore , sstart , send , qstart , qend , sseq , qseq"' - # Create local BLAST database for all alleles in the locus db_name = "/media/lchapado/Reference_data/proyectos_isciii/taranis/new_taranis_result_code/blast/locus_db" # db_name = os.path.join("blast", 'locus_blastdb') @@ -328,7 +325,7 @@ def create_matrix_distance(self): num_threads=4, query=outlier2_file, ) - out, err = cline() + out, _ = cline() outlier2_lines = out.splitlines() outlier2_alleles = [] for outlier2_line in outlier2_lines: @@ -360,7 +357,7 @@ def create_matrix_distance(self): num_threads=4, query=outlier3_file, ) - out, err = cline() + out, _ = cline() outlier3_lines = out.splitlines() outlier3_alleles = [] for outlier3_line in outlier3_lines: From 7b09ba218537a721f308948e0a06f2ecc1ea6dcf Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 8 Jan 2024 21:11:01 +0100 Subject: [PATCH 011/214] fixing latest liting --- taranis/__main__.py | 2 +- taranis/blast.py | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index c3a4112..023a7b6 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -381,7 +381,7 @@ def allele_calling( try: os.makedirs(output) except OSError as e: - log.info("Unable to create folder at %s", output) + log.info("Unable to create folder at %s because %s" , output, e) stderr.print("[red] ERROR. Unable to create folder " + output) sys.exit(1) # Filter fasta files from reference folder diff --git a/taranis/blast.py b/taranis/blast.py index e8b13af..db97836 100644 --- a/taranis/blast.py +++ b/taranis/blast.py @@ -46,7 +46,9 @@ def create_blastdb(self, file_name, blast_dir): except Exception as e: log.error("Unable to create blast db for %s ", self.f_name) log.error(e) - stderr.print(f"[red] Unable to create blast database for sample {self.f_name}") + stderr.print( + f"[red] Unable to create blast database for sample {self.f_name}" + ) exit(1) return @@ -100,8 +102,6 @@ def run_blast( except Exception as e: log.error("Unable to run blast for %s ", self.out_blast_dir) log.error(e) - stderr.print( - f"[red] Unable to run blast for database {self.out_blast_dir}" - ) + stderr.print(f"[red] Unable to run blast {self.out_blast_dir}") exit(1) return out.splitlines() From fdae124664ff667800ceaca750f9bfc5e966e9be Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 8 Jan 2024 21:13:13 +0100 Subject: [PATCH 012/214] fixing latest liting 2 --- taranis/reference_alleles.py | 1 + 1 file changed, 1 insertion(+) diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index 90829bd..b22697e 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -3,6 +3,7 @@ import os import re import rich.console + # import sys import subprocess From ff975a06c353cb4ff2ef18582ddd81fa4569dcd1 Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 8 Jan 2024 21:14:58 +0100 Subject: [PATCH 013/214] fixing latest liting 3 --- taranis/__main__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index 023a7b6..2adf43d 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -381,7 +381,7 @@ def allele_calling( try: os.makedirs(output) except OSError as e: - log.info("Unable to create folder at %s because %s" , output, e) + log.info("Unable to create folder at %s because %s", output, e) stderr.print("[red] ERROR. Unable to create folder " + output) sys.exit(1) # Filter fasta files from reference folder From c5792fec1e267bffbb2df44b9f6cf8d48010ccd8 Mon Sep 17 00:00:00 2001 From: luissian Date: Tue, 16 Jan 2024 16:47:44 +0100 Subject: [PATCH 014/214] checking liting in functions which are defined the type of variable --- taranis/__main__.py | 23 ++++++++++--------- taranis/analyze_schema.py | 47 ++++++++++++++++++++++++++------------- 2 files changed, 44 insertions(+), 26 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index 2adf43d..b67b394 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -199,16 +199,16 @@ def taranis_cli(verbose, log_file): help="Number of cpus used for execution", ) def analyze_schema( - inputdir, - output, - remove_subset, - remove_duplicated, - remove_no_cds, - output_allele_annot, - genus, - species, - usegenus, - cpus, + inputdir: str, + output: str, + remove_subset: bool, + remove_duplicated: bool, + remove_no_cds: bool, + output_allele_annot: bool, + genus: str, + species: str, + usegenus: str, + cpus: int, ): schema_files = taranis.utils.get_files_in_folder(inputdir, "fasta") @@ -270,8 +270,9 @@ def reference_alleles( output, ): # taranis reference-alleles -s ../../documentos_antiguos/datos_prueba/schema_1_locus/ -o ../../new_taranis_result_code - # taranis reference-alleles -s ../../documentos_antiguos/datos_prueba/schema_test/ -o ../../new_taranis_result_code + # taranis reference-alleles -s /media/lchapado/Reference_data/proyectos_isciii/taranis/taranis_testing_data/listeria_testing_schema/ -o /media/lchapado/Reference_data/proyectos_isciii/taranis/test/reference_alleles schema_files = taranis.utils.get_files_in_folder(schema, "fasta") + # Check if output folder exists if taranis.utils.folder_exists(output): q_question = ( diff --git a/taranis/analyze_schema.py b/taranis/analyze_schema.py index 5186a76..d73d740 100644 --- a/taranis/analyze_schema.py +++ b/taranis/analyze_schema.py @@ -10,6 +10,7 @@ # from Bio.SeqRecord import SeqRecord from collections import OrderedDict +from typing import Self import taranis.utils @@ -25,16 +26,32 @@ class AnalyzeSchema: def __init__( - self, - schema_allele, - output, - remove_subset, - remove_duplicated, - remove_no_cds, - genus, - species, - usegenus, - ): + self: Self, + schema_allele: str, + output: str, + remove_subset: bool, + remove_duplicated: bool, + remove_no_cds: bool, + genus: str, + species: str, + usegenus: str, + ) -> "AnalyzeSchema": + """AnalyzeSchema instance creation + + Args: + self (Self): Self + schema_allele (str): Folder path where schema files are located + output (str): Out folder to save result + remove_subset (bool): Remove subset sequences if True + remove_duplicated (bool): Remove duplicated sequences if True + remove_no_cds (bool): Removing non coding sequences if True + genus (str): Genus name for Prokka schema genes annotation + species (str): Species name for Prokka schema genes annotation + usegenus (str): genus-specific BLAST databases for Prokka + + Returns: + AnalyzeSchema: Instance of the created class + """ self.schema_allele = schema_allele self.allele_name = Path(self.schema_allele).stem self.output = output @@ -45,12 +62,12 @@ def __init__( self.species = species self.usegenus = usegenus - def check_allele_quality(self, prokka_annotation): + def check_allele_quality(self: Self, prokka_annotation) -> OrderedDict: a_quality = OrderedDict() allele_seq = {} bad_quality_record = [] - with open(self.schema_allele) as _: - for record in SeqIO.parse(self.schema_allele, "fasta"): + with open(self.schema_allele) as fh: + for record in SeqIO.parse(fh, "fasta"): try: prokka_ann = prokka_annotation[record.id] except Exception: @@ -244,7 +261,7 @@ def stats_graphics(stats_folder): allele_range[1:], group_alleles_df["num_alleles"].to_list(), ["Allele", "number of genes"], - "title", + "Number of alleles per gene", ) # _ = taranis.utils.create_graphic(graphic_folder, "num_genes_per_allele.png", "lines", genes_alleles_df["alleles"].to_list(), genes_alleles_df["genes"].to_list(), ["Allele", "number of genes"],"title") # create pie graph for good quality @@ -284,7 +301,7 @@ def stats_graphics(stats_folder): "", stats_df["mean_length"].to_list(), "", - "Allele variability", + "Allele length variability", ) summary_data = [] From a67d30f022b934f98c53b60413a6d4beb8ed3bf2 Mon Sep 17 00:00:00 2001 From: luissian Date: Wed, 17 Jan 2024 14:12:29 +0100 Subject: [PATCH 015/214] Checking testing file --- .github/workflows/test.yml | 38 ++++++++++ taranis/analyze_schema.py | 106 +++++++++++++++++++--------- taranis/utils.py | 138 +++++++++++++++++++++++++------------ 3 files changed, 205 insertions(+), 77 deletions(-) create mode 100644 .github/workflows/test.yml diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml new file mode 100644 index 0000000..7726272 --- /dev/null +++ b/.github/workflows/test.yml @@ -0,0 +1,38 @@ +name: tests ci +# This workflow runs the pipeline with the minimal test dataset to check that it completes any errors +on: + push: + branches: [develop] + pull_request_target: + branches: [develop] + release: + types: [published] + +jobs: + push_dockerhub: + name: Push new Docker image to Docker Hub (dev) + runs-on: ubuntu-latest + # Only run for the official repo, for releases and merged PRs + if: ${{ github.repository == 'BU-ISCIII/taranis' }} + env: + DOCKERHUB_USERNAME: ${{ secrets.DOCKERHUB_USERNAME }} + DOCKERHUB_PASS: ${{ secrets.DOCKERHUB_PASSWORD }} + steps: + - name: Check out pipeline code + uses: actions/checkout@v2 + + - name: Build new docker image + run: docker build --no-cache pip install . + + - name: Push Docker image to DockerHub (develop) + run: | + echo "$DOCKERHUB_PASS" | docker login -u "$DOCKERHUB_USERNAME" --password-stdin + docker push buisciii/taranis:dev + run-tests: + name: Run tests + needs: push_dockerhub + runs-on: ubuntu-latest + steps: + - name: Run pipeline with test data + run: | + docker run buisciii/taranis:dev bash taranis --help \ No newline at end of file diff --git a/taranis/analyze_schema.py b/taranis/analyze_schema.py index d73d740..a9a84f6 100644 --- a/taranis/analyze_schema.py +++ b/taranis/analyze_schema.py @@ -8,7 +8,6 @@ from Bio import SeqIO -# from Bio.SeqRecord import SeqRecord from collections import OrderedDict from typing import Self @@ -62,7 +61,22 @@ def __init__( self.species = species self.usegenus = usegenus - def check_allele_quality(self: Self, prokka_annotation) -> OrderedDict: + def check_allele_quality(self: Self, prokka_annotation: dict) -> OrderedDict: + """Each allele in the locus file is analyzed its quality by checking + if it can be converted to protein, has start/stop codon, has multiple + stop codon, its a subsequence of the another allele, and if it is + duplicated. + A new schema file is generated, and if remove parameters are set, for + the bad quality, they are removed in this new schema file. + Dictionary with quality information for each allele is returned + + Args: + self (Self): AnalyzeSchema instance + prokka_annotation (dict): Contains the annotation for each allele + + Returns: + OrderedDict: Quality information for each allele + """ a_quality = OrderedDict() allele_seq = {} bad_quality_record = [] @@ -174,7 +188,17 @@ def check_allele_quality(self: Self, prokka_annotation) -> OrderedDict: return a_quality - def fetch_statistics_from_alleles(self, a_quality): + def fetch_statistics_from_alleles(self: Self, a_quality: dict) -> dict: + """By using the information for each allele in the input data create a + dictionary with statistics data about length and quality + + Args: + self (Self): AnalyzeSchema instance + a_quality (dict): Containing allele information + + Returns: + dict: statistics information for all alleles + """ # POSIBLE_BAD_QUALITY = ["not a start codon", "not a stop codon", "Extra in frame stop codon", "is not a multiple of three", "Duplicate allele", "Sub set allele"] record_data = {} bad_quality_reason = {} @@ -205,7 +229,19 @@ def fetch_statistics_from_alleles(self, a_quality): return record_data - def analyze_allele_in_schema(self): + def analyze_allele_in_schema(self: Self) -> list[dict, dict]: + """Analyze the alleles in the schema file by callig the function to + annotate each of the alle and using this info to provide it for checking + allele quality on check_allele_quality. With both info collection + statistics are obtain, returning a list of 2 dict, one with the statistics + information and the quality. + + Args: + self (Self): _description_ + + Returns: + list[dict, dict]: _description_ + """ allele_data = {} # run annotations prokka_folder = os.path.join(self.output, "prokka", self.allele_name) @@ -221,15 +257,30 @@ def analyze_allele_in_schema(self): def parallel_execution( - schema_allele, - output, - remove_subset, - remove_duplicated, - remove_no_cds, - genus, - species, - usegenus, -): + schema_allele: str, + output: str, + remove_subset: bool, + remove_duplicated: bool, + remove_no_cds: bool, + genus: str, + species: str, + usegenus: str, +) -> list[dict, dict]: + """_summary_ + + Args: + schema_allele (str): Folder path where schema files are located + output (str): Out folder to save result + remove_subset (bool): Remove subset sequences if True + remove_duplicated (bool): Remove duplicated sequences if True + remove_no_cds (bool): Removing non coding sequences if True + genus (str): Genus name for Prokka schema genes annotation + species (str): Species name for Prokka schema genes annotation + usegenus (str): genus-specific BLAST databases for Prokka + + Returns: + list[dict, dict]:: _description_ + """ schema_obj = AnalyzeSchema( schema_allele, output, @@ -244,13 +295,18 @@ def parallel_execution( def collect_statistics(data, out_folder, output_allele_annot): - def stats_graphics(stats_folder): - allele_range = [0, 300, 600, 1000, 1500] + def stats_graphics(stats_folder: str) -> None: + """Create the statistics graphics. Pie graphic for allele quality, + bar graphic for number of alleles, and box plot for allele variability + Args: + stats_folder (str): folder path to store graphic + """ + allele_range = [0, 300, 600, 1000, 1500] graphic_folder = os.path.join(stats_folder, "graphics") _ = taranis.utils.create_new_folder(graphic_folder) + # create graphic for alleles/number of genes - # genes_alleles_df = stats_df["num_alleles"].value_counts().rename_axis("alleles").sort_index().reset_index(name="genes") group_alleles_df = stats_df.groupby( pd.cut(stats_df["num_alleles"], allele_range) ).count() @@ -263,18 +319,7 @@ def stats_graphics(stats_folder): ["Allele", "number of genes"], "Number of alleles per gene", ) - # _ = taranis.utils.create_graphic(graphic_folder, "num_genes_per_allele.png", "lines", genes_alleles_df["alleles"].to_list(), genes_alleles_df["genes"].to_list(), ["Allele", "number of genes"],"title") - # create pie graph for good quality - """ - good_percent = [round(stats_df["good_percent"].mean(),2)] - good_percent.append(100 - good_percent[0]) - labels = ["Good quality", "Bad quality"] - # pdb.set_trace() - _ = taranis.utils.create_graphic(graphic_folder, "quality_of_locus.png", "pie", good_percent, "", labels, "Quality of locus") - # create pie graph for bad quality reason. This is optional if there are - # bad quality alleles - """ sum_all_alleles = stats_df["num_alleles"].sum() labels = [] @@ -307,20 +352,17 @@ def stats_graphics(stats_folder): summary_data = [] a_quality = [] for idx in range(len(data)): - # pdb.set_trace() summary_data.append(data[idx][0]) a_quality.append(data[idx][1]) stats_df = pd.DataFrame(summary_data) - # a_quality = data[1] stats_folder = os.path.join(out_folder, "statistics") _ = taranis.utils.create_new_folder(stats_folder) _ = taranis.utils.write_data_to_file(stats_folder, "statistics.csv", stats_df) - # pdb.set_trace() stats_graphics(stats_folder) if output_allele_annot: - # dump allele annotation to file + # if parameter to save allele annotation then write to file ann_heading = [ "gene", "allele", @@ -343,7 +385,7 @@ def stats_graphics(stats_folder): "quality", "reason", ] - # f_name = os.path.join(self.output, self.allele_name +"_allele_annotation.csv") + ann_data = ",".join(ann_heading) + "\n" for gene in a_quality: for allele in gene.keys(): diff --git a/taranis/utils.py b/taranis/utils.py index 8575b97..dcde29e 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -7,9 +7,11 @@ import io import logging import numpy as np +import pandas as pd +import plotly.graph_objects as go import questionary import os -import plotly.graph_objects as go + import re import rich.console @@ -76,14 +78,29 @@ def get_seq_direction(allele_sequence): def create_annotation_files( - fasta_file, - annotation_dir, - prefix, - genus="Genus", - species="species", - usegenus=False, - cpus=3, -): + fasta_file : str, + annotation_dir : str, + prefix : str, + genus : str ="Genus", + species : str ="species", + usegenus : str=False, + cpus : int =3, +) -> str: + """prokka command is executed to generate the annotation files. + Return the folder path where prokka store these files + + Args: + fasta_file (str): fasta file used for annotation + annotation_dir (str): folder where annotation files are saved + prefix (str): string used for naming annotation files + genus (str, optional): parameter used in proka. Defaults to "Genus". + species (str, optional): parameter used in proka. Defaults to "species". + usegenus (str, optional): _description_. Defaults to False. + cpus (int, optional): number of cpus used to run prokka. Defaults to 3. + + Returns: + str: folder path where generated files from prokka are stored + """ try: _ = subprocess.run( [ @@ -114,7 +131,12 @@ def create_annotation_files( return os.path.join(annotation_dir, prefix) -def create_new_folder(folder_name): +def create_new_folder(folder_name : str) -> None: + """Create directory defined in input data. No error occurs if folder exists + + Args: + folder_name (str): folder path to be created + """ try: os.makedirs(folder_name, exist_ok=True) except Exception as e: @@ -124,9 +146,19 @@ def create_new_folder(folder_name): return -def create_graphic(out_folder, f_name, mode, x_data, y_data, labels, title): +def create_graphic(out_folder :str, f_name : str, mode : str, x_data : list, y_data :list, labels : list, title : str) -> None: + """Create the graphic and save it to file + + Args: + out_folder (str): folder path to save the graphic + f_name (str): file name including extension + mode (str): type of graphic + x_data (list): data for x axis + y_data (list): data for y axis + labels (list): labels to be included + title (str): title of the figure + """ fig = go.Figure() - # pdb.set_trace() if mode == "lines": fig.add_trace(go.Scatter(x=x_data, y=y_data, mode=mode, name=title)) elif mode == "pie": @@ -138,18 +170,19 @@ def create_graphic(out_folder, f_name, mode, x_data, y_data, labels, title): fig.update_layout(title_text=title) fig.write_image(os.path.join(out_folder, f_name)) + return -def get_files_in_folder(folder, extension=None): +def get_files_in_folder(folder : str, extension : str =None) -> list[str]: """get the list of files, filtered by extension in the input folder. If extension is not set, then all files in folder are returned Args: - folder (string): folder path - extension (string, optional): extension for filtering. Defaults to None. + folder (str): Folder path + extension (str, optional): Extension for filtering. Defaults to None. Returns: - list: list of files which match the condition + list[str]: list of files which match the condition """ if not folder_exists(folder): log.error("Folder %s does not exists", folder) @@ -158,14 +191,7 @@ def get_files_in_folder(folder, extension=None): if extension is None: extension = "*" folder_files = os.path.join(folder, "*." + extension) - files_in_folder = glob.glob(folder_files) - if len(files_in_folder) == 0: - log.error( - "Folder %s does not have any file which the extension %s", folder, extension - ) - stderr.print("[red] Folder does not have any file which match your request") - sys.exit(1) - return files_in_folder + return glob.glob(folder_files) def file_exists(file_to_check): @@ -240,7 +266,17 @@ def query_user_yes_no(question, default): sys.stdout.write("Please respond with 'yes' or 'no' (or 'y' or 'n').\n") -def read_annotation_file(ann_file): +def read_annotation_file(ann_file : str) -> dict: + """Read the annotation file and return a dictionary where key is the allele + name and the value is the annotation data that prokka was defined for the + allele + + Args: + ann_file (str): annotation file path (gff) + + Returns: + dict: contains the allele name and the predction + """ """example of annotation file lmo0002_782 Prodigal:002006 CDS 1 1146 . + 0 ID=OJGEGONH_00782;Name=dnaN_782;db_xref=COG:COG0592;gene=dnaN_782;inference=ab initio prediction:Prodigal:002006,similar to AA sequence:UniProtKB:P05649;locus_tag=OJGEGONH_00782;product=Beta sliding clamp @@ -271,32 +307,33 @@ def read_fasta_file(fasta_file): return SeqIO.parse(fasta_file, "fasta") -def write_fasta_file(out_folder, seq_data, allele_name=None, f_name=None): +def write_fasta_file(out_folder: str, f_name: str, allele_name:str, seq_data: str) -> str: + """_summary_ + + Args: + out_folder (str): _description_ + seq_data (str): _description_ + allele_name (str, optional): _description_. Defaults to None. + f_name (str, optional): _description_. Defaults to None. + + Returns: + str: _description_ + """ try: os.makedirs(out_folder, exist_ok=True) except OSError as e: print(e) sys.exit(1) - if isinstance(seq_data, dict): - for key, seq in seq_data.items(): - if f_name is None: - # use the fasta name as file name - f_name = key + ".fasta" - f_path_name = os.path.join(out_folder, f_name) - with open(f_path_name, "w") as fo: - fo.write(">" + key + "\n") - fo.write(seq) - else: - if f_name is None: - f_name = allele_name - f_path_name = os.path.join(out_folder, f_name) - with open(f_path_name, "w") as fo: - fo.write(">" + allele_name + "\n") - fo.write(seq_data) - return f_name + + f_path_name = os.path.join(out_folder, f_name + ".fasta") + with open(f_path_name, "w") as fo: + fo.write("> " + allele_name + "\n") + fo.write(seq_data) + return f_path_name def write_data_to_compress_filed(out_folder, f_name, dump_data): + with io.BytesIO() as buffer: with tarfile.open(fileobj=buffer, mode="w:gz") as tar: # Add data to the tar archive @@ -315,8 +352,19 @@ def write_data_to_compress_filed(out_folder, f_name, dump_data): def write_data_to_file( - out_folder, f_name, data, include_header=True, data_type="pandas", extension="csv" -): + out_folder : str, f_name : str, data :pd.DataFrame|list, include_header : bool =True, data_type: str ="pandas", extension :str ="csv" +) -> None: + """write data in the input parameter to disk + + Args: + out_folder (str): Folder path to store file + f_name (str): file name without extension + data (pd.DataFrame | list): data to write. Can be dataframe or list + include_header (bool, optional): for pandas input check if header has to + be included in file. Defaults to True. + data_type (str, optional): type of data pandas or list. Defaults to "pandas". + extension (str, optional): extension of file. Defaults to "csv". + """ f_path_name = os.path.join(out_folder, f_name) if data_type == "pandas": data.to_csv(f_path_name, sep=",", header=include_header) From d1412cfd2cfea48f86214b6f129f5e82f9eb9032 Mon Sep 17 00:00:00 2001 From: luissian Date: Wed, 17 Jan 2024 15:24:55 +0100 Subject: [PATCH 016/214] first draft to run test --- .github/workflows/test.yml | 40 +++++++++++--------------------------- 1 file changed, 11 insertions(+), 29 deletions(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 7726272..4359cb1 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -1,38 +1,20 @@ name: tests ci -# This workflow runs the pipeline with the minimal test dataset to check that it completes any errors +# This workflow runs the pipeline with the minimal test dataset to check +# is completed without any errors on: - push: - branches: [develop] - pull_request_target: - branches: [develop] - release: - types: [published] + pull_request: jobs: - push_dockerhub: - name: Push new Docker image to Docker Hub (dev) + test_ci: + name: Code testing runs-on: ubuntu-latest - # Only run for the official repo, for releases and merged PRs - if: ${{ github.repository == 'BU-ISCIII/taranis' }} - env: - DOCKERHUB_USERNAME: ${{ secrets.DOCKERHUB_USERNAME }} - DOCKERHUB_PASS: ${{ secrets.DOCKERHUB_PASSWORD }} + steps: - name: Check out pipeline code - uses: actions/checkout@v2 + uses: actions/checkout@v4 - - name: Build new docker image - run: docker build --no-cache pip install . + - name: Install taranis + run: pip install . - - name: Push Docker image to DockerHub (develop) - run: | - echo "$DOCKERHUB_PASS" | docker login -u "$DOCKERHUB_USERNAME" --password-stdin - docker push buisciii/taranis:dev - run-tests: - name: Run tests - needs: push_dockerhub - runs-on: ubuntu-latest - steps: - - name: Run pipeline with test data - run: | - docker run buisciii/taranis:dev bash taranis --help \ No newline at end of file + - name: test analyze schema + run: taranis --help \ No newline at end of file From ad0a2cf1d6d543cd6c3eaa1042c67b658207a053 Mon Sep 17 00:00:00 2001 From: luissian Date: Wed, 17 Jan 2024 15:27:47 +0100 Subject: [PATCH 017/214] first draft to run test --- .github/workflows/tests.yml | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) create mode 100644 .github/workflows/tests.yml diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml new file mode 100644 index 0000000..4359cb1 --- /dev/null +++ b/.github/workflows/tests.yml @@ -0,0 +1,20 @@ +name: tests ci +# This workflow runs the pipeline with the minimal test dataset to check +# is completed without any errors +on: + pull_request: + +jobs: + test_ci: + name: Code testing + runs-on: ubuntu-latest + + steps: + - name: Check out pipeline code + uses: actions/checkout@v4 + + - name: Install taranis + run: pip install . + + - name: test analyze schema + run: taranis --help \ No newline at end of file From 38ed82349912bb63553923fc472f951f8ba90f46 Mon Sep 17 00:00:00 2001 From: luissian Date: Wed, 17 Jan 2024 15:30:18 +0100 Subject: [PATCH 018/214] added deps to github action test --- .github/workflows/test.yml | 20 -------------------- .github/workflows/tests.yml | 3 +++ 2 files changed, 3 insertions(+), 20 deletions(-) delete mode 100644 .github/workflows/test.yml diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml deleted file mode 100644 index 4359cb1..0000000 --- a/.github/workflows/test.yml +++ /dev/null @@ -1,20 +0,0 @@ -name: tests ci -# This workflow runs the pipeline with the minimal test dataset to check -# is completed without any errors -on: - pull_request: - -jobs: - test_ci: - name: Code testing - runs-on: ubuntu-latest - - steps: - - name: Check out pipeline code - uses: actions/checkout@v4 - - - name: Install taranis - run: pip install . - - - name: test analyze schema - run: taranis --help \ No newline at end of file diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 4359cb1..0f4079b 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -13,6 +13,9 @@ jobs: - name: Check out pipeline code uses: actions/checkout@v4 + - name: Install dependencies + run: pip install -r requirements.txt + - name: Install taranis run: pip install . From 50011e36cd75cf867e061b57a3ca520e87384795 Mon Sep 17 00:00:00 2001 From: luissian Date: Wed, 17 Jan 2024 16:00:20 +0100 Subject: [PATCH 019/214] Updated test and environment for conda installation --- .github/workflows/tests.yml | 6 +++++- environment.yml | 3 +++ requirements.txt | 1 + 3 files changed, 9 insertions(+), 1 deletion(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 0f4079b..473aa56 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -3,6 +3,7 @@ name: tests ci # is completed without any errors on: pull_request: + push: jobs: test_ci: @@ -14,7 +15,10 @@ jobs: uses: actions/checkout@v4 - name: Install dependencies - run: pip install -r requirements.txt + run: $CONDA/bin/conda env update --file environment.yml --name base + + # - name: install python packages + # run: pip install -r requirements.txt - name: Install taranis run: pip install . diff --git a/environment.yml b/environment.yml index d5ca5b5..9009c37 100644 --- a/environment.yml +++ b/environment.yml @@ -16,3 +16,6 @@ dependencies: - bioconda::blast>=2.9 - bioconda::mash>=2 - bioconda::prodigal=2.6.3 + - pip + - pip : + - -r requirements.txt diff --git a/requirements.txt b/requirements.txt index cffcf88..c744623 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,4 @@ +rich click questionary bio From 38c69c246d526f5987658d41293bae014c9a3d06 Mon Sep 17 00:00:00 2001 From: luissian Date: Wed, 17 Jan 2024 16:07:06 +0100 Subject: [PATCH 020/214] Removed python packages from conda and move to pip --- environment.yml | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/environment.yml b/environment.yml index 9009c37..c85f60a 100644 --- a/environment.yml +++ b/environment.yml @@ -4,14 +4,14 @@ channels: - bioconda - defaults dependencies: - - conda-forge::python>=3.6 - - conda-forge::biopython==1.72 - - conda-forge::pandas==1.2.4 - - conda-forge::openpyxl==3.0.7 - - conda-forge::plotly==5.0.0 - - conda-forge::numpy==1.20.3 - - conda-forge::rich==13.7.0 - - conda-forge::python-kaleido + - conda-forge::python>=3.10 + # - conda-forge::biopython==1.72 + # - conda-forge::pandas==1.2.4 + # - conda-forge::openpyxl==3.0.7 + # - conda-forge::plotly==5.0.0 + # - conda-forge::numpy==1.20.3 + # - conda-forge::rich==13.7.0 + # - conda-forge::python-kaleido - bioconda::prokka>=1.14 - bioconda::blast>=2.9 - bioconda::mash>=2 From a04af8e3a37c65fb84a6d9bf0eb1ba4fc5ab87bc Mon Sep 17 00:00:00 2001 From: luissian Date: Wed, 17 Jan 2024 16:22:10 +0100 Subject: [PATCH 021/214] remove Self from annotation --- .github/workflows/tests.yml | 2 +- taranis/analyze_schema.py | 17 ++++++++--------- 2 files changed, 9 insertions(+), 10 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 473aa56..0038ac5 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -24,4 +24,4 @@ jobs: run: pip install . - name: test analyze schema - run: taranis --help \ No newline at end of file + run: taranis analyze-schema --help \ No newline at end of file diff --git a/taranis/analyze_schema.py b/taranis/analyze_schema.py index a9a84f6..4663b21 100644 --- a/taranis/analyze_schema.py +++ b/taranis/analyze_schema.py @@ -9,7 +9,6 @@ from Bio import SeqIO from collections import OrderedDict -from typing import Self import taranis.utils @@ -25,7 +24,7 @@ class AnalyzeSchema: def __init__( - self: Self, + self, schema_allele: str, output: str, remove_subset: bool, @@ -38,7 +37,7 @@ def __init__( """AnalyzeSchema instance creation Args: - self (Self): Self + self : AnalyzeSchema instance schema_allele (str): Folder path where schema files are located output (str): Out folder to save result remove_subset (bool): Remove subset sequences if True @@ -61,7 +60,7 @@ def __init__( self.species = species self.usegenus = usegenus - def check_allele_quality(self: Self, prokka_annotation: dict) -> OrderedDict: + def check_allele_quality(self, prokka_annotation: dict) -> OrderedDict: """Each allele in the locus file is analyzed its quality by checking if it can be converted to protein, has start/stop codon, has multiple stop codon, its a subsequence of the another allele, and if it is @@ -71,7 +70,7 @@ def check_allele_quality(self: Self, prokka_annotation: dict) -> OrderedDict: Dictionary with quality information for each allele is returned Args: - self (Self): AnalyzeSchema instance + self : AnalyzeSchema instance prokka_annotation (dict): Contains the annotation for each allele Returns: @@ -188,12 +187,12 @@ def check_allele_quality(self: Self, prokka_annotation: dict) -> OrderedDict: return a_quality - def fetch_statistics_from_alleles(self: Self, a_quality: dict) -> dict: + def fetch_statistics_from_alleles(self, a_quality: dict) -> dict: """By using the information for each allele in the input data create a dictionary with statistics data about length and quality Args: - self (Self): AnalyzeSchema instance + self: AnalyzeSchema instance a_quality (dict): Containing allele information Returns: @@ -229,7 +228,7 @@ def fetch_statistics_from_alleles(self: Self, a_quality: dict) -> dict: return record_data - def analyze_allele_in_schema(self: Self) -> list[dict, dict]: + def analyze_allele_in_schema(self) -> list[dict, dict]: """Analyze the alleles in the schema file by callig the function to annotate each of the alle and using this info to provide it for checking allele quality on check_allele_quality. With both info collection @@ -237,7 +236,7 @@ def analyze_allele_in_schema(self: Self) -> list[dict, dict]: information and the quality. Args: - self (Self): _description_ + self : _description_ Returns: list[dict, dict]: _description_ From ee8f6d6844d428a1d045a3df880c8170af1b0b5a Mon Sep 17 00:00:00 2001 From: luissian Date: Wed, 17 Jan 2024 16:39:35 +0100 Subject: [PATCH 022/214] liting --- taranis/utils.py | 64 +++++++++++++++++++++++++++++------------------- 1 file changed, 39 insertions(+), 25 deletions(-) diff --git a/taranis/utils.py b/taranis/utils.py index dcde29e..bbe6961 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -78,16 +78,16 @@ def get_seq_direction(allele_sequence): def create_annotation_files( - fasta_file : str, - annotation_dir : str, - prefix : str, - genus : str ="Genus", - species : str ="species", - usegenus : str=False, - cpus : int =3, + fasta_file: str, + annotation_dir: str, + prefix: str, + genus: str = "Genus", + species: str = "species", + usegenus: str = False, + cpus: int = 3, ) -> str: """prokka command is executed to generate the annotation files. - Return the folder path where prokka store these files + Return the folder path where prokka store these files Args: fasta_file (str): fasta file used for annotation @@ -100,7 +100,7 @@ def create_annotation_files( Returns: str: folder path where generated files from prokka are stored - """ + """ try: _ = subprocess.run( [ @@ -131,12 +131,12 @@ def create_annotation_files( return os.path.join(annotation_dir, prefix) -def create_new_folder(folder_name : str) -> None: - """Create directory defined in input data. No error occurs if folder exists +def create_new_folder(folder_name: str) -> None: + """Create directory defined in input data. No error occurs if folder exists Args: folder_name (str): folder path to be created - """ + """ try: os.makedirs(folder_name, exist_ok=True) except Exception as e: @@ -146,7 +146,15 @@ def create_new_folder(folder_name : str) -> None: return -def create_graphic(out_folder :str, f_name : str, mode : str, x_data : list, y_data :list, labels : list, title : str) -> None: +def create_graphic( + out_folder: str, + f_name: str, + mode: str, + x_data: list, + y_data: list, + labels: list, + title: str, +) -> None: """Create the graphic and save it to file Args: @@ -157,7 +165,7 @@ def create_graphic(out_folder :str, f_name : str, mode : str, x_data : list, y_d y_data (list): data for y axis labels (list): labels to be included title (str): title of the figure - """ + """ fig = go.Figure() if mode == "lines": fig.add_trace(go.Scatter(x=x_data, y=y_data, mode=mode, name=title)) @@ -173,7 +181,7 @@ def create_graphic(out_folder :str, f_name : str, mode : str, x_data : list, y_d return -def get_files_in_folder(folder : str, extension : str =None) -> list[str]: +def get_files_in_folder(folder: str, extension: str = None) -> list[str]: """get the list of files, filtered by extension in the input folder. If extension is not set, then all files in folder are returned @@ -266,13 +274,13 @@ def query_user_yes_no(question, default): sys.stdout.write("Please respond with 'yes' or 'no' (or 'y' or 'n').\n") -def read_annotation_file(ann_file : str) -> dict: +def read_annotation_file(ann_file: str) -> dict: """Read the annotation file and return a dictionary where key is the allele name and the value is the annotation data that prokka was defined for the - allele + allele Args: - ann_file (str): annotation file path (gff) + ann_file (str): annotation file path (gff) Returns: dict: contains the allele name and the predction @@ -307,7 +315,9 @@ def read_fasta_file(fasta_file): return SeqIO.parse(fasta_file, "fasta") -def write_fasta_file(out_folder: str, f_name: str, allele_name:str, seq_data: str) -> str: +def write_fasta_file( + out_folder: str, f_name: str, allele_name: str, seq_data: str +) -> str: """_summary_ Args: @@ -318,13 +328,13 @@ def write_fasta_file(out_folder: str, f_name: str, allele_name:str, seq_data: s Returns: str: _description_ - """ + """ try: os.makedirs(out_folder, exist_ok=True) except OSError as e: print(e) sys.exit(1) - + f_path_name = os.path.join(out_folder, f_name + ".fasta") with open(f_path_name, "w") as fo: fo.write("> " + allele_name + "\n") @@ -333,7 +343,6 @@ def write_fasta_file(out_folder: str, f_name: str, allele_name:str, seq_data: s def write_data_to_compress_filed(out_folder, f_name, dump_data): - with io.BytesIO() as buffer: with tarfile.open(fileobj=buffer, mode="w:gz") as tar: # Add data to the tar archive @@ -352,7 +361,12 @@ def write_data_to_compress_filed(out_folder, f_name, dump_data): def write_data_to_file( - out_folder : str, f_name : str, data :pd.DataFrame|list, include_header : bool =True, data_type: str ="pandas", extension :str ="csv" + out_folder: str, + f_name: str, + data: pd.DataFrame | list, + include_header: bool = True, + data_type: str = "pandas", + extension: str = "csv", ) -> None: """write data in the input parameter to disk @@ -364,7 +378,7 @@ def write_data_to_file( be included in file. Defaults to True. data_type (str, optional): type of data pandas or list. Defaults to "pandas". extension (str, optional): extension of file. Defaults to "csv". - """ + """ f_path_name = os.path.join(out_folder, f_name) if data_type == "pandas": data.to_csv(f_path_name, sep=",", header=include_header) @@ -376,7 +390,7 @@ def find_multiple_stop_codons(seq) : stop_codons = ['TAA', 'TAG','TGA'] c_index = [] for idx in range (0, len(seq) -2, 3) : - c_seq = seq[idx : idx + 3] + c_seq = seq[idx:idx + 3] if c_seq in stop_codons : c_index.append(idx) if len(c_index) == 1: From 9deea44b580cfd155816e38412a6fa7204a6edca Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 22 Jan 2024 14:14:21 +0100 Subject: [PATCH 023/214] Updated with comments in PR#13. Adding testing analyze schema with test data --- .github/workflows/tests.yml | 2 +- environment.yml | 7 - requirements.txt | 1 + taranis/__main__.py | 8 +- taranis/analyze_schema.py | 23 +- taranis/prediction.py | 86 -- test/MLST_listeria/abcZ.fasta | 966 --------------------- test/MLST_listeria/bglA.fasta | 872 ------------------- test/MLST_listeria/cat.fasta | 912 -------------------- test/MLST_listeria/dapE.fasta | 1214 -------------------------- test/MLST_listeria/dat.fasta | 706 --------------- test/MLST_listeria/ldh.fasta | 1382 ------------------------------ test/MLST_listeria/lhkA.fasta | 856 ------------------ test/MLST_listeria/lm0002.fasta | 8 + test/MLST_listeria/lmo0011.fasta | 10 + test/MLST_listeria/lmo0019.fasta | 10 + test/test.sh | 11 +- 17 files changed, 44 insertions(+), 7030 deletions(-) delete mode 100644 taranis/prediction.py delete mode 100644 test/MLST_listeria/abcZ.fasta delete mode 100644 test/MLST_listeria/bglA.fasta delete mode 100644 test/MLST_listeria/cat.fasta delete mode 100644 test/MLST_listeria/dapE.fasta delete mode 100644 test/MLST_listeria/dat.fasta delete mode 100644 test/MLST_listeria/ldh.fasta delete mode 100644 test/MLST_listeria/lhkA.fasta create mode 100644 test/MLST_listeria/lm0002.fasta create mode 100644 test/MLST_listeria/lmo0011.fasta create mode 100644 test/MLST_listeria/lmo0019.fasta diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 0038ac5..9f7d3e6 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -24,4 +24,4 @@ jobs: run: pip install . - name: test analyze schema - run: taranis analyze-schema --help \ No newline at end of file + run: taranis analyze-schema -i $script_dir/MLST_listeria -o analyze_schema_test -cpus 1 \ No newline at end of file diff --git a/environment.yml b/environment.yml index c85f60a..05b18db 100644 --- a/environment.yml +++ b/environment.yml @@ -5,13 +5,6 @@ channels: - defaults dependencies: - conda-forge::python>=3.10 - # - conda-forge::biopython==1.72 - # - conda-forge::pandas==1.2.4 - # - conda-forge::openpyxl==3.0.7 - # - conda-forge::plotly==5.0.0 - # - conda-forge::numpy==1.20.3 - # - conda-forge::rich==13.7.0 - # - conda-forge::python-kaleido - bioconda::prokka>=1.14 - bioconda::blast>=2.9 - bioconda::mash>=2 diff --git a/requirements.txt b/requirements.txt index c744623..a6540f6 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,4 @@ +biopython rich click questionary diff --git a/taranis/__main__.py b/taranis/__main__.py index b67b394..a8bcc94 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -170,7 +170,7 @@ def taranis_cli(verbose, log_file): "--output-allele-annot/--no-output-allele-annot", required=False, default=True, - help="get extension annotation for all alleles in locus", + help="output prokka/allele annotation for all alleles in locus", ) @click.option( "--genus", @@ -212,7 +212,7 @@ def analyze_schema( ): schema_files = taranis.utils.get_files_in_folder(inputdir, "fasta") - """ + """ TODO.DELETE CODE schema_analyze = [] for schema_file in schema_files: schema_obj = taranis.analyze_schema.AnalyzeSchema(schema_file, output, remove_subset, remove_duplicated, remove_no_cds, genus, species, usegenus) @@ -266,8 +266,8 @@ def analyze_schema( help="Output folder to save reference alleles", ) def reference_alleles( - schema, - output, + schema: str, + output: str, ): # taranis reference-alleles -s ../../documentos_antiguos/datos_prueba/schema_1_locus/ -o ../../new_taranis_result_code # taranis reference-alleles -s /media/lchapado/Reference_data/proyectos_isciii/taranis/taranis_testing_data/listeria_testing_schema/ -o /media/lchapado/Reference_data/proyectos_isciii/taranis/test/reference_alleles diff --git a/taranis/analyze_schema.py b/taranis/analyze_schema.py index 4663b21..984de3a 100644 --- a/taranis/analyze_schema.py +++ b/taranis/analyze_schema.py @@ -149,7 +149,7 @@ def check_allele_quality(self, prokka_annotation: dict) -> OrderedDict: a_quality[rec_id]["reason"] = "Duplicate allele" if self.remove_duplicated: bad_quality_record.append(rec_id) - + # check if sequence is a sub allele for rec_id, seq_value in allele_seq.items(): unique_seq.remove(seq_value) if seq_value in unique_seq: @@ -172,19 +172,6 @@ def check_allele_quality(self, prokka_annotation: dict) -> OrderedDict: # update the schema allele with the new file self.schema_allele = new_schema_file - """ - if self.output_allele_annot: - # dump allele annotation to file - ann_heading = ["gene", "allele", "allele direction","nucleotide sequence", "protein sequence", "nucleotide sequence length", "star codon", "CDS coding", "allele quality", "bad quality reason" ] - ann_fields = ["direction", "dna_seq", "protein_seq", "length", "start_codon_alt","cds_coding", "quality", "reason"] - f_name = os.path.join(self.output, self.allele_name +"_allele_annotation.csv") - with open (f_name, "w") as fo: - fo.write(",".join(ann_heading) + "\n") - for allele in a_quality.keys(): - data_field = [a_quality[allele][field] for field in ann_fields] - fo.write(self.allele_name + "," + allele + "," + ",".join(data_field) + "\n") - """ - return a_quality def fetch_statistics_from_alleles(self, a_quality: dict) -> dict: @@ -321,11 +308,9 @@ def stats_graphics(stats_folder: str) -> None: sum_all_alleles = stats_df["num_alleles"].sum() - labels = [] - values = [] - for item in taranis.utils.POSIBLE_BAD_QUALITY: - labels.append(item) - values.append(stats_df[item].sum()) + labels = taranis.utils.POSIBLE_BAD_QUALITY + values = [stats_df[item].sum() for item in labels] + labels.append("Good quality") values.append(sum_all_alleles - sum(values)) _ = taranis.utils.create_graphic( diff --git a/taranis/prediction.py b/taranis/prediction.py deleted file mode 100644 index da2a395..0000000 --- a/taranis/prediction.py +++ /dev/null @@ -1,86 +0,0 @@ -import logging -import os -import rich -import subprocess -import taranis.utils - -from pathlib import Path - -log = logging.getLogger(__name__) -stderr = rich.console.Console( - stderr=True, - style="dim", - highlight=False, - force_terminal=taranis.utils.rich_force_colors(), -) - - -class Prediction: - def __init__(self, genome_ref, sample_file, out_dir): - self.genome_ref = genome_ref - self.sample_file = sample_file - self.sample_name = Path(sample_file).stem - self.out_dir = out_dir - self.train = os.path.join(out_dir, self.sample_name + ".trn") - self.pred_protein = os.path.join(out_dir, self.sample_name + "_prot.faa") - self.pred_gene = os.path.join(out_dir, self.sample_name + "_dna.faa") - self.pred_coord = os.path.join(out_dir, self.sample_name + "_coord.gff") - - if not os.path.exists(self.out_dir): - try: - os.makedirs(self.out_dir, exist_ok=True) - log.debug("Created directory %s for prediction ", self.out_dir) - except OSError as e: - log.error("Cannot create %s directory", self.out_dir) - log.error(e) - stderr.print(f"[red] Unable to create {self.out_dir} folder") - exit(1) - - def training(self): - prodigal_command = ["prodigal", "-i", self.genome_ref, "-t", self.train] - try: - _ = subprocess.run( - prodigal_command, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - check=True, - ) - except Exception as e: - log.error("Unable to execute prodigal command for training") - log.error(e) - stderr.print(f"[red] Unable to run prodigal commmand. ERROR {e} ") - exit(1) - return - - def prediction(self): - prodigal_command = [ - "prodigal", - "-i", - self.sample_file, - "-t", - self.train, - "-f", - "gff", - "-o", - self.pred_coord, - "-a", - self.pred_protein, - "-d", - self.pred_gene, - ] - try: - _ = subprocess.run( - prodigal_command, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - check=True, - ) - except Exception as e: - log.error("Unable to execute prodigal command for training") - log.error(e) - stderr.print(f"[red] Unable to run prodigal commmand. ERROR {e} ") - exit(1) - return - - def get_sequence(self): - return diff --git a/test/MLST_listeria/abcZ.fasta b/test/MLST_listeria/abcZ.fasta deleted file mode 100644 index e42f626..0000000 --- a/test/MLST_listeria/abcZ.fasta +++ /dev/null @@ -1,966 +0,0 @@ ->abcZ_1 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_2 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_3 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_4 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGCTA ->abcZ_5 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGCGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_6 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTTACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAGGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_7 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_8 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_9 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGCGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGATGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_10 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGTTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAACGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_11 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_12 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAACGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_13 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCTCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGCTA ->abcZ_14 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGCGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATACCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_15 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGCGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_16 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_17 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGACTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_18 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTGCTATCCAACATCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGGGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_19 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTGGTTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_20 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_21 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTTATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_22 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGGGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_23 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_24 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTAGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGATGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAATGCCACTTACAAAGGTCAAGTATACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_25 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTGAGTCGCTTCACTAGTGACTTGGATAACATTTCTAACACACTCAACCAAGCATTGATTCAAGTGCTATCAAACATTGCATTAATGATTGGTGTCATAATCATGATGTTCCAGCAAAATGTGGAACTAGCCTTTGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACTATAATCATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAATTAGGTGTTTTGAACGGCTACATTGACGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAACGCAACTTATAAAGGTCAAGTTTACTCTGGTTTACTTTTTCCAATGATGCAAGGTATTTCATTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_26 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTGAGTCGCTTCACTAGTGACTTGGATAACATTTCTAACACACTCAACCAAGCATTGATTCAAGTGCTATCAAACATTGCATTAATGATTGGTGTCATAATCATGATGTTCCAGCAAAATGTGGAACTAGCCTTTGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACTATAATCATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAATTAGGTGTTTTGAACGGCTACATTGACGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTTCCAATGATGCAAGGTATTTCATTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_27 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGGGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_28 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTGAGTCGCTTCACTAGTGACTTGGATAACATTTCTAACACACTCAACCAAGCATTGATTCAAGTGCTATCAAACATTGCATTAATGATTGGTGTCATCATCATGATGTTCCAGCAAAATGTGGAACTAGCCTTTGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACTATAATCATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAATTAGGTGTTTTGAACGGCTACATTGACGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAACGCAACTTATAAAGGTCAAGTTTACTCTGGTTTACTTTTTCCAATGATGCAAGGTATTTCATTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_29 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAATTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATCGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_30 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTAAGCCGCTTCACTAGCGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_31 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTATTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGCTA ->abcZ_32 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAATGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_33 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGAGAAATGCTTAGTCGTTTTACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAGGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_34 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_35 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAATGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_36 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTGAGTCGCTTCACTAGTGACTTGGATAACATTTCTAACACACTCAACCAAGCATTGATTCAAGTGCTATCAAACATTGCATTAATGATTGGTGTCATAATCATGATGTTCCAGCAAAATGTGGAACTAGCCTTTGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACTATAATCATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAATTAGGTGTTTTGAACGGCTACATTGACGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCATTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_37 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_38 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_39 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAACGGTTTAGAAGAAGAAACAATTGACGGCTTTATTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_40 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_41 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTATACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_42 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTCAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_43 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_44 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_45 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_46 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCACCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_47 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGTGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAATGTAGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGATGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAATGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCTCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_48 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAATGTAGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGATGAAAAAATTTCTGGTCAAAAAATTATTATTACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAATGCCACTTACAAAGGTCAAGTATACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_49 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGAAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAATACACTAAACCAAGCATTGATCCAGGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_50 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_51 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_52 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_53 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_54 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATATCCGCAATGATGGCGAAATGCTTAGTCGTTTTACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAGGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_55 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTTACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAGGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTATTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_56 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTATACTCCGGTTTGCTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_57 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGCGACTTAGATGATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_58 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_59 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCTACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_60 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTTGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_61 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_62 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAATGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGGGGATGGTTA ->abcZ_63 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTGGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_64 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAATACACTAAACCAAGCATTGATTCAAGTGCTATCCAACATCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_65 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTGAGTCGCTTCACTAGTGACTTGGATAACATTTCTAACACACTCAACCAAGCATTGATTCAAGTGCTATCAAACATTGCATTAATGATTGGTGTCATAATCATGATGTTCCAGCAAAATGTGGAACTAGCCTTTGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACTATAATCATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAATTAGGTGTTTTGAACGGCTACATTGACGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAACGCAACTTATAAAGGTCAAGTTTACTCTGGTTTACTTTTTCCAATGATGCAAGGTATTTCATTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_66 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGGGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_67 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAATGTAGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGATGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAATGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCTCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_68 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTCAAACAAAACAATATCGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_69 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCCCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_70 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGACAGCCGCAATGATGGCGAAATGCTGAGTCGCTTCACTAGTGACTTGGATAACATTTCTAACACACTCAACCAAGCATTGATTCAAGTGCTATCAAACATTGCATTAATGATTGGTGTCATCATCATGATGTTCCAGCAAAATGTGGAACTAGCCTTTGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACTATAATCATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAATTAGGTGTTTTGAACGGCTACATTGACGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCATTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_71 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_72 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTGAGTCGCTTCACTAGTGACTTGGATAACATTTCTAACACACTCAACCAAGCATTGATTCAAGTGCTATCAAACATTGCATTAATGATTGGTGTCATAATCATGATGTTCCAGCAAAATGTGGAACTAGCCTTTGTTACTTTAATATCTGCTCCATTTGCGATTATAATTGCGACTATAATCATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAATTAGGTGTTTTGAACGGCTACATTGACGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAACGCAACTTATAAAGGTCAAGTCTACTCCGGTTTACTTTTTCCAATGATGCAAGGTATTTCATTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_73 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTTACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAGGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_74 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACATCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_75 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_76 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_77 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCTTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGCTA ->abcZ_78 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGACAGCCGCAATGATGGCGAAATGCTGAGTCGCTTCACTAGTGACTTGGATAACATTTCTAACACACTCAACCAAGCATTGATTCAAGTGCTATCAAACATTGCATTAATGATTGGTGTCATCATCATGATGTTCCAGCAAAATGTGGAACTAGCCTTTGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACTATAATCATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAATTAGGTGTTTTGAACGGCTACATTGACGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAACGCAACTTATAAAGGTCAAGTTTACTCTGGTTTACTTTTTCCAATGATGCAAGGTATTTCATTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_79 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTGAGTCGCTTCACTAGTGACTTGGATAACATTTCTAACACACTCAACCAAGCATTGATTCAAGTGCTATCAAACATTGCATTAATGATTGGTGTCATCATCATGATGTTCCAGCAAAATGTGGAACTAGCCTTTGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACTATAATCATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAATTAGGTGTTTTGAACGGCTACATTGACGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAACGCAACTTACAAAGGTCAAGTCTACTCCGGTTTACTTTTTCCAATGATGCAAGGTATTTCATTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_80 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_81 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_82 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGTGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGCTA ->abcZ_83 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAATGTAGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGATGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAATGCTACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_84 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTAGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGATGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAATGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_85 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAATGTAGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGATGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAATGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_86 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTAGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTAATGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAATGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_87 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATTTTCTTCGGTGGATGGTTA ->abcZ_88 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_89 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGCGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCTTTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_90 -AAATCGACGAATAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTTGATAGCCGTAATGATGGAGAAATGCTTAGTCGCTTCACTAGTGACTTGGATAACATCTCCAACACACTTAACCAAGCTTTGATTCAAGTATTATCAAACATTGCACTGATGATTGGTGTTATTATCATGATGTTCCAACAAAACGTCGAACTAGCCTTCGTTACTTTAATATCTGCACCGTTTGCGATAATTATTGCCACTATCATCATCCGAAAAGCTCGTAAGTACGTGGATATTCAACAAGACGAATTAGGTGTGCTAAATGGTTACATTGATGAAAAAATTTCTGGTCAAAAAATCATTATCACCAATGGTTTAGAAGAAGAAACTATTGATGGCTTTGTAAAACAAAATAATATCGTTAAAGACGCCACTTATAAAGGTCAAGTATACTCTGGTTTACTTTTTCCTATGATGCAAGGTATTTCATTATTAAATACGGCGATCGTCATCTTCTTTGGTGGATGGTTA ->abcZ_91 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_92 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATTCAAGTGCTATCCAACATCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAATGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_93 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_94 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_95 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTTACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAGGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGGAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_96 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCTGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAATGTAGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGATGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAATGCCACTTACAAAGGTCAAGTATACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_97 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTTACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_98 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCGAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGCTA ->abcZ_99 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATAACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_100 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTTACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAGGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTTAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_101 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCAAAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_102 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCAAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_103 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_104 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCATTATCTTCTTCGGTGGATGGTTA ->abcZ_105 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACCATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_106 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAAAGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_107 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTACCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGCTA ->abcZ_108 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAATGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_109 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGGCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGCTA ->abcZ_110 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTTGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_111 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTAATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_112 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAATGTAGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGATGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAAGGTTGTTAAAAATGCCACTTACAAAGGTCAAGTATACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_113 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_114 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAATACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_115 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_116 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATATTCTTCGGTGGATGGTTA ->abcZ_117 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_118 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGTAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_119 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTCAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_120 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGGCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_121 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_122 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_123 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAATGTAGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGATGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAATGCCACTTACAAAGGTCAAGTATACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_124 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_125 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGATCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_126 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGCGACTTAGATAATATTTCCAATACACTAAACCAAGCATTAATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_127 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_128 -AAATCGATGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_129 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_130 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_131 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTATACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_132 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGTAAGATGGAAAAACTATCGATTCGTTTCTTTGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAACATTTCTAACACACTGAATCAGGCGCTTATTCAGGTTCTTTCTAATATTGCGCTAATGGTTGGTGTTATCATCATGATGTTCCAACAAAACGTAGAACTTGCTTTCGTTACACTAATCTCAGCACCATTTGCAGTAGCCATCGCGACACTTATCATCCGAAAAGCCCGCAAGTATGTCGATGTGCAACAAGACGAACTAGGCGTATTAAACGGCTATATCGATGAAAAAATTTCTGGTCAAAAAATTATTATCACTAATGGTTTGGAAGAAGAAACCATTGAAGGCTTTGTCAAACAAAACAATGTCGTAAAAGATGCGACTTACAAAGGCCAAGTTTACTCTGGTTTACTTTTTCCAATGATGCAAGGTATTTCACTACTTAATACAGCTATCGTTATCTTCTTTGGAGGATGGTTA ->abcZ_133 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTTGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTGGATAACATTTCCAACACACTAAATCAGGCGCTTATTCAAGTTCTTTCTAATATCGCACTAATGGTTGGTGTTATCATCATGATGTTCCAACAAAATGTAGAACTTGCTTTTGTTACACTAATTTCAGCACCGTTTGCAGTAGCTATTGCAACGATTATTATCCGAAAAGCCCGTAAGTATGTCGATGTGCAACAAGATGAACTAGGCGTATTAAACGGCTATATCGATGAAAAAATTTCTGGTCAAAAAATTATTATCACTAATGGCTTGGAAGAAGAAACGATTGAAGGCTTTGTCAAACAAAATAATGTCGTAAAAGAGGCGACTTACAAAGGCCAAGTTTACTCTGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTACTTAATACAGCAATTGTTATCTTCTTTGGAGGATGGTTA ->abcZ_134 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTGGATAACATTTCCAACACGCTAAACCAAGCCTTGATTCAAGTGCTTTCCAACATCGCGCTAATGATTGGCGTTATCATCATGATGTTCCAACAAAACGTAGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATAATTATTGCGACTATTATCATCCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTAAACGGCTACATTGATGAAAAAATTTCTGGTCAAAAAATCATTATCACAAACGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAATAATATCGTTAAAAATGCCACTTACAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_135 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACGCTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_136 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGAGGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_137 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGCGGATGGTTA ->abcZ_138 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGCGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_139 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_140 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAATTAGCCTTCGTTACTTTAATATCTGCCCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACCGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_141 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_142 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_143 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTGAGTCGCTTCACTAGTGACTTGGATAACATTTCTAACACACTCAACCAAGCATTGATTCAAGTGCTATCAAACATTGCATTAATGATTGGTGTCATAATCATGATGTTCCAGCAAAATGTGGAACTAGCCTTTGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACTATAATCATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAATTAGGTATTTTGAACGGCTACATTGACGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTTCCAATGATGCAAGGTATTTCATTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_144 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAACGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_145 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTATCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_146 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGTCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_147 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTTACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAGGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAATGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_148 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTGCTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_149 -AAAGCAACGAACCGGATGCGGATTGGGCTTTTCAAGAAAATGGAAAAATTATCGATTCGCTTCTTTGATAGCCACAATGATGGCGAAATGTTGAGTCGCTTTACGAGTGACATGGATAACATTTCAAACACGCTCAACCAAGCGCTTATTCAAGTGCTTTCGAATTTGGCGCTCATGATTGGGGTTATCATCATGATGTTCATGCAAAATGTCGAACTTGCTTTTGTCACACTGATCGCGGCACCATTTGCAGTGATTATTGCAGGGGTTATCATTCGGAAAGCACGCCGTTATGTGGACTTGCAACAAGATAGCTTAGGTCGCTTAAATGCCTATATTGATGAAAAAGTCTCTGGTCAAAAAGTGGTCATCACAAATGGTCTTGAAGAAGAAACCATTAATGGATTCTTGAAGCACAATGAAATCGTTAAAAATGCAACCTATAAAGGTCAAGTTTATTCTGGCTTATTGTTCCCAGTGATGCAAGGGATTTCGCTTGTTAATACGGCGATTGTTATTTTCTTCGGTGGTTATCTT ->abcZ_150 -AAATCGACCAACCGGATGCGGATCGGTTTATTCCGCAAAATGGAGAAACTGTCGATCCGCTTCTTCGATAAGCATAGTGATGGCGAAATGCTGAGCCGGTTTACAAGTGATATGGATAATATTTCCAATACATTAAACCAAGCTATCGTCCAAGTGCTTTCTAACTTTGCATTAATGATCGGAGTCATCATCATGATGTTCAATCAAAATGTGAAGTTGGCGCTGATCACTCTTATCGCTGCGCCATTCGCGATTGTGATCGCAGCACTCATCATCCGCAAAGCACGCCGCTATGTGGATCTGCAACAGGACAGTTTAGGCGAATTGAATGCCTATATCGATGAGAAGATCTCCGGGCAGAAAGTCGTGATTACGAACGGGCTGGAAGAAGAGACGATTGCAGGCTTTACGAAACACAACGAAAAAGTGAAAAATGCTACTTATAAAGGTCAAGTTTATTCCGGGATGTTATTCCCAGTTATGCAGGGGATCTCTCTCTTGAATACGGCGATCGTCATTTTCTTCGGTGGCTACCTA ->abcZ_151 -AAATCGACCAACCGGATGCGGATCGGTTTATTCCGCAAAATGGAGAAACTGTCGATCCGCTTCTTCGATAAGCATAGTGATGGCGAAATGCTGAGCCGGTTTACAAGTGATATGGATAATATTTCCAATACATTAAACCAAGCTATCGTTCAAGTGCTTTCTAACTTTGCATTAATGATCGGAGTCATCATCATGATGTTCAATCAAAATGTGAAGTTGGCGCTGATCACTCTTATCGCTGCGCCATTCGCGATTGTGATCGCAGCACTCATCATCCGCAAAGCACGTCGCTATGTGGATCTGCAACAGGACAGTTTAGGCGAATTGAATGCCTATATCGATGAGAAGATCTCCGGACAGAAAGTCGTGATTACGAACGGGCTGGAAGAAGAGACGATTGCAGGCTTTACGAAACACAACGAAAAAGTGAAAAATGCTACTTATAAAGGTCAAGTTTATTCCGGGATGTTATTCCCAGTTATGCAGGGGATCTCTCTCTTGAATACGGCGATCGTCATTTTCTTCGGTGGCTACCTA ->abcZ_152 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTGAGTCGCTTCACTAGTGACTTGGATAACATTTCTAACACACTCAACCAAGCATTGATTCAAGTGCTATCAAACATTGCATTAATGATTGGTGTCATCATCATGATGTTCCAGCAAAATGTGGAACTAGCCTTTGTTACTTTAATATCTGCTCCATTTGCGATTATAATTGCGACTATAATCATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAATTAGGTGTTTTGAACGGCTACATTGACGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCATTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_153 -AACGCAACGAACCGGATGCGTATCGGATTATTCCGCAAGATGGAAAAATTATCAATTCGTTTCTTCGACGGACATAAAGATGGAGAAATGCTGAGTCGTTTTACAAGTGACTTGGATAACATCTCAAACACACTAAACCAAGCGCTCGTACAAGTTTTGTCCAATGTAGCGCTCATGATTGGTGTTATTATCATGATGTATCAACAAAACGCGAAGCTTGCTACAGTGACGCTTCTAATGGCACCAATTGCGATTTTCATTGCTGCATTAATTATCCGCAAAGCTCGTAAATATGTGGATATGCAACAAGATCGTCTCGGTGAACTAAATGGTTACATTGATGAGAAAATCTCTGGTCAAAAGATCGTTATTACAAATGGTTTAGAAGAAGAAACAATTGAAGGTTTTGTAAAACATAACAATATTGTTAAAGATGCTACATTCAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCTATGATGCAAGGTATTTCTTTAATCAACACAGCTGTCGTGATTTTCTTTGGTGGTTACCTC ->abcZ_154 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATCCGTTTCTTCGATAGCCGCAATGACGGCGAAATGCTTAGCCGTTTCACTAGTGACTTGGATAACATTTCCAACACACTAAACCAAGCCCTGATTCAAGTACTATCAAACGTTGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCAATAATTATTGCGACAGTGATCATCCGAAAAGCTCGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTAAACGGCTACATTGATGAAAAAATTTCTGGTCAAAAAATCATTATCACAAACGGCCTAGAAGAAGAAACAATCGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCTTATTGAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_155 -CGTGCGACGAATCGAATGCGTATTGGCTTGTTCCGCAAAATGGAGAAACTATCGATTCGTTTCTTCGATAGTCATAAAGATGGCGAAATGCTCAGCCGTTTCACCAGTGACCTTGATAACATTTCCAACACGCTGAACCAAGCGTTAGTACAAGTTTTATCCAACGTTGCCCTTATGATCGGTGTTATCATCATGATGTACAACCAAAATGTGAAATTGGCAACTGTAACCCTTATTTTAGCACCAGTTGCGATCATTATTGCCGCTTTGATTATTCGTAAAGCGCGTAAATATGTGGATCTGCAACAAGATCGTCTCGGTGAACTGAATGGTTACATCGATGAGAAGATCTCTGGTCAAAAAATTATTATCACGAACGGCCTGGAAGAAGAAACAGTTGAAGGCTTCGTGAAACATAACAATATCGTTAAAGATGCCACATTTAAAGGCCAAGTATACTCTGGTTTACTTTTCCCGATGATGCAAGGTATTTCGTTAATTAATACCGCTGTCGTTATTTTCTTCGGTGGTTACCTC ->abcZ_156 -AAGGCGACAAACAGAATGCGTATTGGTTTATTCCGCAAAATGGAAAAACTTTCGATTCGCTTTTTCGATCGTCATAATGATGGGGAGATGCTAAGCCGTTTTACAAGTGATATGGATAATATTTCTAATACGCTAAACCAGGCGCTAGTTCAAGTTTTATCCAACTTGGCTTTAATGATTGGTGTTATTATCATGATGTTCAATCAAAATGTAGAGCTGGCACTTGTCACGCTAATTGCATCACCGTTTGCAATTGTGATTGCCGCTATTATTATTCGTAAGGCACGGAGATATGTGGATTTACAACAAGATAGTTTGGGGCATTTAAACGCCTATATTGATGAAAAAATTTCTGGCCAGAAAGTTATTATTACAAACGGCCTTGAAGATGAAACGATTGAAGGTTTTGTGAAGCACAATAATACGGTTAAAAATGCGACTTATAAAGGTCAAGTTTATTCAGGCTTACTTTTCCCAGTGATGCAAGGTATCTCCCTTTTAAACACAGCGATTGTGATTTTCTTTGGTGGATATTTA ->abcZ_157 -AAATCGACTAACCGGATGCGGATCGGTTTGTTCAAAAAAATGGAGAAACTATCAATCCGATTCTTTGACAGTCATAATGACGGTGAAATGCTAAGTCGTTTCACGAGCGATATGGATAATATCTCGAACACGCTTAACCAAGCGCTCGTGCAAGTACTATCCAATCTTGCACTGATGATCGGTGTTATCATCATGATGTTCATGCAAAATGTCGAACTGGCACTCGTTACATTGATTGCTTCACCGTTCGCCGTTGCTATCGCAGCTGTAATCATTCGTAAAGCGCGCCGTTATGTTGATCTTCAACAAGATAGCCTCGGACGTCTTAATGCGTATATTGATGAAAAGATTTCCGGTCAAAAAGTGATCATCACAAACGGACTTGAGGAAGAGACCATTGAAGGCTTTACGAAACACAATAGCATCGTAAAAAATGCGACATATAAAGGACAAGTATATTCAGGGTTGCTTTTCCCAGTTATGCAAGGGATTTCGCTGTTAAACACAGCAATCGTTATTTTCTTCGGTGGATACCTC ->abcZ_158 -AAAGCAACGAACCGGATGCGGATTGGACTTTTCAAGAAAATGGAAAAACTATCGATTCGCTTCTTTGATAGCCACAATGATGGCGAGATGTTGAGTCGCTTTACGAGTGACATGGATAACATTTCAAACACGCTCAACCAAGCGCTTGTTCAAGTGCTTTCGAATTTGGCGCTCATGATCGGGGTTATCATCATGATGTTCACGCAAAATGTAGAACTTGCTCTTGTTACACTGATCGCGGCACCGTTTGCAGTGATTATCGCAGGGATTATCATTCGGAAAGCGCGCCGCTATGTGGACTTGCAACAAGATAGCTTAGGTCGCTTAAATGCCTATATTGATGAAAAAATCTCTGGTCAAAAAGTGGTCATCACAAATGGTCTTGAAGAAGAAACCATTAGCGGTTTCTTAAAACACAATGAAATCGTTAAAAATGCAACGTATAAAGGTCAAGTTTATTCTGGCTTGTTGTTCCCCGTGATGCAGGGTATTTCGCTTGTTAATACGGCGATTGTTATTTTCTTCGGTGGTTATCTT ->abcZ_159 -CGTGCTACAAACCGGATGCGTATTGGTCTGTTCCGCAAAATGGAGAAATTATCAATCCGCTTCTTCGATGGACATAAAGATGGCGAAATGCTAAGCCGCTTCACCAGTGACTTAGATAATATCTCGAACACGCTAAACCAAGCGCTTGTCCAAGTTTTATCCAATGTGGCACTGATGATTGGTGTTATTATCATGATGTACCAACAAAACGTGAAGCTTGCGACAGTAACGTTGCTTTTAGCTCCTGTAGCTATTTTGATTGCTGCGTTAATTATACGTAAGGCTCGTAAATATGTGGATATGCAACAAGATCGTCTTGGCGAACTAAACGGTTATATTGACGAGAAAATCTCTGGTCAAAAAATCGTTATCACAAATGGATTAGAAGAGGAAACTATCGAAGGCTTCGTAAAACATAATAATATCGTGAAAGAGGCTACATTCAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCGATGATGCAAGGTATTTCCTTAGTCAACACAGCCGTTGTTATTTTCTTCGGTGGTTGGTTG ->abcZ_160 -CGCGCGACGAACCGAATGCGTATCGGTTTGTTCCGCAAAATGGAGAAATTATCGATTCGTTTCTTCGATGGGCATAAAGATGGCGAAATGCTGAGTCGTTTTACAAGTGACCTAGATAATATCTCGAACACGCTGAACCAAGCGCTCGTACAAGTTTTATCTAACGTTGCATTAATGATCGGTGTTATTATCATGATGTACCAACAAAACGTGAAGCTCGCGTCTGTCACGCTGATTCTGGCGCCCTTTGCGATTATTATTGCCGCCCTTATCATCCGCAAAGCGCGTAAATATGTGGATCTGCAACAAGATCGTCTCGGTGAACTAAACGGTTACATCGATGAGAAAATTTCTGGTCAAAAAATCGTGATTACGAACGGCTTGGAAGAAGAAACTATCGAAGGCTTCGTGAAACATAACAACATCGTGAAAGAAGCAACATTTAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCGATGATGCAAGGTATTTCGCTGATTAATACGGCTGTCGTTATTTTCTTCGGTGGTTACCTA ->abcZ_161 -CGTGCGACGAACCGGATGCGTATTGGGTTGTTCCGTAAGATGGAGAAATTATCGATTCGTTTCTTCGATGGACACAAAGATGGCGAAATGCTTAGTCGTTTTACAAGTGACCTAGATAACATCTCGAACACGCTGAACCAGGCGCTTGTACAAGTTTTATCTAACGTGGCACTGATGATTGGTGTTATTATCATGATGTACCAACAAAACGTGAAACTCGCGTCTGTTACATTGATTCTAGCTCCTGTTGCGATTATTATTGCGGCCCTCATCATTCGCAAAGCGCGTAAATATGTGGATCTGCAACAAGATCGTCTTGGGGAACTAAACGGCTACATCGATGAAAAAATCTCTGGGCAAAAAATCGTTATCACAAACGGCTTGGAAGAAGAAACCATTGATGGCTTCGTGAAACATAACAATATCGTAAAAGAAGCTACATTTAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCGCTGATCAATACCGCTGTTGTTATTTTCTTCGGTGGTTACCTC ->abcZ_162 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAGAAACTATCGATTCGTTTCTTTGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACAAGTGACTTAGATAACATTTCCAACACACTTAACCAGGCACTTATCCAAGTTCTTTCCAACATCGCACTGATGATTGGTGTTATCATCATGATGTTCCAACAAAATGTAGAACTTGCTTTTGTTACACTGATTTCAGCACCATTTGCAATAATTATTGCCACAGTGATTATTCGTAAAGCTCGCAAATACGTTGATGTGCAACAAGATGAACTAGGCGTATTAAATGGCTATATTGATGAGAAAATCTCTGGTCAAAAAATTATTATCACGAATGGTTTAGAAGAAGAAACCATTGAAGGCTTTGTAAAACAAAATAATGTCGTTAAAGACGCTACTTACAAAGGCCAAGTATACTCAGGCTTACTTTTCCCAATGATGCAAGGTATTTCACTACTTAACACAGCAATCGTTATTTTCTTCGGTGGTTGGTTA ->abcZ_163 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAGAAACTATCGATTCGTTTCTTTGATAGCCGCAATGATGGCGAAATGCTAAGCCGCTTCACAAGTGACTTAGATAACATTTCCAACACACTTAACCAGGCACTTATCCAAGTTCTTTCCAACATCGCACTGATGATTGGTGTTATCATCATGATGTTCCAACAAAATGTAGAACTTGCTTTTGTTACACTGATTTCAGCACCATTTGCAATAATTATTGCCACAGTGATTATTCGTAAAGCTCGCAAATACGTTGATGTGCAACAAGATGAACTAGGCGTATTAAATGGCTATATTGATGAGAAAATCTCTGGTCAAAAAATTATTATCACGAATGGTTTAGAAGAAGAAACCATTGAAGGCTTTGTAAAACAAAATAATGTCGTTAAAGACGCTACTTACAAAGGCCAAGTATACTCAGGCTTACTTTTCCCAATGATGCAAGGTATTTCACTACTTAACACAGCAATCGTTATTTTCTTCGGTGGTTGGTTA ->abcZ_164 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTTGATAGCCGTAATGATGGAGAAATGCTTAGTCGCTTCACTAGTGACTTGGATAACATCTCCAACACACTTAACCAAGCTTTGATTCAAGTATTATCAAACATTGCACTGATGATTGGTGTTATTATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCACCGTTTGCGATAATTATTGCCACTATCATCATCCGAAAAGCTCGCAAGTTCGTGGATATTCAACAAGACGAATTAGGTGTGCTAAATGGTTACATTGATGAAAAAATTTCTGGTCAAAAAATCATTATCACCAATGGTTTAGAAGAAGAAACTATTGATGGCTTTGTAAAACAAAATAATATCGTTAAAGACGCCACTTATAAAGGTCAAGTATACTCTGGGTTACTTTTTCCTATGATGCAAGGTATTTCATTATTAAATACGGCGATTGTCATCTTCTTTGGTGGATGGTTA ->abcZ_165 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTTGATAGCCGTAATGATGGAGAGATGCTTAGTCGCTTCACTAGTGACTTGGATAACATCTCCAACACACTTAACCAAGCTTTGATTCAAGTATTATCAAACATTGCACTGATGATTGGTGTTATTATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCACCGTTTGCGATAATTATTGCCACTATCATTATCCGAAAAGCTCGCAAGTTCGTGGATATTCAACAAGACGAATTAGGTGTGCTAAATGGTTACATTGATGAAAAAATTTCTGGTCAAAAAATCATTATCACCAATGGTTTAGAAGAAGAAACTATTGATGGCTTTGTAAAACAAAATAATATCGTTAAAGACGCCACTTATAAAGGTCAAGTATACTCTGGTTTACTTTTTCCTATGATGCAAGGTATTTCATTATTAAATACGGCGATCGTCATCTTCTTTGGTGGATGGTTA ->abcZ_166 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGACTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGCTA ->abcZ_167 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTTACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_168 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGCGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCATTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_169 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTGGATAATATTTCCAATACACTAAACCAAGCATTGATTCAAGTACTATCAAACATTGCATTAATGATTGGTGTCATCATCATGATGTTCCAGCAAAATGTGGAACTAGCCTTTGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACTATAATCATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAATTAGGTGTTTTGAACGGCTACATTGACGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCATTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_170 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_171 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTTGATAGCCGTAATGATGGAGAAATGCTTAGTCGCTTCACTAGTGACTTGGATAACATCTCCAACACACTTAACCAAGCTTTGATTCAAGTATTATCAAACATTGCACTAATGATTGGTGTTATTATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCACCATTTGCGATAATTATTGCCACTATCATCATCCGAAAAGCTCGCAAGTTCGTGGATATTCAACAAGACGAATTAGGTGTGCTAAATGGTTACATTGATGAAAAAATTTCTGGTCAAAAAATCATTATCACCAATGGTTTAGAAGAAGAAACTATTGATGGCTTTGTAAAACAAAATAATATCGTTAAAGACGCCACTTATAAAGGTCAAGTATACTCTGGTTTACTTTTTCCTATGATGCAAGGTATTTCATTATTAAATACGGCGATCGTCATCTTCTTTGGTGGATGGTTA ->abcZ_172 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_173 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTGAGTCGCTTCACTAGTGACTTGGATAACATTTCTAACACACTCAACCAAGCATTGATTCAAGTGCTATCAAACATTGCATTAATGATTGGTGTCATAATCATGATGTTCCAGCAAAATGTGGAACTAGCCTTTGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACTATAATCATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAATTGGGTGTTTTGAACGGCTACATTGACGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCATTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_174 -CGCGCGACGAACCGGATGCGTATCGGATTGTTCCGCAAAATGGAGAAACTGTCGATTCGTTTCTTCGATGGGCATAAAGATGGCGAAATGCTGAGCCGTTTTACAAGTGACTTAGACAACATCTCGAATACGCTGAACCAAGCGCTCGTGCAAGTTTTGTCAAACGTTGCGTTAATGATTGGTGTTATCATCATGATGTATAACCAAAATGTGAAATTAGCAACGGTGACGCTTCTGATGGCACCAATTGCGATTGTTATTGCCGCGTTGATTATCCGCAAAGCTCGTAAATACGTGGATATGCAACAAGATCGTCTCGGCGAACTAAACGGCTATATCGATGAAAAAATCTCCGGTCAAAAAATCGTTATTACAAACGGTTTAGAGCAAGAAACGATTGAAGGCTTCGTGAAACATAATAACATCGTGAAAGACGCAACTTTTAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCGATGATGCAAGGTATTTCGTTAATCAATACAGCTGTCGTAATCTTCTTCGGTGGTTACCTA ->abcZ_175 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGTGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_176 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGCGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTTCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_177 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCACAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGCGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_178 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACGCTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_179 -AAATCGACGAACAGAATGCGTATAGGGCTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_180 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTTGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTGGATAACATTTCCAACACACTAAATCAGGCGCTTATTCAAGTTCTTTCTAATATCGCACTAATGGTTGGTGTTATCATCATGATGTTCCAACAAAATGTAGAACTTGCTTTTGTTACACTAATTTCAGCACCGTTTGCAGTAGCTATTGCAACGATTATTATCCGAAAAGCCCGTAAGTATGTCGATGTGCAACAAGATGAACTAGGCGTATTAAACGGCTATATCGATGAAAAAATTTCTGGTCAAAAAATTATTATCACTAATGGGTTGGAAGAAGAAACGATTGAAGGCTTTGTCAAACAAAATAATGTCGTGAAAGAGGCGACTTACAAAGGCCAAGTTTACTCTGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTACTTAATACAGCAATTGTTATCTTCTTTGGAGGATGGTTA ->abcZ_181 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAGCTATCAATTCGTTTCTTTGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACAAGTGACTTAGATAACATTTCCAACACACTTAACCAGGCACTTATCCAAGTTCTTTCCAACATCGCACTAATGATCGGTGTTATCATCATGATGTTCCAACAAAATGTAGAACTTGCATTTGTTACACTGATTTCAGCACCATTTGCAATAATTATTGCCACAGTGATTATTCGTAAAGCTCGCAAATACGTTGATGTGCAACAAGATGAACTAGGCGTATTAAATGGTTATATTGATGAAAAAATCTCTGGTCAAAAAATTATTATCACGAATGGTTTAGAAGAAGAAACCATTGAAGGCTTTGTAAAACAAAATAATGTCGTTAAAGACGCTACTTACAAAGGCCAAGTATACTCAGGCTTACTTTTCCCAATGATGCAAGGTATTTCATTACTTAACACAGCAATCGTTATTTTCTTCGGTGGTTGGTTA ->abcZ_182 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTTGATAGCCGTAATGATGGAGAAATGCTTAGTCGCTTCACTAGTGACTTGGATAACATCTCCAACACACTTAACCAAGCTTTGATTCAAGTATTATCAAACATTGCACTGATGATTGGTGTTATTATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCACCATTTGCGATAATTATTGCCACTATCATCATCCGAAAAGCTCGCAAGTTCGTGGATATTCAACAAGACGAATTAGGTGTGCTAAATGGTTACATTGATGAAAAAATTTCTGGTCAAAAGATCATTATCACCAATGGTTTAGAAGAAGAAACTATTGATGGCTTTGTAAAACAAAATAATATCGTTAAAGACGCCACTTATAAAGGTCAAGTATACTCTGGTTTACTTTTTCCTATGATGCAAGGTATTTCATTATTAAATACGGCGATCGTCATCTTCTTTGGTGGATGGTTA ->abcZ_183 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_184 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGCGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAGGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_185 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCGACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_186 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTTACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAGGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_187 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTATAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_188 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGACAGCCGCAATGATGGCGAAATGCTGAGTCGCTTCACTAGTGACTTGGATAACATTTCTAACACACTCAACCAAGCATTGATTCAAGTGCTATCAAACATTGCATTAATGATTGGTGTCATCATCATGATGTTCCAGCAAAATGTGGAACTAGCCTTTGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACTATAATCATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAATTAGGTGTTTTGAACGGCTACATTGACGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAACGCAACTTACAAAGGTCAAGTCTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCATTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_189 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_190 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTGAGTCGCTTCACTAGTGACTTGGATAACATTTCTAACACACTCAACCAAGCATTGATTCAAGTGCTATCAAACATTGCATTAATGATTGGTGTCATAATCATGATGTTCCAGCAAAATGTGGAACTAGCCTTTGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACTATAATCATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAATTAGGTGTTTTGAACGGCTACATTGACGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAACGCAACTTATAAAGGTCAAGTCTACTCCGGTTTACTTTTTCCAATGATGCAAGGTATTTCATTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_191 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTGAGTCGCTTCACTAGTGACTTGGATAACATTTCTAACACACTCAACCAAGCATTGATTCAAGTGCTATCAAACATTGCATTAATGATTGGTGTCATAATCATGATGTTCCAGCAAAATGTGGAACTAGCCTTTGTTACTTTAATATCTGCTCCATTTGCGATTATAATTGCGACTATAATCATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAATTAGGTGTTTTGAACGGCTACATTGACGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTATTAAACAAAATAATGTTGTTAAAAACGCAACTTATAAAGGTCAAGTTTACTCTGGTTTACTTTTTCCAATGATGCAAGGTATTTCATTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_192 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAGCTATCAATTCGTTTCTTTGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACAAGTGACTTAGATAACATTTCCAACACACTTAACCAGGCACTTATCCAAGTTCTTTCCAACATCGCACTAATGATCGGTGTTATCATCATGATGTTCCAACAAAATGTAGAACTTGCATTTGTTACACTGATTTCAGCACCATTTGCAATAATTATTGCCACAGTGATTATTCGTAAAGCTCGCAAATACGTTGATGTGCAACAAGATGAACTAGGCGTATTAAATGGTTATATTGATGAAAAAATCTCTGGTCAAAAAATTATTATCACGAATGGTTTAGAAGAAGAAACCATTGAAGGCTTTGTAAAACAAAATAATGTCGTTAAAGACGCTACTTACAAAGGCCAAGTATACTCAGGCTTACTTTTCCCAATGATGCAAGGTATTTCATTACTTAACACAGCAATCGTTATTTTCTTCGGTGGGTGGTTA ->abcZ_193 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACATCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_194 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_195 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTTGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_196 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTATTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_197 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTATACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_198 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_199 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_200 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATTCAAGTGCTATCCAACATCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTAATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAATTAGGCGTACTTAACGGTTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_201 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTTACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_202 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGCGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAACCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_203 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGCGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTACCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_204 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_205 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_206 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTTACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAGGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_207 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATTTCTGGACAAAAAATTATTATCACAAATGGTTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTTCCAATGATGCAAGGTATTTCATTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_208 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTTCCTATTAAATACAGCTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_209 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTTACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAGGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTAGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_210 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAACCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_211 -AAATCAACAAACCGCATGCGGATTGGTTTGTTTAATAAATTAGAAAAATTGACTATTCGTTTCTTTGATTCTCATCAAGATGGTGAAATTTTAAGTCGTTTTACTAGTGATTTAGACAACATCCAAAACAGTTTAAACCAAGCGTTGCTACAAGTATTAACCAATATTGCCTTATTAGTTGGTGTCTTAATCATGATGTTCCGTCAAAATGTGGAACTGGCATGGGCTACAATTGCTTCTACGCCGATTGCGATTTTAATTGCGGTCTTTGTGATTAGCAAGGCGCGCAAATATGTCGATTTACAGCAAGATGAAGTGGGTAAATTAAATGGCTATATGGATGAAAAAATTAGTGGGCAACGTGTGATTATCACTAATGGCTTACAAGAAGAAACCATTGACGGTTTTTTAGAGCAAAATGAAAAAGTTCGTGCAGCTACGTATAAAGGTCAAGTGTATTCAGGATTACTTTTCCCAATGATGCAAGGAATGTCATTAGTCAATACGGCGATTGTTATTTTCTTTGGTGGTTGGTTA ->abcZ_212 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAATTAGCCTTCGTTACTTTAATATCTGCCCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_213 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAACGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_214 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_215 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_216 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_217 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_218 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_219 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTCGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_220 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTTGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTGGATAACATTTCCAACACACTAAACCAAGCCCTGATTCAAGTACTATCAAACGTTGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTAGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATAATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTAAACGGCTACATTGATGAAAAAATTTCTGGTCAAAAAATCATTATCACAAACGGCCTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAACATCGTTAAAAACGCAACTTACAAAGGTCAAGTATACTCCGGCTTACTTTTCCCAATGATGCAAGGTATTTCCTTATTGAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_221 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTGGATAACATTTCCAACACACTAAACCAAGCCCTGATTCAAGTACTATCAAACGTTGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTAGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATAATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTAAACGGCTACATTGATGAAAAAATTTCTGGTCAAAAAATCATTATCACAAACGGCCTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAACATCGTTAAAAACGCAACTTACAAAGGTCAAGTATACTCCGGCTTACTTTTCCCAATGATGCAAGGTATTTCCTTATTGAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_222 -AAATCGACCAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATCCGTTTCTTCGATAGCCGCAATGACGGCGAAATGCTTAGCCGTTTCACTAGTGACTTGGATAACATTTCCAACACACTAAACCAAGCCCTGATTCAAGTACTATCAAACGTTGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATAATTATTGCGACAGTGATTATTCGAAAAGCTCGTAAATTCGTTGACGTTCAACAAGATGAACTAGGCGTACTAAACGGCTACATTGATGAAAAAATTTCTGGTCAAAAAATCATTATCACAAACGGCCTAGAAGAAGAAACAATCGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCTTATTGAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_223 -AAGGCGACAAACAGAATGCGTATTGGTTTATTCCGCAAAATGGAAAAACTTTCGATTCGCTTTTTCGATCGTCATAATGATGGGGAGATGCTAAGCCGTTTTACAAGTGATATGGATAATATTTCTAATACGCTAAACCAGGCGTTAGTTCAGGTTTTATCCAACCTAGCTTTAATGATTGGTGTTATTATCATGATGTTCAATCAAAATGTGGAGCTGGCACTTGTCACGCTAATTGCATCACCGTTTGCAATTGTGATTGCCGCTATTATTATTCGTAAGGCACGGAGATATGTGGATTTACAACAAGATAGTTTGGGGCATTTAAACGCCTATATTGATGAAAAAATTTCTGGTCAGAAAGTGATTATTACAAACGGCCTTGAAGATGAAACGATTGAAGGTTTTGTGAAGCACAATAATACGGTTAAAAATGCGACTTATAAAGGTCAAGTTTATTCAGGCTTACTTTTCCCAGTGATGCAAGGTATCTCCCTTTTAAACACAGCGATTGTGATTTTCTTTGGTGGATATTTA ->abcZ_224 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGCGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGCACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_225 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_226 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATCATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_227 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATCCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_228 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTGGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGTAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_229 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGTAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_230 -AAATCGACGAACAGAATGCGTATAGGGTTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_231 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGCGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAATTTGGGACTAACTTAACAGATATGGCGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_232 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAACGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_233 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_234 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGCGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_235 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGCTA ->abcZ_236 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_237 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACATCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_238 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCATACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_239 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTAGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_240 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTGTCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_241 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCATTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAATGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_242 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGCGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTATGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_243 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACATCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_244 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_245 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_246 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAACGATGGCGAAATGCTGAGTCGCTTCACTAGTGACTTGGATAACATTTCTAACACACTCAACCAAGCATTGATTCAAGTGCTATCAAACATTGCATTAATGATTGGTGTCATAATCATGATGTTCCAGCAAAATGTGGAACTAGCCTTTGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACTATAATCATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAATTAGGTGTTTTGAACGGCTACATTGACGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAACGCAACTTATAAAGGTCAAGTTTACTCTGGTTTACTTTTTCCAATGATGCAAGGTATTTCATTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_247 -AAATCGACGAACAGAATGCGTATAGGCCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGACTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_248 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTGAGTCGCTTCACTAGTGACTTGGATAACATTTCTAACACACTCAACCAAGCATTGATTCAAGTGCTATCAAACATTGCATTAATGATTGGTGTCATCATCATGATGTTCCAGCAAAATGTGGAACTAGCCTTTGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACTATAATCATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAATTAGGTGTTTTGAACGGCTACATTGACGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCATTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_249 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTTCATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATATTCTTCGGTGGATGGTTA ->abcZ_250 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTGAGTCGCTTCACTAGTGACTTGGATAACATTTCTAACACACTCAACCAAGCATTGATTCAAGTGCTATCAAACATTGCATTAATGATTGGTGTCATAATCATGATGTTCCAGCAAAATGTGGAACTAGCCTTTGTTACTTTAATATCTGCTCCATTTGCGATTATAATTGCGACTATAATCATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAATTAGGTGTTTTGAACGGCTACATTGACGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAACGCAACTTATAAAGGTCAAGTCTACTCCGGTTTACTTTTTCCAATGATGCAAGGTATTTCATTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_251 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTTGATAGCCGTAATGATGGAGAAATGCTTAGTCGCTTCACTAGTGACTTGGATAACATCTCCAACACACTTAACCAAGCTTTGATTCAAGTATTATCAAACATTGCACTGATGATTGGTGTTATTATCATGATGTTCCAACAAAACGTCGAACTAGCCTTCGTTACTTTAATATCTGCACCGTTTGCGATAATTATTGCCACTATCATCATCCGAAAAGCTCGTAAGTACGTGGATATTCAACAAGACGAATTAGGTGTGCTAAATGGTTACATTGATGAAAAAATTTCTGGTCAAAAAATCATTATCACCAATGGTTTAGAAGAAGAAACTATTGATGGCTTTGTAAAACAAAATAATATCGTTAAAGACGCCACTTATAAAGGTCAAGTATACTCTGGTTTACTTTTTCCTATGATGCAAGGTATTTCATTATTAAATACGGCGATCGTCATCTTCTTTGGTGGATGGTTA ->abcZ_252 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGCGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACCAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_253 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGCGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTAGATGGTTA ->abcZ_254 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_255 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAAATGGAGAAACTATCGATTCGTTTCTTTGATAGCCGCAATGATGGCGAAATGCTAAGCCGCTTCACAAGTGACTTAGATAACATTTCCAACACACTTAACCAGGCACTTATCCAAGTTCTGTCCAACATCGCACTGATGATTGGTGTTATCATCATGATGTTCCAACAAAATGTAGAACTTGCTTTTGTTACACTGATTTCAGCACCATTTGCAATAATTATTGCCACAGTGATTATTCGTAAAGCTCGCAAATACGTTGATGTGCAACAAGATGAACTAGGCGTATTAAATGGCTATATTGATGAGAAAATCTCTGGTCAAAAAATTATTATCACGAATGGTTTAGAAGAAGAAACCATTGAAGGCTTTGTAAAACAAAATAATGTCGTTAAAGAAGCTACTTACAAAGGCCAAGTATACTCAGGCTTACTTTTCCCAATGATGCAAGGTATTTCACTACTTAACACAGCAATCGTTATTTTCTTCGGTGGTTGGTTA ->abcZ_256 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTTACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAGGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACAGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_257 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATATTCTTCGGTGGATGGTTA ->abcZ_258 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGAAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_259 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTGAGTCGCTTCACTAGTGACTTGGATAACATTTCTAACACACTCAACCAAGCATTGATTCAAGTGCTATCAAACATTGCATTAATGATTGGTGTCATCATCATGATGTTCCAGCAAAATGTGGAACTAGCCTTTGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACTATAATCATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAATTAGGTGTTTTGAACGGCTACATTGACGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAACGCAACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCATTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_260 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTGAGTCGCTTCACTAGTGACTTGGATAACATTTCTAACACACTCAACCAAGCATTGATTCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAACGCAACTTATAAAGGTCAAGTCTACTCCGGTTTACTTTTTCCAATGATGCAAGGTATTTCGCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_261 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAAATGGAGAAACTATCGATTCGTTTCTTTGATAGCCGCAATGATGGCGAAATGCTAAGCCGCTTCACAAGTGACTTAGATAACATTTCCAACACACTTAACCAGGCACTTATCCAAGTTCTTTCCAACATCGCACTGATGATTGGTGTTATCATCATGATGTTCCAACAAAATGTAGAACTTGCTTTTGTTACACTGATTTCAGCACCTTTTGCAATAATTATTGCCACAGTGATTATTCGTAAAGCTCGCAAATACGTTGATGTGCAACAAGATGAACTAGGCGTATTAAATGGCTATATTGATGAGAAAATCTCTGGTCAAAAAATTATTATCACGAATGGTTTAGAAGAAGAAACCATTGAAGGCTTTGTAAAACAAAATAATGTCGTTAAAGACGCTACTTACAAAGGCCAAGTATACTCAGGCTTACTTTTCCCAATGATGCAAGGTATTTCACTACTTAACACAGCAATCGTTATTTTCTTCGGTGGTTGGTTA ->abcZ_262 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATTCGTTTCTTTGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACAAGTGACTTAGATAACATTTCCAACACACTTAACCAGGCACTTATCCAAGTTCTTTCCAACATCGCACTAATGATCGGTGTTATCATCATGATGTTCCAACAAAATGTAGAACTTGCATTTGTTACACTGATTTCAGCACCATTTGCAATAATTATTGCCACAGTGATTATTCGTAAAGCTCGCAAATACGTTGATGTGCAACAAGATGAACTAGGCGTATTAAATGGTTATATTGATGAAAAAATCTCTGGTCAAAAAATTATTATCACGAATGGTTTAGAAGAAGAAACCATTGAAGGCTTTGTAAAACAAAATAATGTCGTTAAAGACGCTACTTACAAAGGCCAAGTATACTCAGGCTTACTTTTCCCAATGATGCAAGGTATTTCATTACTTAACACAGCAATCGTTATTTTCTTCGGTGGTTGGTTA ->abcZ_263 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAGAAACTATCGATTCGTTTCTTTGATAGCCGCAATGATGGCGAAATGCTAAGCCGCTTCACAAGTGACTTAGATAACATTTCCAACACACTTAACCAGGCACTTATCCAAGTTCTTTCCAACATCGCACTGATGATTGGTGTTATCATCATGATGTTCCAACAAAATGTAGAACTTGCTTTTGTTACACTGATTTCAGCACCATTTGCAATAATTATTGCCACAGTGATTATTCGTAAAGCTCGCAAATACGTTGATGTGCAACAAGATGAACTAGGCGTATTAAATGGCTATATTGATGAGAAAATCTCTGGTCAAAAAATTATTATCACGAATGGTTTAGAAGAAGAAACCATTGAAGGCTTTGTAAAACAAAATAATGTCGTTAAAGACGCTACTTACAAAGGCCAAGTATACTCAGGCTTACTTTTCCCAATGATGCAAGGTATTTCTCTACTTAACACAGCAATCGTTATTTTCTTCGGTGGTTGGTTA ->abcZ_264 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_265 -AAATCGACGAACAGAATGCGCATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_266 -AAGGCAACGAACCGGATGCGGATCGGGCTATTCAAGAAAATGGAGAAACTTTCGATTCGCTTCTTTGATAGTCACAATGATGGCGAAATGCTGAGTCGCTTTACGAGTGATATGGATAACATTTCGAATACGCTCAACCAAGCGCTTGTCCAAGTGCTTTCGAATTTAGCACTCATGATCGGTGTTATCATCATGATGTTCACGCAAAATGTGGAACTTGCTTTTGTCACATTGATTGCGGCTCCGTTTGCTGTGATTATTGCGGGAATCATCATCCGGAAAGCACGCCGCTATGTGGACTTGCAGCAAGATAGTTTGGGCCGCTTGAATGCCTATATTGATGAAAAAATCTCCGGTCAAAAAGTGGTCATCACGAATGGTTTGGAAGAAGAAACGATCCGAGGTTTCTTAAAGCACAATGAAATCGTTAAAAATGCAACGTATAAAGGTCAAGTTTACTCTGGCTTGTTGTTCCCAGTAATGCAAGGGATCTCACTTGTTAACACGGCGATCGTTATTTTCTTCGGTGGCTATCTG ->abcZ_267 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCATTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_268 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTGAGTCGCTTCACTAGTGACTTGGATAACATTTCTAACACACTCAACCAAGCATTGATTCAAGTGCTATCAAACATTGCATTAATGATTGGTGTCATAATCATGATGTTCCAGCAAAATGTGGAACTAGCCTTTGTTACTTTAATATCTGCTCCATTTGCGATTATAATTGCGACTATAATCATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAATTAGGTGTTTTGAACGGCTACATTGACGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCATTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_269 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAATGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_270 -AAAGCAACGAATCGGATGCGGATTGGACTTTTCAAGAAAATGGAAAAACTATCGATTCGCTTCTTTGATAGCCACAATGATGGCGAGATGTTGAGTCGCTTTACGAGTGACATGGATAACATTTCAAACACGCTCAACCAAGCGCTTGTTCAAGTACTTTCGAATTTGGCGCTCATGATCGGGGTTATCATCATGATGTTCACGCAAAATGTAGAACTTGCTCTTGTTACACTGATCGCGGCACCATTTGCGGTGATTATCGCAGGGATTATCATTCGGAAAGCGCGCCGCTATGTGGACTTGCAACAAGATAGCTTAGGTCGCTTAAATGCCTATATCGATGAAAAAATCTCTGGCCAAAAAGTGGTCATCACAAATGGTCTTGAAGAAGAAACCATTGGCGGTTTCTTAAAACACAATGAAATCGTTAAAAATGCAACGTATAAAGGTCAAGTTTATTCTGGCTTGTTGTTCCCAGTGATGCAAGGGATTTCGCTTGTTAATACGGCGATTGTTATTTTCTTCGGTGGTTATCTT ->abcZ_271 -CGCGCGACGAACCGGATGCGTATCGGATTGTTCCGCAAAATGGAGAAACTGTCGATTCGTTTCTTCGATGGGCATAAAGATGGCGAAATGCTGAGCCGTTTTACAAGTGACTTAGACAACATCTCGAATACGCTGAACCAAGCGCTCGTGCAAGTTTTGTCAAACGTTGCGTTAATGATTGGTGTTATCATCATGATGTATAACCAAAATGTGAAATTAGCAACGGTGACGCTTCTGATGGCACCAATTGCGATTGTTATTGCCGCGTTGATTATCCGCAAAGCTCGTAAATACGTGGATATGCAACAAGATCGTCTCGGCGAACTAAACGGCTATATCGATGAAAAAATCTCCGGTCAAAAAATCGTTATTACAAACGGTTTGGAGCAAGAAACAATTGAAGGCTTCGTGAAACATAATAACATCGTGAAAGACGCAACTTTTAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCGATGATGCAAGGTATTTCGTTAATCAATACAGCTGTCGTAATCTTCTTCGGTGGTTACCTA ->abcZ_272 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAAGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_273 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_274 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATTCAAGTGCTATCCAACATCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAATGCCACTTACAAAGGTCAAGTTTACTCCGGTTTGCTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_275 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_276 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_277 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_278 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAATTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_279 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAATGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_280 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAATGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_281 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGTAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_282 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAACGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_283 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACATCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATCGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_284 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCTGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_285 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGATTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_286 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAATGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_287 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATTCAAGTGCTATCCAACATCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAATGCCACTTACAAAGGTCAAGTATACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_288 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_289 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_290 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_291 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_292 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_293 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACGCTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_294 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAATGCCACTTACAAAGGTCAAGTATACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_295 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGACGGCGAAATGCTTAGTCGTTTTACTAGTGACTTGGATAACATTTCTAACACACTTAACCAAGCTTTGATTCAAGTACTATCAAACATTGCGTTAATGATTGGTGTTATTATCATGATGTTCCAACAAAATGTGGAACTAGCACTCGTTACCTTAATATCTGCACCATTTGCAGTAATTATTGCCACAGTGATTATCCGAAAAGCTCGTAAATATGTTGATGTTCAACAAGATGAACTGGGTGTATTAAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAACGGTTTAGAGGAAGAAACAATTGACGGCTTCGTTAAACAAAACAACATCGTTAAGAACGCCACTTATAAAGGACAAGTATACTCTGGTTTACTTTTCCCAATGATGCAAGGTATTTCATTGTTAAATACAGCTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_296 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_297 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTCAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAATGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_298 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_299 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAGCAAAACAATATCGTTAAAAACGCAACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_300 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTCTTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_301 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATTCAAGTGCTATCCAACATCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGTTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_302 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATATTCTTCGGTGGATGGTTA ->abcZ_303 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAATTAGCCTTCGTTACTTTAATATCTGCCCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_304 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTGGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAACGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_305 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_306 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACGCTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAATGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_307 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_308 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATATTCTTCGGTGGATGGTTA ->abcZ_309 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCTACAGTGATTATTCGAAAAGCCCGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_310 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATTCAAGTGCTATCCAACATCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATTACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAATGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_311 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATTGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_312 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTAGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGATGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAATGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_313 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGTAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_314 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_315 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_316 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGTAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTATACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_317 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAATCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_318 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATTTTCTTCGGTGGATGGTTA ->abcZ_319 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_320 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_321 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGAAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGCTA ->abcZ_322 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCATACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_323 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTATATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAATGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_324 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTACTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_325 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_326 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATTCAAGTGCTATCCAACATCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTATATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAATGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_327 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAACGGTTTAGAAGAAGAAACAATCGACGGCTTTGTTAAACAAAACAATATCGTTAAAAATGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_328 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_329 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAAAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_330 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_331 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAACGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_332 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_333 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATAATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAATGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_334 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTATACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_335 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACGCTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTATACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_336 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTATACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_337 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_338 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTGGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGTAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_339 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGTAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTATACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_340 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAATGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_341 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGAAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_342 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATCGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCGATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_343 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_344 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACCAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_345 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_346 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAGTTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_347 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTCAGCCGCTTCACTAGCGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_348 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCTACAGTGATTATTCGAAAAGCCCGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAGGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_349 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATTCAAGTGCTATCCAACATCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAATGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_350 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAACGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_351 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAACGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_352 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTTACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAGGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGTTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_353 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAACGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCTGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCAATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_354 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTACAAAGGCCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_355 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_356 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATTCAAGTGCTATCCAACATCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAATGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_357 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_358 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_359 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAATGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_360 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAATGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_361 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAATTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATCGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_362 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAATGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_363 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATCTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_364 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACATACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_365 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_366 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAGGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_367 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAATGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGGGGATGGTTA ->abcZ_368 -AAAGCAACGAACCGGATGCGGATTGGACTTTTCAAGAAAATGGAAAAACTATCGATTCGCTTCTTTGATAGCCACAATGATGGCGAGATGTTGAGTCGCTTTACGAGTGACATGGATAACATTTCAAACACGCTCAACCAAGCGCTTGTTCAAGTGCTTTCGAATTTGGCGCTCATGATCGGGGTTATCATCATGATGTTCACGCAAAATGTAGAACTTGCTCTTGTTACACTGATCGCGGCACCATTTGCAGTGATTATCGCAGGGATCATCATTCGGAAAGCGCGCCGCTATGTGGACTTGCAACAAGATAGCTTAGGTCGCTTAAATGCCTATATTGATGAAAAAATCTCTGGCCAAAAAGTGGTCATCACAAATGGTCTTGAAGAAGAAACCATTGGCGGTTTCTTAAAACACAATGAAATCGTTAAAAATGCAACGTATAAAGGTCAAGTTTATTCTGGCTTGTTGTTCCCAGTGATGCAGGGTATTTCGCTTGTTAATACGGCGATTGTTATTTTCTTCGGTGGTTATCTT ->abcZ_369 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAATACACTAAACCAAGCATTAATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCATTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_370 -AAAGCAACGAACCGGATGCGGATTGGGCTTTTCAAGAAAATGGAAAAACTATCGATTCGCTTCTTTGATAGCCACAATGATGGGGAAATGTTGAGTCGCTTTACGAGTGACATGGATAACATTTCGAATACGCTCAACCAAGCGCTTGTTCAAGTGCTTTCGAATTTGGCGCTCATGATTGGGGTTATCATCATGATGTTCACGCAAAATGTAGAGCTTGCTTTTGTTACACTAATTGCGGCACCGTTTGCTGTGATTATCGCAGGGATTATCATCCGGAAAGCGCGCCGCTATGTGGATTTGCAACAAGATAGTTTAGGTCGTTTAAATGCCTATATTGATGAAAAAATCTCTGGTCAAAAAGTGGTTATCACAAATGGCCTTGAAGAAGAAACGATCAAAGGTTTCTTAAAGCACAATGAAATCGTTAAAAACGCAACGTATAAAGGTCAGGTTTACTCTGGCTTGTTGTTCCCGGTGATGCAAGGGATTTCGCTTGTGAATACGGCGATCGTTATTTTCTTCGGGGGTTATCTT ->abcZ_371 -AAATCGACGAACAGAATGCGTATAGGACTTTTTCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_372 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTTACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAGGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTTGGTGGATGGTTA ->abcZ_373 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGTGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAATGTAGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGATGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAATGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_374 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCTACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAATGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_375 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTTGATAGTCGTAATGATGGAGAAATGCTTAGTCGCTTCACTAGTGACTTGGATAACATCTCCAACACACTTAACCAAGCTTTGATTCAAGTATTATCAAACATTGCACTGATGATTGGTGTTATTATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCACCGTTTGCGATAATTATTGCCACTATCATCATCCGAAAAGCTCGCAAGTTCGTGGATATTCAACAAGACGAATTAGGTGTGCTAAATGGTTACATTGATGAAAAAATTTCTGGTCAAAAAATCATTATCACCAATGGTTTAGAAGAAGAAACTATTGATGGCTTTGTAAAACAAAATAATATCGTTAAAGACGCCACTTATAAAGGTCAAGTATACTCTGGTTTACTTTTTCCTATGATGCAAGGTATTTCATTATTAAATACGGCGATCGTCATCTTCTTTGGTGGATGGTTA ->abcZ_376 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAGCAAAACAATATCGTTAAAAATGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_377 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAAGTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_378 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_379 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_380 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGTAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_381 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTTACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAGGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTACAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_382 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTCTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_383 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAATGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_384 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGTTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_385 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCAGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAGCAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_386 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGGCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATATTCTTCGGTGGATGGTTA ->abcZ_387 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACATCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTATACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_388 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTATACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_389 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAATGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_390 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAACGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_391 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_392 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_393 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_394 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAACGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_395 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTCAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_396 -AAATCAACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTCAGCCGCTTCACTAGCGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_397 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTTGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATACAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_398 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTTACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAGGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAACGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_399 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGTAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_400 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGTAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_401 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGTAATGATGGCGAAATGCTTAGCCGCTTTACTAGTGACTTGGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_402 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_403 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCGATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_404 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_405 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAATACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_406 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTATACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_407 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATTGACGGCTTTATTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_408 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAACATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_409 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTGGTTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_410 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_411 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_412 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_413 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_414 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_415 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_416 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAACGGTTTAGAAGAAGAAACAATCGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_417 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCCCGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAATATCGTTAAAAATGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_418 -AAATCAACAAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGCGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_419 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTTGATAGCCGTAATGATGGAGAAATGCTTAGTCGCTTCACTAGTGACTTGGATAACATCTCCAACACACTTAACCAAGCTTTGATTCAAGTATTATCAAACATTGCACTGATGATTGGTGTTATTATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCACCGTTTGCGATAATTATTGCCACTATCATCATCCGAAAAGCTCGCAAGTTCGTGGATATTCAACAAGACGAATTAGGTGTGCTAAATGGTTACATTGATGAAAAAATTTCTGGTCAAAAAATCATTATCACCAATGGTTTAGAAGAAGAAACTATTGATGGCTTTGTAAAACAAAATAATATCGTTAAAGACGCCACTTATAAAGGTCAAGTATACTCTGGTTTACTTTTTCCTATGATGCAAGGTATTTCATTATTAAATACGGCGATCGTCATCTTCTTTGGTGGATGGTTA ->abcZ_420 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCATTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_421 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGCTA ->abcZ_422 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTTGATAGCCGTAATGATGGAGAGATGCTTAGTCGCTTCACTAGTGACTTGGATAACATTTCCAACACACTTAACCAAGCTTTGATTCAAGTATTATCAAACATTGCACTGATGATTGGTGTTATTATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCACCGTTTGCGATAATTATTGCCACTATCATCATCCGAAAAGCTCGCAAGTTCGTGGATATTCAACAAGACGAATTAGGTGTGCTAAATGGTTACATTGATGAAAAAATTTCTGGTCAAAAAATCATTATCACCAATGGTTTAGAAGAAGAAACTATTGATGGCTTTGTAAAACAAAATAATATCGTTAAAGACGCCACTTATAAAGGTCAAGTATACTCTGGTTTACTTTTTCCTATGATGCAAGGTATTTCATTATTAAATACGGCGATCGTCATCTTCTTTGGTGGATGGTTA ->abcZ_423 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTTGATAGCCGTAATGATGGAGAAATGCTTAGTCGCTTCACTAGTGACTTGGATAACATCTCCAACACACTTAACCAAGCTTTGATTCAAGTATTATCAAACATTGCACTGATGATTGGTGTTATTATCATGATGTTCCAACAAAACGTGGAACTAGCTTTCGTTACTTTAATATCTGCACCGTTTGCGATAATTATTGCCACTATCATCATCCGAAAAGCTCGCAAGTTCGTGGATATTCAACAAGACGAATTAGGTGTGCTAAATGGTTACATTGATGAAAAAATTTCTGGTCAAAAAATCATTATCACCAATGGTTTAGAAGAAGAAACTATTGATGGCTTTGTAAAACAAAATAATATCGTTAAAGACGCCACTTATAAAGGTCAAGTATACTCTGGTTTACTTTTTCCTATGATGCAAGGTATTTCATTATTAAATACGGCGATCGTCATCTTCTTTGGTGGATGGTTA ->abcZ_424 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTGAGTCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTGCTATCAAACATTGCATTAATGATTGGTGTCATAATCATGATGTTCCAGCAAAATGTGGAACTAGCCTTTGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACTATAATCATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAATTAGGTGTTTTGAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAACGCAACTTATAAAGGTCAAGTCTACTCCGGTTTACTTTTTCCAATGATGCAAGGTATTTCATTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_425 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_426 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTATTTAAACAAAACAATATCGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_427 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTGGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_428 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAACGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_429 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAATTATCAATTCGTTTCTTCGATAGTCGCAATGACGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAACATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAATGTAGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGATGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAATGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_430 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_431 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAGGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_432 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTTACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAGGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAACGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_433 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACGCTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGATGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_434 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTTACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCTACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_435 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGATGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_436 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_437 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATTGTTATCTTCTCCGGTGGATGGTTA ->abcZ_438 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATCCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGACTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_439 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAAGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGCTA ->abcZ_440 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_441 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTTACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_442 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAACGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_443 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTAAAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_444 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGTAATGACGGCGAAATGCTTAGTCGTTTTACTAGTGACTTGGATAACATTTCTAACACACTCAACCAAGCATTGATTCAAGTGCTATCAAACATTGCATTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTTGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACTATAATCATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAATTAGGTGTTTTGAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAACGGTTTAGAAGAAGAAACAATTGACGGCTTCGTTAAACAAAACAACATCGTTAAGAACGCCACTTACAAAGGTCAAGTCTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCATTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_445 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATTCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAATGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_446 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_447 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGTTGGTTA ->abcZ_448 -AAAGCAACGAACCGGATGCGGATTGGGCTTTTCAAGAAAATGGAAAAACTATCGATTCGCTTCTTTGATAGCCACAATGATGGGGAAATGTTGAGTCGCTTTACGAGTGACATGGATAACATTTCGAATACGCTCAACCAAGCGCTTGTTCAAGTGCTTTCGAATTTGGCGCTCATGATTGGGGTTATCATCATGATGTTCACGCAAAATGTAGAGCTTGCTTTTGTTACACTGATTGCGGCACCGTTTGCTGTGATTATCGCAGGGATTATCATCCGGAAAGCGCGCCGCTATGTGGATTTGCAACAAGATAGTTTAGGTCGCTTAAATGCCTATATTGATGAAAAAATCTCTGGTCAAAAAGTGGTCATCACGAATGGTCTTGAAGAAGAAACAATTGATGGGTTCTTAAAACACAATGAAATCGTTAAAAATGCAACGTATAAAGGTCAAGTTTATTCTGGCTTGTTGTTCCCGGTGATGCAAGGGATTTCGCTTGTGAATACGGCGATCGTTATTTTCTTCGGGGGTTATCTT ->abcZ_449 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACCTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_450 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACAAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGCTA ->abcZ_451 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAATAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_452 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_453 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_454 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATAGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_455 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTCGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAATGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTGTTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_456 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTAGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGATGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAATGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_457 -AAATCGACGAACAGAATGCGTATAGGGCTTTTTCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_458 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCAGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACAGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_459 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATTCAAGTGCTATCCAACATCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTAATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGTTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_460 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_461 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTACAAAGGCCAAGTTTACTCTGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTACTTAATACAGCAATTGTTATCTTCTTTGGAGGATGGTTA ->abcZ_462 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTTGATAGCCGTAATGATGGAGAAATGCTTAGTCGCTTCACTAGTGACTTGGATAACATCTCCAACACACTTAACCAAGCTTTGATTCAAGTATTATCAAACATTGCACTGATGATTGGTGTTATTATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCACCATTTGCGATAATTATTGCCACTATCATCATCCGAAAAGCTCGCAAGTTCGTGGATATTCAACAAGACGAATTAGGTGTGCTAAATGGTTACATTGATGAAAAAATTTCTGGTCAAAAAATCATTATCACCAATGGTTTAGAAGAAGAAACTATTGATGGCTTTGTAAAACAAAATAATATCGTTAAAGACGCCACTTATAAAGGTCAAGTATACTCTGGTTTACTTTTTCCTATGATGCAAGGTATTTCATTATTAAATACGGCGATCGTCATCTTCTTTGGTGGATGGTTA ->abcZ_463 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTGAGTCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTGCTATCAAACATTGCATTAATGATTGGTGTCATAATCATGATGTTCCAGCAAAATGTGGAACTAGCCTTTGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACTATAATCATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAATTAGGTGTTTTGAACGGCTACATTGACGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTGTTAAACAAAATAATGTTGTTAAAAACGCAACTTACAAAGGTCAAGTCTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCATTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_464 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAATATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_465 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCTTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGACGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATCGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_466 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGTCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTATTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATTGTTATCTTCTTCGGTGGATGGTTA ->abcZ_467 -AAATCGACAAACCGGATGCGGATCGGCTTATTCCGCAAAATGGAGAAACTGTCGATTCGCTTCTTCGATAAACATAGCGACGGCGAAATGCTGAGCCGGTTTACAAGTGATATGGATAATATTTCCAATACATTAAACCAAGCCATCGTTCAAGTGCTTTCTAACTTTGCATTAATGATCGGAGTCATCATCATGATGTTCAATCAAAACGTGAAGTTGGCACTGATCACCCTTATTGCAGCTCCATTCGCGATTGTGATCGCAGCACTCATCATCCGCAAAGCACGCCGCTATGTGGATCTGCAACAGGACAGTTTAGGCGAATTGAATGCCTATATCGATGAGAAGATCTCCGGACAGAAAGTCGTGATTACGAACGGGCTGGAAGAAGAGACGATTGCAGGCTTTACGAAACACAACGAAAAAGTGAAAAATGCGACTTATAAAGGTCAAGTTTATTCCGGGATGTTATTCCCCGTTATGCAGGGGATCTCTCTCTTGAATACGGCGATCGTCATTTTCTTCGGTGGCTACCTA ->abcZ_468 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_469 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCTGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_470 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTTGATAGCCGTAATGATGGAGAAATGCTTAGTCGCTTCACTAGTGACTTGGATAACATCTCCAACACACTTAACCAAGCTTTGATTCAAGTATTATCAAACATTGCACTGATGATTGGTGTTATTATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCACCGTTTGCGATAATTATTGCCACTATCATCATCCGAAAAGCTCGCAAGTTCGTGGATATTCAACAAGACGAATTAGGTGTGCTAAATGGTTACATTGATGAAAAAATTTCTGGTCAAAAAATCATTATCACCAATGGTTTAGAAGAAGAAACTATTGATGGCTTTGTAAAACAAAATAATATCGTTAAAGACGCCACTTATAAAGGTCAAGTATACTCTGGTTTACTTTTTCCTATGATGCAAGGTATTTCATTATTAAATACGGCAATCGTCATCTTCTTTGGTGGATGGTTA ->abcZ_471 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTATCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_472 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAGGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTGACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGCCTAGAAGAAGAAACAATTGACGGCTTCGTTAAACAAAACAACATCGTTAAAAACGCAACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_473 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTAGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGATGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAAGGCTTTGTTAAACAAAATAATGTTGTTAAAAATGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_474 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTTGATAGCCGTAATGATGGAGAAATGCTTAGTCGCTTCACTAGTGACTTGGATAACATCTCCAACACACTTAACCAAGCTTTGATTCAAGTATTATCAAACATTGCACTGATGATTGGTGTTATTATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCACCGTTTGCGATAATTATTGCCACTATCATCATCCGAAAAGCTCGTAAGTACGTGGATATTCAACAAGACGAATTAGGTGTGCTAAATGGTTACATTGATGAAAAAATTTCTGGTCAAAAAATCATTATCACCAATGGTTTAGAAGAAGAAACTATTGATGGCTTTGTAAAACAAAATAATATCGTTAAAGACGCCACTTATAAAGGTCAAGTATACTCTGGTTTACTTTTTCCTATGATGCAAGGTATTTCATTATTAAATACGGCGATCGTCATCTTCTTTGGTGGATGGTTA ->abcZ_475 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGTTTTACTAGTGACTTGGATAATATTTCTAACACACTTAACCAAGCTTTGATTCAAGTACTATCAAACATTGCGTTAATGATTGGTGTTATTATCATGATGTTCCAACAAAATGTGGAACTAGCACTCGTTACCTTAATATCTGCACCATTTGCAGTAATTATTGCCACAGTGATTATCCGAAAAGCTCGTAAATATGTTGATGTTCAACAAGATGAACTGGGTGTATTAAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATCATTATCACAAACGGTTTAGAGGAAGAAACAATTGACGGCTTCGTTAAACAAAATAATGTTGTTAAAAATGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_476 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATGTTCGTTCAATATTCACAAC ->abcZ_477 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTCACTAGTGACTTAGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAATGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_478 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATTCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_479 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTGAGTCGCTTCACTAGTGACTTGGATAACATTTCTAACACACTCAACCAAGCATTGATTCAAGTGCTATCAAACATTGCATTAATGATTGGTGTCATAATCATGATGTTCCAGCAAAATGTGGAACTAGCCTTTGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCGACTATAATCATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAATTAGGTGTTTTGAACGGCTACATTGACGAAAAAATTTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGAGGGCTTTATTAAACAAAATAATGTTGTTAAAAACGCAACTTATAAAGGTCAAGTTTACTCTGGTTTACTTTTTCCAATGATGCAAGGTATTTCATTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_480 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGTCGTTTTACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAGGTGCTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTTTAATATCTGCTCCATTTGCGATTATTATTGCAACAGTGATTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAGAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGATACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_481 -AAATCGACGAACAGAATGCGTATAGGACTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTAGATAATATTTCCAATACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCAATTATTATTGCGACAGTGACTATTCGAAAAGCACGTAAATTCGTTGATGTTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGACAAAAAATCATTATCACAAATGGTTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTATAAAGGGCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCCTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_482 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCGATTCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTGAGTCGCTTCACTAGTGACTTGGATAACATTTCTAACACACTCAACCAAGCATTGATTCAAGTGCTATCCAACGTTGCGTTAATGATTGGTGTCATCATCATGATGTTCCAACAAAATGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACTATGATCATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAATTAGGTGTACTGAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACTATTGACGGCTTTGTTAAACAAAATAATGTTGTTAAAAACGCAACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAGTATATTTCACTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGTTA ->abcZ_483 -AAATCGACGAACAGAATGCGTATAGGGCTTTTCCGCAAGATGGAAAAACTATCAATCCGTTTCTTCGATAGCCGCAATGATGGCGAAATGCTTAGCCGCTTCACTAGTGACTTGGATAATATTTCCAACACACTAAACCAAGCATTGATCCAAGTACTATCCAACGTCGCGCTAATGATTGGTGTTATCATCATGATGTTCCAACAAAACGTGGAACTAGCCTTCGTTACTCTAATATCTGCTCCATTTGCGATTATTATTGCGACAGTGATTATTCGAAAAGCCCGCAAATTCGTTGATATTCAACAAGATGAACTAGGCGTACTTAACGGCTACATTGACGAAAAAATCTCTGGTCAAAAAATTATTATCACAAATGGCTTAGAAGAAGAAACAATTGACGGCTTTGTTAAACAAAACAATATCGTTAAAAACGCCACTTACAAAGGTCAAGTTTACTCCGGTTTACTTTTCCCAATGATGCAAGGTATTTCCTTATTAAATACAGCTATCGTTATCTTCTTCGGTGGATGGCTA diff --git a/test/MLST_listeria/bglA.fasta b/test/MLST_listeria/bglA.fasta deleted file mode 100644 index 8d7b6e5..0000000 --- a/test/MLST_listeria/bglA.fasta +++ /dev/null @@ -1,872 +0,0 @@ ->bglA_1 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_2 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGATGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_3 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_4 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_5 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_6 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_7 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCACCGTTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAAGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTAAAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCATTATGAAACACCACTTCACTTATCGAAAGCTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_8 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_9 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_10 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_11 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_12 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_13 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_14 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCACCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_15 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_16 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_17 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTTCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_18 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGATGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_19 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTTTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGATTACAATTTTATGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_20 -AATCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGTTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATTGAACCGCTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_21 -AATCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGTTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATTGAACCGCTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_22 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACATACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_23 -AATCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGTTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGATGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATTGAACCGCTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_24 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAGGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAACTAGAAGGAATCGACTTTTATCACCGTTACAAAGATGATGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGCACTTCTATCGCTTGGTCTCGTATTTTCCCAAATGGTGATGAAACTGAACCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAACATTGAACCACTGATTACATTATCTCATTATGAAACGCCACTTCACCTATCTAAAACATACGATGGCTGGGTCAACAGAAAAATGATTGGCTTCTATGAA ->bglA_25 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACATCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_26 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGCCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_27 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_28 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGAACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_29 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGACGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_30 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_31 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_32 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_33 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_34 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGATGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_35 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAATTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_36 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_37 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAATGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_38 -AAACAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_39 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAACAGAAAAATGATTGACTTCTATGAA ->bglA_40 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAGCTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_41 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_42 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTACTCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_43 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTATAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_44 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTTTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCAACTTCTATGAA ->bglA_45 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGTGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_46 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_47 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCACCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_48 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCACCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCGAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCATTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_49 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_50 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACATACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_51 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_52 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_53 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGATGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_54 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACATACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_55 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCAAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_56 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAACAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_57 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_58 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACATACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_59 -AATCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTTTATCATCGTTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAGTTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATTGAACCACTGATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_60 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCGAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_61 -AACCAATTCGAAGGCGCTTACAATGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCCCATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_62 -AATCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGTTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATTGAACCGCTAATCACTTTATCCCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_63 -AATCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAACTAGAAGGAATCGACTTTTATCATCGTTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTTAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGATGAAACTGAGCCAAACGAAGCAGGACTACAATTTTATGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_64 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCAACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_65 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGATGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTTTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_66 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_67 -AATCAATTCGAAGGCGCTTACAACATTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGTTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCTATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATTGAACCGCTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_68 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_69 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCTCATAATATCGAACCACTGATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_70 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAACAGAAAAATGATTGACTTCTATGAA ->bglA_71 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_72 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAGGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAACTAGAAGGAATCGACTTTTATCACCGTTACAAAGATGATGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGCACTTCTATCGCTTGGTCTCGTATTTTTCCAAATGGTGATGAAACTGAACCGAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAACATTGAACCACTGATTACATTATCTCATTATGAAACGCCACTTCACCTATCTAAAACATATGATGGCTGGGTCAACAGAAAAATGATTGACTTCTATGAA ->bglA_73 -AATCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGTTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCTATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATTGAACCGCTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_74 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGTTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTAATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_75 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTTTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_76 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTTTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTAATCACTTTGTCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCAACTTCTATGAA ->bglA_77 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTTTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_78 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAATTTAAAATTAGAAGGAATTGACTTTTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCATTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_79 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCTCATAATATCGAACCACTGATCACTTTATCTCACTATGAAACACCACTTCACTTGTCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_80 -AATCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGTTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATTGAACCGCTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTATGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_81 -AACCAATTCGAAGGCGCTTATAACGTTGATGGGAAAGGACTTACCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGCCATATTACTGACGGTCCAACACCAGACAACTTAAAATTAGAAGGAATTGATTTTTATCACCGTTATAAAGATGATGTAAAACTTTTTGCTGAAATGGGCTTCAAAGTTTTCCGGACATCCATCGCTTGGTCCCGTATCTTCCCAAATGGCGACGAAACAGAACCAAACGAAGCAGGACTACAATTTTACGATGATTTATTTGATGAACTACTAGCACACAACATCGAACCACTCATCACTTTATCACATTATGAAACACCACTTCACTTATCAAAAACATACGACGGTTGGGTTAATCGCAAAATGATCGACTTCTACGAA ->bglA_82 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAGGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAACTAGAAGGAATCGACTTTTATCACCGTTACAAAGATGATGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGCACTTCTATCGCTTGGTCTCGTATTTTCCCAAATGGTGATGAAACTGAACCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAACATTGAACCACTGATTACATTATCTCATTATGAAACGCCACTTCACCTATCTAAAACATACGATGGCTGGGTCAACAGAAAAATGATTGACTTCTACGAA ->bglA_83 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAAGTTTTCCGTACTTCCATCGCTTGGTCCCGGATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_84 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACAAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_85 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_86 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGTTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_87 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTTCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_88 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATCACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAATGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTGGCACATAACATTGAACCACTAATCACTTTATCTCATTATGAAACACCGCTTCACCTATCTAAAACTTACGACGGCTGGGTAAATAGAAAAATGATTGACTTCTACGAA ->bglA_89 -AACCAATTCGAAGGCGCTTACAACGTCGATGAAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_90 -AACCAACTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_91 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCATTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_92 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCAGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_93 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_94 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCTCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_95 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACTTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATTGAACCGCTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTATGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_96 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_97 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_98 -AACCAATACGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_99 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGCCGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCACCGTTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAAGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTAAAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCATTATGAAACACCACTTCACTTATCGAAAGCTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_100 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACATACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_101 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGATGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTTTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_102 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACTACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_103 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCACCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_104 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTTCGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_105 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATAACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACATACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_106 -AACCAATTCGAAGGCGCTTATAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGCGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCATTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATTGACTTCTATGAA ->bglA_107 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACCGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_108 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCATTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_109 -AATCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_110 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAACAGAAAAATGATCGACTTCTATGAA ->bglA_111 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAAAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_112 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_113 -AACCAATTCGAAGGCGCTTATAACGTTGATGGGAAAGGACTTACCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCATATTACTGACGGTCCAACACCAGACAACTTAAAATTAGAAGGCATAGATTTTTATCACCGTTATAAAGATGATGTAAAACTTTTTGCTGAAATGGGTTTCAAAGTTTTCCGTACATCCATCGCTTGGTCCCGTATCTTCCCAAATGGCGACGAAACCGAACCAAACGAAGCAGGACTACAATTCTACGATGATTTATTTGATGAACTTTTAGCACACAACATCGAACCACTTATCACTTTATCTCATTATGAAACACCACTTCACTTATCAAAAACATACGACGGTTGGGTTAATCGCAAAATGATCGACTTCTATGAA ->bglA_114 -AACCAATTCGAAGGCGCTTACAACGTCGATGAAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_115 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGGTTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATTGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_116 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_117 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGTTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAGTTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATTGAACCACTGATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATTGACTTCTATGAA ->bglA_118 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTTTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_119 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAATGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_120 -AATCAATTCGAAGGCGCTTACAATGTTGATGGGAAAGGGCTTACCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGCCACATCACTGACGGCCCCACACCAGACAACTTAAAATTAGAAGGAATTGATTTTTATCACCGTTATAAAGATGATGTAAAACTTTTTGCTGAAATGGGCTTCAAAGTTTTCCGTACATCGATTGCTTGGTCACGTATTTTCCCAAATGGCGATGAAACTGAGCCAAATGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTTTAGCGCACAACATCGAACCACTCATCACTTTATCTCATTATGAAACACCACTTCACTTATCAAAAACATACGATGGTTGGGTTAATCGCAAAATGATCGACTTCTACGAA ->bglA_121 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACATCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_122 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATTGACTTCTATGAA ->bglA_123 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_124 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACAGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATTGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_125 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCACCGTTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAAGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTAAAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCATTATTAAACACCACTTCACTTATCGAAAGCTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_126 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCACCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGATGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_127 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCACCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAACCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_128 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCACCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_129 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACATACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_130 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTTACATTACTGACGGTCCAACACCAGATAATTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_131 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTAAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_132 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTAAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATTTTCCCGAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_133 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTTCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACATACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_134 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATCTATTCGATGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_135 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_136 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACATCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATTGAACCACTTATTACTTTATCTCACTATGAAACACCACTTCACCTATCTAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_137 -AACCAATTCGAAGGCGCTTATAACGTTGATGGGAAAGGACTTACCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGCCATATTACTGACGGTCCAACACCAGACAACTTAAAATTAGAAGGAATTGATTTTTATCACCGTTATAAAGATGATGTAAAACTTTTTGCTGAAATGGGCTTCAAAGTTTTCCGGACATCCATCGCTTGGTCCCGTATCTTCCCAAATGGCGACGAAACAGAACCAAACGAAGCAGGACTACAATTTTACGATGATTTATTTGATGAACTTTTAGCACACAACATCGAACCCCTTATCACTTTATCACATTATGAAACACCACTTCACTTATCAAAAACATACGACGGTTGGGTTAATCGCAAAATGATCGACTTCTACGAA ->bglA_138 -AATCAATTCGAAGGCGCTTACAACGTTGATGGGAAAGGACTTACCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCATATTACTGACGGTCCAACACCAGACAACTTAAAATTAGAAGGAATTGATTTTTATCACCGTTATAAAGATGATGTAAAACTTTTTGCTGAAATGGGCTTCAAAGTTTTCCGGACATCCATCGCTTGGTCTCGTATCTTCCCAAATGGCGACGAAACCGAACCAAACGAAGCAGGACTACAATTTTACGATGATTTATTTGATGAACTACTAGCACACAACATCGAACCACTTATCACTTTATCACATTATGAAACACCACTTCACTTATCAAAAACATACGACGGTTGGGTTAATCGCAAAATGATCGACTTCTACGAA ->bglA_139 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAAGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCACCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_140 -AATCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGTTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCTATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATTGAACCGCTGATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_141 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_142 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAGGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAACTAGAAGGAATCGACTTTTATCACCGTTACAAAGATGATGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGCACTTCTATCGCTTGGTCTCGTATTTTTCCAAATGGTGATGAAACTGAACCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAACATTGAACCACTGATTACATTATCTCATTATGAAACGCCACTTCACCTATCTAAAACATACGATGGCTGGGTCAACAGAAAAATGATTGACTTCTACGAA ->bglA_143 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_144 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGTACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_145 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCGCATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_146 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACGCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_147 -AACCAATTCGAAGGCGCTTACAACGTAGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_148 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACAACAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_149 -AACCAATTTGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_150 -AATCAATTAGAAGGCGCTTATAATGTTGATGGCAAAGGGCTTACCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGCCACATCACTGACGGCCCCACACCAGACAACTTAAAATTAGAAGGAATCGATTTCTATCATCGTTATAAAGATGATGTAAAACTTTTTGCTGAAATGGGCTTCAAAGTTTTCCGGACATCCATCGCTTGGTCTCGTATCTTCCCAAATGGCGACGAAACAGAACCAAACGAAGCAGGACTACAATTTTACGATGATTTATTTGATGAACTTTTAGCACACAACATCGAACCACTTATCACTTTATCACATTATGAAACACCACTTCACTTATCAAAAACATACGACGGTTGGGTTAATCGCAAAATGATCGACTTCTACGAA ->bglA_151 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAATTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAACAGAAAAATGATTGACTTCTATGAA ->bglA_152 -AACCAATTCGAAGGCGCTTATAACGTTGATGGGAAAGGACTTACCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCATATTACTGACGGTCCAACACCAGACAACTTAAAATTAGAAGGCATAGATTTTTATCACCGTTATAAAGATGATGTAAAACTTTTTGCTGAAATGGGTTTCAAAGTTTTCCGTACATCCATCGCTTGGTCCCGTATCTTCCCAAATGGCGACGAAACCGAACCAAACGAAGCAGGACTACAATTCTACGATGATTTATTTGATGAACTTTTAGCGCACAACATCGAACCACTTATCACTTTATCTCATTATGAAACACCACTTCACTTATCAAAAACATACGACGGTTGGGTTAATCGCAAAATGATCGACTTCTACGAA ->bglA_153 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAGGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAACTAGAAGGAATCGACTTTTATCACCGTTACAAAGATGATGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGCACTTCTATCGCTTGGTCTCGTATTTTTCCAAATGGTGATGAAACTGAACCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAACATTGAACCACTGATTACATTATCTCATTATGAAACGCCACTTCACCTATCTAAAACATACGATGGCTGGGTCAACAGAAAAATGATTGACTTCTATGAA ->bglA_154 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCACCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_155 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_156 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_157 -AATCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGTTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATATTACGATGATTTATTCGACGAACTTCTAGCACATAATATTGAACCGCTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_158 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_159 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAACAGAAAAATGATCGACTTCTATGAA ->bglA_160 -AACCAATTCGAAGGCGCTTATAACGTTGATGGGAAAGGACTTACCGTTCAAGATGTTACTCCAAAAGGTGGATTCGGTCATATTACTGACGGTCCAACACCAGACAACTTAAAATTAGAAGGCATAGATTTTTATCACCGTTATAAAGATGATGTAAAACTTTTTGCTGAAATGGGTTTCAAAGTTTTCCGTACATCCATCGCTTGGTCCCGTATCTTCCCAAATGGCGACGAAACCGAACCAAACGAAGCAGGACTACAATTCTACGATGATTTATTTGATGAACTTTTAGCGCACAACATCGAACCACTTATCACTTTATCTCATTATGAAACACCACTTCACTTATCAAAAACATACGACGGTTGGGTTAATCGCAAAATGATCGACTTCTATGAA ->bglA_161 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCACTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_162 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCATTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_163 -AACCAATTCGAAGGCGCTTACAACCTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_164 -AACCAATTCGAAGGCGCTTATAACGTTGATGGGAAAGGACTTACCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCATATTACTGACGGTCCAACACCAGACAACTTAAAATTAGAAGGAATTGATTTTTATCACCGTTATAAAGATGATGTAAAACTTTTTGCTGAAATGGGCTTCAAAGTTTTCCGGACATCCATCGCTTGGTCTCGTATCTTCCCAAATGGCGACGAAACCGAACCAAACGAAGCAGGACTACAATTTTACGATGATTTATTTGATGAACTACTAGCACACAACATCGAACCACTTATCACTTTATCACATTATGAAACACCACTTCACTTATCAAAAACATACGACGGTTGGGTTAATCGCAAAATGATCGACTTCTACGAA ->bglA_165 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_166 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_167 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATCATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_168 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACGCCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_169 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_170 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAGATGATCGACTTCTATGAA ->bglA_171 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATCACTTTATCTCACTATGAAACACCACTTCACCTATCTAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_172 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_173 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCACCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCGAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_174 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGAGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_175 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAAAAAAATGATCGACTTCTATGAA ->bglA_176 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATAGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_177 -AACCAATTCGAAGGCGCTTACAACATCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_178 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGATTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_179 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGATGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_180 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_181 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAATTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_182 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGATGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAACAGAAAAATGATCGACTTCTATGAA ->bglA_183 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_184 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTTCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAACAGAAAAATGATTGACTTCTATGAA ->bglA_185 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_186 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACAGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACATACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_187 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCATATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_188 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACATCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATTGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACCTATCTAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_189 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACATCAGATAACTTAAAATTAGAAGGAATCGACTTTTACCATCGTTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATTGAACCACTTATTACTTTATCTCACTATGAAACACCACTTCACCTATCTAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_190 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACATCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATTGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACCTATCTAAAACTTATGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_191 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTATCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_192 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAACCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAACAGAAAAATGATTGACTTCTATGAA ->bglA_193 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCGCTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_194 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGCTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_195 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACTATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_196 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_197 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGCTGGGTAAATAGAAAAATGGTCGACTTCTATGAA ->bglA_198 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACTATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_199 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTAATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_200 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGTTACAAAGACGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATTTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_201 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCACCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCGAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCATTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_202 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCACCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAATTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_203 -AACCAATTCGAAGGCGCTTACAACGCCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_204 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAGATAGAAAAATGATCGACTTCTATGAA ->bglA_206 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAACAGAAAAATGATTGACTTCTATGAA ->bglA_207 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGATCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCACCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_208 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTAATCACTTCATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_209 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACATGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_210 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAATATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_211 -AATCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGTTACAAAAACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATTGAACCGCTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_212 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCAATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_213 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAAAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_214 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCCAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_215 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGCACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_216 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCACCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_217 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGTTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCTATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATTGAACCGCTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_218 -AATCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGTCTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGTTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCTATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATTGAACCGCTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_219 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_220 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAGGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAACTAGAAGGAATCGACTTTTATCACCGTTACAAAGATGATGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGCACTTCTATCGCTTGGTCTCGTATTTTTCCAAATGGTGATGAAACTGAACCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAACATTGAACCACTGATTACATTATCTCATTATGAAACGCCACTTCACCTATCTAAAACATACGATGGCTGGGTCAACAGAAAAATGATTGGCTTCTATGAA ->bglA_221 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAGGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAACTAGAAGGAATCGACTTTTATCACCGTTACAAAGATGATGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGCACTTCTATCGCTTGGTCTCGTATTTTCCCAAATGGTGATGAAACTGAACCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAACATTGAACCACTGATTACATTATCTCATTATGAAACGCCACTTCACCTATCTAAAACATATGATGGCTGGGTCAACAGAAAAATGATTGACTTCTATGAA ->bglA_222 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACATCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAAGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_223 -AACCAATTCAAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_224 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_225 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_226 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACTAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAACAGAAAAATGATCGACTTCTATGAA ->bglA_227 -AATCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGTTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATTGAACCGCTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGGCTTCTATGAA ->bglA_228 -AATCAATTCGAAGGCGCTTACAATGTTGATGGGAAAGGGCTTACCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGCCACATCACTGACGGCCCCACACCAGACAACTTAAAATTAGAAGGAATTGATTTTTATCACCGTTATAAAGATGATGTAAAACTTTTTGCTGAAATGGGCTTCAAAGTTTTCCGGACATCCATCGCTTGGTCTCGTATCTTCCCAAATGGCGACGAAACAGAACCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTTTAGCGCACAACATCGAACCACTCATCACTTTATCTCATTATGAAACACCACTTCACTTATCAAAAACATACGATGGTTGGGTTAATCGCAAAATGATCGACTTCTACGAA ->bglA_229 -AATCAATTCGAAGGCGCTTACAATGTTGATGGGAAAGGGCTTACCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGCCACATCACTGACGGCCCCACACCAGACAACTTAAAATTAGAAGGAATCGATTTCTATCATCGTTATAAAGATGATGTAAAACTTTTTGCTGAAATGGGCTTCAAAGTTTTCCGTACATCGATTGCTTGGTCACGTATTTTCCCAAATGGCGATGAAACTGAGCCAAATGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTTTAGCGCACAACATCGAACCACTCATCACTTTATCTCATTATGAAACACCACTTCACTTATCAAAAACATACGATGGTTGGGTTAATCGCAAAATGATTGACTTCTACGAA ->bglA_230 -AACCAATTCGAAGGCGCTTATAACGTTGATGGGAAAGGACTTACCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCATATTACTGACGGTCCAACACCAGACAACTTAAAATTAGAAGGCATAGATTTTTATCACCGTTATAAAGATGATGTAAAACTTTTTGCTGAAATGGGTTTCAAAGTTTTCCGTACATCCATCGCTTGGTCCCGTATCTTCCCAAATGGCGACGAAACCGAACCAAACGAAGCAGGACTACAATTCTACGATGATTTATTTGATGAACTTTTAGCACATAACATCGAACCACTTATCACTTTATCACATTATGAAACACCACTTCACTTATCAAAAACATACGACGGTTGGGTTAATCGCAAAATGATCGACTTCTATGAA ->bglA_231 -AATCAATTCGAAGGCGCTTACAACGTTGATGGGAAAGGACTTACCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCATATTACTGACGGTCCAACACCAGACAACTTAAAATTAGAAGGAATTGATTTTTATCACCGTTATAAAGATGATGTAAAACTTTTTGCTGAAATGGGCTTCAAAGTTTTCCGGACATCCATCGCTTGGTCCCGTATCTTCCCAAATGGCGACGAAACCGAACCAAACGAAGCAGGACTACAATTTTACGATGATTTATTTGATGAACTACTAGCACACAACATCGAACCACTTATCACTTTATCACATTATGAAACACCACTTCACTTATCAAAAACATACGACGGTTGGGTTAATCGCAAAATGATCGACTTCTACGAA ->bglA_232 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGTCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_233 -AACCAATTCGAAGGCGCTTATAACGTTGATGGGAAAGGACTTACCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCATATTACTGACGGTCCAACACCAGACAACTTAAAATTAGAAGGAATTGATTTTTATCACCGTTATAAAGATGATGTAAAACTTTTTGCTGAAATGGGCTTCAAAGTTTTCCGGACATCCATCGCTTGGTCCCGTATCTTCCCAAATGGCGACGAAACCGAACCAAACGAAGCAGGACTACAATTTTACGATGATTTATTTGATGAACTACTAGCACACAACATCGAACCACTTATCACTTTATCACATTATGAAACACCACTTCACTTATCAAAAACATACGACGGTTGGGTTAATCGCAAAATGATCGACTTCTACGAA ->bglA_234 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAACCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_235 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAAGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_236 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGAAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_237 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTTGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_238 -AACCAATTCGAAGGCGCTTACAACGTCAATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_239 -AATCAATTCGAAGGCGCTTACAATGTTGATGGGAAAGGGCTTACCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGCCACATCACGGACGGCCCCACACCAGACAACTTAAAATTAGAAGGAATCGATTTCTATCATCGTTATAAAGATGATGTAAAACTTTTTGCTGAAATGGGCTTCAAAGTTTTCCGTACATCGATTGCTTGGTCACGTATTTTCCCAAATGGCGATGAAACTGAGCCAAATGAAGCAGGACTACAATTTTACGATGATTTATTTGATGAACTTTTAGCACACAACATCGAACCACTCATCACTTTATCTCATTATGAAACACCACTTCACTTATCAAAAACATACGATGGTTGGGTTAATCGCAAAATGATCGACTTCTACGAA ->bglA_240 -AATAAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGTTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATTGAACCGCTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_241 -AATCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGTTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATTGAACCGCTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATAAA ->bglA_242 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAAGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAACAGAAAAATGATTGACTTCTATGAA ->bglA_243 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATTGAACCGCTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_244 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAAGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_245 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGACGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_246 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGGCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCACCGTTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAAGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTAAAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCATTATGAAACACCACTTCACTTATCGAAAGCTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_247 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAAGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_248 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTTTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_249 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_250 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_251 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGTAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_252 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGAACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_253 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGATGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_254 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGTAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_255 -AACCAATTCGAAGGCGCTTACAACGTCTATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_256 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_257 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCACCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGATTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_258 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGCACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_259 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_260 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACAGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_261 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCTGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAACAGAAAAATGATTGACTTCTATGAA ->bglA_262 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGTTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAATGAAGCAGGATTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_263 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCATAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_264 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCACCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTAGAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_265 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_266 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATAACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_267 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGATTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_268 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCCGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_269 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGATGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_270 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACAGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_271 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTACCATCGCTACAAAGATGATGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_272 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCTCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCATTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_273 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGCCCCATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_274 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_275 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGCACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTGCAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_276 -AACCAATTCGAAGGCTCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_277 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTAACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCTCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCTCTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_278 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTGTCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_279 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_280 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_281 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTAATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_282 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_283 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATAACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_284 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGGCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_285 -AACCAATACGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_286 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAACAGAAAAATGATCGACTTCTATGAA ->bglA_287 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAACAGAAAAATGATCGACTTCTATGAA ->bglA_288 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAATGAAGCAGGACTGCAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATTGACTTCTATGAA ->bglA_289 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAATTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGATGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_290 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAGATGATCGACTTCTATGAA ->bglA_291 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAGCTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_292 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAATGAAGCAGGATTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATTGACTTCTATGAA ->bglA_293 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAACAGAAAAATGATTGACTTCTATGAA ->bglA_294 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTAAAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATTGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATTGACTTCTATGAA ->bglA_295 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_296 -AACCAATTCGAAGGCGCTTATAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_297 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGTGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_298 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTTTATCATCGTTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAACCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCTCATAATATCGAACCACTGATCACTTTATCTCACTATGAAACACCTCTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_299 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCTAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_300 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCACCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATTGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_301 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_302 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_303 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAACAGAAAAATGATCGACTTCTATGAA ->bglA_304 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_305 -AACCAATTCGAAGGCGCTTACAAGGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_306 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_307 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTTCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_308 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGTTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATTGACTTCTATGAA ->bglA_309 -AACCAATTCGAAGGCGCTTACAACGTCGATGAAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_310 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGAAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_311 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCACCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCATTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_312 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAAGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATTGACTTCTATGAA ->bglA_313 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCACTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_314 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_315 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGGTTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATTGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATTGACTTCTATGAA ->bglA_316 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAATTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_317 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_318 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAGTTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATTGAACCACTGATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATTGACTTCTATGAA ->bglA_319 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATTGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_320 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_321 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGTTACAAAGACGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCGCTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_322 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_323 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGCGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACATACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_324 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACGCCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_325 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_326 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_327 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGTGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTTTATCATCGCTACAAAGATGACGTGAAACTTTTTGCAGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_328 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTTTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGATGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCATTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_329 -AACCAATTCGAAGGCGCTTATAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCACCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_330 -AATCAATTAGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGTTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATTGAACCGCTAATCACTTTATCCCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_331 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTATTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_332 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGTCTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_333 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGAATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_334 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTAGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_335 -AACCAATTCGAAGGCGCTTATAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCATTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATTGACTTCTATGAA ->bglA_336 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACAAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGATGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATTGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_337 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACAGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_338 -AATCAATTAGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGTTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATAGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATTGAACCGCTAATCACTTTATCCCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_339 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGTTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_340 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAAGATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_341 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAAGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_342 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAACAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_343 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTACGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_344 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGTTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAACCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_345 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAACCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCTCATAATATCGAACCACTGATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_346 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCCACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAACAGAAAAATGATTGACTTCTATGAA ->bglA_347 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACATCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAACAGAAAAATGATTGACTTCTATGAA ->bglA_348 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCGCTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_349 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATTGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_350 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACAAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGATGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCATTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_351 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGAACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_352 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACAGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACATACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_353 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAACAGAAAAATGATCGACTTCTATGAA ->bglA_354 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAATGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_355 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCCACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAACAGAAAAATGATCGACTTCTATGAA ->bglA_356 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAACAGAAAAATGATCGACTTCTATGAA ->bglA_357 -AACCAATTCGAAGGCGCTTACAACGTCGATGAAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCGAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_358 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAACAGAAAAATGATCGACTTCTATGAA ->bglA_359 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACGCCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_360 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACAGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTGTCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACATACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_361 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGTAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAACAGAAAAATGATCGACTTCTATGAA ->bglA_362 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAAGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_363 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCATATTACTGACGGTCCAACATCAGATAACTTAAAATTAGAAGGAATCGACTTTTACCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAAGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAACCAAACGAAGCAGGACTACAATTTTACGATGATTTATTTGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_364 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAGCTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCTCATAATATCGAACCACTGATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_365 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACGCCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_366 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_367 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_368 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTTTATCATCGTTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_369 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGCCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_370 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGAACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_371 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_372 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAGGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAACTAGAAGGAATCGACTTTTATCATCGTTACAAAGATGATGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGCACTTCTATCGCTTGGTCTCGTATTTTCCCAAATGGTGATGAAACTGAACCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAACATTGAACCACTGATTACATTATCTCATTATGAAACGCCACTTCACCTATCCAAAACATACGATGGCTGGGTCAACAGAAAAATGATTGACTTCTATGAA ->bglA_373 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAGGATGTTACTCCAAAAGGCGGATTCGGTCACATAACTGACGGTCCAACACCAGATAACTTAAAACTAGAAGGAATCGACTTTTATCACCGTTACAAAGATGATGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGCACTTCTATCGCTTGGTCTCGTATTTTCCCAAATGGTGATGAAACTGAACCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAACATTGAACCACTGATTACATTATCTCATTATGAAACGCCACTTCACCTATCTAAAACATACGATGGCTGGGTCAACAGAAAAATGATTGACTTCTACGAA ->bglA_374 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCACCGTTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAAGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTAAAATTTTACGCTGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCATTATGAAACACCACTTCACTTATCGAAAGCTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_375 -AATCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGTTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCTATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTTGACGAACTTCTAGCACATAATATTGAACCGCTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_376 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACAGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_377 -AACCAATTCAAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_378 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_379 -AACCAATTCGAAGAAGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAAAAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTATTACATTTATTTTTTATGATTTATTCGATGAACATGATTCTAGCACATATGATATCGAACCACTGATTACTTTATCTCACTATGAACAACACTTTCACTTATCGAAAACTTACGACTGGCTGGGTAAATAGAAAAATGATCGACTCTTCTATGAA ->bglA_380 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGGCTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_381 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATGACATTGAGCCACTTGTGACACTTTCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_382 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTGGCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_383 -AATCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_384 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTTTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGATGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCATTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATTGACTTCTATGAA ->bglA_385 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACAGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCACGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_386 -AACCAATTCGAAAGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_387 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_388 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAAGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_389 -AACCAATTCGAAGGCGCTTACAACGTTGATGGGAAAGGACTTACCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCATATTACTGACGGTCCAACACCAGACAACTTAAAATTAGAAGGAATTGATTTTTATCACCGTTATAAAGATGATGTAAAGCTTTTTGCTGAAATGGGCTTCAAAGTTTTCCGGACATCCATCGCTTGGTCTCGTATCTTCCCAAATGGCGACGAAACCGAACCAAACGAAGCAGGACTACAATTTTACGATGATTTATTTGATGAACTACTAGCACACAACATCGAACCACTTATCACTTTATCACATTATGAAACACCACTTCACTTATCAAAAACATACGACGGTTGGGTTAATCGCAAAATGATCGACTTCTACGAA ->bglA_390 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGTCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_391 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATAATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_392 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_393 -AACCAATTCGAAGGCGCTTACAACGTCGACGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_394 -AACCAATTTGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_395 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTCCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_396 -AATCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTTTATCATCGTTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGATGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCATTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATTGACTTCTATGAA ->bglA_397 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_398 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCTCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_399 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_400 -AATCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGTTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATTGACTTCTATGAA ->bglA_401 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAACGGTGACGAAACCGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_402 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACATTTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_403 -AATCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTTTATCATCGTTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGATGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_404 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCCTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_405 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAAACACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_406 -AACCAATTCAAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_407 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGTTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATTGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAACCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATCACTTTATCTCACTATGAAACACCTCTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_408 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATTGACTTCTATGAA ->bglA_409 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAGGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCGAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_410 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGTGGATTCGGTCAAATTACTGACGGTCCAACAACAGATAACTTAAAATTAGAAGGAATTGACTTTTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_411 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTTTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_412 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAAGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGGA ->bglA_413 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCACCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCGCTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCAAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_414 -AATCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_415 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATAGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_416 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTATAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_417 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAGGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAACTAGAAGGAATCGACTTTTATCACCGTTACAAAGATGATGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGCACTTCTATCGCTTGGTCTCGTATTTTTCCAAATGGTGATGAAACTGAACCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAACATTGAACCACTGATTACATTATCTCATTATGAAACGCCACTTCACCTATCTAAAACATATGATGGCTGGGTCAACAGAAAAATGATTGACTTCTATGAA ->bglA_418 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATCATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_419 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCAGATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAACAGAAAAATGATCGACTTCTATGAA ->bglA_420 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACATACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_421 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAACTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_422 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGCAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_423 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTACCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTACATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_424 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTCTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_425 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCGCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGGCTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGATTTCTATGAA ->bglA_426 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTTTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACGCCACTTCATTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_427 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACGCCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_428 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAGCTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_429 -AACCAATTCGAAGGCGCTTATAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGATGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_430 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGTGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTTTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCATTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_431 -AACCAATTCGAAGGCGCTTACAACGTTGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGTGGATTCGGTCAAATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTTTATCATCGCTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACCGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTAATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_432 -AACCAATTCGAAGGCACTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_433 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCACCGTTACAAAGATGACGTGAAACTTTTTGCTGAAATGGGCTTCAAAGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACAGAGCCAAACGAAGCAGGACTAAAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCATTATGAAACACCACTTCACTTATCGAAAACTTACGACGGATGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_434 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGACGACGTGAAACTTTTTGCTGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTACAATTTTACGATGATTTATTCGACGAACTTCTAGCACATAATATCGAACCGCTGATCACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_435 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTGAATAGAAAAATGATCGACTTCTATGAA ->bglA_436 -AACCAATTCGAAGGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTTGGCCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATTGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGATTACAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCATTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA ->bglA_437 -AACCAATTCGAAAGCGCTTACAACGTCGATGGAAAAGGACTTTCCGTTCAAGATGTTACTCCAAAAGGCGGATTCGGTCACATTACTGACGGTCCAACACCAGATAACTTAAAATTAGAAGGAATCGACTTCTATCATCGCTACAAAGATGACGTGAAACTTTTTGCCGAAATGGGCTTCAAGGTTTTCCGTACTTCCATCGCTTGGTCCCGTATCTTCCCAAATGGTGACGAAACTGAGCCAAACGAAGCAGGACTTCAATTTTACGATGATTTATTCGATGAACTTCTAGCACATAATATCGAACCACTGATTACTTTATCTCACTATGAAACACCACTTCACTTATCGAAAACTTACGACGGCTGGGTAAATAGAAAAATGATCGACTTCTATGAA diff --git a/test/MLST_listeria/cat.fasta b/test/MLST_listeria/cat.fasta deleted file mode 100644 index 767e588..0000000 --- a/test/MLST_listeria/cat.fasta +++ /dev/null @@ -1,912 +0,0 @@ ->cat_1 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_2 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_3 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGGACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGAGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_4 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGAGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_5 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACTGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_6 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACAATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_7 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATAGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCTGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_8 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_9 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_10 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_11 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_12 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_13 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_14 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_15 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACAATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_16 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_17 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_18 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAATGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_19 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAATGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_20 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAATGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_21 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGATACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATATTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_22 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTGCCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_23 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_24 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGATT ->cat_25 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGGACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGAGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGAATTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGATT ->cat_26 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACAATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_27 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATAGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_28 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACAATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACGGTTTATGTAAAACTGCGCTGGGTT ->cat_29 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACCTTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGTACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATCACATACCTATTCAGTGATGAAGGGACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGATT ->cat_30 -GCTCGAGGTGCTGGGGCTCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACTATCGCTAATTTTTTACAAGAAGAAGGGACAGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGATCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAAGGAAATTATGACTTTGTCGGAAATAACTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTCTGAAACCTGATCCACGCACGAATATTCAAGATGGTAACCGCTACTGGGATTTCTTTAGCTTGAGTCCTGAAGCTGCAACTATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCTTCTTATCGCGAAATCCGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAGGGGAAAACTGTTTACGTAAAACTACGCTGGGTA ->cat_31 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGATACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATATTTATTCAGTGATGAAGGAACGCCAGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_32 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTGCCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_33 -GCTCGAGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACTATCGCTAATTTTTTACAAGAAGAAGGGACAGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGATCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAAGGAAATTATGACTTTGTCGGAAATAACTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTCTGAAACCTGATCCACGCACGAATATTCAAGATGGTAACCGCTACTGGGATTTCTTTAGCTTGAGTCCTGAAGCTACAACTATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCTTCTTATCGCGAAATCCGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAGGGGAAAACTGTTTACGTAAAACTACGCTGGGTA ->cat_34 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGCTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTGCCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_35 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_36 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGGACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_37 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTAAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_38 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATAGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGATACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATATTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_39 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACAATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_40 -GCTCGAGGTGCTGGGGCTCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACTATCGCTAATTTTTTACAAGAAGAAGGGACAGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGATCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAAGGAAATTATGACTTTGTCGGAAATAACTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTCTGAAACCTGATCCACGCACGAATATTCAAGATGGTAACCGCTACTGGGATTTCTTTAGCTTGAGTCCTGAAGCTACAACTATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCTTCTTATCGCGAAATCCGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAGGGGAAAACTGTTTACGTAAAACTACGCTGGGTA ->cat_41 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAATTTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_42 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAATGGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_43 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_44 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_45 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACCGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGTGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_46 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_47 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTGCGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_48 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTCGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_49 -GCTCGTGGTGCTGGTGCGCACGGGTAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGAGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_50 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_51 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCTGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_52 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTGCCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGGAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_53 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGGAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_54 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_55 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACCTTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGTACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGGACGCCGGCTTCTTACCGCGAAATAAGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGATT ->cat_56 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCACGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_57 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGGAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_58 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCATTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_59 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTTAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_60 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCCTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_61 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGGCAACATTCTCCAGAAACCTTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_62 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGGACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGAGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGAATTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_63 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_64 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_65 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGTCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_66 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGGACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGAGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGGCCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_67 -GCTCGAGGTGCTGGGGCTCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACTATCGCTAATTTTTTACAAGAAGAAGGGACAGAAAAAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGACCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAGGGAAATTATGATTTTGTTGGGAATAACTTGCCTGTATTCTTTATTCGTGACGCGATTAAGTTTCCGGATGTTATTCATTCTTTGAAACCTGATCCACGCACGAATATTCAAGATGGTAACCGCTACTGGGATTTCTTTAGCTTGAGTCCTGAAGCTACAACTATGATTATGTATTTATTTAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGTGGCTCTAGTGTTCATGCCTTTAAATGGATTAATGAAGAAGGGAAAACTGTTTACGTAAAACTACGCTGGGTA ->cat_68 -GCTCGTGGTGCTGGGGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_69 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGATACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATATTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_70 -GCTCGAGGTGCTGGGGCTCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACTATCGCTAATTTTTTACAAGAAGAAGGGACAGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGACCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAGGGAAATTATGATTTTGTTGGGAATAACTTGCCGGTATTCTTTATTCGTGACGCGATTAAGTTTCCGGATGTTATTCATTCTTTGAAACCTGATCCACGCACGAATATTCAAGATGGTAACCGCTACTGGGATTTCTTTAGCTTGAGTCCTGAAGCTACAACTATGATTATGTATTTATTTAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGTGGCTCTAGTGTTCATGCCTTTAAATGGATTAATGAAGAAGGGAAAACTGTTTACGTAAAACTACGCTGGGTA ->cat_71 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTTGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_72 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGTGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_73 -GCTCGAGGTGCTGGGGCTCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACTATCGCTAATTTTTTACAAGAAGAAGGGACAGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGATCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAAGGAAATTATGACTTTGTCGGAAATAACTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTCTGAAACCTGATCCACGCACGAATATTCAAGATGGTAACCGCTACTGGGATTTCTTTAGCTTGAGTCCTGAAGCTACAACTATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCTTCTTACCGCGAAATCCGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAGGGGAAAACTGTTTACGTAAAACTGCGCTGGATA ->cat_74 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGATATTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_75 -GCTCGAGGTGCTGGGGCTCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACTATCGCTAATTTTTTACAAGAAGAAGGGACAGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGATCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAAGGAAATTATGACTTTGTCGGAAATAACTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTCTGAAACCTGATCCACGCACGAATATTCAAGATGGTAACCGCTACTGGGATTTCTTTAGCTTGAGTCCTGAAGCTACAACTATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCTTCTTATCGCGAAATCCGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAGGGGAAAACGGTTTACGTGAAACTGCGCTGGGTA ->cat_76 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_77 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_78 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGATATTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_79 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGGAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGATACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_80 -GCTCGAGGTGCTGGGGCTCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACTATCGCTAATTTTTTACAAGAAGAAGGGACAGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGATCCGCGTGGATTCTCTGTTAAATTTTATACTGAAGAAGGAAATTATGACTTTGTCGGAAATAACTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTCTGAAACCTGATCCACGCACGAATATTCAAGATGGTAACCGCTACTGGGATTTCTTTAGCTTGAGTCCTGAAGCTACAACTATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCTTCTTATCGCGAAATCCGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAGGGGAAAACGGTTTACGTGAAACTGCGCTGGGTA ->cat_81 -GCTCGCGGTGCTGGGGCGCACGGAAAGTTTGTTACTAAAAAAAGCATGAAAAAATATACAATCGCTAATTTTTTACAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGCCAACATTCTCCAGAAACATTGCGTGATCCACGTGGATTCTCTGTTAAATTTTATACGGAAGAAGGGAATTATGACTTTGTCGGAAATAACTTGCCGGTGTTCTTTATTCGTGATGCGATTAAGTTTCCGGACGTTATTCATTCCTTGAAGCCCGATCCGCGCACGAATATTCAAGATGGTAATCGCTATTGGGATTTCTTTAGTTTATCTCCAGAAGCTACCACGATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCATCTTATCGTGAAATTCGTGGATCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAAGGTAAAACTGTCTATGTAAAACTGCGCTGGATT ->cat_82 -GCTCGAGGTGCTGGGGCTCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACTATCGCTAATTTTTTACAAGAAGAAGGGACAGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGATCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAAGGAAATTATGACTTTGTCGGAAATAACTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTCTGAAACCTGATCCACGCACGAATATTCAAGATGGTAACCGCTACTGGGATTTCTTTAGCTTGAGTCCTGAAGCTACAACTATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCTTCTTACCGCGAAATCCGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAGGGGAAAACTGTTTACGTAAAACTACGCTGGGTA ->cat_83 -GCTCGAGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACTATCGCTAATTTTTTACAAGAAGAAGGGACAGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGATCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAAGGAAATTATGACTTTGTCGGAAATAACTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTCTGAAACCTGATCCACGCACGAATATTCAAGATGGTAACCGCTACTGGGATTTCTTTAGCTTGAGTCCTGAAGCTACAACTATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCTTCTTATCGCGAAATCCGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAGGGGAAAACTGTTTACGTAAAACTACGCTGGGTA ->cat_84 -GCTCGAGGTGCTGGGGCTCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACTATCGCTAATTTTTTACAAGAAGAAGGGACAGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGATCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAAGGAAACTATGATTTTGTTGGGAATAACTTGCCGGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTCTGAAACCTGATCCACGCACGAATATTCAAGATGGTAACCGCTACTGGGATTTCTTTAGCTTGAGTCCTGAAGCTACAACTATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCTTCTTATCGCGAAATCCGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAGGGGAAAACGGTTTACGTGAAACTGCGCTGGGTA ->cat_85 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAGAAAAGTATGAAAAAATATACAATGGCTAAATTCTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGATCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAAGGAAACTATGATTTTGTTGGGAATAACTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTTTGAAACCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGTCTTACACCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_86 -GCTCGTGGTGCTGGTGCGCACGGCAAATTTGTAACGAAAAAAAGCATGAAAAAATATACGAAAGCACAATTTTTACAAGAAGAAGGAACAGAGACAGAGGTTTTTGCGCGTTTTTCTACAGTAATCCATGGTCAACATTCACCAGAAACGCTTCGTGATCCACGTGGTTTTTCGGTTAAATTTTATACGGAAGAAGGTAACTATGACTTTGTCGGTAACAACTTACCTGTATTCTTTATCCGTGATGCTATCAAGTTTCCTGACGTAATTCACTCCTTGAAGCCAGATCCACGTACTAACATTCAAGATGGCAATCGTTACTGGGACTTCTTTAGTTTAACTCCGGAAGCGACGACGATGATTACTTATTTATTTAGCGATGAGGGGACTCCGGCATCTTACCGCGAAATTCGTGGTTCAAGCGTACATGCCTTTAAATGGATAAACGAAGAAGGTAAGACTGTTTATGTAAAACTACGCTGGGTT ->cat_87 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACAGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGAGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_88 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAGCAGTTTATGTAAAACTGCGCTGGGTT ->cat_89 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGATACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_90 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTAACGAAAAAGAGCATGAAAAAGTATACGAAAGCAAAATTTTTACAAGAAGAAGGAACGGAGACAGAAGTTTTTGCGCGCTTTTCAACGGTTATTCACGGACAGCACTCACCAGAAACTTTGCGCGACCCACGTGGTTTTTCTGTTAAATTTTATACGGAAGAAGGGAACTACGATTTTGTTGGGAACAATTTACCAGTATTCTTTATTCGTGATGCAATTAAGTTTCCGGATGTGATCCATTCCTTGAAGCCAGATCCACGTACGAATATTCAAGATGGGAACCGTTATTGGGATTTCTTTAGTTTGACACCTGAAGCAACAACGATGATTACGTACTTGTTTAGTGATGAAGGAACACCAGCGTCTTATCGCGAAATTCGTGGTTCTAGTGTTCATGCTTTTAAATGGATTAACGCTGAGGGGAAAACAGTGTATGTTAAACTTCGCTGGGTT ->cat_91 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACCTTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGTACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGGACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGATT ->cat_92 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACCTTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTTTGAAGCCTGATCCACGTACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATCACATACCTATTCAGTGATGAAGGCACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGATT ->cat_93 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACCTTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGTACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATCACATACCTATTCAGCGATGAAGGCACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGATT ->cat_94 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACCTTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTTTGAAGCCTGATCCACGTACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATCACATATCTATTCAGTGATGAAGGGACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGATT ->cat_95 -GCTCGTGGTGCTGGTGCGCACGGCAAATTTGTAACGAAAAAAAGTATGAAAAAATATACGAAAGCACAATTTTTACAAGAAGAAGGAACAGAGACGGAGGTTTTTGCGCGTTTTTCTACAGTAATCCATGGTCAACATTCACCAGAGACGCTTCGTGATCCACGTGGTTTTTCAGTTAAATTTTATACGGAAGAAGGTAACTATGACTTTGTCGGTAACAACTTACCTGTATTCTTTATCCGTGATGCTATCAAGTTTCCTGACGTAATTCATTCCTTGAAGCCAGATCCACATACTAACATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGTTTAACTCCGGAAGCGACAACGATGATTACTTATTTATTTAGTGATGAGGGAACTCCGGCATCTTACCGCGAAATTCGTGGTTCAAGCGTACATGCCTTTAAATGGATAAACGAAGAAGGTAAGACTGTTTATGTAAAACTACGCTGGGTT ->cat_96 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTAACGAAAAAAAGTATGAAAAAATATACGAAAGCACAATTTTTACAAGAAGAAGGAACAGAGACAGAGGTTTTTGCGCGTTTTTCTACAGTAATCCATGGTCAACATTCACCAGAAACGCTTCGTGATCCACGTGGTTTTTCAGTTAAATTTTATACGGAAGAAGGTAACTATGACTTTGTCGGTAACAACTTACCTGTATTCTTTATCCGTGATGCTATCAAGTTTCCTGACGTAATTCATTCCTTGAAGCCAGATCCACGTACTAACATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGTTTAACTCCGGAAGCGACAACGATGATTACTTATTTATTTAGTGATGAGGGAACTCCGGCATCTTACCGCGAAATTCGTGGTTCAAGCGTACATGCCTTTAAATGGATAAACGAAGAAGGTAAGACTGTTTATGTAAAACTACGCTGGGTT ->cat_97 -GCTCGTGGTGCTGGTGCGCACGGCAAATTTGTAACGAAAAAAAGCATGAAAAAATATACGAAAGCACAATTTTTACAAGAAGAAGGAACAGAGACAGAGGTTTTTGCGCGTTTTTCTACAGTAATCCATGGTCAACATTCACCAGAAACGCTTCGTGATCCACGTGGTTTTTCGGTTAAATTTTATACGGAAGAAGGTAACTATGACTTTGTCGGTAACAACTTACCTGTATTCTTTATCCGTGATGCTATCAAGTTTCCTGATGTAATTCATTCCTTGAAGCCAGATCCACATACTAACATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGTTTAACTCCGGAAGCGACAACGATGATTACTTATTTATTTAGTGATGAGGGAACTCCGGCATCTTACCGCGAAATTCGTGGTTCAAGCGTACATGCCTTTAAATGGATAAACGAAGAAGGTAAGACTGTTTATGTAAAACTACGCTGGGTT ->cat_98 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGTGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_99 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCTGCTTCTTATCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_100 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_101 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAACGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_102 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_103 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATATTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_104 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACATGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_105 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACTGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_106 -GCTCGTGGTGCTGGGGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAGAAATATACAATGGCTAACTTTTTGCAAGACGAAGGGACAGAAACAGAGGTTTTCGCTCGTTTTTCAACAGTGATTCATGGTCAACACTCTCCAGAAACTTTGCGTGATCCACGTGGATTCTCGGTTAAGTTTTATACGGAGGAGGGGAATTATGATTTTGTCGGAAATAACTTGCCGGTATTCTTTATTCGTGATGCAATTAAGTTTCCGGATGTTATTCATTCCTTGAAACCTGACCCGCGCACGAACATTCAAGATGGTAATCGCTACTGGGATTTCTTTAGTTTAACGCCAGAAGCTACAACGATGATTATGTACTTATTCAGTGATGAAGGGACGCCTGCTTCGTACCGTGAAATTCGTGGTTCTAGTGTTCATGCTTTTAAATGGATTAATGAAGAAGGAAAAACGGTATACGTAAAGTTACGCTGGATT ->cat_107 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTTTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_108 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGGACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGAGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATACGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_109 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAACCTTACACCGGAAGCTACGACAATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_110 -GCTCGTGGTGCTGGTGCGCACGGGAGATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_111 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAATTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACAATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_112 -GCTCGTGGTACTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_113 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTGCCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_114 -GCTCGTGGTGCTGGTGCGCACGGGAAATCTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_115 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACCTTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGTACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGGACACCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGATT ->cat_116 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGCGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_117 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_118 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCCCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_119 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGCGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_120 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACAGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_121 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTATTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_122 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTAATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_123 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAGAAAAGTATGAAAAAATATACAATGGCTAAATTCTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGATCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAAGGAAACTATGATTTTGTTGGGAATAACTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTTTGAAACCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGTCTTACACCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_124 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTGCCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGTGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_125 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATATTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_126 -GCTCGTGGTTCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_127 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACCTTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_128 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTATCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_129 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_130 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACCTTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTCCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_131 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACAATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTTAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_132 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGATTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_133 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGAGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGATGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_134 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCGGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTCCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_135 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGAGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_136 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTAACGAAAAGAGCATGAAAAGTATACGAAAGCAAAATTTTTACAAGAAGAAGGAACGGAGACAGAAGTTTTTGCGCGCTTTTCAACGGTTATTCACGGACAGCACTCACCAGAACTTTGCGCGACCCACGTGGTTTTTCTGTTAAATTTTATACGGAAGAAGGGAACTACGATTTTGTTGGGAACAATTTACCAGTATTCTTTATTCGTGATGCAATTAAGTTTCCGGATGTGATCCATTCCTTGAAGCCAGATCCACGTACGAATATTCAAGATGGGAACCGTTATTGGGATTTCTTTAGTTTGACACCTGAAGCAACAACGATGATTACGTACTTGTTTAGTGATGAAGGAACACCAGCGTCTTATCGCGAAATTCGTGGTTCTAGTGTTCATGCTTTTAAATGGATTAACGCTGAGGGGAAAACAGTGTATGTTAAACTTCGCTGGGTT ->cat_137 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTAACGAAGAAGAGTATGAAAAAGTATACGAAAGCACAATTTTTACAAGAAGAAGGAACAGAGACAGAAGTTTTTGCACGCTTCTCAACGGTTATTCACGGACAACACTCACCAGAAACTTTGCGGGACCCACGTGGTTTTTCTGTTAAATTTTATACGGAAGAAGGGAACTACGATTTTGTTGGGAACAATTTGCCAGTATTCTTTATTCGTGATGCAATTAAGTTTCCGGATGTAATCCATTCCTTGAAGCCAGATCCACATACGAATATTCAAGATGGGAATCGTTATTGGGATTTCTTTAGTTTAACTCCTGAAGCGACAACGATGATTACATACTTGTTTAGTGATGAAGGAACACCAGCATCTTATCGCGAAATTCGGGGTTCTAGTGTACATGCTTTTAAATGGATTAATGATGAAGGGAAAACAGTGTATGTTAAACTTCGTTGGATT ->cat_138 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGGACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGAGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGCT ->cat_139 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTCTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_140 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGCGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_141 -GCTCGTGGTGCTGGAGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTGCCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTATGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGTGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_142 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_143 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGAACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_144 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGTACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_145 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGAACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_146 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGGACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCACGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_147 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTCATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACAATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_148 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCCCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATATTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_149 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_150 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTGCCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_151 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTTATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGATACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATATTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_152 -GCACGTGGAGCAGGGGCACACGGAAAGTTTGTAGTTAAAAATAGCATGAAAAAATATACAATGGCACACTTTTTACAAGAAGAAGGACAAGAAACAGAGGTTTTTGCTCGTTTTTCTACCGTTATTCACGGACAACACTCGCCTGAGACGTTGCGAGATCCACGCGGTTTTTCGATCAAGTTTTATACAGAAGAGGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCGCTCAAGCCAGATCCAGCGACGAATATCCAAGATCCAAATCGTTACTGGGACTTCATGAGCCTAACGCCGGAAGCAGTCACGATGCTTACATATCTTTTCAGCGATGAAGGGACACCTGCTTCTTACCGTGAGATGCGCGGTTCGAGCGTACATGCTTTTAAATGGATCAATGAGGCAGGAAAAACGGTTTATGTGAAGCTACGCTGGCAA ->cat_153 -GCTCGTGGTGCAGGAGCACACGGTGTTTTTGTAACTAAAAAAAGCATGAAAAAATATACGAAGGCGGCTTTCCTTGCAGAAGAAGGTACAGAAACAGAAGTTTTTGCACGATTTTCGACGGTTATTCATGGACAGCACTCGCCGGAAACTTTACGAGATCCGCGTGGCTTTTCGATCAAGTTTTATACGGAAGAGGGCAATTATGATTTTGTGGGAAATAATTTACCGGTATTCTTTATTCGCGATGCCATCAAATTCCCAGATGTGATCCATTCCTTGAAACCAGATCCAACGACAAATATCCAAGATGGAAATCGGTATTGGGATTTTTTCAGTATGTCGCCAGAAGCAACGACGATGATCATGTATCTTTTTAGTGATGAAGGGACACCGGCTTCTTACCGTGAAATCCGGGGATCGAGCGTTCATGCTTTTAAATGGGTGAATGAAGAAGGAAAGACGGTTTACGTGAAATTACGCTGGATT ->cat_154 -GCTCGTGGTGCAGGAGCACACGGTGTTTTTGTAACTAAAAAAAGCATGAGAAAATATACGAAGGCGGCTTTCCTTGCAGAAGAAGGTATAGAAACAGAAGTTTTTGCACGATTTTCGACGGTTATTCATGGACAGCACTCGCCGGAAACTTTACGAGATCCGCGTGGCTTTTCGATCAAGTTTTATACGGAAGAGGGCAATTATGATTTTGTGGGAAATAATTTACCGGTATTCTTTATTCGCGATGCCATCAAATTCCCAGATGTGATCCATTCCTTGAAACCAGATCCAACGACAAATATCCAAGATGGAAATCGGTATTGGGATTTTTTCAGTATGTCGCCAGAAGCAACGACGATGATCATGTATCTTTTTAGTGATGAAGGGACACCGGCTTCTTACCGTGAAATCCGGGGATCGAGCGTTCATGCTTTTAAATGGGTGAATGAAGAAGGAAAGACGGTTTACGTGAAATTACGCTGGATT ->cat_155 -GCTCGCGGTGCTGGGGCGCACGGAAAGTTTGTTACTAAAAAAAGTATGAAAAAATATACAATCGCTAATTTTTTACAAGAAGAAGGAACTGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGCCAACATTCTCCAGAAACATTGCGTGATCCACGTGGGTTCTCTGTTAAATTTTATACGGAAGAAGGGAATTACGACTTTGTCGGAAATAACTTGCCGGTGTTCTTTATTCGTGATGCGATTAAGTTTCCAGACGTTATTCATTCCTTGAAGCCCGATCCGCGCACGAATATTCAAGATGGTAATCGCTATTGGGATTTCTTTAGTTTATCTCCAGAAGCTACCACGATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCATCTTATCGTGAAATTCGTGGATCTAGTGTTCACGCGTTTAAATGGATTAATGAAGAAGGTAAAACTGTCTATGTAAAACTGCGCTGGATT ->cat_156 -GCTCGTGGAATCGGTGCGCATGGTGTTTTTACAGTTAAAAATAGTATGAAGAAGTACACGAAAGCAGCATTTTTACAAGAAGTGGGGAAAGAAACAGAGGTTTTTGTTCGTTTTTCAACGGTAATTCATGGTTTGCATTCGCCGGAGACATTGCGTGACCCACGTGGTTTTGCGGTTAAATTTTATACAGAAGAGGGAAATTATGATTTTGTAGGTAATAATTTGCCTGTATTCTTCATTCGTGATGCGATTAAGTTCCCAGATGTTATTCATTCTTTGAAGCCGGATCCAAGTACGAATATGCAAGATGCTAACCGTTATTGGGATTTCTTCAGCTTGACTCCTGAGGCTACGACGATGATTACATATTTGTTTAGTGATGAGGGTATCCCGGCTTCTTACCGCCAAATTCGTGGTTCGAGTGTTCATGCTTTTAAATGGACGAATGAGGAGGGCAAAACGGTTTATATTAAAATGCGTTGGGTG ->cat_157 -GCTCGGGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAGAGCATGAAAAAATATACAATGGCTAATTTTTTACAAGAAGAAGGGGCGGAAACAGAGGTTTTTGTTCGTTTCTCAACAGTAATTCATGGTCAACATTCTCCGGAAACTTTGCGTGATCCACGTGGGTTCTCGGTTAAGTTTTATACGGAAGAGGGAAATTATGATTTTGTTGGCAATAATTTACCAGTATTCTTCATCCGTGATGCGATCAAGTTCCCGGACGTTATTCACTCTTTGAAGCCGGATCCGCGCACGAATATTCAAGATGGCAATCGTTACTGGGACTTCTTTAGCTTAACCCCAGAAGCTACAACGATGATTATGTACTTGTTCAGTGATGAGGGAACTCCGGCATCTTATCGCGAAATTCGCGGTTCTAGTGTGCATGCCTTTAAATGGATTAATGAAGAAGGAAAAACAGTCTATGTAAAATTACGCTGGGTT ->cat_158 -GCTCGTGGAATCGGCGCGCATGGCGTATTTACAGTTAAGAATAGTATGAAGAAATACACGAAAGCTGCATTTTTACAAGAAGAAGGGCAAGAGACAGAGGTTTTCGCTCGTTTTTCTACTGTAATTCATGGCTTACATTCTCCTGAAACGTTGCGTGATCCACGTGGTTTTGCGGTTAAATTTTACACGGAAGAGGGAAATTACGATTTTGTTGGTAATAACTTGCCTGTATTCTTTATTCGTGATGCGATTAAATTCCCGGATGTTATTCATTCTTTGAAGCCAGATCCAAGTACAAACATGCAAGATGCGAACCGTTATTGGGACTTTTTCAGCTTGACTCCTGAGGCTACGACGATGATTACATACCTATTTAGTGATGAGGGTATTCCTGCTTCGTTCCGCCAAATTCGTGGTTCGAGTGTTCATGCTTTTAAATGGACAAATGAAGAGGGCAAAACGGTTTACATCAAAATGCGTTGGGTG ->cat_159 -GCCAGAGGCGCGGGGGCGCACGGGAAGTTTGTCGTGAAAAATAGCATGAAAAAGTACACTATGGCGCATTTTTTACAAGAAGTTGGACAAGAAACAGAAGTTTTTGCTCGTTTTTCAACCGTTATTCACGGTATGCACTCTCCGGAAACTTTACGTGATCCACGCGGCTTCTCTATTAAGTTTTATACAGAAGAAGGAAATTATGATTTTGTAGGAAATAATTTACCTGTTTTCTTCATTCGTGATGCGATTAAGTTTCCTGATGTTATTCATTCTCTTAAGCCAGACCCACGAACAAACATTCAAGACCCTAATCGGTACTGGGATTTTATGAGCTTAACACCGGAAGCAGTAACGATGCTTACATATTTATTTAGTAACGAAGGAACACCAGCTTCTTACCGGGAAATTAGGGGTTCAAGTGTGCATGCGTTTAAATGGATAAATGAAGAAGGGAAAACGGTTTATGTAAAATTACGCTGGCAA ->cat_160 -GCACGCGGCGTTGGAGCACATGGTAAATTTGTTGTGAAAAACAGTATGAAGAAGTATACTATGGCGCATTTTTTACAAGAAGAAGGGCAAGAAACAGAAGTTTTTGTGAGGTTTTCTACGGTTATTCATGGGTTGCATTCTCCCGAAACGCTTCGTGATCCGCGTGGCTTTTCTGTAAAATTCTACACAGAAGAAGGGAATTTTGATTTTGTTGGGAATAATTTACCAGTCTTCTTTATTCGCGACGCAATCAAGTTTCCAGATGTTATTCATTCACTTAAACCAGATCCGACAACAAATATTCAAGATCCGAATCGTTACTGGGATTACTTTAGTTTAACACCGGAAGCGACGACAATGATTACTTATTTGTTCAGCGACGAAGGGATTCCTGCATCATATCGCCAAATGCGGGGTTCGAGCGTCCATGCTTTTAAATGGATTAATGAGGAAGGAAAGACGGTCTATGTGAAGCTTCGCTGGCAG ->cat_161 -GCACGTGGAGCAGGGGCACACGGAAAGTTTGTAGTTAAAAATAGCATGAAAAAATATACAATGGCACATTTTTTACAAGAAGAAGGACAAGAAACAGAGGTTTTTGCTCGTTTTTCTACCGTTATTCACGGGCAACACTCGCCTGAGACGTTGCGAGATCCACGAGGTTTTTCGATCAAGTTTTATACAGAAGAGGGAAATTATGATTTTGTCGGAAATAATTTGCCTGTATTTTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCGCTCAAGCCAGATCCAGCGACGAATATCCAAGATCCAAATCGTTACTGGGACTTCATGAGCCTAACGCCGGAAGCAGTCACGATGCTTACATATCTTTTCAGCGATGAAGGGACACCTGCTTCTTACCGTGAGATGCGCGGTTCGAGCGTACATGCTTTTAAATGGATCAATGAGGCAGGAAAAACGGTTTATGTGAAGCTACGCTGGCAA ->cat_162 -GCTCGTGGAACTGGAGCACATGGTGTTTTCACAGTGAAGAATAGTATGAAGAAATATACAAAAGCGGGATTCTTACAAAAAGAAGGACAACAAACCGAAGTCTTTGCTCGTTTTTCTACAGTTATTCATGGCTTACATTCTCCTGAAACATTGCGTGATCCGCGTGGCTTTGCCGTGAAATTTTATACAGAAGAGGGAAATTACGATTTTGTTGGTAATAACTTGCCTGTATTCTTTATTCGCGATGCTATTAAGTTCCCGGATGTTATTCATTCACTGAAACCAGATCCAAGCACAAACATGCAAGATGCTAATCGGTATTGGGATTTTATCAGCTTGACTCCTGAAGCTACAACTATGATTACATATTTATTCAGTGACGAAGGAACGCCAGCTTCATACCGCCAAATTCGTGGTTCTAGTGTTCACGCGTTCAAATGGACAAATGAAGAGGGCAAAACAGTTTATATCAAAATGCGTTGGGTG ->cat_163 -GCGCGTGGAATTGGTGCGCATGGCGTATTTACAGTTAAAAATAGCATGAAGAAATATACGAAGGCAGCATTTTTACAAGAAGAAGGACAAGAGACAGAGGTTTTTGCTCGTTTTTCTACTGTAATCCATGGCTTGCATTCTCCTGAAACATTGCGTGATCCACGTGGATTTGCGGTTAAGTTTTATACAGAAGAGGGAAATTACGATTTTGTTGGTAATAACTTGCCTGTATTCTTTATTCGCGACGCAATTAAATTCCCGGATGTTATTCATTCTCTGAAACCTGATCCAAGTACGAATATGCAAGATGCGAACCGTTATTGGGACTTTTTCAGTTTAACTCCTGAGGCTACAACGATGATTACATACCTATTCAGTGATGAGGGTATTCCAGCTTCGTTCCGCCAAATTCGTGGTTCGAGTGTTCATGCTTTTAAATGGACGAATGAAGAGGGCAAAACGGTTTACATCAAAATGCGTTGGGTG ->cat_164 -GCTCGTGGAACTGGTGCACATGGCGTATTTAAAGTGAAGAATAGCATGAAGAAATATACGAAAGCAGCATTTTTACAAGAAGAAGGACAAGAAACAGAGGTTTTTGCCCGTTTTTCTACTGTAATTCATGGTTTGCATTCTCCGGAAACATTGCGTGATCCACGTGGATTTGCGGTTAAATTTTATACAGAAGAGGGAAATTATGATTTTGTTGGTAATAACTTGCCTGTATTCTTTATTCGCGATGCAATTAAATTCCCGGATGTTATCCATTCGCTCAAGCCTGATCCAAGTACGAATATGCAAGACGGAAACCGTTACTGGGACTTCTTTAGCTTGACTCCGGAGGCTGTGACAATGCTTACATACTTGTTCAGCGATGAAGGAATTCCTGCTTCTTACCGCCAAATTCGTGGTTCTAGTGTCCATGCATTCAAATGGACAAACGAAGAAGGCAAAACCGTTTATATCAAAATGCGTTGGGTG ->cat_165 -GCTCGTGGTGCTGGTGCGCACGGCAAATTTGTAACGAAAAAAAGCATGAAAAAATATACGAAAGCACAATTTTTACAAGAAGAAGGAACAGAGACAGAGGTTTTTGCGCGTTTTTCTACAGTAATCCATGGTCAACATTCACCAGAAACGCTTCGTGATCCACGTGGTTTTTCAGTTAAATTTTATACGGAAGAAGGTAACTATGACTTTGTCGGTAACAACTTACCTGTATTCTTTATCCGTGATGCTATCAAGTTTCCTGACGTAATTCATTCCTTGAAGCCAGATCCACATACTAACATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGTTTAACTCCGGAAGCGACAACGATGATTACTTATTTATTTAGTGATGAGGGAACTCCGGCATCTTACCGCGAAATTCGTGGTTCAAGCGTACATGCCTTTAAATGGATAAACGAAGAAGGTAAGACTGTTTATGTAAAACTACGCTGGGTT ->cat_166 -GCTCGTGGTGCTTGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_167 -GCTCGCGGTGCTGGGGCGCACGGAAAGTTTGTTACTAAAAAAAGCATGAAAAAATATACAATCGCTAATTTTTTACAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGCCAACATTCTCCAGAAACATTGCGTGATCCACGTGGATTCTCTGTTAAATTTTATACGGAAGAAGGGAATTATGACTTTGTCGGAAATAACTTGCCGGTGTTCTTTATTCGTGATGCGATTAAGTTTCCGGACGTTATTCATTCCTTGAAGCCCGATCCGCGCACGAATATTCAAGATGGTAATCGCTATTGGGATTTCTTTAGTTTATCTCCAGAAGCTACCACGATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCATCTTATCGTGAAATTCGTGGATCTAGTGTTCACGCTTTTAAATGGATTAATGAAGAAGGTAAAACTGTCTATGTAAAACTGCGCTGGATT ->cat_168 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACAATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_169 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAGAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGATCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAAGGAAACTATGATTTTGTTGGGAATAACTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTTTGAAACCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGTCTTACACCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAGGGGAAAACTGTTTACGTGAAACTGCGCTGGGTA ->cat_170 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACTGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTTCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_171 -GCTCGTGGTGCTGGTGCGCGCGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGGCAACATTCTCCAGAAACCTTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_172 -GCTCGCGGTGCTGGAGCGCACGGAAAGTTTGTTACTAAAAAAAGCATGAAAAAATATACAATCGCTAATTTTTTACAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGCCAACATTCTCCAGAAACATTGCGTGATCCACGTGGATTCTCTGTTAAATTTTATACGGAAGAAGGGAATTATGACTTTGTCGGAAATAACTTGCCGGTGTTCTTTATTCGTGATGCGATTAAGTTTCCGGACGTTATTCATTCCTTGAAGCCCGATCCGCGCACGAATATTCAAGATGGTAATCGCTATTGGGATTTCTTTAGTTTATCTCCAGAAGCTACCACGATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCATCTTATCGTGAAATTCGTGGATCTAGTGTTCACGCTTTTAAATGGATTAATGAAGAAGGTAAAACTGTCTATGTAAAACTGCGCTGGATT ->cat_173 -GCTCGAGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACTATCGCTAATTTTTTACAAGAAGAAGGGACAGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAACATTCACCAGAAACTTTACGTGATCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAAGGAAATTATGACTTTGTCGGAAATAACTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTCTGAAACCTGATCCACGCACGAATATTCAAGATGGTAACCGCTACTGGGATTTCTTTAGCTTGAGTCCTGAAGCTACAACTATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCTTCTTACCGCGAAATCCGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAGGGGAAAACTGTTTACGTAAAACTACGCTGGGTA ->cat_174 -GCTCGTGGAATTGGTGCGCATGGCGTATTTACAGTTAAAAATAGCATGAAGAAATACACGAAAGCTGCTTTTTTACAAGAAGAAGGACAGCAAACAGAGGTTTTCGCGCGTTTTTCTACGGTAATTCACGGCTTGCATTCTCCTGAAACGTTGCGTGATCCACGTGGTTTTGCGGTTAAATTTTACACGGAAGAGGGAAATTACGATTTTGTTGGTAATAACTTGCCTGTATTCTTTATTCGTGATGCGATTAAATTCCCGGATGTTATTCATTCTTTGAAGCCGGATCCAAGCACGAATATGCAAGATGCGAACCGTTATTGGGACTTTTTCAGCTTGACTCCTGAGGCTACGACGATGATTACATACCTATTCAGTGATGAGGGTATTCCTGCTTCGTTCCGTCAAATTCGTGGTTCGAGTGTGCATGCTTTTAAATGGACGAATGAAGAAGGCAAAACGGTTTACATCAAAATGCGTTGGGTG ->cat_175 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTATTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_176 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACCAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_177 -GCTCGTGGTGCTGGTGCGCACGGCAAATTTGTAACGAAAAAAAGCATGAAAAAATATACGAAAGCGCAATTTTTACAAGAAGAAGGAACTGAGACAGAAGTTTTTGCACGTTTTTCTACAGTAATTCATGGTCAACATTCACCAGAAACTCTTCGTGATCCACGTGGTTTTTCGGTTAAATTTTATACTGAAGAAGGTAACTATGACTTTGTTGGTAACAACTTACCTGTATTCTTTATCCGTGATGCCATCAAGTTTCCTGACGTAATTCACTCCTTGAAGCCAGATCCGCGTACCAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGTTTAACTCCGGAAGCGACGACGATGATTACTTATTTATTTAGCGATGAGGGGACTCCGGCATCTTACCGCGAAATTCGTGGTTCGAGCGTACATGCCTTTAAATGGATAAACGAAGAAGGTACGACTGTTTATGTAAAACTACGCTGGGTT ->cat_178 -GCTCGCGGTGCTGGGGCGCACGGAAAGTTTGTTACTAAAAAAAGTATGAAAAAATATACAATCGCTAATTTTTTACAAGAAGAAGGAACTGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGCCAACATTCTCCAGAAACATTGCGTGATCCACGTGGGTTCTCTGTTAAATTTTATACGGAAGAAGGGAATTATGACTTTGTCGGGAATAACTTGCCGGTGTTCTTTATTCGTGATGCGATTAAGTTTCCGGACGTTATTCATTCCTTGAAGCCCGATCCGCGCACGAATATTCAAGATGGTAATCGCTATTGGGATTTCTTTAGTTTATCTCCAGAAGCTACCACGATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCATCTTATCGTGAAATTCGTGGATCTAGTGTTCACGCTTTTAAATGGATTAATGAAGAAGGTAAAACTGTCTATGTAAAACTGCGCTGGATT ->cat_179 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACCTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_180 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGTGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_181 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAAACGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_182 -GCTCGAGGTGCTGGGGCTCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACTATCGCTAATTTTTTACAAGAAGAAGGGACAGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGATCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAAGGAAATTATGACTTTGTTGGGAATAACTTGCCGGTATTCTTCATTCGTGACGCGATTAAGTTTCCGGATGTTATTCATTCTTTGAAACCTGATCCACGCACAAATATTCAAGATGGTAACCGCTACTGGGATTTCTTTAGCTTGAGTCCTGAAGCTGCAACTATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCTTCTTATCGCGAAATCCGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAGGGGAAAACTGTTTACGTAAAACTGCGCTGGATA ->cat_183 -GCTCGAGGTGCTGGGGCTCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACTATCGCCAATTTTTTACAAGAAGAAGGGACAGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGATCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAAGGAAATTATGACTTTGTCGGAAATAACTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTCTGAAACCTGATCCACGCACGAATATTCAAGATGGTAACCGCTACTGGGATTTCTTTAGCTTGAGTCCTGAAGCTACAACTATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCTTCTTATCGCGAAATCCGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAGGGGAAAACTGTTTACGTAAAACTACGCTGGGTA ->cat_184 -GCTCGAGGTGCTGGGGCTCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACTATCGCCAATTTTTTACAAGAAGAAGGGACAGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGATCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAAGGAAATTATGACTTTGTCGGAAATAACTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTCTGAAACCTGATCCACGCACGAATATTCAAGATGGTAATCGCTACTGGGATTTCTTTAGCTTGAGTCCTGAAGCTACAACTATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCTTCTTATCGCGAAATCCGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAGGGGAAAACTGTTTACGTAAAACTACGCTGGGTA ->cat_185 -GCTCGTGGTGCTGGTGCGCACGGCAAATTTGTAACGAAAAAAAGCATGAAAAAATATACGAAAGCGCAATTTTTACAAGAAGAAGGAACTGAGACAGAAGTTTTTGCACGTTTTTCTACAGTAATTCATGGTCAACATTCACCAGAAACTCTTCGTGATCCGCGTGGTTTTTCGGTTAAATTTTATACTGAAGAAGGTAACTATGACTTTGTTGGTAACAACTTACCTGTATTCTTTATCCGTGATGCCATCAAGTTTCCTGACGTAATTCACTCCTTGAAGCCAGATCCGCGTACCAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGTTTAACTCCGGAAGCGACGACGATGATTACTTATTTATTTAGCGATGAGGGGACTCCGGCTTCTTACCGCGAAATTCGTGGTTCGAGCGTACATGCCTTTAAATGGATAAACGAAGAAGGTACGACTGTTTATGTAAAACTACGCTGGGTT ->cat_186 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTTGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_187 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTATGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTATGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_188 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGGCCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACAATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_189 -GCTCGTGGTGCTGGTGCGCACGGCAAATTTGTAACGAAAAAAAGCATGAAAAAATATACGAAAGCACAATTTTTACAAGAAGAAGGAACAGAGACAGAGGTTTTTGCGCGTTTTTCTACAGTAATCCATGGTCAACATTCACCAGAGACGCTTCGTGATCCACGTGGTTTTTCGGTTAAATTTTATACGGAAGAAGGTAACTATGACTTTGTCGGTAACAACTTACCTGTATTCTTTATCCGTGATGCTATCAATTTTCCTGACGTAATTCATTCCTTGAAGCCAGATCCACGTACTAACATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGTTTAACTCCGGAAGCGACAACGATGATTACTTATTTATTTAGTGATGAGGGAACTCCGGCATCTTACCGCGAAATTCGTGGTTCAAGCGTACATGCCTTTAAATGGATAAACGAAGAAGGTAAGACTGTTTATGTAAAACTACGCTGGGTT ->cat_190 -GCTCGTGGTGCTGGTGCGCACGGCAAATTTGTAACGAAAAAAAGCATGAAAAAATATACGAAAGCACAATTTTTACAAGAAGAAGGAACAGAGACAGAGGTTTTTGCGCGTTTTTCTACAGTAATCCATGGTCAACATTCACCAGAGACGCTTCGTGATCCACGTGGTTTTTCGGTTAAATTTTATACGGAAGAAGGTAACTATGACTTTGTCGGTAACAACTTACCTGTATTCTTTATCCGTGATGCTATCAAGTTTCCTGACGTAATTCATTCCTTGAAGCCAGATCCACATACTAACATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGTTTAACTCCGGAAGCGACAACGATGATTACTTATTTATTTAGTGATGAGGGAACTCCGGCATCTTACCGCGAAATTCGTGGTTCAAGCGTACATGCCTTTAAATGGATAAACGAAGAAGGTAAGACTGTTTATGTAAAACTACGCTGGGTT ->cat_191 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTAGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACAATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_192 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGATGGGTT ->cat_193 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_194 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_195 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGAAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_196 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGGACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_197 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCAGAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_198 -GCTCGTGGTGCTGGGGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_199 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCGGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_200 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCCCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_201 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_202 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTGCCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_203 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACCGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGTAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_204 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_205 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_206 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAACCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGTCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_207 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTGCCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGGAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_208 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAGAGCATGAAAAAATATACAATGGCTAATTTTTTACAAGAAGAAGGTGCAGAAACAGAGGTTTTTGTGCGCTTTTCAACAGTAATTCACGGTCAGCACTCTCCGGAAACGTTGCGTGATCCACGTGGGTTTTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGATTTTGTTGGCAATAATTTACCGGTATTCTTTATCCGTGATGCAATCAAGTTCCCGGACGTTATTCACTCTTTGAAGCCGGATCCGCGCACGAATATTCAAGATGGTAATCGTTACTGGGACTTCTTTAGCTTAACCCCAGAAGCTACAACGATGATTATGTACTTGTTCAGTGATGAGGGAACTCCGGCATCTTATCGCGAAATTCGCGGTTCTAGTGTGCATGCCTTTAAATGGATTAACGAAGAAGGAAAAACAGTCTATGTAAAATTACGCTGGGTT ->cat_209 -GCTCGGGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAGAGCATGAAAAAATATACAATGGCTAATTTTTTACAAGAAGAAGGGGCGGAAACAGAGGTTTTTGTTCGTTTCTCAACAGTAATTCATGGTCAACATTCTCCGGAAACTTTGCGTGATCCACGTGGGTTCTCGGTTAAGTTTTATACGGAAGAGGGAAATTATGATTTTGTTGGCAATAATTTACCAGTATTCTTCATCCGTGATGCGATCAAGTTCCCGGACGTTATTCACTCTTTGAAGCCGGATCCGCGCACGAATATTCAAGATGGCAATCGTTACTGGGACTTCTTTAGCTTAACCCCAGAAGCTACAACGATGATTATGTACTTGTTCAGTGATGAGGGAACTCCGGCATCTTATCGCGAAATTCGCGGTTCTAGTGTGCATGCCTTTAAATGGATTAACGAAGAAGGAAAGACAGTCTATGTAAAATTACGCTGGGTT ->cat_210 -GCCAGAGGCGCGGGGGCACACGGAAAGTTTGTCGTGAAAAATAGCATGAAAAAGTATACTATGGCGCATTTTTTACAAGAAGTTGGACAAGAAACAGAAGTTTTTGCTCGTTTTTCAACCGTTATTCACGGTATGCACTCTCCGGAAACTTTACGTGATCCACGTGGCTTCTCTATTAAGTTTTATACAGAAGAAGGAAATTATGATTTTGTAGGAAATAATTTACCTGTTTTCTTTATTCGTGATGCGATTAAGTTTCCTGATGTTATTCATTCTCTTAAGCCAGACCCACGAACAAACATTCAAGACCCTAATCGGTACTGGGATTTTATGAGCTTAACACCGGAAGCAGTAACGATGCTTACATATTTATTTAGTAACGAAGGAACACCAGCTTCTTACCGGGAAATTAGGGGTTCAAGTGTGCACGCGTTTAAATGGATAAATGAAGAAGGGAAAACGGTTTATGTGAAGTTACGCTGGCAA ->cat_211 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTCATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAATGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_212 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAGAAAAGTATGAAAAAATATACAATGGCTAAATTCTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGATCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAAGGAAACTATGATTTTGTTGGGAATAACTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTTTGAAACCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGTCTTACACCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_213 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGAGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCCTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_214 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAACCTTATACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_215 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGTCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_216 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_217 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCGCGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_218 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGTCTTACACCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_219 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGTCTTACACCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_220 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGGACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_221 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGGACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGAGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATAATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_222 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAGAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_223 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTTCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_224 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATAACGTGATCCACGAGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_225 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGAGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATCCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_226 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGAACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGAAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_227 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACCTTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_228 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACCTTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTATTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_229 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGTGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_230 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTAAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_231 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACAATGATTATGTACTTATTCAGTGATGAAGAAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_232 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGTCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_233 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACTTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_234 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCAGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_235 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCCAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_236 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGGACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGAGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_237 -GCTCGAGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACTATCGCTAATTTTTTACAAGAAGAAGGGACAGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGATCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAAGGAAATTATGACTTTGTCGGAAATAACTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTCTGAAACCTGATCCACGCACGAATATTCAAGATGGTAACCGCTACTGGGATTTCTTTAGCTTGAGTCCTGAAGCTACAACTATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCTTCTTATCGCGAAATCCGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAGGGGAAAACGGTTTACGTAAAACTGCGCTGGGTA ->cat_238 -GCTCGCGGTGCTGGGGCGCACGGAAAGTTTGTTACTAAAAAAAGTATGAAAAAATATACAATCGCTAATTTTTTACAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGCCAACATTCTCCAGAAACATTGCGTGATCCACGTGGGTTCTCTGTTAAATTTTATACGGAAGAAGGGAATTATGACTTTGTCGGGAATAACTTGCCGGTGTTCTTTATTCGTGATGCGATTAAGTTTCCGGACGTTATTCATTCCTTGAAGCCCGATCCGCGCACGAATATTCAAGATGGTAATCGCTATTGGGATTTCTTTAGTCTATCTCCAGAAGCTACCACGATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCATCTTATCGTGAAATTCGTGGATCTAGTGTTCACGCGTTTAAATGGATTAATGAAGAAGGTAAAACTGTCTATGTAAAACTGCGCTGGATT ->cat_239 -GCTCGTGGTGCTGGTGCGCACGGCAAATTTGTAACGAAAAAAAGCATGAAAAAATATACGAAAGCACAATTTTTACAAGAAGAAGGAACAGAGACAGAGGTTTTTGCGCGTTTTTCTACAGTAATCCATGGTCAACATTCACCAGAAACGCTTCGTGATCCACGTGGTTTTTCAGTTAAATTTTATACGGAAGAAGGTAACTATGACTTTGTCGGTAACAACTTACCTGTATTCTTTATCCGTGATGCTATCAAGTTTCCTGATGTAATTCATTCCTTGAAGCCAGATCCACATACTAACATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGTTTAACTCCGGAAGCGACAACGATGATTACTTATTTATTTAGTGATGAGGGAACTCCGGCATCTTACCGCGAAATTCGTGGTTCAAGCGTACATGCCTTTAAATGGATAAACGAAGAAGGTAAGACTGTTTATGTAAAACTACGCTGGGTT ->cat_240 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAACCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_241 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGGCGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_242 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCCGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_243 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATAGGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_244 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGCGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_245 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGTCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCTGCTTCTTATCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_246 -GCTCGTGGTGCTGGGGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_247 -GCTCGAGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACTATCGCTAATTTTTTACAAGAAGAAGGGACAGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGATCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAAGGAAATTATGACTTTGTCGGAAATAACTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTCTGAAACCTGATCCACGCACGAATATTCAAGATGGTAACCGCTACTGGGATTTCTTTAGCTTGAGTCCTGAAGCTACAACTATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCTTCTTATCGCGAAATCCGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAGGGGAAAACTGTTTACGTAAAACTGCGCTGGATA ->cat_248 -GCTCGTGGTGCTGGTGCGCACGGCAAATTTGTAACGAAAAAAAGTATGAAAAAATATACGAAAGCGCAATTTTTACAAGAAGAAGGAACAGAGACAGAGGTTTTTGCGCGTTTTTCTACAGTAATCCATGGTCAACATTCACCAGAAACGCTTCGTGATCCACGTGGTTTTTCAGTTAAATTTTATACGGAAGAAGGTAACTATGACTTTGTCGGTAACAACTTACCTGTATTCTTTATCCGTGATGCTATCAAGTTTCCTGATGTAATTCATTCCTTGAAGCCAGATCCACATACTAACATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGTTTAACTCCGGAAGCGACAACGATGATTACTTATTTATTTAGTGATGAGGGAACTCCGGCATCTTACCGCGAAATTCGTGGTTCAAGCGTACATGCCTTTAAATGGATAAACGAAGAAGGTAAGACTGTTTATGTAAAACTACGCTGGGTT ->cat_249 -GCTCGTGGTGCAGGAGCACACGGTGTTTTTGTAACTAAAAAAAGCATGAGAAAATATACGAAGGCGGCTTTCCTTGCAGAAGAAGGTATAGAAACAGAAGTTTTTGCACGATTTTCGACGGTTATTCATGGACAGCACTCGCCGGAAACTTTACGAGATCCGCGTGGCTTTTCGATCAAGTTTTATACGGAAGAGGGCAATTATGATTTTGTGGGAAATAATTTACCGGTATTCTTTATTCGCGATGCCATCAAATTCCCAGATGTGATCCATTCCTTGAAACCAGATCCAACGACAAATATCCAAGATGGAAATCGGTATTGGGATTTTTTCAGTATGTCGCCAGAAGCAACGACGGTGATCATGTATCTTTTTAGTGATGAAGGGACACCGGCTTCTTACCGTGAAATCCGGGGATCGAGCGTTCATGCTTTTAAATGGGTGAATGAAGAAGGAAAGACGGTTTACGTGAAATTACGCTGGATT ->cat_250 -GCTCGTGGTGCTGGTGCGCACGGCAAATTTGTAACGAAAAAAAGCATGAAAAAATATACGAAAGCACAATTTTTACAAGAAGAAGGAACAGAGACAGAGGTTTTTGCGCGTTTTTCTACAGTAATCCATGGTCAACATTCACCAGAGACGCTTCGTGATCCACGTGGTTTTTCGGTTAAATTTTATACGGAAGAAGGTAACTATGACTTTGTCGGTAACAACTTACCTGTATTCTTTATCCGTGATGCTATCAAGTTTCCTGACGTAATTCATTCCTTGAAGCCAGATCCACGTACTAACATTCAAGATGGCAATCGTTACTGGGACTTCTTTAGTTTAACTCCGGAAGCGACAACGATGATTACTTATTTATTTAGTGATGAGGGAACTCCGGCATCTTACCGCGAAATTCGTGGTTCAAGCGTACATGCCTTTAAATGGATAAACGAAGAAGGTAAGACTGTTTATGTAAAACTACGCTGGGTT ->cat_251 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAGAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGATCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAAGGAAACTATGATTTTGTTGGGAATAACTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAACCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGTCTTACACCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_252 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAGAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAGGAAGGAACGGAAACAGAGGTCTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGAGGTTTCTCTGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_253 -GCACGTGGAGCAGGGGCACACGGAAAGTTTGTAGTCAAAAATAGCATGAAAAAATATACGATGGCACACTTTTTACAAGAAGAAGGACAAGAAACAGAAGTTTTTGCGCGTTTTTCTACGGTTATCCATGGACAGCATTCTCCTGAGACGTTGCGTGATCCACGTGGGTTTTCGATCAAATTTTATACCGAAGAAGGAAATTATGATTTTGTTGGAAATAATTTGCCTGTATTTTTCATTCGTGATGCGATTAAATTTCCAGACGTGATTCATTCGCTCAAGCCAGATCCGGCGACGAATATCCAAGATCCAAATCGTTACTGGGACTTCATGAGCCTAACACCGGAAGCAGTCACGATGCTTACTTATCTCTTTAGCGATGAAGGGACGCCTGCTTCTTACCGCGAGATGCGTGGTTCGAGCGTACATGCTTTTAAATGGATTAATGAGGCAGGAAAAACGGTCTATGTGAAACTACGCTGGCAA ->cat_254 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTCTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGATCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAAGGAAACTATGATTTTGTTGGGAATAACTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAACCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGTCTTACACCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_255 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAGAAAAGTATGAAAAAATATACAATGGCTAAATTCTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGATCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAAGGAAACTATGATTTTGTTGGGAATAACTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTTTGAAACCTGATCCACGCACAAATATTCAAGATAGCAACCGTTACTGGGATTTCTTTAGTCTTACACCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_256 -GCACGTGGGGCAGGGGCACACGGGAAGTTTGTAGTTAAAAATAGCATGAAAAAATATACAATGGCACACTTTTTACAAGAAGAAGGACAAGAAACAGAGGTTTTTGCTCGTTTTTCTACCGTTATTCACGGACAACACTCGCCTGAGACGTTGCGAGATCCACGAGGTTTTTCGATCAAGTTTTATACAGAAGAGGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCGCTCAAGCCAGATCCAGCGACGAATATCCAAGATCCAAATCGTTACTGGGACTTCATGAGCCTAACGCCGGAAGCAGTCACGATGCTTACATATCTTTTCAGCGATGAAGGGACACCTGCTTCTTACCGTGAGATGCGCGGTTCGAGCGTACATGCTTTTAAATGGATCAATGAGGCAGGAAAAACGGTTTATGTGAAGCTACGCTGGCAA ->cat_257 -GCTCGTGGAATTGGTGCGCATGGCGTATTTACAGTTAAAAATAGCATGAAGAAATACACGAAAGCTGCTTTTTTACAAGAAGAAGGACAGCAAACAGAGGTTTTCGCGCGTTTTTCTACGGTAATTCACGGTTTGCATTCTCCTGAAACGTTGCGTGATCCACGTGGTTTTGCGGTTAAATTTTACACGGAAGAGGGAAATTACGATTTTGTTGGTAATAACTTGCCTGTATTCTTTATTCGTGATGCGATTAAATTCCCGGATGTTATTCATTCTTTGAAGCCGGATCCAAGCACGAATATGCAAGATGCGAACCGTTATTGGGACTTTTTCAGCTTGACTCCTGAGGCTACGACGATGATTACATACCTATTCAGTGATGAGGGTATTCCTGCTTCGTTCCGTCAAATTCGTGGTTCGAGTGTGCATGCTTTTAAATGGACGAATGAAGAAGGCAAAACGGTTTACATCAAAATGCGTTGGGTG ->cat_258 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGATACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATATTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_259 -GCTCGTGGTGCTGGTGCTCACGGGGAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_260 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCGGTTAAGTTTTATACAAAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_261 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_262 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTGAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_263 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTTTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTGCCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTTTGAAGCCTGATCCACGTACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_264 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACAATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCATTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_265 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_266 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACTGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACCTTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGTACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATCACATACCTATTCAGTGATGAAGGGACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGATT ->cat_267 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGATTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTGCCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_268 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGGACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_269 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTGCCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_270 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_271 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_272 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCCCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAGGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_273 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_274 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_275 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTGGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_276 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_277 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACAACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_278 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACCTTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGATT ->cat_279 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAAGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACCTTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_280 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_281 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_282 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_283 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGGACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGAGGTTTCTCCGTTAAGTTTTATACAGAAGAGGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_284 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_285 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATTCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_286 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGCGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGATACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_287 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGTAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_288 -GCTCGTGGTGCTGGTGCGCACGGGAATTTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_289 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACCGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACCTTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_290 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGATACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_291 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGATACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_292 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_293 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_294 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_295 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGCTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_296 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCGGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_297 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGTAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_298 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTGCCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_299 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTAATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_300 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_301 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGCTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_302 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACCGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGTGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_303 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACCGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGGAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_304 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACAAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_305 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGAAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCCCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_306 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAGCGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_307 -GCTCGTGGTGCTGGTGCACACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTTAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_308 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAAGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACCTTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_309 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTGAAACTGCGCTGGATT ->cat_310 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGCACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_311 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACCTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_312 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTGCTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_313 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_314 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGCAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_315 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAATTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_316 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCACACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_317 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGAAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_318 -GCTCGTGGTGCTGGTGCGCATGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACCTTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_319 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTCTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_320 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_321 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACCTTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_322 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAACCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGTCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_323 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAAAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_324 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAATGCCAGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_325 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGATACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_326 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_327 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTTTGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_328 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTTTACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_329 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_330 -GCTCGTGGTGCTGGGGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_331 -GCTCGTGGTGCTAGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGAGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_332 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCATTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_333 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACACCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_334 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_335 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACCTTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGATT ->cat_336 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGAACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_337 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACCGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_338 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACTGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCAGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_339 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCAATAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAACCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGTCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_340 -ACTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGTGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_341 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_342 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_343 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTGCCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_344 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGTGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_345 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAATATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_346 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTCCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_347 -GCACGTGGGGCAGGGGCACACGGAAAGTTTGTAGTCAAAAATAGCATGAAAAAATATACGATGGCACATTTTTTACAAGAAGAAGGGCAAGAAACAGAAGTTTTTGCTCGCTTTTCTACGGTTATCCATGGACAGCATTCTCCTGAGACGTTGCGCGATCCACGTGGATTTTCGATCAAATTTTATACAGAAGAAGGAAATTATGATTTTGTTGGAAATAATTTGCCTGTATTTTTCATTCGTGATGCGATTAAATTTCCAGACGTGATTCATTCGCTCAAGCCAGATCCAGCGACGAATATCCAAGATCCAAATCGTTACTGGGACTTCATGAGCTTAACGCCAGAAGCGGTCACGATGCTTACTTATCTCTTTAGCGATGAAGGGACGCCTGCTTCTTACCGCGAGATGCGTGGTTCGAGCGTACATGCTTTTAAATGGATTAATGAGGCAGGAAAAACGGTCTATGTGAAGCTACGTTGGCAA ->cat_348 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTACTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_349 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGAAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_350 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCCCGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACAATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_351 -GCTCGCGGTGCTGGGGCGCACGGAAAGTTTGTTACTAAAAAAAGTATGAAAAAATATACAATCGCTAATTTTTTACAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGCCAACATTCTCCAGAAACATTGCGTGATCCACGTGGGTTCTCTGTTAAATTTTATACGGAAGAAGGGAATTATGACTTTGTCGGGAATAACTTGCCGGTGTTCTTTATTCGTGATGCGATTAAGTTTCCGGACGTTATTCATTCCTTGAAGCCCGATCCGCGCACGAATATTCAAGATGGTAATCGCTATTGGGATTTCTTTAGTTTATCTCCAGAAGCTACCACGATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCATCTTATCGTGAAATTCGTGGATCTAGTGTTCACGCGTTTAAATGGATTAATGAAGAAGGTAAAACTGTCTATGTAAAACTGCGCTGGATT ->cat_352 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGTTCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_353 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCCCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_354 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAACATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_355 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGGTTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_356 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCTGCTTCTTATCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_357 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGAAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGAGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_358 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAGGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_359 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAATGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGAGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_360 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGGACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGAGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACTGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_361 -GCTCGAGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACTATCGCTAATTTTTTACAAGAAGAAGGGACAGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGATCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAAGGAAATTATGACTTTGTCGGAAATAACTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTCTGAAACCTGATCCACGCACGAATATTCAAGATGGTAACCGCTACTGGGATTTCTTTAGCTTGAGTCCTGAAGCTACAACTATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCTTCTTATCGCGAAATCCGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAGGGGAAAACGGTTTACGTGAAACTGCGCTGGGTA ->cat_362 -GCTCGAGGTGCTGGGGCTCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACTATCGCTAATTTTTTACAAGAAGAAGGGACAGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGATCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAAGGAAACTATGATTTTGTTGGGAATAACTTTCCGGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTCTGAAACCTGATCCACGCACGAATATTCAAGATGGTAACCGCTACTGGGATTTCTTTAGCTTGAGTCCTGAAGCTACAACTATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCTTCTTATCGCGAAATCCGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAGGGGAAAACGGTTTACGTGAAACTGCGCTGGGTA ->cat_363 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTAATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_364 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTAATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAATGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_365 -GTTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_366 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATCTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_367 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_368 -GCTCGTGGTGCTGGTGCGTACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_369 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACCGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_370 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGAAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_371 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTGCCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGGAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGTGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_372 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_373 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAAGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_374 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTGCCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_375 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_376 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCCCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_377 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAGATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTGCCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_378 -GCTCGTGGTGCTGGTGCGCACGGAAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTAATCCACGTACAAATATTCAAGATGGTAACCGTTACTGGGAGTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCTGCTTCTTATCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_379 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTGCCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_380 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAATAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_381 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_382 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTTCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_383 -GCTCGTGGTGCTGGGGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGAAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_384 -GCTCGTGGTGCTGGTGCGCGCGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGGCAACATTCTCCAGAAACCTTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGTCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_385 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTGCGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_386 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACAAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_387 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGATGGGTT ->cat_388 -GCTCGTGGTGCTGGGGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGAAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_389 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACTCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_390 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTCTTTTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_391 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_392 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGTGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_393 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGACTTTGTCGGAAATAATTTGCCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_394 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAATGGAAACAGAAGTTTTTGCTCGTTTTTCAACGGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCTGCTTCTTATCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_395 -GCTCGTGGTACTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAATGGAAACAGAAGTTTTTGCTCGTTTTTCAACGGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTGTTCAGTGATGAAGGAACGCCTGCTTCTTATCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_396 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCTGCTTCTTATCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_397 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGACAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGTACAAATATTCAAGATGGTAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCTGCTTCTTATCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_398 -GCTCGTGGTGCTGGTGCGCACGGAAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGTACAAATATTCAAGATGGTAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCTGCTTCTTATCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_399 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_400 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGTACAAATATTCAAGATGGTAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCTGCTTCTTATCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_401 -GCTCGCGGTGCTGGGGCGCACGGAAAGTTTGTTACTAAAAAAAGTATGAAAAAATATACAATCGCTAATTTTTTACAAGAAGAAGGAACTGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGCCAACATTCTCCAGAAACATTGCGTGATCCACGTGGGTTCTCTGTTAAATTTTATACGGAAGAAGGGAATTACGACTTTGTCGGAAATAACTTGCCGGTGTTCTTTATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCCGATCCGCGCACGAATATTCAAGATGGTAATCGCTATTGGGATTTCTTTAGTTTATCTCCAGAAGCTACCACGATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCATCTTATCGTGAAATTCGTGGATCTAGTGTTCACGCGTTTAAATGGATTAATGAAGAAGGTAAAACTGTCTATGTAAAACTGCGCTGGATT ->cat_402 -GCTCGCGGTGCTGGGGCGCACGGAAAGTTTGTTACTAAAAAAAGTATGAAAAAATATACAATCGCTAATTTTTTACAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGTCAACATTCTCCAGAAACATTGCGTGATCCACGTGGGTTCTCTGTTAAATTTTATACGGAAGAAGGGAATTATGACTTTGTCGGGAATAACTTGCCAGTGTTCTTTATTCGTGATGCGATTAAGTTTCCAGACGTTATTCATTCCTTGAAGCCCGATCCGCGCACGAATATTCAAGATGGTAATCGCTATTGGGATTTCTTTAGTCTATCTCCAGAAGCTACCACGATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCATCTTATCGTGAAATTCGTGGATCTAGTGTTCACGCGTTTAAATGGATTAATGAAGAAGGTAAAACTGTCTATGTAAAACTGCGCTGGATT ->cat_403 -GCTCGAGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACTATTGCTAATTTTTTACAAGAAGAAGGGTCAGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGATCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAAGGAAATTATGACTTTGTTGGGAATAACTTGCCGGTATTCTTTATTCGTGACGCGATTAAGTTTCCGGATGTTATTCATTCTCTGAAGCCTGATCCACGCACAAATATTCAAGATGGTAACCGCTACTGGGATTTCTTTAGCTTGAGTCCTGAAGCTACAACTATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCTTCTTATCGCGAAATCCGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAGGGGAAAACTGTTTACGTGAAACTGCGCTGGGTA ->cat_404 -GCTCGAGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACTATTGCTAATTTTTTACAAGAAGAAGGGACAGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGATCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAAGGAAATTATGACTTTGTTGGGAATAACTTGCCGGTATTCTTTATTCGTGACGCGATTAAGTTTCCGGATGTTATTCATTCTCTGAAGCCTGATCCACGCACAAATATTCAAGATGGTAACCGCTACTGGGATTTCTTTAGCTTGAGTCCTGAAGCTACAACTATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCTTCTTATCGCGAAATCCGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAGGGGAAAACTGTTTACGTGAAACTGCGCTGGGTA ->cat_405 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGGACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGAGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_406 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTTCGCTGGATT ->cat_407 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATACACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_408 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCAGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_409 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGGACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATTCACGAGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_410 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGGACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGAGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_411 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGCTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_412 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACCTTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGTACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGGACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGATT ->cat_413 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_414 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGAACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGTAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_415 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACCTTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGTACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_416 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCCAACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACAATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_417 -GCTCGTGGTGCTGGTGCGCACGGCAAATTTGTAACGAAAAAAAGTATGAAAAAATATACGAAAGCACAATTTTTACAAGAAGAAGGAACAGAGACAGAGGTTTTTGCGCGTTTTTCTACAGTAATCCATGGTCAACATTCACCAGAGACGCTTCGTGATCCACGTGGTTTTTCAGTTAAATTTTATACGGAAGAAGGTAACTATGACTTTGTCGGTAACAACTTACCTGTATTCTTTATCCGTGATGCTATCAAGTTTCCTGACGTAATTCATTCCTTGAAGCCAGATCCACATACTAACATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGTTTAACTCCGGAAGCGACAACGATGATTACTTATTTATTTAGTGATGAGGGAACTCCGGCATCTTACCGCGAAATTCGTGGTTCAAGCGTACATGCCTTTAAATGGATAAACGAAGAAGGTAAGACTGTTTATGTAAAACTACGCTGGGTT ->cat_418 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACAATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTTAAACTGCGCTGGGTT ->cat_419 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGAGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCTGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_420 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGCTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACAATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_421 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGAGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_422 -GCTCGAGGTGCTGGGGCTCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACTATCGCTAATTTTTTACAAGAAGAAGGGACAGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGACCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAGGGAAATTATGATTTTGTTGGGAATAACTTGCCGGTATTCTTTATTCGTGACGCGATTAAGTTTCCGGATGTTATTCATTCTTTGAAACCTGATCCACGCACGAATATTCAAGATGGTAACCGCTACTGGGATTTCTTTAGCTTGAGTCCTGAAGCTACAACTATGATTATGTATTTATTTAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGTGGCTCTAGTGTTCATGCCTTTAAATGGATTAATGAAGAGGGGAAAACTGTTTACGTAAAACTACGCTGGGTA ->cat_423 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGATCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAAGGAAACTATGATTTTGTTGGGAATAACTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAACCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGTCTTACACCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_424 -GCTCGAGGTGCTGGGGCTCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACTATCGCTAATTTTTTACAAGAAGAAGGGATAGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGACCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAGGGAAATTATGATTTTGTTGGGAATAACTTGCCGGTATTCTTTATTCGTGACGCGATTAAGTTTCCGGATGTTATTCACTCTTTGAAACCTGATCCACGCACGAATATTCAAGATGGTAACCGCTACTGGGATTTCTTTAGCTTGAGTCCTGAAGCTACAACTATGATTATGTATTTATTTAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGTGGCTCTAGTGTTCATGCCTTTAAATGGATTAATGAAGAAGGGAAAACTGTTTACGTAAAACTACGCTGGGTA ->cat_425 -GCTCGAGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACTATCGCTAATTTTTTACAAGAAGAAGGGACAGAAACAGAGGTTTTTGCTCGTTTTTCAACGGTAATTCATGGTCAGCATTCACCAGAAACTTTGCGTGATCCGCGTGGATTCTCTGTTAAGTTTTATACTGAAGAAGGAAATTATGACTTTGTTGGGAATAACTTGCCGGTATTCTTTATTCGTGACGCGATTAAGTTTCCGGATGTTATTCATTCTCTGAAGCCTGATCCACGCACGAATATTCAAGATGGTAACCGCTACTGGGATTTCTTTAGCTTGAGTCCTGAAGCTACAACTATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCTTCTTATCGCGAAATCCGTGGCTCTAGTGTTCATGCGTTTAAATGGATTAATGAAGAGGGGAAAACTGTTTACGTAAAACTACGCTGGGTA ->cat_426 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCGGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGATT ->cat_427 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCCGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAATGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_428 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGACTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_429 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACCTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGTAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_430 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGATTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_431 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGTGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_432 -GCTCGTGGTGCTGGTGCGCACGGTAAATTTGTAACGAAAAAAAGCATGAAAAAATATACGAAAGCGCAATTTTTACAAGAAGAAGGAACTGAGACGGAAGTTTTTGCACGTTTTTCTACAGTAATTCATGGTCAACATTCACCAGAAACTCTTCGTGATCCACGTGGTTTTTCGGTTAAATTTTATACTGAAGAAGGTAACTATGACTTTGTCGGTAACAACTTACCTGTATTCTTTATCCGTGATGCCATCAAGTTTCCTGACGTAATTCACTCCTTGAAGCCAGATCCGCGTACCAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGTTTGACTCCGGAAGCGACAACGATGATTACTTATTTATTTAGTGATGAGGGGACTCCAGCATCTTACCGCGAAATTCGTGGTTCAAGCGTACATGCCTTTAAATGGATTAACGCAGAAGATAAGACTGTTTATGTAAAATTACGCTGGGTT ->cat_433 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCATTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_434 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGAACCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_435 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACCTTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGTACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGGACACCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGATT ->cat_436 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCCCCAGAAACATTACGTGACCCACGAGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCGAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_437 -GCTCGTGGTGCAGGAGCACACGGTGTTTTTGTAACTAAAAAAAGCATGAAAAAATATACGAAGGCGGCTTTTTTAGCAGAAGAAGGTACAGAAACAGAAGTTTTTGCACGATTTTCGACGGTTATTCATGGACAGCACTCGCCGGAAACTTTACGAGATCCGCGCGGCTTTTCGATCAAGTTTTATACGGAAGAGGGCAATTATGATTTTGTGGGAAATAACTTACCAGTATTCTTTATCCGAGATGCCATCAAATTCCCAGATGTGATCCATTCCTTAAAACCAGATCCAACGACAAATATCCAAGATGGAAATCGGTATTGGGATTTTTTCAGCATGTCGCCAGAAGCAACGACGATGATCATGTATCTTTTCAGTGATGAAGGGACACCGGCTTCTTACCGTGAAATCCGAGGATCGAGCGTTCATGCTTTTAAATGGGTGAATGAAGAAGGAAAGACGGTTTACGTGAAATTACGATGGATT ->cat_438 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGCTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTACCTGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGGAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGGTT ->cat_439 -GCTCGCGGTGCTGGGGCGCACGGAAAGTTTGTTACTAAAAAAAGTATGAAAAAATATACAATCGCTAATTTTTTACAAGAAGAAGGAACTGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGCCAACATTCTCCAGAAACATTGCGTGATCCACGTGGGTTCTCTGTTAAATTTTATACGGAAGAAGGGAATTATGACTTTGTCGGAAATAACTTGCCGGTGTTCTTTATTCGTGATGCGATTAAGTTTCCGGACGTTATTCATTCCTTGAAGCCCGATCCGCGCACGAATATTCAAGACGGTAATCGCTATTGGGATTTCTTTAGTTTATCTCCAGAAGCTACCACGATGATTATGTATTTATTTAGTGATGAAGGTACTCCGGCATCTTATCGTGAAATTCGTGGATCTAGTGTTCACGCTTTTAAATGGATTAATGAAGAAGGTAAAACTGTCTATGTAAAACTGCGCTGGATT ->cat_440 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTCACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGACCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCTGTCTTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCTTTGAAGCCTGATCCACGTACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACGCCGGAAGCTACGACGATGATCACATACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGCAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_441 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGAAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_442 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGATCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_443 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACAATGATTATGTACTTATTCAGTGATGAAGGAATGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_444 -GCTCGTGGTGCTGGTGCTCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGACCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_445 -GCTCGTGGTGCTGGTGCGCACGGAAAATTTGTCACTAAAAAAAGTATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACCTTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGTACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATCACATACCTATTCAGCGATGAAGGCACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGATT ->cat_446 -GCTCGCGGTGCTGGAGCGCACGGAAAGTTTGTTACTAAAAAAAGTATGAAAAAATATACAATCGCTAATTTTTTACAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGCCAACATTCTCCAGAAACATTGCGTGATCCACGTGGATTCTCTGTTAAATTTTATACGGAAGAAGGGAATTATGACTTTGTCGGAAATAACTTGCCGGTGTTCTTTATTCGTGATGCGATTAAGTTTCCGGACGTTATTCATTCCTTGAAGCCCGATCCGCGCACGAATATTCAAGATGGTAATCGCTATTGGGATTTCTTTAGTTTATCTCCAGAAGCTACCACGATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCATCTTATCGTGAAATTCGTGGATCTAGTGTTCACGCTTTTAAATGGATTAATGAAGAAGGTAAAACTGTCTATGTAAAACTGCGCTGGATT ->cat_447 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGACAACATTCTCCAGAAACCTTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAGGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGTACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATCACATACCTATTCAGTGATGAAGGGACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTCTATGTAAAACTGCGCTGGATT ->cat_448 -GCTCGTGGTGCTGGGGCGCACGGAAAGTTTGTTACTAAAAAAAGCATGAAAAAATATACAATCGCTAATTTTTTACAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGCCAACATTCTCCAGAAACATTGCGTGATCCACGTGGGTTCTCTGTTAAATTTTATACGGAAGAAGGGAATTACGACTTTGTCGGAAATAACTTGCCGGTGTTCTTTATTCGTGATGCGATTAAGTTTCCAGACGTTATTCATTCCTTGAAGCCCGATCCGCGCACGAATATTCAAGATGGTAATCGCTATTGGGATTTCTTTAGTTTATCTCCAGAAGCTACCACGATGATTATGTATTTATTTAGTGATGAAGGAACTCCGGCATCTTATCGTGAAATTCGTGGATCTAGTGTTCACGCTTTTAAATGGATTAATGAAGAAGGTAAAACTGTCTATGTAAAACTGCGCTGGATT ->cat_449 -GCTCGTGGTGCTGGTGCTCACGGGCAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGGGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_450 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGGACGGAAACAGAGGTTTTTGCTCGTTTTTCAACTGTAATTCATGGACAACATTCTCCAGAAACATTACGTGATCCACGAGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTTTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_451 -GCTCGTGGTGCTGGTGCGCACGGTAAATTTGTTACTAAAAAAAGCATGAAACAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_452 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACTATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_453 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACAAAGAAGGCAAAACAGTTTATGTAAAATTGCGCTGGGTT ->cat_454 -GCTCGTGGTGCTGGTGCGCACGGGAAATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTAACCCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT ->cat_455 -GCTCGTGGTGCTGGCGAGCACGGGAAATTTGTCACTAAGAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACGGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACAGAAGAAGGGAATTATGATTTTGTCGGAAATAATTTGCCGGTATTCTTCATTCGTGATGCGATTAAGTTTCCGGATGTTATTCATTCCTTGAAGCCTGATCCACGCACAAATATTCAAGATGGCAACCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAATACGTGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGATT ->cat_456 -GCTCGTGGTGCTGGTGCGCACGGGAGATTTGTTACTAAAAAAAGCATGAAAAAATATACAATGGCTAAATTTTTGCAAGAAGAAGGAACGGAAACAGAGGTTTTTGCTCGTTTTTCAACAGTAATTCATGGGCAACATTCTCCAGAAACATTACGTGATCCACGCGGTTTCTCCGTTAAGTTTTATACGGAAGAGGGAAATTATGACTTTGTCGGAAATAATTTGCCAGTATTCTTCATTCGTGATGCGATTAAGTTTCCAGATGTTATTCATTCCTTGAAGCCTGACCCGCGCACAAATATTCAAGATGGCAATCGTTACTGGGATTTCTTTAGCCTTACACCGGAAGCTACGACGATGATTATGTACTTATTCAGTGATGAAGGAACGCCGGCTTCTTACCGCGAAGTCCGGGGCTCTAGTGTTCATGCGTTCAAATGGATTAACGAAGAAGGCAAAACAGTTTATGTAAAACTGCGCTGGGTT diff --git a/test/MLST_listeria/dapE.fasta b/test/MLST_listeria/dapE.fasta deleted file mode 100644 index f3170c9..0000000 --- a/test/MLST_listeria/dapE.fasta +++ /dev/null @@ -1,1214 +0,0 @@ ->dapE_1 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_2 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGGCACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_3 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_4 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_5 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_6 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_7 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_8 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_9 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATATCGTTAAATCC ->dapE_10 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_11 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCATTAAATCC ->dapE_12 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGTGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAATCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_13 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_14 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_15 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_16 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCATTAAATCC ->dapE_17 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_18 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGTAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACTGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_19 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACACAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_20 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACCTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_21 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_22 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGTAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_23 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_24 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_25 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_26 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAAATAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_27 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATATGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_28 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCGATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_29 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_30 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCTGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACAAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGTGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_31 -TTGCAAGAGTTGTTAGCTGGATACGGAATTGAGTCTGAAAAGATACATTATGATGTAGACAGAGCAAGCCTAGTAAGCGAAATTGGTTCCAGTTACGAGAAGATTTTGGCATTTTCAGGGCATATGGATGTGGTTGATGCGGGGGATGTGTCTAAGTGGAAATTCCCGCCTTTTGAAGCGACAGAACATGAAGGGAAACTATATGGACGTGGCGCGACAGATATGAAGTCAGGTCTAGCAGCGATGGTTATTGCAATGATTGAACTTCAGGAAGAAAAACAAAAACTAAACGGTAAGATCAGATTATTAGCAACAGTTGGTGAAGAAGTCGGCGAACTTGGAGCAGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCAAGTGGACACAGAATCGTATTTGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_32 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTAAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_33 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTAAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_34 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAATGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_35 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTAAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGCGGACACAGAATCGTATATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_36 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_37 -TTACAGAAGTTGTTTGCTGTGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGCGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_38 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCATTAAATCC ->dapE_39 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACAGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_40 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAAGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_41 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGTAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACTGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_42 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGCTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_43 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGTGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_44 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTAGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_45 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGGAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGCCTAGCTGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGCGGACACAGAATCGTATATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_46 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_47 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAATAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAACATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_48 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCTGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_49 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCTGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAACATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCG ->dapE_50 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCG ->dapE_51 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_52 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAATAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAACATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCG ->dapE_53 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_54 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGTCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_55 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATAC ->dapE_56 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATAATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_57 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAATCTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_58 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGAGGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCATTAAATCC ->dapE_59 -CTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_60 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTTGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_61 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATCAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_62 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_63 -TTGCAAGAGTTGTTAGCTGGATACGGAATTGAGTCTGAAAAGGTACATTATGATGTAGACAGAGCAAGCCTAGTAAGCGAAATTGGTTCCAGTTACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGGGATGTGTCTAAGTGGAAATTCCCGCCTTTTGAAGCGACAGAACATGAAGGAAAACTATATGGACGTGGCGCGACAGATATGAAGTCAGGTCTAGCAGCGATGGTTATTGCAATGATTGAACTTCAGGAAGAAAAACAAAAACTAAACGGTAAGATCAGATTATTAGCAACAGTTGGTGAAGAAGTCGGCGAACTTGGAGCAGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCAAGTGGACACAGAATCGTATATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_64 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAGCGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_65 -TTGCAAGAGTTGTTAGCTGGATACGGAATTGAGTCTGAAAAGGTAGATTATGATGTAGACAGAGCAAGCCTAGTAAGCGAAATTGGTTCCAGTTACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGGGATGTGTCTAAGTGGAAATTCCCGCCTTTTGAAGCGGACAGAACATGAAGGAAAACTATATGGACGTGGCGCGACAGATATGAAGTCAGGTCTAGCAGCGATGGTTATTGCAATGATTGAACTTCAGGAAGAAAAACAAAAACTAAACGGTAAGATCAGATTATTAGCAACAGTTGGTGAAGAAGTCGGCGAACTTGGAGCAGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCAAGTGGACACAGAATCGTATATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_66 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCCGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_67 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_68 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAGATTAGATTATTAGCAACTGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_69 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCAAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_70 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAATTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGGCACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_71 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_72 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCTGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_73 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_74 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_75 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATCAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_76 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAACCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_77 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACGGAGCACGAAGGAAAACTTTACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTAATTGCGATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_78 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGCTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_79 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAATCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCACGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGCCTAGCTGCGATGGTTATTGCAATGATTGAGCTTCACGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACACAAAAAGGTTATGCAGATGATTTAGATGGTCTTATTATCGGCGAACCGAGTGGACACAGAATCGTATATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_80 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGATGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCGGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAAATAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_81 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCAACGGAGCACGAAGGAAAACTTTACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTAATTGCGATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAGATTAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACGCTAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGTGGGCACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_82 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAATCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCACGAAGGGAAAATATACGGACGCGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACAGAATCGTATATGCGCATAAAGGTTCCATCAATTATACCGTTAAATCC ->dapE_83 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATATGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCTGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAGATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_84 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_85 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGTAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACTGTTGGTGAAGAAGTCGGTGAACTTGGAGTTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_86 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTAAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGGGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGGTGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_87 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAGTGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_88 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTAAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTAAAATTGGTTCTAATGATGGAAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGCCTAGCTGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGCGGACACAGAATCGTATATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_89 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_90 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAATCTGAAAAAGTACAATATGATGTAGACCGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCATTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACCGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_91 -TTACAGAAGTTGTTTGCTGTGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_92 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGTAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_93 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCGGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAAATATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_94 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_95 -TTACAGAAGTTGTTAGCTGAATATGGGATTGAATCTGAGAAAGTGCAGTATGATGAAGACAGGGCAAGTCTTGTTAGTGAAATAGGTTCTAGCGATGAGCAAGTTTTGGCTTTTTCAGGGCATATGGATGTGGTTGATGCAGGAGATGTGTCAAAATGGAAGTTTCCTCCTTTTGAAGCAACGGAGCATGAAGGGAGAATATACGGCCGTGGTGCCACGGATATGAAGTCAGGTTTAGCAGCGATGGTCATTGCAATGATTGAGCTGCAAGAAGAAAAGCAAAAACTAAATGGGAAAATTAGATTATTAGCTACAGTTGGTGAAGAAGTTGGCGAACTTGGAGCAGAACAACTAACTCAAAAAGGTTATGCAGATGACTTAGATGGTTTAATTATTGGGGAACCAAGTGGACACCGAATTGTATATGCGCATAAAGGTTCGATTAATTATACCGTTAAATCT ->dapE_96 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTAAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGCCTAGCTGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_97 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTAAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACAGAATCGTATATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_98 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATATGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_99 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCAAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_100 -TTGCAAGAGTTGTTAGCTGGATACGGAATTGAGTCTGAAAAGGTAGATTATGATGTAGACAGAGCAAGCCTAGTAAGCGAAATTGGTTCCAGTTACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGGGATGTGTCTAAGTGGAAATTCCCGCCTTTTGAAGCGACAGAACATGAAGGAAAACTATATGGACGTGGCGCGACAGATATGAAGTCAGGTCTAGCAGCGATGGTTATTGCAATGATTGAACTTCAGGAAGAAAAACAAAAACTAAACGGTAAGATCAGATTATTAGCAACAGTTGGTGAAGAAGTCGGCGAACTTGGAGCAGAACAACTAAGGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCAAGTGGACACAGAATCGTATATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_101 -TTGCAAGAGTTGTTAGCTGGATACGGAATTGAGTCTGAAAAGGTAGATTATGATGTAGACAGAGCAAGCCTAGTAAGCGAAATTGGTTCCAGTTACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGGGATGTGTCTAAGTGGAAATTCCCGCCTTTTGAAGCGACAGAACATGAAGGGAAACTATATGGACGTGGCGCGACAGATATGAAGTCAGGTCTAGCAGCGATGGTTATTGCAATGATTGAACTTCAGGAAGAAAAACAAAAACTAAACGGTAAGATCAGATTATTAGCAACAGTTGGTGAAGAAGTCGGCGAACTTGGAGCAGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCAAGTGGACACAGAATCGTATATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_102 -TTGCAAGAGTTGTTAGCTGGATACGGAATTGAGTCTGAAAAGGTACATTATGATGTAGACAGAGCAAGCCTAGTAAGCGAAATTGGTTCCAGTTACGAGAAGATTTTGGCATTTTCAGGGCATATGGATGTGGTTGATGCGGGGGATGTGTCTAAGTGGAAATTCCCGCCTTTTGAAGCGACAGAACATGAAGGGAAACTATATGGACGTGGCGCGACAGATATGAAGTCAGGTCTAGCAGCGATGGTTATTGCAATGATTGAACTTCAGGAAGAAAAACAAAAACTAAACGGTAAGATCAGATTATTAGCAACAGTTGGTGAAGAAGTCGGCGAACTTGGAGCAGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCAAGTGGACACAGAATCGTATATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_103 -TTGCAAGAGTTGTTAGCTGGATACGGAATTGAGTCTGAAAAGGTACATTATGATGTAGACAGAGCAAGCCTAGTAAGCGAAATTGGTTCCAGTTACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGGGATGTGTCTAAGTGGAAATTCCCGCCTTTTGAAGCGACAGAACATGAAGGGAAACTATATGGACGTGGCGCGACAGATATGAAGTCAGGTCTAGCAGCGATGGTTATTGCAATGATTGAACTTCAGGAAGAAAAACAAAAACTAAACGGTAAGATCAGATTATTAGCAACAGTTGGTGAAGAAGTCGGCGAACTTGGAGCAGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCAAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_104 -TTGCAAGAGTTGTTAGCTGGATACGGAATTGAGTCTGAAAAGGTACATTATGATGTAGACAGAGCAAGCCTAGTAAGCGAAATTGGTTACAGTTACGAGAAGATTTTGGCATTTTCAGGGCATATGGATGTGGTTGATGCGGGGGATGTGTCTAAGTGGAAATTCCCGCCTTTTGAAGCGACAGAACATGAAGGGAAACTATATGGACGTGGCGCGACAGATATGAAGTCAGGTCTAGCAGCGATGGTTATTGCAATGATTGAACTTCAGGAAGAAAAACAAAAACTAAACGGTAAGATCAGATTATTAGCAACAGTTGGTGAAGAAGTCGGCGAACTTGGAGCAGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATCATCGGCGAACCAAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_105 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_106 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTACATTAATTATACCGTTAAATCC ->dapE_107 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGAAGACAGAGTTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_108 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTAAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACAGAATCGTATATGCGCATAAAGGTTCCATCAATTATACCGTTAAATCC ->dapE_109 -TTGCAAAAATTGTTAGCGGAATACAACATCCAAGCTGAAAAAGTGCAGTATGACAAGGATCGCGCAAGCTTAGTAAGTGAAATAGGCGCGGAAAAAGGTCGCGTTTTAGCTTTTTCTGGACATATGGATGTTGTTGAAGCGGGAGATGTTTCTAAGTGGACTTTTCCGCCATTTGAAGCGACAGAATCGGATGGCAAAATTTATGGTCGCGGTGCTACTGATATGAAATCTGGTTTGGCCGCGATGGTCATTGCGATGATTGAACTCCATGAAGAAAAAACTAAATTAAATGGGAAAATTAAATTATTGGCGACAGTTGGAGAAGAAGTTGGAGAACTCGGGGCGGAACAACTTACCACACAAGGTTATGCGGATGATTTAGATGGTTTAATTATCGGCGAGCCAAGTGGACATCGGATTGTTTATGCGCATAAAGGTTCAATTAATTACACGATTAAATCG ->dapE_110 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTAAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_111 -TTGCAAAAATTATTAGCGGAATACAACATCAAAGCTGAGAAAGTGCAGTATGACAAGGATCGCGCAAGCCTTGTAAGTGAAGTAGGTACAGATAATGGCCCTGTTTTAGCTTTTTCTGGACATATGGATGTTGTTGATGCGGGTGATGTTTCCAAGTGGACTTTTCCTCCATTTGAAGCAACGGAATCAGATGGTAAAATTTACGGACGTGGTGCTACTGATATGAAATCAGGGCTGGCTGCGATGGTCATTGCAATGATTGAACTCCATGAAGAAAAAACGAAATTAAATGGGAAAATCAAATTATTGGCGACAGTTGGAGAAGAAGTTGGAGAACTCGGCGCGGAACAACTTACGACAAAAGGTTATGCGGATGATTTAGATGGTTTAATTATCGGCGAGCCAAGTGGACATCGGATTGTTTATGCGCATAAAGGTTCAATTAATTACACGATTAAATCG ->dapE_112 -TTACAGAAGTTGTTAGCTGAATATGGGATTGAATCTGAGAAAGTGCAGTATGATGAAGATAGGGCAAGTCTTGTTAGTGAAATAGGTTCTAGCGATGAGCAAGTTTTGGCTTTTTCAGGGCATATGGATGTGGTTGATGCAGGAGATGTGTCAAAATGGAAGTTTCCTCCTTTTGAAGCAACGGAGCATGAAGGGAGAATATACGGCCGTGGTGCCACGGATATGAAGTCAGGTTTAGCAGCGATGGTCATTGCAATGATTGAGCTGCAAGAAGAAAAGCAAAAACTAAATGGGAAAATTAGATTATTAGCTACAGTTGGTGAAGAAGTTGGCGAACTTGGAGCAGAACAACTAACTCAAAAAGGTTATGCAGATGACTTAGATGGTTTAATTATTGGGGAACCAAGTGGACACCGAATTGTATATGCGCATAAAGGTTCGATTAATTATACCGTTAAATCT ->dapE_113 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAGTGGCAAAATTAGATTATTAGCAACGGTTGGGGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_114 -TTGCAAGAGTTGTTAGCTGGATACGGAATTGAGTCTGAAAAGGTAGATTATGATGTAGACAGAGCAAGCCTAGTAAGCGAAATTGGTTCCAGTTACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGGGATGTGTCTAAGTGGAAATTCCCGCCTTTTGAAGCGACAGAACATGAAGGAAAACTATATGGACGTGGCGCGACAGATATGAAGTCAGGTCTAGCAGCGATGGTTATTGCAATGATTGAACTTCAGGAAGAAAAACAAAAACTAAACGGTAAGATCAGATTATTAGCAACAGTTGGTGAAGAAGTCGGCGAACTTGGAGCAGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCAAGTGGACACAGAATCGTATATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_115 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACTGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_116 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGTAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_117 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGAGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_118 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_119 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGACGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_120 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACATAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGATGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAATTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAGCCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_121 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGTATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_122 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGGCGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGTCATATGGATGTAGTTGATGCGGGTGATGTCCCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_123 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAAGTTCCATTAATTATACCGTTAAATCC ->dapE_124 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGAGGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_125 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_126 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAATCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_127 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTAGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_128 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCAGAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_129 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAATGGTTCCATTAATTATACCGTTAAATCC ->dapE_130 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGCCTAGCTGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGCGGACACAGAATCGTATATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_131 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_132 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGATGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_133 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGGGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_134 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGGCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_135 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACACAGATGATTTGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_136 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGTCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_137 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGTGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_138 -TTGCAAGAGTTGTTAGCTGGATACGGAATTGAGTCTGAAAAGGTACATTATGATGTAGACAGAGCAAGCCTAGTAAGCGAAATTGGTTCCAGTTACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGGGATGTGTCTAAGTGGAAATTCCCGCCTTTTGAAGCGACAGAACATGAAGGAAAACTATATGGACGTGGCGCGACAGATATGAAGTCAGGTCTAGCAGCGATGGTTATTGCAATGATTGAACTTCAGGAAGAAAAACAAAAACTAAACGGTAAGATCAGATTATTAGCAACAGTTGGTGAAGAAGTCGGCGAACTTGGAGCAGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCAAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_139 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCTGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACAAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_140 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGGCACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_141 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCAGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCATTAAATCC ->dapE_142 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGATGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCGGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCACAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAAATAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTAATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_143 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGAGGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_144 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCTGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAGATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_145 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGAGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_146 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAAATATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_147 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACATAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_148 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_149 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACATAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_150 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGATGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCG ->dapE_151 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACCATACGGACGTGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_152 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAGCTATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACATCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_153 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAATGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_154 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCAGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_155 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGATGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAGTGGCAAAATTAGATTATTAGCAACGGTTGGGGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_156 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGATGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAGATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_157 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCACGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAGTGGCAAAATTAGATTATTAGCAACGGTTGGGGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_158 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_159 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGCCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCACGAAGGGAAAATATACGAACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAGTGGCAAAATTAGATTATTAGCAACGGTTGGGGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_160 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_161 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCAAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_162 -TTGCAAAAATTGTTAGCAGAATACAGCATCCAAGCTGAAAAGGTGCACTATGACACAGATCGTGCGAGTCTTATAAGTGAAATCGGCGCGGAGCAAGGACGAGTGTTGGCTTTTTCCGGACATATGGATGTGGTTGATGCTGGGGATGTTTCTAAATGGACTTTTCCGCCATTTGAAGCAACAGAATCGGATGGTAAAATATATGGCCGTGGTTCTACTGATATGAAATCTGGTTTAGCCGCGATGGTCATTGCGATGATTGAACTTCATGAAGAAAAAACCAAATTAAATGGGAAAATCAAATTATTAGCGACAGTTGGAGAAGAAGTTGGAGAGCTTGGAGCGGAACAACTTACCACACAAGGTTATGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGTGGACATCGAATTGTTTATGCACATAAAGGTTCGATTAATTACACGGTCAAATC ->dapE_163 -TTGCAAAAATTGTTAGCGGAATACAGCATCCAAGCTGAAAAGGTGCACTATGACACAGATCGTGCGAGTCTTATAAGCGAAATCGGCGCAGAGCAAGGACGAGTGTTAGCTTTTTCCGGTCATATGGATGTGGTGGATGCTGGAGATGTTTCTAAGTGGACCTTTCCGCCATTTGAAGCAACCGAATCGGATGGAAAAATTTATGGCCGTGGTTCTACCGATATGAAATCTGGTTTAGCTGCGATGGTCATTGCGATGATTGAGCTTCATGAAGAAAAAACTAAATTAAATGGGAAAATTAAATTATTGGCAACAGTTGGAGAAGAAGTTGGAGAGCTCGGAGCGGAACAGCTCACTACGCAAGGTTACGCAGATGACATAGATGGTTTGATTATCGGCGAACCAAGTGGACATCGGATTGTTTATGCGCATAAAGGTTCAATTAATTATACAGTCAAATC ->dapE_164 -TTGCAAAAATTGTTAGCAGAATACAGCATCCAAGCTGAAAAGGTGCACTATGACACAGATCGTGCGAGTCTTATAAGTGAAATCGGCGCGGAGCAAGGACGAGTGTTGGCTTTTTCCGGACATATGGATGTGGTTGATGCTGGGGATGTTTCTAAATGGACCTTTCCGCCATTTGAAGCAACAGAATCGGATGGTAAAATATATGGCCGTGGTTCTACTGATATGAAATCTGGTTTAGCCGCGATGGTCATTGCGATGATTGAACTTCATGAAGAAAAAACCAAATTAAATGGGAAAATCAAATTATTAGCGACAGTTGGAGAAGAAGTTGGAGAGCTTGGAGCGGAACAACTTACCACACAAGGTTATGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGTGGACATCGAATTGTTTATGCACATAAAGGTTCGATTAATTATACGGTCAAATC ->dapE_165 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCTTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_166 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGTAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAAATTAGATTATTAGCAACTGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_167 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTAAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGCGGACACAGAATCGTATATGCGCATAAAGGTTCCATCAATTATACCGTTAAATCC ->dapE_168 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCTGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAGATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_169 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAGTATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_170 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_171 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACTGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_172 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAGATTAGATTATTAGCAACTGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_173 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACTGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_174 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_175 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_176 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGAGGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGGCACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_177 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGAAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_178 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCTCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_179 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACAAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_180 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGTGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_181 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCACGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_182 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_183 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTACAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCG ->dapE_184 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCG ->dapE_185 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTACAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_186 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_187 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAAACAGAGCTAGCCTAGTAAGCGAAATTGGTTCAAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_188 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAAATAGAGCTAGCCTAGTAAGCGAAATTGGTTCAAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_189 -TTACAGAAGTTGTTAGCTGAATATGGGATTGAATCTGAGAAAGTGCTGTATGATGAAGACAGGGCAAGTCTTGTTAGTGAAATAGGTTCTAGCGATGAGCAAGTTTTGGCTTTTTCAGGGCATATGGATGTGGTTGATGCAGGAGATGTGTCAAAATGGAAGTTTCCTCCTTTTGAAGCAACGGAGCAGGAAGGGAAAATATACGGCCGTGGTGCCACGGATATGAAGTCAGGTTTAGCAGCGATGGTCATTGCAATGATTGAGCTGCAAGAAGAAAAGCAAAAACTAAATGGGAAAATTAGATTATTAGCTACAGTTGGTGAAGAAGTTGGCGAACTTGGAGCAGAACAACTAACTCAAAAAGGTTATGCAGATGACTTAGATGGTTTAATTATTGGGGAACCAAGTGGACACCGAATTGTATATGCGCATAAAGGTTCGATTAATTATACCGTTAAATCT ->dapE_190 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACAGAATCGTATATGCGCATAAAGGTTCCATCAATTATACCGTTAAATCC ->dapE_191 -TTACAGAAGTTGTTAAGTGAATATGGGATTGAATCTGAAAAGGTGCAGTATGATGTGGACAGAGCCAGTTTGGTTAGTGAAATTGGTTCTAGCGACGGGCGGATTTTGGCTTTTTCAGGGCATATGGATGTGGTTGATGCCGGAGATGAGTCCAAGTGGAAGTTCCCACCTTTTGAAGCGACGGAGCATGACGGGAAAATATACGGACGTGGTGCCACTGATATGAAATCAGGTTTAGCAGCGATGGTTATTGCGATGATTGAACTTCAAGAAGAGAAGCAAAAGTTAAACGGAAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGTGCAGAACAACTAACGCAAAAAGGGTATGCGGATGATTTAGATGGATTGATTATTGGTGAACCAAGCGGGCATCAGATTGTTTACGCCCATAAAGGATCGATTAATTATACCGTTAAATCT ->dapE_192 -CTTTCTGATCTGTTTGCAAGACACGGAATTGATTCAAAGAAAGTGCAGTTTGCTGAAAAGCGGGCCAACTTAGTAAGTGAAATTGGTGAATCTGGAAAACGAGTGTTAGGATTTTCGGGGCATATGGATGTTGTAGATGCCGGTGATCTATCGAAATGGAGTTTTCCACCGTTTGAAGCTACTGAAAAAGATGGAAAGCTTTACGGCAGAGGTTCAACAGATATGAAATCCGGTCTTGCGGCAATGGTTATTGCGATGATTGAATTAAAAGACGAGAAAAAAGCGTTACCAGGCAAAGTGAAATTGCTTGCAACGGTAGGGGAAGAAGTTGGAGAGTTGGGTGCTGAGCAGTTGACTTCAAATGGTTATGCAGATGACCTTGATGCGCTTGTCATTGGTGAGCCGAGCGGTCCGCAAATTTGCTTTGCGCACAAAGGCTCGATGAATTATACAGTTACTTCA ->dapE_193 -TTATTGAAACAGTTCGCTAAATATGGGATTTTATCAGAAAAAGTGGCATACTCGGAGGCACGAACTAATTTAGTAAGTAAAATTGGTGGAAAAAATGGGAAAGTACTAGGTTTTTCTGGGCATATGGACGTTGTGGATGCGGGGGATAGGAAAGCATGGAAAACGCCACCTTTTCAAGCAACGGAATCCGATGGGAAGTTATTCGGCCGTGGCTCGACAGATATGAAATCTGGTTTGGCGGCAATGGTAATCGCGATGATTGAGCTAGAGCAGGAAGGCAAACTAATAGATGGGCAAATTAAATTACTAGCCACGATAGGCGAAGAAATTGGCGAGATTGGGGCAGCGCAACTGACGACGTTGGGCTATGCGGATGATTTAGACGGCTTGATTATCGGTGAACCGAGTGTACGCCAAATTATATATGCGCACAAAGGATCGATGAATTATACGCTCGTATCT ->dapE_194 -TTATTGAAACAACTTACTAAATACGGAATTTCTTCTGAAACAGTAGCATATACAGATAAGCGAGTCAATTTAGTAAGCTGTATTGGCAAAAAAACTGGTAAAGTTCTAGGTTGCTCTGGCCATATGGATGTCGTAGATGCAGGCGATAGTAAAGCTTGGAAAACACCCCCTTTTCAGGCAACAGAGATTGATGGAAAGTTATTTGGTCGTGGGTCAACAAATATGAAGTCTGGTTTAGCAGCAATAGTGATTGCGATGATTGAGATAGAACAAGAAGGTACATTAGTAGACGGACAAATCAAACTGTTAGCTACAGTAAGCGAAGAAATTGAGATTGAGGAGGTTGGTGCAGCACAGTTAACGAAGCTAGGCTATGCGGATGATTTGAATGGCTTAATTATTAGCAAGCCAAGTGTGCGTCAAATTATCTATGCCCATAAAGGTTTGATGAATTACACAGTAGTATCT ->dapE_195 -TAGGGGAGTTACTTTCAAGACACGGAATCGACTCGAAAAAAGTACAGTACGCGGAAAAGCGTGCGAGCTTAGTCAGTGAAATTGGCGATGGCGGGAGCCGTGTACTAGGACTTTCTGGACACATGGATGTTGTGGATGCGGGGGACCCTTCAAAATGGACTTATCCACCTTTTGAAGCGACGGAAAAAGATGGTAAACTCTATGGTCGGGGCTCAACGGACATGAAATCGGGACTTGCGGCGATGGTGATCGCGATGATCGAACTAAAAGATGAAAAAAAGAACTTACCAGGAAAGGTAAAACTATTAGCCACTGTCGGAGAAGAAGTTGGAGAGCTAGGAGCAGAACAGCTTACATCGGAAGGTTATGCGGATGATCTGGATGCGCTCGTTATCGGGGAACCAAGCGGTCCACGGATTTGGTATGCCCACAAAGGCTCGATGAATTACACGGTGACTTCT ->dapE_196 -TTGCAAAAATTATTAGCGGAATACAACATCAAAGCTGAGAAAGTGCAGTATGACAAGGATCGCGCAAGCCTTGTAAGTGAAGTAGGTACAGATAATGGCCCTGTTTTAGCTTTTTCTGGACATATGGATGTTGTTGATGCGGGAGATGTTTCCAAGTGGACTTTTCCTCCATTTGAAGCAACGGAATCAGATGGTAAAATTTACGGACGTGGTGCTACTGATATGAAATCAGGGCTGGCTGCGATGGTCATTGCAATGATTGAACTCCATGAAGAAAAAACGAAATTAAATGGGAAAATCAAATTATTGGCGACAGTTGGAGAAGAAGTTGGAGAACTCGGCGCGGAACAACTTACGACACAAGGTTATGCGGATGATTTAGATGGTTTAATTATCGGCGAGCCAAGTGGACATCGGATTGTTTATGCGCATAAAGGTTCAATTAATTACACGATTAAATCG ->dapE_197 -TTGCAAAAATTATTAGCGGAATACAACATCAAAGCTGAGAAAGTGCAGTATGACAAGGATCGCGCAAGCCTTGTAAGTGAAGTAGGTACAGATAATGGCCCTGTTTTAGCCTTTTCTGGACATATGGATGTTGTTGATGCGGGGGACGTTTCCAAGTGGACTTTTCCTCCATTTGAAGCAACGGAATCAGATGGTAAAATTTACGGACGTGGTGCTACTGATATGAAATCAGGGCTGGCTGCGATGGTCATTGCAATGATTGAACTCCATGAAGAAAAAACGAAATTAAATGGGAAAATTAAATTATTGGCGACAGTTGGGGAAGAAGTTGGAGAACTCGGCGCGGAACAACTTACCACAAAAGGTTATGCGGATGATTTAGATGGTTTAATTATCGGCGAGCCAAGTGGACATCGGATTGTTTATGCGCATAAAGGTTCAATTAATTACACGGTTAAATCG ->dapE_198 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCATCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_199 -TTACAGAAGTTGTTAGCTGAATATGGGATTGAATCTGAGAAAGTGCAGTATGATGAAGACAGGGCAAGTCTTGTTAGTGAAATAGGTTCTAGCGATGAGCAAGTTTTGGCTTTTTCAGGGCATATGGATGTGGTTGATGCAGGAGATGTGTCAAAATGGAAGTTTCCTCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGCCGTGGTGCCACGGATATGAAGTCAGGTTTAGCAGCAATGGTCATTGCAATGATTGAGCTGCAAGAAGAAAAGCAAAAACTAAATGGGAAAATTAGATTATTAGCTACAGTTGGTGAAGAAGTTGGCGAACTTGGAGCAGAACAACTAACTCAAAAAGGTTATGCAGATGACTTAGATGGTTTAATTATTGGGGAACCAAGTGGACACCGAATTGTATACGCGCATAAAGGTTCGATTAATTATACCGTTAAATCT ->dapE_200 -TTACAGAAGTTGTTAGCTGAATATGGGATTGAATCTGAGAAAGTGCAGTATGATGAAGACAGGGCAAGTCTTGTTAGTGAAATAGGTTCTAGCGATGAGCAAGTTTTGGCTTTTTCAGGGCATATGGATGTGGTTGATGCAGGAGATGTGTCAAAATGGAAGTTTCCTCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGCCGTGGTGCCACGGATATGAAGTCAGGTTTAGCAGCGATGGTCATTGCAATGATTGAGTTGCAAGAAGAAAAGCAAAAACTAAATGGGAAAATTAGATTATTAGCTACAGTTGGTGAAGAAGTTGGCGAACTTGGAGCAGAACAACTAACTCAAAAAGGTTATGCAGATGACTTAGATGGTTTAATTATTGGGGAACCAAGTGGACACCGAATTGTATATGCGCATAAAGGTTCGATTAATTATACCGTTAAATCT ->dapE_201 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAATTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCATTAAATCC ->dapE_202 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACCCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_203 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGAAGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGCCTAGCTGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGCGGACACAGAATCGTATATGCGCATAAAGGTTCAATTAATTATACCGTTAAATCC ->dapE_204 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_205 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAATTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCTGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_206 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAGATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_207 -TTACAGAAGTTGTTAGCTGAATATGGGATTGAATCTGAGAAAGTGCAGTATGATGAAGACAGGGCAAGTCTTGTTAGTGAAATAGGTTCTAGCGATGAGCAAGTTTTGGCTTTTTCAGGGCATATGGATGTGGTTGATGCAGGAGATGTGTCAAAATGGAAGTTTCCTCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGCCGTGGTGCCACGGATATGAAGTCAGGTTTAGCAGCGATGGTCATTGCAATGATTGAGCTGCAAGAAGAAAAGCAAAAACTAAATGGGAAAATTAGATTATTAGCTACAGTTGGTGAAGAAGTTGGCGAACTTGGAGCAGAACAACTAACTCAAAAAGGTTATGCAGATGACTTAGATGGTTTAATTATTGGGGAACCAAGTGGACACCGAATTGTATATGCGCATAAAGGTTCGATTAATTATACCGTTAAATCT ->dapE_208 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTAAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGCCTAGCTGCGATGGTTATTGCTATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACAGAATCGTATATGCGCATAAAGGTTCCATCAATTATACCGTTAAATCC ->dapE_209 -TGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGCGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_210 -TTGCAAAAGTTGTTAGCTGAACACGGCATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_211 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGAGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_212 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACCTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTTATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_213 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCGGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_214 -TTGCAAAAATTGTTAGCGGAATATAACATCCAAGCTGAAAAAGTGCAATATGACAAGGATCGCGCAAGCCTTGTAAGTGAAGTAGGTGCAGATAATGGCCCTGTTTTAGCCTTTTCTGGACATATGGATGTTGTTGATGCGGGTGATGTTTCCAAGTGGACTTTTCCTCCATTTGAAGCAACGGAATCAGAGGGTAAAATTTATGGACGTGGTGCTACTGATATGAAATCAGGGCTGGCTGCGATGGTCATTGCAATGATTGAACTCCATGAAGAAAAAACGAAATTAAATGGGAAAATCAAATTATTGGCGACAGTTGGAGAAGAAGTTGGAGAACTCGGGGCGGAACAACTTACCACACAAGGTTATGCAGATGATTTAGATGGTTTAATTATCGGCGAACCAAGTGGACACCGAATTGTTTATGCGCATAAAGGTTCAATTAATTATACGGTTAAATCT ->dapE_215 -TTACAGAAGTTGTTAGCTGAATATGGGATTGAATCTGAGAAAGTGCAGTATGATGAAGACAGGGCAAGTCTTGTTAGTGAAATAGGTTCTAGCGATGAGCAAGTTTTGGCTTTTTCAGGGCATATGGATGTGGTTGATGCAGGAGATGTGTCAAAATGGAAGTTTCCTCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGCCGTGGTGCCACGGATATGAAGTCAGGTTTAGCAGCGATGGTCATTGCAATGATTGAGTTGCAAGAAGAAAAGCAAAAACTAAATGGGAAAATTAGATTATTAGCTACAGTTGGTGAAGAAGTTGGCGAACTTGGAGCAGAACAACTAACTCAAAAAGGTTATGCAGATGACTTAGATGGCTTAATTATTGGGGAACCAAGTGGACACCGAATTGTATATGCGCATAAAGGTTCGATTAATTATACCGTTAAATCT ->dapE_216 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTAATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_217 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGTCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_218 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_219 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAGACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_220 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGATAAGGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCTGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAGATTAGATTATTAGCAACAGTGGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTGAAATCT ->dapE_221 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAACTATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_222 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTGTTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_223 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATATGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACAGAATCGTATATGCGCATAAAGGTTCCATCAATTATACCGTTAAATCC ->dapE_224 -TTGCAAAAGTTATTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGTAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACATCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_225 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTCGTCGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_226 -TTGCAAAAATTGTTAGCGGAATATAACATCCAAGCTGAAAAAGTGCAATATGACAAGGAGCGCGCAAGCCTTGTAAGTGAAGTAGGTACAGATAATGGCCCTGTTTTAGCCTTTTCTGGACATATGGATGTTGTTGATGCAGGTGATGTTTCCAAGTGGACTTTTCCTCCATTTGAAGCAACGGAATCAGATGGTAAAATTTATGGACGTGGTGCTACTGATATGAAATCAGGGCTGGCTGCGATGGTCATTGCAATGATTGAACTCCATGAAGAAAAAACGAAATTAAATGGGAAAATCAAATTATTGGCGACAGTTGGAGAAGAAGTTGGAGAACTCGGCGCGGAACAACTTACGAAAAAAGGGTATGCGGATGATTTAGATGGTTTAATTATCGGTGAGCCAAGTGGACATCGGATTGTTTATGCGCATAAAGGTTCAATTAATTACACGGTTAAATCG ->dapE_227 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_228 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_229 -TTGCAAAATTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTCGTCGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAGTGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_230 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAATTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTACAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGGCACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_231 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAAATTAGATTATTGGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_232 -TTGCAAAAATTATTAGCGGAATACAACATCAAAGCTGAGAAAGTGCAGTATGACAAGGATCGCGCAAGCCTTGTGAGTGAAGTTGGTGCAGATAATGGCCCTGTTTTAGCCTTTTCTGGACATATGGATGTTGTTGATGCGGGGGACGTTTCCAAGTGGACTTTTCCTCCATTTGAAGCAACGGAATCAGATGGTAAAATTTACGGACGTGGTGCTACTGATATGAAATCAGGGCTGGCTGCGATGGTCATTGCAATGATTGAACTCCATGAAGAAAAAACGAAATTAAATGGGAAAATTAAATTATTGGCGACAGTTGGAGAAGAAGTTGGAGAACTCGGCGCGGAACAACTTACCACAAAAGGTTATGCGGATGATTTAGATGGTTTAATTATCGGCGAGCCAAGTGGACATCGGATTGTTTATGCGCATAAAGGTTCAATTAATTACACGGTTAAATCG ->dapE_233 -TTGCAAAAATTATTAGCGGAATACAACATCAAAGCTGAGAAAGTGCAGTATGACAAGGATCGCGCAAGCCTTGTGAGTGAAGTTGGTGCAGATAATGGCCCTGTTTTAGCTTTTTCTGGACATATGGATGTTGTTGATGCGGGTGATGTTTCCAAGTGGACTTTTCCTCCATTTGAAGCAACGGAATCAGATGGTAAAATTTACGGACGTGGTGCTACTGATATGAAATCAGGGCTGGCTGCGATGGTCATTGCAATGATTGAACTCCATGAAGAAAAAATGAAATTAAATGGGAAAATTAAATTATTGGCGACAGTTGGAGAAGAAGTTGGAGAACTCGGCGCGGAACAACTTACCACAAAAGGTTATGCGGATGATTTAGATGGTTTAATTATCGGCGAGCCAAGTGGACATCGGATTGTTTATGCGCATAAAGGTTCAATTAATTACACGGTTAAATCG ->dapE_234 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAATTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGAAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGGCACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_235 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGATGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_236 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATAACGTTAAATCC ->dapE_237 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGATGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAACATGAAGGGAAACTATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCTATTAATTATACCGTTAAATCC ->dapE_238 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTCTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_239 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCATCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_240 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGATGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_241 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_242 -TTACAGAAGTTGTTAAGTGAATATGGGATTGAATCTGAAAAGGTGCAGTATGATGTGGACAGAGCCAGTTTGGTTAGTGAAATTGGTTCTAGCGACGGGCAGGTTTTGGCTTTTTCAGGGCATATGGATGTGGTTGATGCGGGAGATGAGTCCAAGTGGAAGTTCCCACCTTTTGAAGCAACGGAGCATGACGGGAAAATATACGGACGCGGAGCTACTGATATGAAATCAGGTTTAGCAGCGATGGTTATTGCGATGATTGAACTTCAAGAAGAGAAGCAAAAGTTAAACGGAAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGTGCAGAACAACTAACGCAAAAAGGGTATGCGGATGATTTAGATGGATTGATTATTGGTGAACCAAGCGGGCATCAGATTGTTTACGCCCATAAAGGATCGATTAATTATACCGTTAAATCT ->dapE_243 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAATTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAGCTATACGGACGTGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGTCACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_244 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCG ->dapE_245 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCTGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_246 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATCGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_247 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAAGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_248 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGACGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_249 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAAATAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_250 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGATGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAGCTATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAGATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTGAAATCT ->dapE_251 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACATCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_252 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACTTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGTATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAAATAAATGGCAAAATTAGATTATTAGCAACGATTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCG ->dapE_253 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCTGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_254 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCGGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAAATATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_255 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAGCAAAAAATAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_256 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCTGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGGTTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_257 -TTGCAAGAGTTGTTAGCTGGATACGGAATTGAGTCTGAAAAGGTAGATTATGATGTAGACAGAGCAAGCCTAGTAAGCGAAATTGGTTCCAGTTACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGGGATGTGTCTAAGTGGAAATTCCCGCCTTTTGAAGCGACAGAACATGAAGGGAAACTATATGGACGTGGCGCGACAGATATGAAGTCAGGTCTAGCAGCGATGGTTATTGCAATGATTGAACTTCAGGAAGAAAAACAAAAACTAAACGGTAAGATCAGATTATTAGCAACAGTTGGTGAAGAAGTCGGCGAACTTGGAGCAGAACAACTAAGGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCAAGTGGACACAGAATCGTATATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_258 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAATCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCAGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_259 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGATGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCGGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAAATATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_260 -CTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_261 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGTGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_262 -TTGCAGAAGTTGTTAAGTGAATATGGGATTGAATCTGAAAAGGTGCAGTATGATGTGGACAGAGCCAGTTTGGTTAGCGAAATTGGTTCTAGCGACGGGCCGGTTTTGGCTTTTTCAGGGCATATGGATGTGGTTGATGCGGGAGATGAGTCCAAGTGGAAGTTCCCACCTTTTGAAGCAACGGAGCATGACGGGAAAATATACGGACGTGGTGCCACTGATATGAAATCAGGTTTAGCAGCGATGGTTATTGCGATGATTGAACTTCAAGAAGAGAAGCAAAAGTTAAACGGAAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGTGCAGAACAACTAACGCAAAAAGGGTATGCGGATGATTTAGATGGATTGATTATTGGTGAACCAAGCGGGCATCAGATTGTTTACGCCCATAAAGGATCGATTAATTATACCGTTAAATCT ->dapE_263 -TTACAGAAGTTGTTAAGTGAATATGGGATTGAATCTGAAAAGGTGCAGTATGATGTGGACAGAGCCAGTTTGGTTAGTGAAATTGGTTCTAGCGACGGGCGGATTTTGGCTTTTTCAGGGCATATGGATGTGGTTGATGCCGGAGATGAGTCCAAGTGGAAGTTCCCACCTTTTGAAGCGACGGAGCATGACGGGAAAATATACGGACGTGGCGCCACTGATATGAAATCAGGTTTAGCAGCGATGGTTATTGCGATGATTGAACTTCAAGAAGAGAAGCAAAAGTTAAACGGAAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGTGCAGAACAACTAACGCAAAAAGGGTATGCGGATGATTTAGATGGATTGATTATTGGTGAACCAAGCGGGCATCAGATTGTTTACGCCCATAAAGGATCGATTAATTATACCGTTAAATCT ->dapE_264 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACCTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATTAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_265 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACGGAATCGTTTATGCGCATAAAGGTTCCATTAATTATATCGTTAAATCC ->dapE_266 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGCGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCAAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_267 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGTGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_268 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCTGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_269 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGCGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_270 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGATAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_271 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACATTATGATGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGAGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_272 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAAGGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACACAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_273 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGTAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGGTTATAGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_274 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGGAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_275 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAAACGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_276 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAGACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_277 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTATGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_278 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGTAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_279 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAACGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_280 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAAAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCAACGGAGCACGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAGACTAAATGGTAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACTCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_281 -TTACAGAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATATGATGTAGACCGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAGTGGTAAAATTAGATTATTAGCAACGGTTGGGGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_282 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCTGCGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_283 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTACGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_284 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGTCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_285 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACTGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_286 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGTAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_287 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATTCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGTAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACTGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_288 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAAGTTCCATTAATTATACCGTTAAATCC ->dapE_289 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAAAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_290 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGCTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCATTAAATCC ->dapE_291 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCACGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_292 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAATACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_293 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATATCGTTAAATCC ->dapE_294 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_295 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATATAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGAAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGCCTAGCTGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCAAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_296 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAAATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_297 -TTGCAAAAATTATTAGCGGAATACAACATCAAAGCTGAGAAAGTGCAGTATGACAAGGATCGCGCAAGCCTTGTAAGTGAAGTAGGTACAGATAATGGCCCTGTTTTAGCCTTTTCTGGACATATGGATGTTGTTGATGCGGGGGACGTTTCCAAGTGGACTTTTCCTCCATTTGAAGCAACGGAATCAGATGGTAAAATTTACGGACGTGGTGCTACTGATATGAAATCAGGGCTGGCTGCGATGGTCATTGCAATGATTGAACTCCATGAAGAAAAAACGGAATTAAATGGGAAAATTAAATTATTGGCGACAGTTGGAGAAGAAGTTGGAGAACTCGGCGCGGAACAACTTACCACAAAAGGTTATGCGGATGATTTAGATGGTTTAATTATCGGCGAGCCAAGTGGACATCGGATTGTTTATGCGCATAAAGGTTCAATTAATTACACGGTTAAATCG ->dapE_298 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTAAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGGGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCGGATGATTTAGATGGTCTGATCATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_299 -TTACAGAAGTTGTTAGCTGAATATGGGATTGAATCTGAGAAAGTGCAGTATGATGAAGACAGGGCAAGTCTTGTTAGTGAAATAGGTTCTAGCGATGAGCAAGTTTTGGCTTTTTCAGGGCATATGGATGTGGTTGATGCAGGAGATGTGTCAAAATGGAAGTTTCCTCCTTTTGAAGCAACGGAGCAGGAAGGGAAAATATACGGCCGTGGTGCCACGGATATGAAGTCAGGTTTAGCAGCGATGGTCATTGCAATGATTGAGCTGCAAGAAGAAAAGCAAAAACTAAATGGGAAAATTAGATTATTAGCTACAGTTGGTGAAGAAGTTGGCGAACTTGGAGCAGAACAACTAACTCAAAAAGGTTATGCAGATGACTTAGATGGTTTAATTATTGGGGAACCAAGTGGACACCGAATTGTATATGCGCATAAAGGTTCGATTAATTATACCGTTAAATCT ->dapE_300 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACTGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTAATCATCGGCGAATCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_301 -TTACAGAAGTTGTTAGCTGAATATGGGATTGAATCTGAGAAAGTGCAGTATGATGAAGACAGGGCAAGTCTTGTTAGTGAAATAGGTTCTAGCGATGAGCAAGTTTTGGCTTTTTCAGGGCATATGGATGTGGTTGATGCAGGAGATGTGTCAAAATGGAAGTTTCCTCCTTTTGAAGCAATGGAGCATGAAGGGAAAATATACGGCCGTGGTGCCACGGATATGAAGTCAGGTTTAGCAGCGATGGTCATTGCAATGATTGAGCTGCAAGAAGAACAGCAAAAACTAAATGGGAAAATTAGATTATTAGCTACAGTTGGTGAAGAAGTTGGCGAACTTGGAGCAGAACAACTAACTCAAAAAGGTTATGCAGATGACTTAGACGGTTTAATTATTGGGGAACCAAGTGGACACCGAATTGTATATGCGCATAAAGGTTCGATTAATTATACCGTTAAATCT ->dapE_302 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGGTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_303 -TTGCAAAAATTATTAGCGGAATACAACATCAAAGCTGAGAAAGTGCAGTATGACAAGGATCGCGCAAGCCTTGTAAGTGAAGTAGGTACAGATAATGGCCCTGTTTTAGCCTTTTCTGGACATATGGATGTTGTTGATGCGGGGGACGTTTCCAAGTGGACTTTTCCTCCATTTGAAGCAACGGAATCAGATGGTAAAATTTACGGACGTGGTGCTACTGATATGAAATCAGGGCTGGCTGCGATGGTCATTGCAATGATTGAACTCCATGAAGAAAAAACGAAATTAAATGGGAAAATTAAATTATTGGCGACAGTTGGAGAAGAAGTTGGAGAACTCGGCGCGGAACAACTTACCACAAAAGGTTATGCGGATGATTTAGATGGTTTAATTATCGGCGAGCCAAGTGGACATCGGATTGTTTATGCGCATAAAGGTTCAATTAATTACACGATTAAATCG ->dapE_304 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGATGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_305 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACATCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_306 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTTGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_307 -TTGCAAAAATTGTTAGCGGAATATAACATCCAAGCTGAAAAAGTGCAATATGACAAGGATCGCGCAAGCCTTGTGAGTGAAGTTGGTGCAGAAAAAGGTCGTGTTTTAGCCTTTTCTGGACATATGGATGTTGTTGATGCGGGGGATGTTTCCAAGTGGACTTTTCCTCCATTTGAAGCAACGGAATCAGATGGTAAAATTTACGGACGTGGTGCTACTGATATGAAATCAGGGCTGGCTGCGATGGTCATTGCAATGATTGAACTCCATGAAGAAAAAACGAAATTAAATGGGAAAATTAAATTATTGGCGACAGTTGGAGAAGAAGTTGGAGAACTCGGCGCGGAACAACTTACCACAAAAGGTTATGCGGATGATTTAGATGGTTTAATTATCGGCGAGCCAAGTGGACATCGGATTGTTTATGCGCATAAAGGTTCAATTAATTACACGGTTAAATCG ->dapE_308 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGATTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_309 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_310 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTGGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCATTAAATCC ->dapE_311 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGTAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACTGTTGGTGAAGAAGTCAGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_312 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATTGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_313 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_314 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAATAAAAACTAAATGGCAAGATTAGATTATTAGCAACTGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_315 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACAGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAGTCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_316 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_317 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTATATCC ->dapE_318 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTAAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGAAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_319 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_320 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGCCTAGCTGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCAAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_321 -TTGCAAAAATTGTTAGCGGAATATAACATCCAAGCTGAAAAAGTGCAATATGACAAGGATCGCGCAAGCCTTGTAAGTGAAGTAGGTGCAGATAATGGCCCTGTTTTAGCCTTTTCTGGACATATGGATGTTGTTGATGCGGGTGATGTTTCCAAGTGGACTTTTCCTCCATTTGAAGCAACGGAATCAGAGGGTAAAATTTATGGACGTGGTGCTACTGATATGAAATCAGGGCTGGCTGCGATGGTCATTGCAATGATTGAACTCCATGAAGAAAAAACGAAATTAAATGGGAAAATCAAATTATTGGCGACAGTTGGAGAAGAAGTTGGAGAACTCGGCGCGGAACAACTTACGAAAAAAGGGTATGCAGATGATTTAGATGGTTTAATTATCGGTGAGCCAAGTGGACATCGGATTGTTTATGCGCATAAAGGTTCAATTAATTACACGGTTAAATCG ->dapE_322 -TTGCAAAAATTATTAGCGGAATACAACATCAAAGCTGAGAAAGTGCAGTATGACAAGGATCGCGCAAGCCTTGTAAGTGAAGTAGGTACAGATAATGGCCCTGTTTTAGCCTTTTCTGGACATATGGATGTTGTTGATGCGGGGGACGTTTCCAAGTGGACTTTTCCTCCATTTGAAGCAACGGAATCAGATGGTAAAATTTACGGACGTGGTGCTACTGATATGAAATCAGGGCTGGCTGCGATGGTCATTGCAATGATTGAACTCCATGAAGAAAAAACGAAATTAAATGGGAAAATTAAATTATTGGCGACAGTTGGAGAAGAAGTTGGAGAACTCGGCGCGGAACAACTTACCAAAAAAGGTTATGCGGATGATTTAGATGGTTTAATTATCGGCGAGCCAAGTGGACATCGGATTGTTTATGCGCATAAAGGTTCAATTAATTACACGATTAAATCG ->dapE_323 -TTGCAAAAATTATTAGCGGAATACAACATCAAAGCTGAGAAAGTGCAGTATGACAAGGATCGCGCAAGCCTTGTAAGTGAAGTTGGTACAGATAATGGCCCTGTTTTAGCCTTTTCTGGACATATGGATGTTGTTGATGCGGGGGACGTTTCCAAGTGGACTTTTCCTCCATTTGAAGCAACGGAATCAGATGGTAAAATTTACGGACGTGGTGCTACTGATATGAAATCAGGGCTGGCTGCGATGGTCATTGCAATGATTGAACTCCATGAAGAAAAAACGGAATTAAATGGGAAAATTAAATTATTGGCGACAGTTGGAGAAGAAGTTGGAGAACTCGGCGCGGAACAACTTACCACAAAAGGTTATGCGGATGATTTAGATGGTTTAATTATCGGCGAGCCAAGTGGACATCGGATTGTTTATGCGCATAAAGGTTCAATTAATTACACGGTTAAATCG ->dapE_324 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGCCTAGCTGCGATGGTTATTGCAATGATTGAGCTTCACGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCAAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_325 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACATAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAGCCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_326 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTTGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_327 -TAGGAGAATTACTTTCAAGACACGGAATTGACTCGAAAAAAGTTCAGTACGCGGAAAAGCGCGCTAGCTTAGTCAGTGAAATCGGCGATGGCGGGAACCGAGTATTGGGACTTTCTGGTCACATGGATGTCGTGGATGCGGGGGATCCTTCAAAATGGACGTACCCACCTTTTGAAGCGACGGAAAAAGATGGAAAACTTTACGGACGTGGCTCAACAGACATGAAATCAGGACTTGCGGCGATGGTGATCGCGATGATTGAACTGAAAGATGAAAAGAAAAATTTACCAGGAAAGGTAAAACTGTTAGCCACCGTCGGAGAAGAGGTTGGAGAGCTAGGAGCAGAACAACTTACATCGGAAGGTTATGCGGATGATCTGGATGCCCTCATCATTGGGGAACCAAGCGGTCCACGGATTTGGTATGCCCACAAAGGCTCAATGAATTATACAGTCACTTCT ->dapE_328 -TAGGGGAATTACTTTCAAGACACGGAATCGACTCAAAAAAAGTACAGTACGCGGAAAAGCGTGCGAGCTTAGTCAGTGAAATTGGCGATGGCGGGAGCCGTGTACTGGGACTTTCTGGACACATGGATGTTGTGGATGCGGGAGACCCTTCAAAGTGGACTTATCCACCTTTTGAAGCGACGGAAAAAGACGGCAAGCTCTATGGTCGGGGCTCAACGGACATGAAATCGGGACTTGCGGCGATGGTGATCGCGATGATCGAACTAAAAGATGAAAAGAAAAACTTACCAGGAAAGGTAAAACTGTTAGCCACTGTCGGAGAAGAAGTTGGAGAGCTAGGAGCAGAACAGCTTACATCGGAAGGTTATGCGGATGATCTGGATGCGCTCGTTATCGGGGAACCAAGCGGTCCGCGGATTTGGTATGCCCACAAAGGCTCGATGAATTATACGATGACTTCT ->dapE_329 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTTGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_330 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGATTCCATTAATTATACCGTTAAATCC ->dapE_331 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACATAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAATAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_332 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCAGCAGAGCACGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGGGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_333 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGTCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_334 -TTACAGAAGTTGTTTGCTGTGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_335 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCTGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_336 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_337 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGTGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_338 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTTCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_339 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAATCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_340 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAATGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTATCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAACTATACGGACGTGGCGCGACAGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_341 -TTGCAAAAGTTGTTAGCTGAACATGGTATTAAGTCCGAAAAGGTGCAATATGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGGTTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_342 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGAAGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_343 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGTCTAGTAAGTGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGGAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_344 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTCGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGTGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_345 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_346 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGGCACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_347 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAATCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_348 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGGAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_349 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACAATGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACATCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_350 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAATGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACAGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCG ->dapE_351 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGTCTAGTAAGTGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTAGTCGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTAGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_352 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTCGTCGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_353 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATGTACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_354 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCGGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTTCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAAATAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_355 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAGTGGCAAAATTAGATTATTAGCAACGGTTGGGGAAGAAGTTGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_356 -TTACAGAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAGTGGTAAAATTAGATTATTAGCAACGGTTGGGGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_357 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCATTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_358 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATCAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_359 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAATAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAACATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGTAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGGTTATAGGCGAACCGAGCGGACATCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_360 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAAATTAGATTATTGGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_361 -TTGCAAAAGTTGTTAGCTGAACATAGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTCGTCGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAGTGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_362 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_363 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_364 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATATCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCAACAGAGCATGAAGGGAAGCTATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTACAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGGCACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_365 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCAGCAGAGCACGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAGTGGCAAAATTAGATTATTAGCAACGGTTGGGGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_366 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_367 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_368 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGATGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGCGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGTAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_369 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGCGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_370 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGATGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_371 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCAAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_372 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGACAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_373 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_374 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCGGGGCATATGGATGTCGTTGATGCGGGTGATATCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCAACAGAGCATGAAGGGAAGCTATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTACAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCG ->dapE_375 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAAAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATACGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTATATCC ->dapE_376 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGACTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_377 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAGTGGCAAAATTAGATTATTAGCAACGGTTGGGGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_378 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAGCAAAAACTAAATGGCAAGATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCG ->dapE_379 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCAAGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCTGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_380 -TTGCAAAAGTTGTTAGCTGAATATGGTATTGAGTCCGAAAAGGTACAATACGATGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_381 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_382 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_383 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_384 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCTGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCA ->dapE_385 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_386 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCG ->dapE_387 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGACGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_388 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGATGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCGGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAAATAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTAATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_389 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCTGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_390 -TTGCAAAAGTTGTTAGCTGAACACGGCATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_391 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_392 -TTGCAAAAGTTGTTAGCCGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAGTGGCAAAATTAGATTATTAGCAACGGTTGGGGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_393 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACTGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_394 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAACGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAGTGGCAAAATTAGATTATTAGCAACGGTTGGGGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_395 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCTGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAACATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCTATTAATTATACCGTTAAATCC ->dapE_396 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_397 -CTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_398 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_399 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGATGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAGACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_400 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTGAAATCT ->dapE_401 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAGTGGTAAAATTAGATTATTAGCAACGGTTGGGGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_402 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAATGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_403 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCTGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_404 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGTGAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_405 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_406 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACAAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_407 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_408 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTATCTAAGTGGAAATTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_409 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_410 -TTACAGAAGTTGTTAAGTGAATATGGGATTGAATCTGAAAAGGTGCAGTATGATGTGGACAGAGCCAGTTTGGTTAGTGAAATTGGTTCTAGCGACGGGCGGATTTTGGCTTTTTCAGGGCATATGGATGTGGTTGATGCCGGAGATGAGTCCAAGTGGAAGTTCCCACCTTTTGAAGCAACGGAGCATGACGGGAAAATATACGGACGTGGCGCCACTGATATGAAATCAGGTTTAGCAGCGATGGTTATTGCGATGATTGAACTTCAAGAAGAGAAGCAAAAGTTAAACGGAAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGTGCAGAACAACTAACGCAAAAAGGGTATGCGGATGATTTAGATGGATTGATTATTGGTGAACCAAGCGGGCATCAGATTGTTTACGCCCATAAAGGATCAATTAATTATACCGTTAAATCT ->dapE_411 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGATGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCAAGTGACGAGAAAGTTTTGGCGTTTTCGGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAACATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_412 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_413 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_414 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAATCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_415 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACCGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAGTGGCAAAATTAGATTATTAGCAACGGTTGGGGAAGAAGTTGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_416 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGACATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAAATAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_417 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGTTACAATACGACGTAGACAGAGCTAGCCTAGTAAGTGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_418 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACTGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCATTAAATCC ->dapE_419 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAACTATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCG ->dapE_420 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGATGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCGGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_421 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGACATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGTAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGGTTATAGGCGAACCGAGCGGACATCGGACTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_422 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGATGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGTAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGATACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_423 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAATAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_424 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_425 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_426 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATACACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_427 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGATGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCTGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_428 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCAAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_429 -TTGCAAGAGTTGTTAGCTGGATACGGAATTGAGTCTGAAAAGGTACATTATGATGTAGACAGAGCAAGCCTAGTAAGCGAAATTGGTTCCAGTTATGAGAAGATTTTGGCATTTTCAGGGCATATGGATGTGGTTGATGCGGGGGATGTGTCTAAGTGGAAATTCCCGCCTTTTGAAGCGACAGAACATGAAGGGAAACTATATGGACGTGGCGCGACAGATATGAAGTCAGGTCTAGCAGCGATGGTTATTGCAATGATTGAACTTCAGGAAGAAAAACAAAAACTAAACGGTAAGATCAGATTATTAGCAACAGTTGGTGAAGAAGTCGGCGAACTTGGAGCAGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCAAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_430 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCACGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCGATGATTGAGCTTCATGAAGAAAAACAAAAACTAAGTGGCAAAATTAGATTATTAGCAACGGTTGGGGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_431 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACGAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_432 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGAGCAATATGACGTAGACAGAGCCAGCCTAGTAAGCGAAGTTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_433 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAGCTATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCG ->dapE_434 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGTCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAGATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_435 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGTAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_436 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGGAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_437 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_438 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAGACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_439 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCGGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_440 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCTGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACAAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_441 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_442 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGGTCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATAATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_443 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_444 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGTGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_445 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_446 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGATGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGTAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_447 -TTACAGAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAGTGGCAAAATTAGATTATTAGCAACGGTTGGGGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_448 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGCGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_449 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCTTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGTAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_450 -TTGCAAAAGTTGTTAGCTGAACATGGTGTTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCAATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_451 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGGCACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_452 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAATTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAGCTATACGGACGTGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAAATAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_453 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_454 -TTACAGAAGTTGTTAGTTGAACATGGTATTGAGTCCGAAAAGGTACAATATGATGTAGACCGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAGTGGCAAAATTAGATTATTAGCAACGGTTGGGGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_455 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGATGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_456 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGTAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_457 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCAACGGATATGAAGTCTGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_458 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAGATTAGATTATTAGCAACTGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTAATCATCGGTGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_459 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_460 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGAAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGTAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCAAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_461 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAATTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAAATATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACATCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_462 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGATGTAGACAGAGCTAGCCTAGTAGGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCAGCAGAGCACGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAGTGGCAAAATCAGATTATTAGCAACGGTTGGGGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_463 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGATGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCAAGTGACGAGAAAGTTTTGGCGTTTTCGGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAACATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAATGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_464 -TAGGGGAATTACTTTCAAGACACGGAATTGACTCGAAAAAAGTTCAGTACGCGGAAAAGCGCGCTAGCTTAGTCAGTGAAATTGGCGATGGCGGGAACCGAGTATTGGGACTTTCTGGTCACATGGACGTCGTGGATGCTGGGGATCCTGCAAAATGGATGTACCCACCTTTTGAAGCGACGGAAAAAGATGGAAAACTTTACGGACGTGGCTCAACAGACATGAAATCAGGACTTGCGGCGATGGTGATCGCGATGATTGAACTGAAAGATGAAAAGAAAGATTTACCAGGAAAGGTAAAACTGTTAGCCACCGTCGGGGAAGAAGTTGGAGAGCTAGGAGCAGAACAGCTTACATCGGAAGGTTATGCGGATGATTTGGATGCCCTCGTCATTGGGGAACCAAGCGGTCCGCGGATTTGGTATGCCCACAAAGGCTCAATGAATTACACAGTCACTTCT ->dapE_465 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTAAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTCTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGCGGACACAGAATCGTATATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_466 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTTGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_467 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCTGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_468 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCTGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_469 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATAGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_470 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGTGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_471 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATTAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_472 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_473 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGGCGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCATTAAATCC ->dapE_474 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_475 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGACATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_476 -TTGCAAAAGTTGTTAGCTAAACACGGTATTGAGTCCGAAAAGGTACAATACGACATAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_477 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACATAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCAGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_478 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAATGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_479 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACAACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCATTAAATCC ->dapE_480 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTAAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATATGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCACGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGCGGATACAGAATCGTATATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_481 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTAAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGGGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGCCTAGCTGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_482 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGAACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_483 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTATATGCGCATAAAGGTTCCATTAATTATATCGTTAAATCC ->dapE_484 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGAGCATAAAGGTTCCATTAATTATACCATTAAATCC ->dapE_485 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGTCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_486 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_487 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGTCTAGTAAGTGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_488 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_489 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGATGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_490 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_491 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCTGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAGATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCG ->dapE_492 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACAAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_493 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_494 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAAATTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_495 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGAAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_496 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGTTCCATTAATTATACCGTTAAATCC ->dapE_497 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_498 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAAATAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTAATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_499 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTTTACCGTTAAATCC ->dapE_500 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACAAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGCTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_501 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATATGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_502 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCTGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAGATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_503 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGACGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCTGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_504 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAAATATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_505 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCTGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_506 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCGGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAAATATACGGACGCGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGTAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_507 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_508 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_509 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_510 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_511 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_512 -CTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCTGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_513 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGAATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_514 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_515 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAAATAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGGTTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_516 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCTGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCGGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTTCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGTAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_517 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATAGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTGAAATCT ->dapE_518 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_519 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_520 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCTGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACAAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_521 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAGACTAAATGGTAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACTCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_522 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAATCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_523 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAATCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGTGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_524 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCTGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAACATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACATCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_525 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACTTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGTATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAAATAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCG ->dapE_526 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAATAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTTCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCG ->dapE_527 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCTGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAACATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCG ->dapE_528 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGATGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCAAGTGACGAGAAAGTTTTGGCGTTTTCGGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGTAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_529 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCTGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCAAGTGACGAGAAAGTTTTGGCGTTTTCGGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAAATAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTAATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_530 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAAGTGCAATACGACGTAGACAGAGCCAGCCTAATAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAAATAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTAATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_531 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCAAGTGACGAGAAAGTTTTGGCGTTTTCGGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAAATAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTAATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_532 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACTTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGTAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACATCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_533 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAATAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACACAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_534 -TTACAGAAGTTGTTAGCTGTATATGGGATTGAATCTGAGAAAGTGCAGTATGATGAAGATAGGGCAAGTCTTGTTAGTGAAATAGGTTCTAGCGATGAGCAAGTTTTGGCTTTTTCAGGGCATATGGATGTGGTTGATGCAGGAGATGTGTCAAAATGGAAGTTTCCTCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGCCGTGGTGCCACGGATATGAAGTCAGGTTTAGCAGCGATGGTCATTGCAATGATTGAGCTGCAAGAAGAAAAGCAAAAACTAAATGGGAAAATTAGATTATTAGCTACAGTTGGTGAAGAAGTTGGCGAACTTGGAGCAGAACAACTAACTCAAAAAGGTTATGCAGATGACTTAGATGGTTTAATTATTGGGGAACCAAGTGGACACCGAATTGTATACGCGCATAAAGGTTCGATTAATTATACCGTTAAATCT ->dapE_535 -ACTCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_536 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTAAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGCGGACACAGAATCGTATATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_537 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_538 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATAGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCATTAAATCC ->dapE_539 -CAAAAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTTGGCGTTTTCAGGGCATATGGATGTCGTTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATCATGAAGAAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_540 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTTAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_541 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCG ->dapE_542 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGATGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_543 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAAGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_544 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAATCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGACGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_545 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGTCACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_546 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_547 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCAGTAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_548 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTATACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_549 -TTGCAAAAATTATTAGCGGAATACAACATCAAAGCTGAGAAAGTGCAGTATGACAAGGATCGCGCAAGCCTTGTGAGTGAAGTAGGTACAGATAATGGCCCTGTTTTAGCCTTTTCTGGACATATGGATGTTGTTGATGCGGGGGACGTTTCCAAGTGGACTTTTCCTCCATTTGAAGCAACGGAATCAGATGGTAAAATTTACGGACGTGGTGCTACTGATATGAAATCAGGGCTGGCTGCGATGGTCATTGCAATGATTGAACTCCATGAAGAAAAAACGAAATTAAATGGGAAAATTAAATTATTGGCGACAGTTGGGGAAGAAGTTGGAGAACTCGGCGCGGAACAACTTACCACAAAAGGTTATGCGGATGATTTAGATGGTTTAATTATCGGCGAGCCAAGTGGACATCGGATTGTTTATGCGCATAAAGGTTCAATTAATTACACGGTTAAATCG ->dapE_550 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGATGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_551 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTACCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_552 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGAGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_553 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTAATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_554 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAGTTCGATGCCGGAATTTGGTGTTAATGCG ->dapE_555 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCAAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_556 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_557 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCTGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCG ->dapE_558 -CTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_559 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCCGAAAAGGTGCAATACGATGTAGACCGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCACGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACAGAATCGTATATGCGCATAAAGGTTCCATCAATTATACCGTTAAATCC ->dapE_560 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGCCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCACGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAGTGGTAAAATTAGATTATTAGCAACGGTTGGGGAAGAAGTCGGTGAACTTGGAGCCAAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_561 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCTGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_562 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTAAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGAAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGCCTAGCTGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCAAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_563 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCAGCAGAGCACGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAGTGGCAAAATTAGATTATTAGCAACGGTTGGGGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_564 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGTATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_565 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGGGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_566 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCCGAAAAGGTGCAATACGATGTAGACCGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCACGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACAGGATCGTATATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_567 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_568 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_569 -TAGGAGAATTACTTTCAAGACACGGAATTGACTCGAAAAAAGTTCAGTATGCGGAAAAGCGCGCTAGCTTAGTCAGTGAAATTGGCGATGGCGGGAACCGAGTATTGGGACTTTCTGGTCACATGGATGTCGTGGATGCGGGGGATCCTGCAAAATGGACGTACCCACCTTTTGAAGCGACGGAAAAAGATGGAAAACTTTACGGACGTGGCTCAACAGACATGAAATCAGGACTTGCGGCGATGGTGATCGCGATGATTGAACTGAAAGATGAAAAGAAAAATTTACCAGGAAAGGTAAAACTGTTAGCCACCGTCGGAGAAGAGGTTGGAGAGCTAGGAGCAGAACAACTTACATCGGAAGGTTATGCGGATGATCTGGATGCCCTCGTCATTGGGGAACCAAGCGGTCCGCGGATTTGGTATGCCCACAAAGGCTCAATGAATTATACAGTCACTTCT ->dapE_570 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATACGACGTAGACAGAGCCAGCCTAATAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAACATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGGTTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_571 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGTATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_572 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTCATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_573 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_574 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCTGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGATTCCAGTAACAAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGTGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_575 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGAGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_576 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_577 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCTACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCATTAAATCC ->dapE_578 -CTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCATTTGAAGCAACAGAGCATGAAGGGAAACTATACGGACGTGGCGCAACGGATATGAAGTCTGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAGCAAAAACTAAATGGCAAGATTAGATTATTAGCAACAGTGGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTGAAATCT ->dapE_579 -TTGCAAGAGTTGTTAGCTGGATACGGAATTGAGTCTGAAAAGGTAGATTATGATGTAGACAGAGCAAGCCTAGTAAGCGAAATTGGTTCCAGTTACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGAGGATGTGTCTAAGTGGAAATTCCCGCCTTTTGAAGCGACAGAACATGAAGGAAAACTATATGGACGTGGCGCGACAGATATGAAGTCAGGTCTAGCAGCGATGGTTATTGCAATGATTGAACTTCAGGAAGAAAAACAAAAACTAAACGGTAAGATCAGATTATTAGCAACAGTTGGTGAAGAAGTCGGCGAACTTGGAGCAGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCAAGTGGACACAGAATCGTATATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_580 -TTGCAAGAGTTGTTAGCTGGATACGGAATTGAGTCTGAAAAGGTAGATTATGATGTAGAAAGAGCAAGCCTAGTAAGCGAAATTGGTTCCAGTTACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGGGATGTGTCTAAGTGGAAATTCCCGCCTTTTGAAGCGACAGAACATGAAGGGAAACTATATGGACGTGGCGCGACAGATATGAAGTCAGGTCTAGCAGCGATGGTTATTGCAATGATTGAACTTCAGGAAGAAAAACAAAAACTAAACGGTAAGATCAGATTATTAGCAACAGTTGGTGAAGAAGTCGGCGAACTTGGAGCAGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCAAGTGGACACAGAATCGTATATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_581 -TTACAGAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAGTGGCAAAATTAGATTATTAGCAACGGTTGGGGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_582 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTGCAATATGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_583 -TTACAGAAGTTGTTAGCTGAATATGGGATTGAATCTGAGAAAGTGCAGTATGATGAAGACAGGGCAAGTCTTGTTAGTGAAATAGGTTCTAGCGATGAGCAAGTTTTGGCTTTTTCAGGGCATATGGATGTGGTTGATGCAGGAGATGTGTCAAAATGGAAGTTTCCTCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGCCGTGGTGCCACGGATATGAAGTCAGGTTTAGCAGCGATGGTCATTGCAATGATTGAGCTGCAAGAAGAAAAGCAGAAACTAAATGGGAAAATTAGATTATTAGCTACAGTTGGTGAAGAAGTTGGCGAACTTGGAGCAGAACAACTAACTCAAAAAGGTTATGCAGATGACTTAGATGGTTTAATTATTGGGGAACCAAGTGGACACCGAATTGTATATGCGCATAAAGGTTCGATTAATTATACCGTTAAATCT ->dapE_584 -TTGCAAAAATTGTTAGCGGAATATAACATCCAAGCTGAAAAAGTGCAATATGACAAGGATCGCGCAAGCCTTGTAAGTGAAGTAGGTACAGATAATGGCCCTGTTTTAGCCTTTTCTGGACATATGGATGTTGTTGATGCGGGTGATGTTTCCAAGTGGACTTTTCCTCCATTTGAAGCAACGGAATCAGAGGGTAAAATTTATGGACGTGGTGCTACTGATATGAAATCAGGGCTGGCTGCGATGGTCATTGCAATGATTGAACTCCATGAAGAAAAAACGAAATTAAATGGGAAAATCAAATTATTGGCGACAGTTGGAGAAGAAGTTGGAGAACTCGGCGCGGAACAACTTACGACAAAAGGGTATGCGGATGATTTAGATGGTTTAATTATCGGTGAGCCAAGTGGACATCGGATTGTTTATGCGCATAAAGGTTCAATTAATTACACGGTTAAATCG ->dapE_585 -TTGCAGAAGTTGTTAAGTGAATATGGGATTGAATCTGAAAAGGTGCAGTATGATGTGGACAGAGCCAGTTTGGTTAGTGAAATTGGTTCTAGCGACGGGCGGATTTTGGCTTTTTCAGGGCATATGGATGTGGTTGATGCCGGAGATGAGTCCAAGTGGAAGTTCCCACCTTTTGAAGCAACGGAGCATGACGGGAAAATATACGGACGCGGAGCTACTGATATGAAATCAGGTTTAGCAGCGATGGTTATTGCGATGATTGAACTTCAAGAAGAGAAGCAAAAATTAAACGGAAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGTGCAGAACAACTAACGCAAAAAGGGTATGCGGATGATTTAGATGGATTGATTATTGGTGAACCAAGCGGGCATCAGATTGTTTACGCCCATAAAGGATCGATTAATTATACCGTTAAATCT ->dapE_586 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCATAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGTGAACCGAGCGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_587 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCG ->dapE_588 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAAATATACGGACGTGGCACGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_589 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAGGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_590 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_591 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_592 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTAAGCGAAATTGGTTCCAGTGACGAGAAAATTTTGGCGTTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCGCCTTTTGAAGCGACAGAGCATGAAGGGAAGCTATACGGACGTGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCT ->dapE_593 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCACCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_594 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATATGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_595 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACTGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTCTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_596 -TTGCAAGAGTTGTTAGCTGGATACGGAATTGAGTCTGAAAAGGTAGATTATGATGTAGACAGAGCAAGCCTAGTAAGCGAAATTGGTTCCAGTTACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGGGATGTGTCTAAGTGGAAATTCCCGCCTTTTGAAGCGACAGAACATGAAGGAAAACTATATGGACGTGGCGCGACAGATATGAAGTCAGGTCTAGCAGCGATGGTTATTGCAATGATTGAACTTCAGGAAGAAAAACAAAAACTAAACGGTAAGATCAGATTATTAGCAACAGTTGGTGAAGAAGTCGGCGAACTTGGAGCAGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCAAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_597 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTAAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAACGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAATCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTTATGAAGAAAAACAAAAACTAAACGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCGAGCGGACACAGAATCGTATATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_598 -TTACAGAAGTTGTTTGCTGAGTATGGTATTGAGTCTGAAAAGGTGCAATATGATGTAGACCGAGCTAGTCTTGTTAGTGAAATTGGTTCTAATGATGGCAAAGTTTTGGCGTTTTCAGGGCATATGGATGTGGTTGATGCTGGCGATGTATCTAAATGGAAGTTCCCGCCTTTTGAAGCAGCGGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGTAAAATTAAATTATTAGCAACAGTTGGTGAAGAAGTCGGTGAACTTGGAGCTGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGCGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_599 -TTGCAAGAGTTATTAGCTGGATACGGAATTGAGTCTGAAAAGGTACATTATGATGTAGACAGAGCAAGCCTAGTAAGCGAAATTGGTTCCAGTTACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGGGATGTGTCTAAGTGGAAATTCCCGCCTTTTGAAGCGACAGAACATGAAGGAAAACTATATGGACGTGGCGCGACAGATATGAAGTCAGGTCTAGCAGCGATGGTTATTGCAATGATTGAACTTCAGGAAGAAAAACAAAAACTAAACGGTAAGATCAGATTATTAGCAACAGTTGGTGAAGAAGTCGGCGAACTTGGAGCAGAACAACTAAGGCAAAAAGGTTACGCAGATGATTTAGATGGTTTGATTATCGGCGAACCAAGTGGACACAGAATCGTATATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_600 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTAAAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_601 -TTACAGAAGTTGTTAGCTGAATATGGGATTGAATCTGAGAAAGTGCAGTATGATGAAGATAGGGCAAGTCTTGTTAGTGAAATAGGTTCTAGCGATGAGCAAGTTTTGGGTTTTTCAGGGCATATGGATGTGGTTGATGCAGGAGATGTGTCAAAATGGAAGTTTCCTCCTTTTGAAGCAACGGAGCATGAAGGGAGAATATACGGCCGTGGTGCCACGGATATGAAGTCAGGTTTAGCAGCGATGGTCATTGCAATGATTGAGCTGCAAGAAGAAAAGCAAAAACTAAATGGGAAAATTAGATTATTAGCTACAGTTGGTGAAGAAGTTGGCGAACTTGGAGCAGAACAACTAACTCAAAAAGGTTATGCAGATGACTTAGATGGTTTAATTATTGGGGAACCAAGTGGACACCGAATTGTATATGCGCATAAAGGTTCGATTAATTATACCGTTAAATCT ->dapE_602 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAATGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCATTAAATCC ->dapE_603 -TTGCAAAAGCTGTTAGCTGAACATGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_604 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAACAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_605 -TTGCAAAAGTTGTTAGCTGAACATGGTATTGAGTCCGAAGAGGTACAATACGACGTAGACAGAGCTAGCCTAGTAAGCGAAATTGGTTCCAGTAACGAGAAGGTTTTGGCATTTTCAGGGCATATGGATGTAGTTGATGCGGGTGATGTATCTAAGTGGAAGTTCCCACCTTTTGAAGCGACAGAGCATGAAGGGAAACTATACGGACGCGGCGCAACGGATATGAAGTCAGGTCTAGCGGCGATGGTTATTGCAATGATTGAACTTCATGAAGAAAAACAAAAACTAAACGGCAAGATCAGATTATTAGCAACAGTTGGGGAAGAAATCGGTGAACTTGGAGCAGAACAACTAACACAAAAAGGTTACGCAGATGATTTAGATGGTTTAATCATCGGCGAACCGAGTGGACACAGAATCGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_606 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAGTCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCTAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGCGCATAAAGGTTCCATTAATTATACCGTTAAATCC ->dapE_607 -TTGCAAAAGTTGTTAGCTGAACACGGTATTGAATCCGAAAAGGTACAATACGACGTAGACAGAGCCAGCCTAGTTAGCGAAATTGGTTCCAGTGACGAGAAAGTTTTGGCGTTTTCAGGGCATATGGATGTCGTTGATGCGGGTGATGTCTCGAAGTGGAAGTTCCCACCTTTTGAAGCAGCAGAGCATGAAGGGAAAATATACGGACGTGGCGCGACGGATATGAAGTCAGGTCTAGCGGCGATGATTATTGCAATGATTGAGCTTCATGAAGAAAAACAAAAACTAAATGGCAAAATTAGATTATTAGCAACGGTTGGTGAAGAAGTCGGTGAACTTGGAGCCGAACAACTAACGCAAAAAGGTTACGCAGATGATTTAGATGGCTTGATTATCGGCGAACCGAGTGGACACCGGATTGTTTATGTGCATAAAGGTTCCATTAATTATACCGTTAAATCC diff --git a/test/MLST_listeria/dat.fasta b/test/MLST_listeria/dat.fasta deleted file mode 100644 index 124253b..0000000 --- a/test/MLST_listeria/dat.fasta +++ /dev/null @@ -1,706 +0,0 @@ ->dat_1 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_2 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCGAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_3 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_4 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCACGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_5 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCTATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_6 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACAGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_7 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAATCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_8 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTAAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCTATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_9 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGTATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_10 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAAAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_11 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTCGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACAGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_12 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACTGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCCGCTTCAAACGTTTCTATTATT ->dat_13 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCCGCTTCAAACGTTTCTATTATT ->dat_14 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCGGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_15 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGATTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_16 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCATTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_17 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_18 -GAAGTAGTTCGACTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAATCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_19 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACAGAATGCTCTGCTTCAAACGTTTCTATTATT ->dat_20 -GAAGTTGTTCGTTTATATAATGGTAAATTTTTTACTTATAACGAGCATATCGATCGTTTATACGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAACATTACGAGCGTTATTAGACAAATTAGTAGCTGAAAATAATATCAATACCGGAAATGTATATTTACAAGTAACAAGAGGAGTTCAAAATCCTCGTAATCACGTTCTTCCAGATGATTTCCCATTAGAAGGAGTATTAACTGCAGCAGCTCGTGAAGTACCAAGAAATGAGCGTCAATTCATAGAAGGCGGCTCAGCAATTACCGAAGAAGATGTACGCTGGTTACGTTGTGATATTAAAAGCTTAAACTTACTTGGAAATATTTTAGCGAAAAACAAAGCGCATCAACAAAACGCACTTGAAGCTATTTTACATCGCGGGGAGCAAGTTACCGAGTGTTCTGCTTCTAACGTATCCATTATT ->dat_21 -GAAGTTGTTCGTTTATATAATGGTAAATTTTTTACTTATAACGAGCATATCGATCGTTTATACGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAACATTACGAGCGTTATTAGACAAATTAGTAGCTGAAAATAATATCAATACCGGAAATGTATATTTACAAGTAACAAGAGGAGTTCAAAATCCTCGTAATCACGTTCTTCCAGATGATTTCCCATTAGAAGGAGTATTAACTGCAGCAGCTCGTGAAGTACCAAGAAATGAGCGTCAATTCATAGAAGGCGGCTCAGCAATTACCGAAGAAGATGTACGCTGGTTACGTTGTGATATTAAAAGCTTAAACTTACTTGGAAATATTTTAGCGAAAAACAAAGCACATCAACAAAACGCACTTGAAGCTATTTTACATCGCGGTGAGCAAGTTACCGAGTGTTCTGCTTCTAACGTATCCATTATT ->dat_22 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAATAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_23 -GAAGTTGTTCGTTTATATAATGGTAAATTTTTTACTTATAACGAGCATATCGATCGTTTATACGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAACATTACGAGCGTTATTAGACAAATTAGTAGCTGAAAATAATATCAATACCGGAAATGTATATTTACAAGTAACAAGAGGAGTTCAAAATCCTCGTAATCACGTTCTTCCAGATGATTTCCCCTTAGAAGGAGTATTAACTGCAGCAGCTCGTGAAGTACCAAGAAATGAGCGTCAATTCATAGAAGGCGGCTCAGCAATTACCGAAGAAGATGTACGCTGGTTACGTTGTGATATTAAAAGCTTAAACTTACTTGGAAATATTTTAGCGAAAAACAAAGCACATCAACAAAACGCGCTTGAAGCTATTTTACATCGCGGGGAGCAAGTTACCGAGTGTTCTGCTTCTAACGTATCCATTATT ->dat_24 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGCGAGCAAGTAACTGAGTGTTCGGCTTCCAATATTTCTATTATT ->dat_25 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_26 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATACGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACAGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_27 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_28 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTTATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_29 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTAATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_30 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTTCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_31 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGTTGTGACATCAAGAGTTTGAATTTACTCGGAAACATTTTAGCAAAAAACAAAGCTCATCAGCAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGTTCAGCTTCCAATATTTCTATTATT ->dat_32 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_33 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACGACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCTCATCAGCAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGTTCAGCTTCCAATATTTCTATTATT ->dat_34 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_35 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCTATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAACTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_36 -GAAGTAGTTCGTCTATATAATGGGAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCAAGTGCTGCAAAAATTGACTTAGTTATTCCTTACTCTAAAGAAGAATTACGGGCATTACTTGAAAAATTAGTTGCTGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGGGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGACTTCCCGCTGGAAGGCGTTTTAACAGCGGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGGCCAGTAATTACAGAAGAAGACGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTGGCGAAAAATAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGCTCAGCTTCCAATATTTCTATTATT ->dat_37 -GAAGTAGTTCGTCTATATAATGGGAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCAAGTGCTGCAAAAATTGATTTAGTTATTCCTTACTCTAAAGAAGAATTACGGGCATTACTTGAAAAATTAGTTGCTGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGGGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGACTTCCCGCTGGAAGGCGTTTTAACAGCGGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGACGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTGGCGAAAAATAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGCTCAGCTTCCAATATTTCTATTATT ->dat_38 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAATGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_39 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAATCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_40 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACTGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCTATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_41 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCAAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_42 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAATGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_43 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACCTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGGCCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_44 -GAAGTTGTTCGTTTATATAATGGTAAATTTTTTACTTATAACGAGCATATCGATCGTTTATACGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAACATTACGAGCATTATTAGACAAATTAGTAGCTGAAAACAATATTAATACCGGAAATGTATATTTACAAGTAACAAGAGGAGTTCAAAATCCTCGTAATCACGTTCTTCCAGATGATTTCCCGTTAGAAGGAGTATTAACTGCAGCAGCTCGTGAAGTACCAAGAAACGAGCTTCAATTCATAGAAGGCGGCACAGCAATTACTGAAGAAGATGTACGCTGGTTACGTTGTGATATTAAAAGCTTAAATTTACTTGGCAATATTTTAGCGAAAAATAAAGCACATCAACAAAACGCACTTGAAGCTATTTTACATCGCGGGGAGCAAGTTACCGAGTGTTCTGCTTCTAACGTATCGATTATT ->dat_45 -GAAGTTGTTCGTTTATATAATGGTAAATTTTTTACTTATAACGAGCATATCGATCGTTTATACGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAACATTACGAGCGTTATTAGACAAATTAGTAGCTGAAAATAATATCAATACCGGAAATGTATATTTACAAGTAACAAGAGGAGTTCAAAATCCTCGTAATCACGTTCTTCCAGATGATTTCCCGTTAGAAGGAGTATTGACTGCGGCAGCTCGTGAAGTACCAAGAAATGAGCGTCAATTCATAGAAGGCGGCTCAGCAATTACTGAAGAAGATGTACGCTGGTTACGTTGTGATATTAAAAGCTTAAACTTACTTGGAAATATTTTAGCGAAAAACAAAGCACATCAACAAAACGCACTTGAAGCTATTTTACATCGCGGGGAGCAAGTTACCGAGTGTTCTGCTTCTAACGTATCCATTATT ->dat_46 -GAAGTTGTTCGTTTATATAATGGTAAATTTTTTACTTATAACGAGCATATCGATCGTTTATACGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAACATTACGAGCATTATTAGACAAATTAGTAGCTGAAAACAATATTAATACCGGAAATGTATATTTACAAGTAACAAGAGGAGTTCAAAATCCTCGTAATCACGTTCTTCCAGATGATTTCCCGTTAGAAGGTGTATTAACTGCAGCAGCACGTGAAGTACCAAGAAATGAGCTTCAATTCATAGAAGGCGGCACAGCAATTACTGAAGAAGATGTACGCTGGTTACGTTGTGATATTAAAAGCTTAAATTTACTTGGCAATATTTTAGCGAAAAACAAAGCACATCAACAAAACGCACTTGAAGCTATTTTACATCGCGGGGAGCAAGTTACCGAGTGTTCTGCTTCTAACGTATCGATTATT ->dat_47 -GAAGTAGTTCGTCTATATAATGGGAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_48 -GAAGTTGTTCGTTTATATAATGGTAAATTTTTTACTTATAACGAGCATATCGATCGTTTATACGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAACATTACGAGCGTTATTAGACAAATTAGTAGCTGAAAATAATATCAATACCGGAAATGTATATTTACAAGTAACAAGAGGAGTTCAAAATCCTCGTAATCACGTTCTTCCAGATGATTTCCCATTAGAAGGAGTATTAACTGCAGCAGCTCGTGAAGTACCAAGAAATGAGCGTCAATTCATAGAAGGCGGCTCAGCAATTACTGAAGAAGATGTACGCTGGTTACGTTGTGATATTAAAAGCTTAAACTTACTTGGAAATATTTTAGCGAAAAACAAAGCACATCAACAAAACGCGCTTGAAGCTATTTTACATCGCGGGGAGCAAGTTACCGAGTGTTCTGCTTCTAACGTATCCATTATT ->dat_49 -GAAGTTGTTCGTTTATATAATGGTAAATTTTTTACTTATAACGAGCATATCGATCGTTTATACGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAACATTACGAGCGTTATTAGACAAATTAGTAGCTGAAAATAATATCAATACCGGAAATGTATATTTACAAGTAACAAGAGGAGTTCAAAATCCTCGTAATCACGTTCTTCCAGATGATTTCCCCTTAGAAGGAGTATTAACTGCAGCAGCTCGTGAAGTACCAAGAAATGAGCGTCAATTCATAGAAGGCGGCTCAGCAATTACCGAAGAAGATGTACGCTGGTTACGTTGTGATATTAAAAGCTTAAACTTACTTGGAAATATTTTAGCGAAAAACAAAGCACATCAACAAAACGCACTTGAAGCTATTTTACATCGCGGGGAGCAAGTTACCGAGTGTTCTGCTTCTAACGTATCCATTATT ->dat_50 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATAGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCGAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_51 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGCGAGCAAGTAACTGAGTGTTCGGCTTCCAATATTTCTATTATT ->dat_52 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGTAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_53 -GAAGTTGTTCGTTTATATAATGGTAAATTTTTTACTTATAACGAGCATATCGATCGTTTATACGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAACATTAAGAGCGTTATTAGAAAAATTAGTAGCTGAAAATAATATCAATACCGGAAATGTATATTTACAAGTAACAAGAGGAGTTCAAAATCCTCGTAATCACGTTCTTCCAGATGATTTCCCGTTAGAAGGGGTATTAACTGCAGCAGCTCGTGAAGTACCAAGAAATGATCGTCAATTCATAGAAGGTGGCTCAGCAATTACTGAAGAAGATGTACGTTGGTTACGTTGTGATATTAAAAGCTTAAATTTACTTGGAAATATTTTAGCGAAAAACAAAGCACATCAACAAAACGCACTTGAAGCTATTTTACATCGCGGGGAGCAAGTTACTGAGTGTTCTGCTTCTAACGTATCCATTATT ->dat_54 -GAAGTAGTTCGTCTGTATAATGGAAAATTCTTTACATATGATGAACATATTGATCGCCTATACGCTAGCGCCGCAAAAATCGACTTAGTTATTCCTTATTCCAAAGAAACGTTACGTGAATTATTAGAGAAATTAGTAGCTGAAAATAATATTAGTACTGGTAATGTCTATTTGCAAGTAACTCGAGGTGTTCAAAAACCACGTAATCACGTGTTCCCAGATGATTACGCACTAGAAGGTGTCTTAACTGCCGCAGCTCGTGAAGTACCTCGTAATGAGCGTCAATTTGTAGAAGGTGGAACAGCAATTACTGAAGAAGATATACGTTGGTTGCGTTGCGATATTAAGAGTTTAAATCTACTTGGAAATATTTTAGCTAAAAACAAAGCACACCAACAAAATGCGTTAGAAGCAATTCTTCACCGCGGAGAACAGGTTACTGAATGTTCAGCTTCCAATGTTTCTATTATT ->dat_55 -GAAGTTGTTCGTTTATATAATGGTAAATTTTTTACTTATAACGAGCATATCGATCGTTTATACGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAACATTACGAGCGTTATTAGACAAATTAGTAGCTGAAAATAATATCAATACCGGAAATGTATATTTACAAGTAACAAGAGGAGTTCAAAATCCTCGTAATCACGTTCTTCCAGATGATTTCCCATTAGAAGGAGTATTAACTGCAGCAGCTCGTGAAGTACCAAGAAATGAGCGTCAATTCATAGAAGGCGGCTCAGCAATTACCGAAGAAGATGTACGCTGGTTACGTTGTGATATTAAAAGCTTAAACTTACTTGGAAATATTTTAGCGAAAAACAAAGCACATCAACAAAACGCACTTGAAGCTATTTTACATCGCGGGGAGCAAGTTACCGAGTGTTCTGCTTCTAACGTATCCATTATT ->dat_56 -GAAGTTGTTCGTTTGTATAATGGCCAATTTTTTACATATGAAGAACATATCGAACGCTTGTATGCTAGCGCAGCAAAAATCGATTTAGTCATTCCTTATTCCAAAGAAGAACTACGTGAATTAATTGATAAGTTAGTTGCAGAAAATAACATTCATACTGGAAATGTCTATTTACAAGTGACCCGAGGCGTTCAAAATCCTCGAAACCATGTTATTCCAGATGATTTTCCATTAGAAGGAGTTCTCACAGCAGCAGCACGTGAGGTTCCTCGGAATGAAAAACAATTTATCGAAGGCGGAACTGCAATTACAGAAGAAGATGTACGCTGGTTACGCTGCGATATTAAAAGTTTAAGTTTACTAGGTAATATTATGGCCAAAAATAAAGCGCATCAACAAAATGCACTAGAAGCTATTTTGCACCGCGGGGAGCAAGTTACCGAATGTTCTGCGTCGAATGTTTCAATCATT ->dat_57 -GAAGTTGTTCGTTTGTATAATGGCAAATTCTTTACATATGATGAACATATCGACCGTTTATATGCCAGCGCTGCAAAAATTGATTTAGTCATACCATATTCCAAAGAAGAACTACGTAAATTAATTGAAGAGTTAGTGGCGGCAAATAATATTCATACAGGAAATGTTTATTTACAGGTAACTCGTGGCGTCCAAAATCCTCGTAACCATGTGATTCCAGATGATTTCCCATTAGAAGGAGTACTAACAGCAGCAGCTCGCGAGGTTCCTAGAAATGAAAAACAATTTATCGAAGGTGGAACAGCGATTACAGAAGAAGATGTGCGCTGGTTACGTTGTGACATTAAAAGTTTAAGCTTACTTGGAAACATTATGGCCAAGAATAAAGCACATCAACAAGATGCTTTAGAAGCTATTTTGCACCGCGGTGAACAAGTTACCGAATGTTCCGCATCCAATGTTTCTATTATT ->dat_58 -GAAGTAGTTCGTCTATATAATGGGAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCAAGTGCTGCAAAAATTGACTTAGTTATTCCTTACTCTAAAGAAGAATTACGGGCATTACTTGAAAAATTAGTTGCTGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGGGGTGTTCAAAACCCGCGTAACCATGTTATGCCAGATGACTTCCCGCTGGAAGGCGTTTTAACAGCGGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGACGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTGGCGAAAAATAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGCTCAGCTTCCAATATTTCTATTATT ->dat_59 -GAAGTAGTTCGTCTATATAATGGGAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCAAGTGCTGCAAAAATTGACTTAGTTATTCCTTACTCTAAAGAAGAATTACGGGCATTACTTGAAAAATTAGTTGCTGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTAGGGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGACTTCCCGCTGGAAGGCGTTTTAACAGCGGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGACGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTGGCGAAAAATAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGCTCAGCTTCCAATATTTCTATTATT ->dat_60 -GAAGTAGTTCGTCTATATAATGGGAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCAAGTGCTGCAAAAATTGACTTAGTTATTCCTTACTCTAAAGAAGAATTACGAGCATTACTTGAAAAATTAGTTGCTGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGGGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGACTTCCCGCTGGAAGGCGTTTTAACAGCGGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGACGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTGGCGAAAAATAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGCTCAGCTTCCAATATTTCTATTATT ->dat_61 -GAAGTAGTTCGTCTATATAATGGGAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCAAGTGCTGCAAAAATTGACTTAGTTATTCCTTACTCTAAAGAAGAATTACGGGCATTACTTGAAAAATTAGTTGCTGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGGGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGACTTCCCGCTGGAAGGCGTTTTAACAGCGGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGACGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTGGCGAAAAATAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGCTCAGCTTCCAATATTTCTATTATT ->dat_62 -GAAGTAGTTCGTCTATATAATGGGAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCAAGTGCTGCAAAAATTGATTTAGTTATTCCTTACTCTAAAGAAGAATTACGAGCATTACTTGAAAAATTAGTTGCTGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGGGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGACTTCCCGCTGGAAGGCGTTTTAACAGCGGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGGCCAGTAATTACAGAAGAAGACGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTGGCGAAAAATAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGCTCAGCTTCCAATATTTCTATTATT ->dat_63 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTAATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCTATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_64 -GAAGTAGTTCGTTTGTATAATGGCCAATTTTTTACATATGAAGAACATATCGAACGCTTGTATGCTAGCGCAGCAAAAATCGATTTAGTCATTCCTTATTCCAAAGAAGAACTACGTGAATTAATTGATAAGTTAGTTGCAGAAAATAACATTCATACTGGAAATGTCTATTTACAAGTGACCCGAGGCGTTCAAAATCCTCGAAACCATGTTATTCCAGATGATTTTCCATTAGAAGGAGTTCTCACAGCAGCTGCACGTGAGGTTCCTCGGAATGAAAAACAATTTATCGAAGGCGGAACTGCAATTACAGAAGAAGATGTACGCTGGTTACGCTGCGATATTAAAAGTTTAAGTTTGCTAGGTAATATTATGGCCAAAAATAAAGCGCATCAACAAAATGCATTAGAAGCTATTTTGCACCGCGGGGAGCAAGTTACCGAATGTTCTGCGTCGAATGTTTCAATCATT ->dat_65 -GAAGTTGTTCGTTTATATAATGGTAAATTTTTTACTTATAACGAGCATATCGATCGTTTATACGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAACATTACGAGCGTTATTAGACAAATTAGTAGCTGAAAATAATATCAATACCGGAAATGTATATTTACAAGTAACAAGAGGAGTTCAAAATCCTCGTAATCACGTTCTTCCAGATGATTTCCCCTTAGAAGGAGTATTAACTGCAGCAGCTCGTGAAGTACCAAGAAATGAGCGTCAATTCATAGAAGGCGGCTCAGCAATTACCGAAGAAGATGTACGCTGGTTACGTTGTGATATTAAAAGCTTAAACTTACTTGGAAATATTTTAGCGAAAAACAAAGCGCATCAACAAAACGCACTTGAAGCTATTTTACATCGCGGGGAGCAAGTTACCGAGTGTTCTGCTTCTAACGTATCCATTATT ->dat_66 -GAAGTTGTTCGTTTGTATAATGGCCAATTTTTTACATATGAAGAACATATCGAACGCTTGTATGCTAGCGCAGCAAAAATCGATTTAGTCATTCCTTATTCCAAAGAAGAACTACGTGAATTAATTGATAAGTTAGTTGCAGAAAATAACATTCATACTGGAAATGTCTATTTACAAGTGACCCGAGGCGTTCAAAATCCTCGAAACCATGTTATTCCAGATGATTTTCCATTAGAAGGAGTTCTCACAGCAGCAGCACGTGAGGTTCCTCGGAATGAAAAACAATTTATCGAAGGCGGAACTGCAATTACAGAAGAAGATGTACGCTGGTTACGCTGCGATATTAAAAGTTTAAGTTTACTAGGTAATATTATGGCCAAAAATAAAGCGCATCAACAAAATGCACTAGAAGCTATTTTGCACCGCGGGGAACAAGTTACCGAATGTTCTGCGTCGAATGTTTCAATCATT ->dat_67 -GAAGTAGTTCGTCTGTATAATGGAAAATTCTTTACATATGATGAACATATTGATCGCCTATACGCTAGCGCCGCAAAAATCGACTTAGTTATTCCTTATTCCAAAGAAACGTTACGTGAATTATTAGAGAAATTAGTAGCTGAAAATAATATTAGTACTGGTAATGTCTATTTGCAAGTAACTCGAGGTGTTCAAAAACCACGTAATCACGTGTTCCCAGATGATTACGCACTAGAAGGTGTCTTAACTGCCGCAGCTCGTGAAGTACCTCGTAATGAGCGTCAATTTGTAGAAGGTGGAACAGCAATTACTGAAGAAGATATACGTTGGTTGCGTTGCGATATTAAGAGTTTAAATCTACTTGGAAATATTTTAGCTAAAAACAAAGCACATCAACAAAATGCATTAGAAGCAATTCTTCACCGCGGAGAACAGGTTACTGAATGTTCAGCTTCCAATGTTTCTATTATT ->dat_68 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGAGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCACTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGTTCAGCTTCCAATATTTCCATTATT ->dat_69 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCGAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_70 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGAGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCATTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_71 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCATTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_72 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_73 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCGGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_74 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTTGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_75 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_76 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGACCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACAGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_77 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTATTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACAGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_78 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTATTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_79 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCGAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTAGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_80 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCGAAAGAAGAGTTACGAGTGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_81 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACATAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCTATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_82 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACCTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCTATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_83 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCGAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_84 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATCATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACAGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_85 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAAACCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_86 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGTTGTGACATCAAGAGTTTGAATTTACTCGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_87 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAAAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_88 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_89 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_90 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCTCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_91 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGAGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCACTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGTTCAGCTTCCAATATTTCTATTATT ->dat_92 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTCTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_93 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGACTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGTTCAGCTTCCAATATTTCTATTATT ->dat_94 -GAAGTATTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_95 -GAAGTTGTTCGTTTGTATAATGGTAAATTTTTTACATATGATGAACATATCGAACGTTTATATGCTAGCGCTGCAAAAATTGATTTAGTTATTCCGTATTCCAAAGAAGAACTACGTAATTTAATTGAAGAGTTAGTGGAGGTAAATGATATTCATACAGGAAATGTTTATTTACAAGTAACTCGTGGCGTCCAAAATCCTCGCAACCATGTGATTCCAGATGATTTCCCATTAGAAGGTGTTTTGACTGCTGCGGCTCGAGAGGTTCCTAGAAATGAAAAACAATTTATCGAAGGCGGAACAGCGATTACAGAAGAAGATGTACGCTGGTTACGTTGTGACATTAAAAGTTTAAGCTTACTTGGAAACATTATGGCCAAAAATAAAGCACATCAACAAGATGCTTTAGAAGCTATTTTACACCGCGGTGAACAGGTTACCGAATGTTCCGCATCTAATGTTTCTATCATT ->dat_96 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGAGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_97 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTCGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_98 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCGAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAAACCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_99 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCATTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACGACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCTCATCAGCAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGTTCAGCTTCCAATATTTCTATTATT ->dat_100 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTGCTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAATAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_101 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_102 -GAAGTAGTACGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAAGGGCGAGCTTCAAACGTTTCTTCTAGT ->dat_103 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACGACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCTCATCAGCAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGCACTAAAAGGGCGAGCTTCAAACGTTTCTTCTAGC ->dat_104 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACGACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCTCATCAGCAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGTTCAGCTTCAAACGTTTCTTCTAAC ->dat_105 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACGACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCTCATCAGCAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGTTCAGCTTCCAATGTTTCTTCTAAC ->dat_106 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACGACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCTCATCAGCAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGTTCAGCTTCCAACGTTTCTTCTAGC ->dat_107 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTAAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_108 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCTCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_109 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAAAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_110 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCAAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_111 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAAGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCCGCTTCAAACGTTTCTATTATT ->dat_112 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTCGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_113 -GAAGTAGTACGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_114 -GAAGTTGTTCGTCTTTACAACGGTCAGTTTTTTACATTTGATGAACATATTGATCGCTTGTACGAAAGCGCAGCCAAGATCGACCTTGTCATTCCTTACGCAAAGGATGTCTTAAGAAATTTGTTACAAAAGCTTGCAAAAGAAAACAATATTCATACTGGAAACATTTACCTGCAAGTAACTCGCGGCATCCAAATTCCGAGAAATCATATTATTCCTGACGATTTTCCACTTGAGGGCATCTTAACAGCTTCTGCTCGTGAAGTTCCACGGGATGAAGCACTCTTTTTAGAAGGTGGACGAGCGATAACTGATGAAGATATCCGCTGGTTACGTTGCGACATCAAGAGTATCAGTCTACTTGGAAATATTTTAGCTAAAAATAAAGCACACCGGGCTGGTGCACTAGAAGCTATTTTACATCGAGGTGGCACCGTGACGGAGTGCTCTGCTTCAAACGTTTTTATAGTT ->dat_115 -GAAGTAATTCGGCTTTATAATAAAAAATTCTTTACATTTGAAGAGCACATCGACCGTTTATTTGCGAGTGCAGCGAAAATCGAACTAGGAATTCCTTATTCTAAAGAAAAATTGCGGGAACTTTTAGAAAATTTGGTAAAAGAAAATGATATCGACACAGGGAATGTTTATCTACAAGTATCTCGAGGCGTGCAACAACCCCGCAACCATATCATTCCAGATGACCTTGCCTTAGTGGGCATCTTGACAGCTTCGGCACAAGAAGTGCCACGCAATCCTCATTTATTCGAAGATGGTGGAACGGCGATCATAGAACCAGATACAAGATGGCTGCATTGTGATATCAAAAGTATCAGTTTGCTGGGAAATGTACTGGCTAAAAATCGGGCGCATCGTGCTGGTGCAATGGAAGCGATTTTGCATCGCGATGGCGAAGTTACCGAATGCTCGGCTTCCAATGTATATATGATC ->dat_116 -GAAGTAATTCGGCTTTATAATAAAAAATTCTTTACATTTGAAGAGCACATCGACCGTTTATTTGCGAGTGCAGCGAAAATCGAACTAGGAATTCCTTATTCCAAAGAAAAATTGCGGGAACTTTTAGAAAATTTGGTAAAAGAAAATGATATCGACACAGGGAATGTTTATCTACAAGTATCTCGAGGCGTGCAACAACCCCGCAACCATATCATTCCAGATGACCTTGCCTTAGTGGGCATCTTGACAGCTTCGGCACAAGAAGTGCCACGCAATCCTCATTTATTCGAAGATGGTGGAACGGCGATCATAGAACCAGATACAAGATGGCTACATTGTGATATCAAAAGTATCAGTTTGCTGGGAAATGTACTGGCTAAAAATCGGGCGCATCGTGCTGGTGCAATGGAAGCGATTTTGCATCGCGATGGTGAAGTTACCGAATGCTCGGCTTCCAATGTATATATGATC ->dat_117 -GAAGTAGTTCGTCTGTATAATGGAAAATTCTTTACATATGATGAACATATTGATCGCCTATACGCTAGCGCCGCAAAAATCGACTTAGTTATTCCTTATTCCAAAGAAACGTTACGTGAATTATTAGAGAAATTAGTAGCTGAAAATAATATTAGTACTGGTAATGTCTATTTGCAAGTAACTCGAGGTGTTCAAAAACCACGTAATCACGTGTTCCCAGATGATTACGCACTAGAAGGTGTCTTAACTGCCGCAGCTCGTGAAGTACCTCGTAATGAGCGTCAATTTGTAGAAGGTGGAACAGCAATTACTGAAGAAGATATACGTTGGTTACGTTGCGATATTAAGAGTTTAAATCTACTTGGAAATATTTTAGCTAAAAACAAAGCACATCAACAAAATGCATTAGAAGCAATTCTTCACCGCGGAGAACAGGTTACTGAATGTTCAGCTTCCAATGTTTCTATTATT ->dat_118 -GAAGTAGTTCGTTTATATAATGGTAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGTGAATTATTAGAAAAATTAGTTGCCGAAAATAATATTAATACAGGAAATGTATACTTGCAAGTGACACGTGGGGTTCAAAATCCACGTAATCATGTGATGCCAGATGATTTCCCATTAGAAGGCGTTTTAACTGCTGCAGCTCGCGAAGTACCAAGAAATGAGCAGCAATTTGTAGAAGGCGGACCGGTTATTACAGAAGAAGATGTGCGCTGGTTACGTTGCGATATCAAGAGCTTGAACTTACTTGGAAACATTTTAGCGAAAAACAAAGCGCATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACCGAGTGTTCTGCTTCCAATGTTTCTATTATT ->dat_119 -GAAGTTGTACGTATGTATAATGGCAAGTTTTTTACATATGAAGAACATATAGATCGTCTGTATGCGAGCGCGGCTAAGATTGACCTAGTTATTCCTTATAGCAAACCAGAATTGCGTGCGATTTTGGACAGTTTAGTAACAGCGAATAACGTTGGAACTGGTAATGTGTATCTTCAAGTGACGCGAGGCATTCAATCTCCGCGTAATCATGTTATTCCTGAGACACCTCTAAAAGCAGTCTTGACGGCTTCCACAAGTGAAGTTCCGCGTGACATGACGCTGTTTGAAGATGGTCGTAAAGCGATTGTCGAAGAAGATGTTCGCTGGTTGCGTTGCGACATTAAGAGCTTGAACTTACTAGGTAATTCGATGGCTAAAAATAAAGCGCACCAAGCAGGAGCTTTGGAAGCGATTTTGCATCGTGATGGAAGCGTGACGGAAGGTTCGGCAACCAACGCGTATATGATA ->dat_120 -GAAGTCGTACGGATGTACAACGGGAAATTTTTCACAGCAGAGGAACATATTGATCGTTTATATGCGAGTGCAGCAAAAATAGATTTAGTCATTCCTTACTCAAAAGAAGTATTATATAAATCCCTTTTAAAACTTGCAGAGGAAAATGGGATTTCGACAGGAAATATATACTTACAAGTAACAAGAGGCGTTCAACAACCACGTAATCATATTATTCCAGACGAGTTTCCACTTGAAGGTGTTCTGACAGCAGCGGCTAGAGAAGTTCCGCGTAATGAAAACCAATTTGTGGTCGGGGGAACGGCAATTACAGAGGAAGATGTACGTTGGTTACGATGTGATATTAAAAGTATTAGTTTACTAGGCAATATTTTAGCTAAAAATAAAGCTCATCAGAAAGGGGCTTTAGAAGCGATTTTGCACCGTGGCGATATGGTAACAGAATGCTCAGCCTCGAATGTTTCGATTATA ->dat_121 -GAAGTTGTTCGTTTATATAATGGACAATTCTTTACTTTTGACGAACATATTGATCGACTTTATGCGAGTGCAGCTAAGATTGACTTAGTCATTCCTTATCCAAAAGATATTTTGAAAAACCTTCTGAAACAGCTTGCCTCTGAAAACAATATCCATACAGGGAATGTGTATCTACAAGTAACACGCGGGATTCAAATTCCTAGAAATCACGTGATTCCGGATGACTTACCTTTGGAAGGCGTTTTAACAGCCACCGCGCGTGAAGTTCCTCGAAACGAAGCTCTTTTTGTGACCGGCGGAAAAGCGATTACGGACGAAGATGTTCGCTGGCTTCGCTGTGACATCAAAAGCATCAGCCTGCTTGGAAATATAATGTCAAAAAACAAAGCACATCGTGAAGGTGCTCTTGAAGCTATTTTACATCGCGGTGATACGATTACAGAGTGTTCTGCTTCGAATGTGTTTATCATT ->dat_122 -GAAGTTGTTCGTCTTTACAACGATCAGTTTTTTACATTTGATGAACATATTAATCGCTTGTACGAAAGCGCAGCCAAGATCGACCTTGTCATTCCTTACGCAAAGGATGTCTTAAGAAACTTGTTACAAAAACTTGCAAAAGAAAACAATATCCATACTGGAAACGTTTACCTGCAAGTAACTCGCGGCATCCAAATTCCGAGAAATCATATCATTCCTGACGATTTTCCACTTGAGGGCATCTTAACAGCTTCTGCTCGTGAAGTTCCACGGAATGAAGCACTCTTTGTAGAAGGTGGGCGAGCGATAACTGATGAAGATATCCGCTGGTTACGTTGCGACATCAAGAGTATCAGCCTACTTGGAAATATTTTAGCCAAAAATAAAGCACACCGAGCTGGTGCACTAGAAGCTATTTTACATCGAGGTGGCACCGTGACGGAGTGCTCTGCTTCAAACGTTTTTATAGTT ->dat_123 -GAAGTTGTTCGTGCTTATAATGGCCAATTCTTTACATATGAAGAGCACATTGATCGCCTGTACGCCAGTGCGAATAAGATTGATCTTGTTATTCCTTTTGAAAAAGCAGAATTACGTGAATTACTGGAAGGCTTATTAAAAGCAAATAATATTGGAACAGGGAATGTGTACTTGCAAGTTACGCGCGGTATTCAATCACCGCGAAATCATGTAGTGCCTGATCTTCCTTTAGAAGGTGTGCTGACGGCTTCTGCAAGTGAAGTGTCGCGTGACACGACGCTATTTGAACAAGGACGCAAAGCGATTTTGGAAGAGGATGTTCGTTGGCTACGTTGTGATATTAAGAGCTTGAACTTGCTTGGAAACACGATGGCAAAGAACAAGGCGCACCAAGCTGGAGCTTTCGAAGCGATTCTGCACCGTGGTGAGGAAGTGACGGAAGGCTCGTCTACCAATGCGTATATTATT ->dat_124 -GAGGTTATTCGTATGTATAATGGCCAATTTTTTACATATGATGAGCATATCGAGCGTTTGTATGCTAGCGCGGCCAAAATTGATCTTGTGATTCCTTATAGCAAGCCGGAATTGCGCGCGCTTGTGGACAGTTTGATGAAAGCGAACAATATTGGGACTGGTAATGTGTATCTTCAAATCACGCGTGGGGAGCAGTCGCCACGTAATCATGTGATTCCAAGCACGCCGCCTGAAGCTGTTCTCACGGCCTCCGCTAGCGAAGTTCCGCGTGATACGACGCTATTTGAGAACGGCCGAAAAGCAATTCTGGAAGAGGATGTTCGCTGGTTGCGCTGCGATATTAAGAGCCTTAACTTACTAGGAAATTCGATTGCAAAAAATAAAGCGCATCAAGCCGGGGCGTTTGAAGCGATTCTGCACCGTGGGGAAAACGTGACAGAAGGTTCTGCTGCGAATGCTTACATGATT ->dat_125 -GAGGTTGTTCGTATGTATAATGGCACGTTTTTTACATATGATGAGCACATTGAGCGCTTGTATGCCAGTGCAGCTAAGCTTGACCTTGTGATTCCGTACAGCAAGCCAGAATTACGCAAACTTTTGGACGGTCTTGTGAAAGCAAACAATGTTGGGACTGGTAATGTGTATCTTCAAATCACGCGAGGTGTGCAATCTCCACGTAATCACGTGATTCCAAGTACACCACTGGAGGCAGTGCTTACAGCTTCGACGAGTGAGGTGCCAAGGGATACGACGCTGTTTGAGAACGGTCGTAAAGCGATTCTTGAAGAGGACATTCGTTGGTTGCGTTGTGATATTAAGAGCTTGAATTTACTTGGAAATACCATTGCAAAGAATAAAGCGCACCAAGCAGGTGCATTTGAAGCGATTTTGCATCGTGGGGAGAACGTGACGGAAGGCTCTGCGGCGAATGCTTACATTATT ->dat_126 -GAGGTTATTCGTATGTACAACGGGCAGTTTTTTACATATGATGAGCATATTGAGCGTTTGTATGCCAGCGCGGCTAAAATCGATCTTGATATCCCATACAGCAAGCCAGAATTGCGCAAGCTCGTAGATGGTTTGATGAAGGCGAATAATATTGGGACTGGTAATGTGTATCTGCAGATCACGCGTGGAGAGCAGTCGCCTCGTAATCACGTTATTCCAAGCACGCCGCCAGAAGCTGTACTTACTGCATCAGCGAGTGAAGTGCCACGTGATAAGACATTGTTTGAGAATGGACGTAAAGCTATTCTGGAAGAGGACGTTCGCTGGTTGCGATGCGATATTAAGAGCCTTAACCTATTAGGGAATTCAATTGCTAAAAATAAAGCGCATCAAGCTGGTGCATTTGAAGCTATCCTGCATCGTGGGGAGAGCGTGACAGAAGGTTCAGCAGCAAATGCCTATATTGTT ->dat_127 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAACAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_128 -GAAGTAGTTCGTCTGTATAATGGAAAATTCTTTACATATGATGAACATATTGATCGCCTATACGCTAGCGCCGCAAAAATCGACTTAGTTATTCCTTATTCCAAAGAAACGTTACGTGAATTATTAGAGAAATTAGTAGCTGAAAATAATATTAGTACTGGTAATGTCTATTTGCAAGTAACTCGAGGTGTTCAAAAACCACGTAATCACGTGTTCCCAGATGATTACGCACTAGAAGGTGTCTTAACCGCCGCAGCTCGTGAAGTACCTCGTAATGAGCGTCAATTTGTAGAAGGTGGAACAGCAATTACTGAAGAAGATATACGTTGGTTGCGTTGCGATATTAAGAGTTTAAATCTACTTGGAAATATTTTAGCTAAAAACAAAGCACATCAACAAAATGCATTAGAAGCAATTCTTCACCGCGGAGAACAGGTTACTGAATGTTCAGCTTCCAATGTTTCTATTATT ->dat_129 -GAAGTAGTCCGTCTGTATAATGGAAAATTCTTTACATATGATGAACATATTGATCGCCTATACGCTAGCGCCGCAAAAATCGACTTAGTTATTCCTTATTCCAAAGAAACGTTACGTGAATTATTAGAGAAATTAGTAGCTGAAAATAATATTAGTACTGGTAATGTCTATTTGCAAGTAACTCGAGGTGTTCAAAAACCACGTAATCACGTGTTCCCAGATGATTACGCACTAGAAGGTGTCTTAACTGCCGCAGCTCGTGAAGTACCTCGTAATGAGCGTCAATTTGTAGAAGGTGGAACAGCAATTACTGAAGAAGATATACGTTGGTTGCGTTGCGATATTAAGAGTTTAAATCTACTTGGAAATATTTTAGCTAAAAACAAAGCACACCAACAAAATGCGTTAGAAGCAATTCTTCACCGCGGAGAACAGGTTACTGAATGTTCAGCTTCCAATGTTTCTATTATT ->dat_130 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAAAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_131 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCATTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACAGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_132 -GAAGTTGTTCGTTTATATAATGGTAAATTTTTTACTTATAACGAGCATATCGATCGTTTATACGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAACATTACGAGCGTTATTAGACAAATTAGTAGCTGAAAATAATATCAATACCGGAAATGTATATTTACAAGTAACAAGAGGAGTTCAAAATCCTCGTAATCACGTTCTTCCAGATGATTTCCCCTTAGAAGGAGTATTAACTGCAGCAGCTCGTGAAGTACCAAGAAATGATCGTCAATTCATAGAAGGTGGCTCAGCAATTACTGAAGAAGATGTACGTTGGTTACGTTGTGATATTAAAAGCTTAAATTTACTTGGAAATATTTTAGCGAAAAATAAAGCACATCAACAAAACGCACTTGAAGCTATTTTACATCGCGGGGAGCAAGTTACTGAGTGTTCTGCTTCTAACGTATCCATTATT ->dat_133 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACTCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_134 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGATTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_135 -GAAGTAGTTCGTCTGTATAATGGAAAATTCTTTACATATGATGAACATATTGATCGCCTATACGCTAGCGCCGCAAAAATCGACTTAGTTATTCCTTATTCCAAAGAAACGTTACGTGAATTATTAGAGAAATTAGTAGCTGAAAATAATATTAGTACCGGTAATGTCTATTTGCAAGTAACTCGAGGTGTTCAAAAACCACGAAATCACGTGTTCCCAGATGATTACGCACTAGAAGGTGTCTTAACTGCTGCAGCTCGTGAAGTACCTCGTAATGAGCGTCAATTTGTAGAAGGTGGAACAGCAATTACTGAAGAAGATATACGTTGGTTGCGTTGCGATATTAAGAGTTTAAATCTACTTGGAAATATTTTAGCTAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCAATTCTTCACCGCGGAGAACAGGTTACTGAATGTTCAGCTTCCAATGTTTCTATTATT ->dat_136 -GAAGTTGTTCGTTTATATAATGGTAAATTTTTTACTTATAACGAGCATATCGATCGTTTATACGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAACATTAAGAGCGTTATTAGAAAAATTAGTAGCTGAAAATAATATCAATACCGGAAATGTATATTTACAAGTAACAAGAGGCGTTCAAAATCCTCGTAATCACGTTCTTCCAGATGATTTCCCGTTAGAAGGGGTATTAACTGCAGCAGCTCGTGAAGTACCAAGAAATGATCGTCAATTCATAGAAGGTGGCTCAGCAATTACTGAAGAAGATGTACGTTGGTTACGTTGTGATATTAAAAGCTTAAATTTACTTGGAAATATTTTAGCGAAAAATAAAGCACATCAACAAAACGCACTTGAAGCTATTTTACATCGCGGGGAGCAAGTTACTGAGTGTTCTGCTTCTAACGTATCCATTATT ->dat_137 -GAGGTTATTCGTATGTATAATGGCCAATTTTTTACATATGATGAGCATATCGAGCGTTTGTATGCGAGCGCGGCTAAAATTGATCTTGTGATTCCTTATAGCAAGCCGGAATTGCGTGCGCTTGTGGACAGTTTGATGGAAGCGAACAATATTGGGACTGGTAATGTGTATCTTCAAATCACGCGTGGGGAGCAGTCGCCACGTAATCATGTGATTCCAAGCACGCCGCCGGAAGCTGTGCTCACCGCCTCTGCTAGTGAAGTTCCGCGTGATACGACGCTATTTGAGAACGGCCGCAAAGCGATTCTGGAAGAGGACGTTCGCTGGTTGCGTTGCGATATTAAGAGCCTTAATTTACTGGGAAATTCGATTGCAAAAAATAAAGCGCATCAAGCTGGGGCGTTTGAAGCAATTCTGCATCGCGGGGAAAACGTGACGGAAGGCTCTGCGGCGAATGCTTACATGATT ->dat_138 -GAAGTAGTTCGTTTGTATAATGGTCAATTTTTTACATATGAAGAACATATCGAACGCTTGTATGCTAGCGCAGCAAAAATCGATTTAGTTATTCCTTATTCCAAAGAAGAACTACGTAAATTAATTGATAAGTTAGTTGCAGAAAATAATATTCATACTGGAAATGTCTATTTACAAGTGACCCGAGGCGTTCAAAATCCTCGAAACCATGTTATTCCAGATGATTTTCCATTAGAAGGAGTTCTCACAGCAGCAGCACGTGAGGTTCCTCGAAATGAAAAACAATTTATCGAGGGCGGAACTGCAATTACAGAAGAAGATGTACGCTGGTTACGCTGCGATATTAAAAGTTTAAGTTTGCTAGGTAACATTATGGCCAAAAATAAAGCGCATCAACAAAATGCACTAGAAGCTATTTTGCACCGCGGGGAGCAAGTTACCGAATGTTCTGCGTCGAATGTTTCAATCATT ->dat_139 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_140 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTAGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_141 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGAATTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTTCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_142 -GAAGTAGTTCGTTTGTATAATGGCCAATTTTTTACATATGAAGAACATATCGAACGCTTGTATGCTAGCGCAGCAAAAATCGATTTAGTTATTCCTTATTCCAAAGAAGAACTACGTAAATTAATTGATAAGTTAGTTGCAGAAAATAATATTCATACTGGAAATGTCTATTTACAAGTGACCCGAGGCGTTCAAAATCCTCGAAACCATGTTATTCCAGATGATTTTCCATTAGAAGGAGTTCTCACAGCAGCAGCACGTGAGGTTCCTCGAAATGAAAAACAATTTATCGAGGGCGGAACTGCAATTACAGAAGAAGATGTACGCTGGTTACGCTGCGATATTAAAAGTTTAAGTTTGCTAGGTAACATTATGGCCAAAAATAAAGCGCATCAACAAAATGCACTAGAAGCTATTTTGCACCGCGGGGAGCAAGTTACCGAATGTTCTGCGTCGAATGTTTCAATCATT ->dat_143 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGCTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTCGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_144 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACACTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_145 -GAAGTAGTTCGTTTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCTATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_146 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCGGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_147 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGACTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_148 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGAGGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACAGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_149 -GAAGTTGTTCGTTTATATAATGGTAAATTTTTTACTTATAACGAGCATATCGATCGTTTATACGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAACATTAAGAGCGTTATTAGAAAAATTAGTAGCTGAAAATAATATCAATACCGGAAATGTATATTTACAAGTAACAAGAGGCGTTCAAAATCCTCGTAATCACGTTCTTCCAGATGATTTCCCGTTAGAAGGAGTATTAACTGCAGCAGCTCGTGAAGTACCAAGAAATGACCGTCAATTCATAGAAGGTGGCTCAGCAATTACTGAAGAAGATGTACGTTGGTTACGTTGTGATATTAAAAGCTTAAATTTACTTGGAAATATTTTAGCGAAAAATAAAGCACATCAACAAAACGCACTTGAAGCTATTTTACATCGCGGGGAGCAAGTTACTGAGTGTTCTGCTTCTAACGTATCCATTATT ->dat_150 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCCGCTTCAAACGTTTCTATTATT ->dat_151 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCAAAAAATAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_152 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAACTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_153 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATTCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCTATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_154 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCGAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_155 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCACGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAGCAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_156 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACATAGTTATTCCTTATTCCAAAGAAGAGCTACGTAAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCTATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_157 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGACTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCACTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGTTCAGCTTCCAATATTTCTATTATT ->dat_158 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGATTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCATCTTCCAATATTTCTATTATT ->dat_159 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCACTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACGACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_160 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACGACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCTCATCAGCAAAATGCGCTAGAAGCGGTTTTACACCGCGGAGAGCAAGTAACTGAATGTTCAGCTTCCAATATTTCTATTATT ->dat_161 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_162 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAATCCGCGTAATCATGTTATGCCAGATGATTTCCCGTTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTCGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_163 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCAAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAAACCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_164 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_165 -GAAGTAGTTCGTTTATATAATGGTAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGTGAATTATTAGAAAAATTAGTTGCCGAAAATAACATTAATACAGGAAATGTATACTTGCAAGTGACACGTGGGGTTCAAAATCCACGTAATCATGTGATGCCAGATGATTTCCCATTAGAAGGCGTTTTAACTGCTGCAGCTCGCGAAGTACCAAGAAATGAGCAGCAATTTGTAGAAGGCGGACCGGTTATTACAGAAGAAGATGTGCGCTGGTTACGTTGCGATATCAAGAGCTTGAATTTACTTGGAAACATTTTAGCGAAAAACAAAGCGCATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACCGAGTGTTCTGCTTCCAATGTTTCTATTATT ->dat_166 -GAAGTAGTTCGTTTATATAATGGTAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGTGAATTATTAGAAAAATTAGTTGCCGAAAATAATATTAATACAGGAAATGTATACTTGCAAGTGACACGTGGGGTTCAAAATCCACGTAATCATGTGATGCCAGATGATTTCCCATTAGAAGGCGTTTTAACTGCTGCAGCTCGCGAAGTACCAAGAAATGAGCAGCAATTTGTAGAAGGCGGACCGGTTATTACAGAAGAAGATGTGCGCTGGTTACGTTGCGATATCAAGAGCTTGAATTTACTTGGAAACATTTTAGCGAAAAACAAAGCGCATCAACAAAATGCGTTAGAAGCTGTTTTGCACCGCGGAGAGCAAGTAACCGAGTGTTCTGCTTCCAATGTTTCTATTATT ->dat_167 -GAAGTAGTTCGTTTATATAATGGTAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGTGAATTATTAGAAAAATTAGTTGCCGAAAATAATATTAATACAGGAAATGTATACTTGCAAGTGACACGTGGGGTTCAAAATCCACGTAATCATGTAATGCCAGATGATTTCCCATTAGAAGGCGTTTTAACTGCTGCAGCTCGCGAAGTACCAAGAAATGAGCAGCAATTTGTAGAAGGCGGACCGGTTATTACAGAAGAAGATGTGCGCTGGTTACGTTGCGATATCAAGAGCTTGAACTTACTTGGAAACATTTTAGCGAAAAACAAAGCGCATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCAGAGAGCAAGTAACCGAATGTTCTGCTTCCAATGTTTCTATTATT ->dat_168 -GAAGTCGTGCGGATGTACAACGGGAAATTTTTTACAGCAGAGGAACATATTGATCGTCTATATGCGAGTGCAGCCAAAATAGATTTAGTCATTCCTTACTCAAAAGAAGTATTATATAAATCCCTTTTAAAACTTGCAGAGGAAAATGGGATTTCGACAGGAAATATATACTTACAAGTAACAAGAGGCGTTCAACAACCACGTAATCATATTATTCCAGACGACTTTCCGCTTGAAGGTGTTTTGACAGCAGCTGCTAGAGAAGTTCCGCGTAATGAAAACCAGTTTGTGGTCGGGGGAACAGCAATTACAGAGGAAGATGTACGCTGGTTACGATGTGATATTAAAAGTATTAGTTTACTAGGCAATATTTTGGCTAAAAATAAAGCTCATCAAAAAGGGGCTTTAGAAGCAATTTTGCATCGTGGCGATATGGTAACAGAATGTTCAGCCTCAAATGTATCGATTATA ->dat_169 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_170 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGCGAGCAAGTAACTGAGTGTTCGGCTTCCAATATTTCTATTATT ->dat_171 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCGGCTTCCAATATTTCTATTATT ->dat_172 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAAAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_173 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAATAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_174 -GAAGTAGTTCATCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCGAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_175 -GAAGTAGTTCGTCTATATAATGGAAAATTTTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_176 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAACTTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_177 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTCGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCCGCTTCAAACGTTTCTATTATT ->dat_178 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCAAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCCGCTTCAAACGTTTCTATTATT ->dat_179 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGACTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCACTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGTTCAGCTTCCAATATTTCCATTATT ->dat_180 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCATTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCACTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGTTCAGCTTCCAATATTTCCATTATT ->dat_181 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAACTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_182 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGAAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACTGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCCGCTTCAAACGTTTCTATTATT ->dat_183 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAAACCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_184 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGGGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_185 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCATTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_186 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCTATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAATCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_187 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCTATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCCGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_188 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACATAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_189 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATAATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_190 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGACGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_191 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGTGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_192 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGATAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_193 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAATTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_194 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCTTCTAGAAGGCGTTTTAACTGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCCGCTTCAAACGTTTCTATTATT ->dat_195 -GAAGTTGTTCGTTTATATAATGGTAAATTTTTTACTTATAACGAGCATATCGATCGTTTATACGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAACATTACGAGCGTTATTAGACAAATTAGTAGCTGAAAATAATATCAATACCGGAAATGTATATTTACAAGTAACAAGAGGAGTTCAAAATCCTCGTAATCACGTTCTTCCAGATGATTTCCCGTTAGAAGGAGTATTGACTGCGGCAGCTCGTGAAGTACCAAGAAATGAGCGTCAATTCATAGAAGGCGGCTCAGCAATTACTGAAGAAGATGTACGCTGGTTACGTTGTGATATTAAAAGCTTAAACTTACTTGGAAATATTTTAGCGAAAAACAAAGCACATCAACAAAACGCGCTTGAAGCTATTTTACATCGCGGGGAGCAAGTTACCGAGTGTTCTGCTTCTAACGTATCCATTATT ->dat_196 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCTCATCAGCAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGTTCAGCTTCCAATATTTCTATTATT ->dat_197 -GAAGTAGTTCGTCTGTATAATGGAAAATTCTTTACATATGATGAACATATTGATCGCCTATACGCTAGCGCCGCAAAAATCGACTTAGTTATTCCTTATTCCAAAGAAACGTTACGTGAATTATTAGAGAAATTAGTAGCTGAAAATAATATTAGTACCGGTAATGTCTATTTGCAAGTAACTCGAGGTGTTCAAAAACCACGTAATCACGTGTTCCCAGATGATTACGCACTAGAGGGTGTCTTAACTGCCGCAGCTCGTGAAGTACCTCGTAATGAGCGTCAATTTGTAGAAGGTGGAACAGCAATTACTGAAGAAGATATACGTTGGTTGCGTTGCGATATTAAGAGTTTAAATCTACTTGGAAATATTTTAGCTAAAAACAAAGCACATCAACAAAATGCATTAGAAGCAATTCTTCACCGCGGAGAACAGGTTACTGAATGTTCAGCTTCCAATGTTTCTATTATT ->dat_198 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACAGTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_199 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGTTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_200 -GAAGTAGTTCGTTTGTATAATGGCCAATTTTTTACATATGAAGAACATATCGAACGCTTGTATGCTAGCGCAGCAAAAATCGATTTAGTCATTCCTTATTCCAAAGAAGAACTACGTGAATTAATTGATAAGTTAGTTGCAGAAAATAACATTCATACTGGAAATGTCTATTTACAAGTGACCCGAGGCGTTCAAAATCCTCGAAACCATGTTATTCCAGATGATTTTCCATTAGAAGGAGTTCTCACAGCAGCTGCACGTGAGGTTCCTCGGAATGAAAAACAATTTATCGAAGGCGGAACTGCAATTACAGAAGAAGATGTACGCTGGTTACGCTGTGATATTAAAAGTTTAAGTTTGCTAGGTAATATTATGGCCAAAAATAAAGCGCATCAACAAAATGCATTAGAAGCTATTTTGCACCGCGGGGAGCAAGTTACCGAATGTTCTGCGTCGAATGTTTCAATCATT ->dat_201 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCGAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCACGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_202 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTAAATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCGAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_203 -GAAGTAGTTCGTATATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_204 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATATAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACAGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_205 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCTAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_206 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCTATTACAGAAGAAGATGTGCGCTGGTTAGGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_207 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTGTATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_208 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATAATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACAGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_209 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTTATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAATAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_210 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCTGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_211 -GAAGTTGTTCGTTTATATAATGGTAAATTTTTTACTTATAACGAGCATATCGATCGTTTATACGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAACATTACGAGCGTTATTAGACAAATTAGTAGCTGAAAATAATATCAATACCGGAAATGTATATTTACAAGTAACAAGAGGAGTTCAAAATCCTCGTAATCACGTTCTTCCAGATGATTTCCCCTTAGAAGGAGTATTAACTGCAGCAGCTCGTGAAGTACCAAGAAATGAGCGTCAATTCATAGAAGGCGGCTCAGCAATTACTGAAGAAGATGTACGCTGGTTACGTTGTGATATTAAAAGCTTAAACTTACTTGGAAATATTTTAGCGAAAAACAAAGCGCATCAACAAAACGCACTTGAAGCTATTTTACATCGCGGGGAGCAAGTTACCGAGTGTTCTGCTTCTAACGTATCCATTATT ->dat_212 -GAAGTTGTTCGTTTATATAATGGTAAATTTTTTACTTATAACGAGCATATCGATCGTTTATACGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAACATTAAGAGCGTTATTAGAAAAATTAGTAGCTGAAAATAATATCAATACCGGAAATGTATATTTACAAGTAACAAGAGGAGTTCAAAATCCTCGTAATCACGTTCTTCCAGATGATTTCCCGTTAGAAGGAGTATTAACTGCAGCAGCTCGTGAAGTACCAAGAAATGATCGTCAATTCATAGAAGGTGGCTCAGCAATTACTGAAGAAGATGTACGTTGGTTACGTTGTGATATTAAAAGCTTAAATTTACTTGGAAATATTTTAGCGAAAAATAAAGCACATCAACAAAACGCACTTGAAGCTATTTTACATCGCGGGGAGCAAGTTACCGAGTGTTCTGCTTCTAACGTATCCATTATT ->dat_213 -GAAGTTGTTCGTTTATATAATGGTAAATTTTTTACTTATAACGAGCATATCGATCGTTTATACGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAACATTAAGAGCGTTATTAGAAAAATTAGTAGCTGAAAATAATATCAATACCGGAAATGTATATTTACAAGTAACAAGAGGAGTTCAAAATCCTCGTAATCACGTTCTTCCAGATGATTTCCCGTTAGAAGGGGTATTAACTGCAGCAGCTCGTGAAGTACCAAGAAATGATCGTCAATTCATAGAAGGTGGCTCAGCAATTACTGAAGAAGATGTACGTTGGTTACGTTGTGATATTAAAAGCTTAAATTTACTTGGAAATATTTTAGCGAAAAATAAAGCACATCAACAAAACGCACTTGAAGCTATTTTACATCGCGGGGAGCAAGTTACTGAGTGTTCTGCTTCTAACGTATCCATTATT ->dat_214 -GAAGTTGTTCGTTTATATAATGGTAAATTTTTTACTTATAACGAGCATATCGATCGTTTATACGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAACATTACGAGCGTTATTAGACAAATTAGTAGCTGAAAATAATATCAATACCGGAAATGTATATTTACAAGTAACAAGAGGAGTTCAAAATCCTCGTAATCACGTTCTTCCAGATGATTTCCCGTTAGAAGGAGTATTGACTGCGGCAGCTCGTGAAGTACCAAGAAATGAGCTTCAATTCATAGAAGGCGGCTCAGCAATTACTGAAGAAGATGTACGCTGGTTACGTTGTGATATTAAAAGCTTAAACTTACTTGGAAATATTTTAGCGAAAAACAAAGCACATCAACAAAACGCACTTGAAGCTATTTTACATCGCGGGGAGCAAGTTACCGAGTGTTCTGCTTCTAACGTATCCATTATT ->dat_215 -GAGGTAGTTCGTCTTTATAACGGTCAGTTTTTCACATTCGATGAGCATATTGATCGGTTATACGAAAGCGCAGCCAAGATCGAACTTGTCATTCCTTACTCAAAGGATGTTTTACGAGACTTGCTACAAAGCCTTGCAAAAGAAAACGACATTCATACAGGAAACATTTACTTGCAAGTAACACGAGGCATCCAAATCCCGAGAAATCATATTATCCCCGACGATTTTCCGCTTGAAGGTATCTTAACAGCTTCTGCAAGAGAAGTTCCACGAAATGAAGCACTTTTTGTAGAGGGTGGTACCGCGATAACCAATGAAGATATTCGCTGGTTACGTTGCGACATCAAGAGTATCAGCTTACTTGGAAACATCTTGGCCAAAAACAAAGCACACCGAGCTGGCGCTTTAGAAGCGATTTTACATCGTGGTGGAACAGTTACGGAATGTTCTGCTTCTAATGTATTTATCGTG ->dat_216 -GAAGTTGTTCGTTTATATAATGGTAAATTTTTTACTTATAACGAGCATATCGATCGTTTATACGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAACATTAAGAGCGTTATTAGAAAAATTAGTAGCTGAAAATAATATCAATACCGGAAATGTATATTTACAAGTAACAAGAGGAGTTCAAAATCCTCGTAATCACGTTCTTCCAGATGATTTCCCGTTAGAAGGAGTATTAACTGCAGCAGCTCGTGAAGTACCAAGAAATGATCGTCAATTCATAGAAGGTGGCTCAGCAATTACTGAAGAAGATGTACGTTGGTTACGTTGTGATATTAAAAGCTTAAATTTACTTGGAAATATTTTAGCGAAAAATAAAGCACATCAACAAAACGCACTTGAAGCTATTTTACATCGCGGGGAGCAAGTTACTGAGTGTTCTGCTTCTAACGTATCCATTATT ->dat_217 -GAAGTTGTTCGTTTATATAATGGTAAATTTTTTACTTATAACGAGCATATCGATCGTTTATACGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAACATTACGAGCGTTATTAGACAAATTAGTAGCTGAAAATAATATCAATACCGGAAATGTATATTTACAAGTAACAAGAGGAGTTCAAAATCCTCGTAATCACGTTCTTCCAGATGATTTCCCATTAGAAGGAGTATTAACTGCAGCAGCTCGTGAAGTACCAAGAAATGAGCGTCAATTCATAGAAGGCGGCTCAGCAATTACCGAAGAAGATGTACGCTGGTTACGTTGTGATATTAAAAGCTTAAACTTACTTGGAAATATTTTAGCGAAAAACAAAGCACATCAACAAAACGCGCTTGAAGCTATTTTACATCGCGGGGAGCAAGTTACCGAGTGTTCTGCTTCTAACGTATCCATTATT ->dat_218 -GAGGTTGTTCGTTTATATAATGGTAAATTTTTTACTTATAACGAGCATATCGATCGTTTATACGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAACATTAAGAGCGTTATTAGAAAAATTAGTAGCTGAAAATAATATCAATACCGGAAATGTATATTTACAAGTAACAAGAGGAGTTCAAAATCCTCGTAATCACGTTCTTCCAGATGATTTCCCGTTAGAAGGGGTATTAACTGCAGCAGCTCGTGAAGTACCAAGAAATGATCGTCAATTCATAGAAGGTGGCTCAGCAATTACTGAAGAAGATGTACGTTGGTTACGTTGTGATATTAAAAGCTTAAATTTACTTGGAAATATTTTAGCGAAAAATAAAGCACATCAACAAAACGCACTTGAAGCTATTTTACATCGCGGGGAGCAAGTTACTGAGTGTTCTGCTTCTAACGTATCCATTATT ->dat_219 -GAAGTTGTTCGTCTTTACAACGGTCAGTTTTTTACATTTGATGAACATATTGATCGCTTGTACGAAAGCGCAGCCAAGATCGACCTTGTCATTCCTTACGCAAAGGATGTCTTAAGAAATTTGTTACAAAAACTTGCAAAAGAAAACAATATTCATACTGGAAACATTTACCTGCAAGTAACTCGCGGCATCCAAATTCCGAGAAATCATATTATTCCTGACGATTTTCCACTTGAGGGCATCTTAACAGCTTCTGCTCGTGAAGTTCCACGGGATGAAGCACTCTTTGTAGAAGGTGGGCGAACGATAACTGATGAAGATATCCGCTGGTTACGTTGCGATATCAAGAGTATCAGTCTACTTGGAAATATTTTAGCCAAAAATAAAGCACACCGGGCTGGTGCACTAGAAGCTATTTTACATCGAGGTGGCACCGTGACGGAGTGCTCTGCTTCAAACGTTTTTATAGTT ->dat_220 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGACTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCACTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGTTCAGCTTCCAATATTTCCATTATT ->dat_221 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGCGAGCAAGTAACTGAGTGTTCGGCTTCCAATATTTCTATTATT ->dat_222 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCTGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACAGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_223 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATACTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_224 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGAGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCTATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_225 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCATTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGAAATCATGTTATACCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_226 -GAAGTAGTTCGTTTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTAAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCTATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_227 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTTCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCTATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_228 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTTACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACTGCAGCAGCTCGTGAAGTACCTAGAAACGAGAGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCCGCTTCAAACGTTTCTATTATT ->dat_229 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACATCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_230 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGACTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_231 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGAGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGTTCAGCTTCCAATATTTCTATTATT ->dat_232 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCATTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_233 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCAAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGATCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_234 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACGACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTCGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_235 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACAGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_236 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGTTCAGCTTCCAATATTTCTATTATT ->dat_237 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCATTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGCTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTCGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_238 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCATTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAATCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_239 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGAGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGTTCAGCTTCCAATATTTCTATTATT ->dat_240 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCTCATCAGCAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGTTCAGCTTCCAATATTTCTATTATT ->dat_241 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCTGCTTCCAATATTTCTATTATT ->dat_242 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_243 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCATTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAAGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_244 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCGAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCAGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_245 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCATTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCTGCTTCCAATATTTCTATTATT ->dat_246 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCATTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACACGTGGTGTTCAAAATCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_247 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCATTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_248 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCACGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_249 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAGTGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_250 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCATTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCGCATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_251 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACGACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_252 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCATTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCACTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGTTCAGCTTCCAATATTTCCATTATT ->dat_253 -GAAGTAGTTCGTCTATATAATGGGAAATTCTTCACTTATAATGAACACATTGACCGTTTATATGCAAGTGCTGCAAAAATTGACTTAGTTATTCCTTACTCTAAAGAAGAATTACGGGCATTACTTGAAAAATTAGTTGCTGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGGGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGACTTCCCGCTGGAAGGCGTTTTAACAGCGGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGACGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTGGCGAAAAATAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGCTCAGCTTCCAATATTTCTATTATT ->dat_254 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCAAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACAGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_255 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTCATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCCGCTTCAAACGTTTCTATTATT ->dat_256 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCTTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_257 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTAGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_258 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGATTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGAGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCACTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGTTCAGCTTCCAATATTTCTATTATT ->dat_259 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTTATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_260 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_261 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCATTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGGGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_262 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGCGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_263 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGAGTTACTAGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_264 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTACCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCGAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_265 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCGAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAATTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_266 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTGAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACAGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_267 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATATGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACAGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_268 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAACATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_269 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_270 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_271 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTGCTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_272 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACGACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_273 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_274 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAATTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_275 -GAAATAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCGAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_276 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAATCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCTGCTTCCAATATTTCTATTATT ->dat_277 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGACTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGTTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCACTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGTTCAGCTTCCAATATTTCCATTATT ->dat_278 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGCGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATATAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_279 -GAGGTAGTTCGTCTTTATAACGGCCAGTTTTTTACATTCGATGAGCATATTGATCGGTTATACGAAAGCGCAGCCAAGATCGAACTTGTCATTCCTTACTCAAAGGATGTTTTACGAGACTTGCTACAAAGCCTTGCAAAAGAAAACGACATTCATACTGGAAACATTTACTTGCAAGTAACACGAGGCATCCAAATTCCGAGAAATCATATTATCCCTGACGATTTTCCGCTTGAAGGCGTCTTAACAGCTTCTGCAAGAGAAGTTCCACGAAATGAAGCGCTTTTTGTAGAGGGTGGGACCGCGATAACCGATGAAGATATTCGCTGGTTACGTTGCGACATCAAGAGTATCAGCTTGCTTGGAAACATCTTAGCCAAGAACAAAGCACACCGAGCTGGCGCTTTAGAAGCGATTTTGCATCGTGGTGGAACAGTTACAGAATGTTCTGCTTCCAATGTATTTATTGTG ->dat_280 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCTCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCTATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_281 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGTAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCATTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_282 -GAAGTAGTTCGTCTGTATAATGGAAAATTCTTTACATATGATGAACATATTGATCGCCTATACGCTAGCGCCGCAAAAATCGACTTAGTTATTCCTTATTCCAAAGAAACGTTACGTGAATTATTAGAGAAATTAGTAGCTGAAAATAATATTAGTACCGGTAATGTCTATTTGCAAGTAACTCGAGGTGTTCAAAAACCACGTAATCACGTGTTCCCAGATGATTACGCACTAGAAGGTGTCTTAACTGCCGCAGCTCGTGAAGTACCTCGTAATGAGCGTCAATTTGTAGAAGGTGGAACAGCAATTACTGAAGAAGATATACGTTGGTTGCGTTGCGATATTAAGAGTTTAAATCTACTTGGAAATATTTTAGCTAAAAACAAAGCACACCAACAAAATGCGTTAGAAGCAATTCTTCACCGCGGAGAACAGGTTACTGAATGTTCAGCTTCCAATGTTTCTATTATT ->dat_283 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCATATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_284 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAATCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTTATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_285 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCGAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCGTCCAATATTTCTATTATT ->dat_286 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGATTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCGAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_287 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTCTCTATTATT ->dat_288 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCGAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_289 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCATTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCACTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_290 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAACTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_291 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCGAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAAGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_292 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATACCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCTATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_293 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGAGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_294 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAACAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGCGAGCAAGTAACTGAGTGTTCGGCTTCCAATATTTCTATTATT ->dat_295 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGATCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAGGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_296 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACTGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTACAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGCGAGCAAGTAACTGAGTGTTCGGCTTCCAATATTTCTATTATT ->dat_297 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_298 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTGAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_299 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAATGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACAGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_300 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGCGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_301 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCATTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAACTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_302 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTACAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_303 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCAAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGCGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_304 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCATTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAATAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_305 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACATATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCGGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_306 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_307 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGGCCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_308 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTTTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCCGCTTCAAACGTTTCTATTATT ->dat_309 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTTCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGTGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_310 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_311 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACATCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_312 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCATTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGAGCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_313 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCGAAAGAAGAGCTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_314 -GAAGTAGTTCGTCTGTATAATGGAAAATTCTTTACATATGATGAACATATTGATCGCCTATACGCTAGCGCCGCAAAAATCGACTTAGTTATTCCTTATTCCAAAGAAACGTTACGTGAATTATTAGAGAAATTAGTAGCTGAAAATAATATTAGTACCGGTAATGTCTATTTGCAAGTAACTCGAGGTGTTCAAAAACCACGTAATCACGTGTTCCCAGATGATTGCGCACTAGAAGGTGTCTTAACTGCCGCAGCTCGTGAAGTACCTCGTAATGAGCGTCAATTTGTAGAAGGTGGAACAGCAATTACTGAAGAAGATATACGTTGGTTGCGTTGCGATATTAAGAGTTTAAATCTACTTGGAAATATTCTAGCTAAAAACAAAGCACATCAACAAAATGCATTAGAAGCAATTCTTCACCGCGGAGAACAGGTTACTGAATGTTCAGCTTCCAATGTTTCTATTATT ->dat_315 -GAAGTAGTTCGTCTGTATAATGGAAAATTCTTTACATATGATGAACATATTGATCGCCTATACGCTAGCGCCGCAAAAATCGACTTAGTTATTCCTTATTCCAAAGAAACGTTACGTGAATTATTAGAGAAATTAGTAGCTGAAAATAATATTAGTACTGGTAATGTCTATTTGCAAGTAACTCGAGGTGTTCAAAAACCACGTAATCACGTGTTCCCAGATGATTACGCACTAGAAGGTGTCTTAACTGCCGCAGCTCGTGAAGTACCTCGTAATGAGCGTCAATTTGTAGAAGGTGGAACAGCAATTACTGAAGAAGATATACGTTGGTTGCGTTGCGATATTAAGAGTTTAAATCTACTTGGAAATATTTTAGCTAAAAACAAAGCACATCTACAAAATGCATTAGAAGCAATTCTTCACCGCGGAGAACAGGTTACTGAATGTTCAGCTTCCAATGTTTCTATTATT ->dat_316 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATACTTGACGAGTA ->dat_317 -GAAGTAGTTCGTCTATATAATGGAAATTCTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_318 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCCCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_319 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCCTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_320 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTTGTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATCATT ->dat_321 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTAACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_322 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGGAATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_323 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGACGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_324 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGACCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_325 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGTAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACAGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_326 -GAAGTAGTTCGTTTGTATAATGGCCAATTTTTTACATATGAAGAACATATCGAACGCTTGTATGCTAGCGCAGCAAAAATCGATTTAGTCATTCCTTATTCCAAAGAAGAACTACGTGAATTAATTGATAAGTTAGTTGCAGAAAATAACATTCATACTGGAAATGTCTATTTACAAGTGACCCGAGGCGTTCAAAATCCTCGAAACCATGTTATTCCAGATGATTTTCCATTAGAAGGAGTTCTCACAGCAGCAGCACGTGAGGTTCCTCGGAATGAAAAACAATTTATCGAAGGCGGAACTGCAATTACAGAAGAAGATGTACGCTGGTTACGCTGCGATATTAAAAGTTTAAGTTTGCTAGGTAATATTATGGCCAAAAATAAAGCGCATCAACAAAATGCATTAGAAGCTATTTTGCACCGCGGGGAGCAAGTTACCGAATGTTCTGCGTCGAATGTTTCAATCATT ->dat_327 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCCCATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACAGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_328 -GAAGTTGTTCGTTTATATAATGGTAATTTTTTTACTTATAACGAGCATATCGATCGTTTATACGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAACATTACGAGCATTATTAGACAAATTAGTAGCTGAAAATAATATTAATACCGGAAATGTATATTTACAAGTAACAAGAGGAGTTCAAAATCCTCGTAATCACGTTCTTCCAGATGATTTCCCGTTAGAAGGTGTATTAACTGCAGCAGCACGTGAAGTACCAAGAAATGAGCTTCAATTCATAGAAGGCGGCACAGCAATTACTGAAGAAGATGTACGCTGGTTACGTTGTGATATTAAAAGCTTAAATTTACTTGGCAATATTTTAGCGAAAAACAAAGCACATCAACAAAATGCGCTTGAAGCTATTTTACATCGCGGGGAGCAAGTTACCGAGTGTTCTGCTTCTAACGTATCGATTATT ->dat_329 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGAGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGCGAGCAAGTAACTGAGTGTTCGGCTTCCAATATTTCTATTATT ->dat_330 -GAAGTTGTTCGTTTATATAATGGTAAATTTTTTACTTATAACGAGCATATCGATCGTTTATACGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAACATTACGATCATTATTAGACAAATTAGTAGCTGAAAACAATATTAATACCGGAAATGTATATTTACAAGTAACAAGAGGAGTTCAAAATCCTCGTAATCACGTTCTTCCAGATGATTTCCCGTTAGAAGGAGTATTAACTGCAGCAGCTCGTGAAGTACCAAGAAACGAGCTTCAATTCATAGAAGGCGGCACAGCAATTACTGAAGAAGATGTACGCTGGTTACGTTGTGATATTAAAAGCTTAAATTTACTTGGCAATATTTTAGCGAAAAATAAAGCACATCAACAAAACGCACTTGAAGCTATTTTACATCGCGGGGAGCAAGTTACCGAGTGTTCTGCTTCTAACGTATCGATTATT ->dat_331 -GAAGTTGTTCGTTTATATAATGGTAAATTTTTTACTTATAACGAGCATATCGATCGTTTATACGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAACATTACGAGCGTTATTAGACAAATTAGTAGATGAAAATAATATCAATACCGGAAATGTATATTTACAAGTAACAAGAGGAGTTCAAAATCCTCGTAATCACGTTCTTCCAGATGATTTCCCGTTAGAAGGAGTATTGACTGCGGCAGCTCGTGAAGTACCAAGAAATGAGCGTCAATTCATAGAAGGCGGCTCAGCAATTACCGAAGAAGATGTACGCTGGTTACGTTGTGATATTAAAAGCTTAAACTTACTTGGAAATATTTTAGCGAAAAACAAAGCGCATCAACAAAACGCACTTGAAGCTATTTTACATCGCGGGGAGCAAGTTACCGAGTGTTCTGCTTCTAACGTATCCATTATT ->dat_332 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGATACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACTGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCCGCTTCAAACGTTTCTATTATT ->dat_333 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCGAAAGAAGAGTTACGAGCGTTACCTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_334 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCGGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAAAGTTTGAATTTACTCGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACATCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_335 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCGCATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_336 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTAACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCCGCTTCAAACGTTTCTATTATT ->dat_337 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTCGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_338 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGACGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_339 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCACGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGCGAGCAAGTAACTGAGTGTTCGGCTTCCAATATTTCTATTATT ->dat_340 -GAAGTAGTTCGTCTGTATAATGGAAAATTCTTTACATATGATGAACATATTGATCGCCTATACGCTAGCGCCGCAAAAATCGACTTAGTTATTCCTTATTCCAAAGAAACGTTACGTGAATTATTAGAGAAATTAGTAGCTGAAAATAATATTAGTACCGGTAATGTCTATTTGCAAGTAACTCGAGGTGTTCAAAAACCACGTAATCACGTGTTCCCAGATGATTACGCACTAGAAGGTGTCTTAACTGCCGCAGCTCGTGAAGTACCTCGTAATGAGCGTCAATTTGTTGAAGGTGGAACAGCAATTACTGAAGAAGATATACGTTGGTTGCGTTGCGATATTAAGAGTTTAAATCTACTTGGAAATATTTTAGCTAAAAACAAAGCACATCAACAAAATGCATTAGAAGCAATTCTTCACCGCGGAGAACAGGTTACTGAATGTTCAGCTTCCAATGTTTCTATTATT ->dat_341 -GAAGTAATTCGGCTTTATAATAAAAAATTCTTTACATTTGAAGAGCACATCGATCGTTTATTTGCGAGTGCAGCGAAAATCGAACTGGGAATTCCTTATTCCAAAGAAAAATTGCGGGAACTTTTAGAAAATTTGGTAAAAGAAAATGATATCGACACAGGAAATGTTTATCTACAAGTATCTCGAGGTGTGCAACAACCCCGCAATCATATCATTCCAGATGACCTTGCCTTAGTGGGCATCTTGACAGCTTCGGCACAAGAAGTGCCACGCAATCCTCATTTATTCGAAGATGGTGGAACAGCAATCATAGAACCAGATACAAGATGGTTGCATTGTGATATCAAAAGTATCAGTTTGCTGGGAAATGTACTGGCTAAAAATCGGGCGCATCGTGCTGGTGCAATGGAAGCGATTTTGCATCGCGATGGCGAAGTTACCGAATGCTCGGCTTCCAATGTATATATGATC ->dat_342 -GAAGTAGTTCGTCTGTATAATGGAAAATTCTTTACATATGATGAACATATTGATCGCCTATACGCTAGCGCCGCAAAAATCGACTTAGTTATTCCTTATTCCAAAGAAACGTTACGTGAATTATTAGAGAAATTAGTAGCTGAAAATAATATTAGTACCGGTAATGTCTATTTGCAAGTAACTCGAGGTGTTCAAAAACCACGTAATCACGTGTTCCCAGATGATTACGCACTAGAAGGTGTCTTAACTGCCGCAGCTCGTGAAGTACCTCGTAATGAGCGTCAATTTGTAGAAGGTGGAACAGCAATTACTGAAGAAGATATACGTTGGTTGCGTTGCGATATTAAGAGTTTAAATCTACTTGGAAATATTTTAGCTAAAAACAAAGCACATCAACAAAATGCATTAGAAGCAATTCTTCACCGCGGAGAACAGGTTACTGAATGTTCAGCTTCCAATGTTTCTATTATT ->dat_343 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACGTGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAATGGCTATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_344 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTAACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_345 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAATTACGGGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAATATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_346 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAATAAAGCACATCAACAAAATGCGCTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_347 -GAAGTAGTTCGTCTATATAATGGGAAATTTTTCACTTATAATGAACACATTGATCGTTTATATGCAAGTGCTGCAAAAATTGACTTAGTTATTCCTTACTCTAAAGAAGAATTACGGGCATTACTTGAAAAATTAGTTGCTGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGGGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGACTTCCCGCTGGAAGGCGTTTTAACAGCGGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGACGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTGGCGAAAAATAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGCTCAGCTTCCAATATTTCTATTATT ->dat_348 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGAGTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGATTTCCCGCTGGAAGGTGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCACTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGTTCAGCTTCCAATATTTCTATTATT ->dat_349 -GAAGTAGTTCGTCTATATAATGGGAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCAAGTGCTGCAAAAATTGATTTAGTTATTCCTTACTCTAAAGAAGAATTACGAGCATTACTTGAAAAATTAGTTGCTGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGGGGTGTTCAAAACCCGCGTAATCATGTTATGCCAGATGACTTCCCGCTGGAAGGCGTTTTAACAGCGGCTGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGGCCAGTAATTACAGAAGAAGACGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTGGCGAAAAATAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAATGCTCAGCTTCCAATATTTCTATTATT ->dat_350 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCAAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCTAAAGAAGAGTTACGGGCTTTACTTGAAAAATTAGTTGCCGAAAATAACATCAATACAGGGAATGTTTATTTGCAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCGATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_351 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTTACTTATAATGAACACATTGATCGCTTATATGCTAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCCAAAGAAGAGCTACATGAATTACTTGAAAAATTAGTTGCCGAAAATAATATCAATACAGGGAATGTCTATTTACAAGTGACTCGTGGTGTTCAAAACCCACGTAATCATGTAATCCCTGATGATTTCCCTCTAGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCTAGAAACGAGCGTCAATTCGTTGAAGGTGGAACGGCTATTACAGAAGAAGATGTGCGCTGGTTACGCTGTGATATTAAGAGCTTAAACCTTTTAGGAAATATTCTAGCAAAAAATAAAGCACATCAACAAAATGCTTTGGAAGCTATTTTACATCGCGGGGAACAAGTAACGGAATGTTCTGCTTCAAACGTTTCTATTATT ->dat_352 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCGAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAATGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGTTGGTTACGCTGTGACATCAAGAGTTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGAGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT ->dat_353 -GAAGTAGTTCGTCTATATAATGGAAAATTCTTCACTTATAATGAACACATTGATCGTTTATATGCGAGTGCAGCAAAAATTGACTTAGTTATTCCTTATTCGAAAGAAGAGTTACGAGCGTTACTTGAAAAATTAGTTGCTGAAAATAATATTAATACAGGAAATGTCTATTTACAAGTGACTCGAGGTGTTCAAAACCCGCGTAATCACGTTATGCCAGATGATTTCCCGCTGGAAGGCGTTTTAACAGCAGCAGCTCGTGAAGTACCAAGAAACGAACAACAATTTGTGCAAGGTGGACCAGTAATTACAGAAGAAGATGTTCGCTGGTTACGCTGTGACATCAAGAGCTTGAATTTACTTGGAAACATTTTAGCAAAAAACAAAGCACATCAACAAAATGCGTTAGAAGCTGTTTTACACCGCGGAGAGCAAGTAACTGAGTGTTCAGCTTCCAATATTTCTATTATT diff --git a/test/MLST_listeria/ldh.fasta b/test/MLST_listeria/ldh.fasta deleted file mode 100644 index 5163c7d..0000000 --- a/test/MLST_listeria/ldh.fasta +++ /dev/null @@ -1,1382 +0,0 @@ ->ldh_1 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_2 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_3 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_4 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_5 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTTTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCCGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_6 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_7 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_8 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_9 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTCCGTAAGTGTTCGTGATGCA ->ldh_10 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGGCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_11 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAATTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_12 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATAAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_13 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATAAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_14 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_15 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTCCGTAAGTGTTCGTGATGCA ->ldh_16 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGACACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_17 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAACGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_18 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTTTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCCGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAATTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_19 -TATAGCGACTGCCATGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGATGGCATCTTTTTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTAAAAGTGGACGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTTGGTGGCCTTCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_20 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCGAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTAAAAGTGGATGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTTGGTGGCCTTCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_21 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_22 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_23 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGCACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_24 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_25 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTCCGTAAGTGTTCGTGATGCA ->ldh_26 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_27 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_28 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGGATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_29 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTAGTAAGTGTTCGTGATGCA ->ldh_30 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAACCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_31 -TATAGCGATTGCCACGATGCAAATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCGCTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_32 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAAAGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_33 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGACATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_34 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACCGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_35 -TATAGCGATTGCCACGATGCAAATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGACCTTCCAATCGCTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_36 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAACGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_37 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_38 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGGCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_39 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGCTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_40 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGCCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_41 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAATTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_43 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAAAGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_44 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGACCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_45 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGGATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_46 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACACAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_47 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGACCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_48 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCAAACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_49 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTGCGTAAGTGTTCGTGATGCA ->ldh_50 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCATTTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_51 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTGTCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_52 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGGCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_53 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAACCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_54 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTAAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_55 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGACACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_56 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCGCACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_57 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGGATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_58 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACTTCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_59 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGATCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_60 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTTTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCCGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTCCGTAAGTGTTCGTGATGCA ->ldh_61 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACAATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_62 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTAGTAAGTGTTCGTGATGCA ->ldh_63 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAACCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_64 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTTTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCCGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGTTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_65 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCGCACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_66 -TATAGCGATTGCCATGATGCGGACTTAGTTGTTGTAACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTATGCGACTTGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCCATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTATATCCTTGGCGAACATGGCGATACAGGATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCC ->ldh_67 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTTTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCCGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAACCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_68 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAACCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_69 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCATCTAACCCAGTAGACATTTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAGGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCCGATTACTTAAAAGTGGATGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTTGGTGGCCTTCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_70 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCGCTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_71 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGGCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_72 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCTCACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_73 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_74 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCGAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTAAAAGTGGATGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACACAATTCCCAGCATGGAGCCATACAACAGTTGGTGGCCTTCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_75 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTCTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_76 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACCATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_77 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGGATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_78 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTTTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCCGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTGTCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_79 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGACCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_80 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCATTTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_81 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCAGACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_82 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCGAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTAAAAGTGGATGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGACATACAACAGTTGGTGGCCTTCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_83 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCAGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_84 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATAAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_85 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGACCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_86 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTTTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCCGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCATATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_87 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTGTCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_88 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGCGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_89 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCGCACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_90 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGACACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_91 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATAAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_92 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGATCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_93 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTAGTAAGTGTTCGTGATGCA ->ldh_94 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTCGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_95 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTACCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_96 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_97 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACCATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_98 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTTTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCCGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGGATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_99 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCATGATGCA ->ldh_100 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTTATGCA ->ldh_101 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTGTCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_102 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTGTCGTAAGTGTTCGTGATGCA ->ldh_103 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGACGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_104 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGGACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_105 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATGCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_106 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAATCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_107 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_108 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCCGGTGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTATGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_109 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCAGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_110 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCCGGTGAAACTCGTTTAGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_111 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCAGTAGACATTTTAACTTACGCTACTTGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGTGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_112 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTACGCTACTTGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_113 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCCGGTGAAACTCGTTTAGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCTATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_114 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCCGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTATGCTACATGGAAATTCTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCTATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_115 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCAGTAGACATTTTAACTTACGCTACTTGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_116 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCCAAGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_117 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAATTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_118 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCAGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_119 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGTGAAGATGAACAAGGTGCAATGGATACTATTTCCGTAAGTGTTCGTGATGCA ->ldh_120 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGATCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_121 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTGTCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_122 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGCATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_123 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTACGCTACTTGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_124 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGCTCCGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_125 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTCCGTAAGTGTTCGTGATGCA ->ldh_126 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCATTTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_127 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCATTCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_128 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAACATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_129 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_130 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGAATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_131 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTACGTAAGTGTTCGTGATGCA ->ldh_132 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_133 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATAAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_134 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCATTTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_135 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGGCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_136 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTGTCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_137 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAACCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_138 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTCCGTAAGTGTTCGTGATGCA ->ldh_139 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTACCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATAAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_140 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAACCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_141 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTGGTAAGTGTTCGTGATGCA ->ldh_142 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCGCACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_143 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTCGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGGATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_144 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTTTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCCGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATAAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_145 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCCTGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTACGCTACTTGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_146 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTACGCTACTTGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATAAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_147 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTACGCTACTTGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAATAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_148 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAGGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_149 -TATAGCGATTGCCATGATGCGGACTTAGTTGTTGTAACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGCATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTATGCGACTTGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCCATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTATATCCTTGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_150 -TATAGCGATTGCCATGATGCGGACTTAGTTGTTGTAACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGCATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTATGCGACTTGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCCATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTATATCCTTGGCGAACATGGCGATACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCC ->ldh_151 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_152 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCAAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_153 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGGGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_154 -TATAGCGACTGCCACGATGCAGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_155 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACAGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_156 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACAGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_157 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTACGCTACTTGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_158 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGATATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_159 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_160 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_161 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGGACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_162 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAATAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_163 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTATGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_164 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTACGCTACTTGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGTGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_165 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAATTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_166 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGGTGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_167 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGACGGCATCTTCTTAATCGCGTCTAACCCAGTAGACATCCTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCTGATTACTTAAAAGTGGATGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_168 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACAGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_169 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACCTCGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGGTGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_170 -TATAGCGATTGCCACGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCGAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTAAAAGTGGATGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTTGGTGGCCTTCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_171 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGACGGCATCTTCTTAATCGCGTCTAACCCAGTAGACATCCTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCTGATTACTTAAAAGTGGATGCTCGTAACGTGCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_172 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATATTTTCGTAAGTGTTCGTGATGCA ->ldh_173 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTCTGTAAGTGTTCGTGATGCA ->ldh_174 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGCGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_175 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGCATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_176 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGGATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_177 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGATCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_178 -TATAGCGATTGCCATGATGCGGACTTAGTTGTTGTAACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTATGCGACTTGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCCATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTATATCCTTGGCGAACATGGCGATACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCC ->ldh_179 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGCGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGATGGCATCTTTTTAATCGCGTCTAACCCAGTAGACATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAGGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCTGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGGGGCCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCG ->ldh_180 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGATGGCATCTTTTTAATCGCGTCTAACCCAGTAGACATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAGGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCTGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGTGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGGGGCCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_181 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGCCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_182 -TATAGCGACTGCCACGATACGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAAAGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_183 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAATTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_184 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAACGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_185 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACACAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_186 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGATCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_187 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAACTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGGACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_188 -TATAGCGACTGCCATGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCATCTAACCCAGTAGACATCTTAACTTATGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTAAAAGTAGACGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTTGGTGGCCTTCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_189 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGACACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_190 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACAATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_191 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTGCGTAAGTGTTCGTGATGCA ->ldh_192 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCGAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTAAAAGTGGATGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTTGGTGGCCTTCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_193 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGTACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_194 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGCCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_195 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGACACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_196 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGCATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_197 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_198 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAACCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_199 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_200 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGACACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_201 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_202 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGGATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_203 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAATTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_204 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAATTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_205 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGTGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_206 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_207 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGATGGCATCTTTTTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCTGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACATGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGTGGCCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCC ->ldh_208 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGATGGCATCTTTTTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCTGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACATGGCGACACAGAATTCCCAGCATGGAGACATACAACAGTCGGTGGCCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCC ->ldh_209 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGATGGCATCTTTTTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCTGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACATGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGTGGCCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTAGTAAGTGTTCGTGATGCC ->ldh_210 -TATAGTGATTGCCACGATGCAGACCTTGTTGTTGTAACTGCTGGAACTGCGCAAAAACCTGGCGAAACTCGCTTAGACCTAGTAAACCGTAATATTAAAATCATGAAAGGCATTGTAGATGAAGTTATGGCAAGCGGATTTGACGGTATTTTCCTTATCGCGTCTAACCCAGTCGACATCTTAACTTATGCTACATGGAAATTCTCAGGGCTTCCAAAAGAACGCGTTATCGGTTCCGGAACAAGCCTTGATACCGCTCGTTTCCGCATGTCAATTGCAGACTATTTAAAAGTAGATGCGCGTAATGTTCATGGTTATATCCTTGGCGAACATGGAGATACAGAATTCCCAGCTTGGAGTCATACAACAGTCGGTGGTCTTCCAATCACTGAGTGGATTAACGAAGATGAACAAGGTGCAATGGAAACCATATTCGTAAGTGTACGTGACGCA ->ldh_211 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCGAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAGGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCTGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGGGGCCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTCCATAAGTGTTCGTGATGCA ->ldh_212 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGATGGCATCTTTTTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCTGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACATGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGTGGCCTTCCATTCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCC ->ldh_213 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCATCTAACCCAGTAGACATTTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTAAAAGTGGATGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTTGGTGGCCTTCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_214 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTAAAAGTAGACGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTTGGTGGCCTTCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_215 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCATCTAACCCAGTAGACATTTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTAAAAGTGGATGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGCTGGTGGCCTTCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_216 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGATGGCATCTTTTTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCTGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACATGGCGACACAGAATTCCCAGCATGGAGCCATACAATAGTCGGTGGCCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCC ->ldh_217 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGATTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_218 -TATAGCGACTGCCATGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGATGGCATCTTTTTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTAAAAGTGGATGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTTGGTGGCCTTCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_219 -TATAGCGACTGTCACGATGCTGACTTAGTTGTTGTCACTGCAGGAACAGCGCAAAAACCTGGTGAAACTCGTCTAGATTTAGTAAACCGTAATATCAAGATTATGAAAGGTATTGTCGATGAAGTTATGGCAAGCGGTTTTGATGGTATTTTCCTAATTGCATCAAATCCAGTAGATATTTTGACTTATGCGACATGGAAATTCTCTGGACTTCCTAAAGAACGCGTGATTGGTTCAGGCACAAGTCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTAAAAGTTGATGCTCGTAACGTCCATGGCTATATTCTCGGTGAACACGGTGATACAGAATTCCCAGCTTGGAGCCATACAACAGTCGGCGGTCTTCCAATTACCGAGTGGATTAGCGAAGACGAACAAGGCGCAATGGAAACTATTTTCGTGAGTGTGCGTGATGCT ->ldh_220 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTTTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCCGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCGCACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_221 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGCGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAATGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_222 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAATAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGACACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_223 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAATAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_224 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATTATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTCGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_225 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGGCTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_226 -TATAGCGACTGCCATGATGCTGACTTAGTTGTTGTTACAGCAGGTACAGCACAAAAACCTGGCGAGACTCGTTTAGATTTAGTAAATCGTAACATTAAGATTATGAAAGGCATTGTCGATGAAGTAATGGCAAGTGGTTTTGATGGTATTTTCCTAATCGCATCAAACCCTGTGGATATTTTAACTTACGCGACATGGAAATTTTCTGGACTTCCTAAAGAACGAGTTATCGGTTCTGGAACAAGTTTAGATACAGCGCGCTTCCGTATGTCAATTGCCGATTATTTAAAAGTGGATGCTCGTAATGTCCATGGCTATATTCTAGGTGAACACGGTGATACCGAATTCCCAGCTTGGAGCCATACAACAGTTGGTGGACTCCCAATTACAGAATGGATTAATGAAGATGAACAAGGCGCAATGGACACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_227 -TATAGCGATTGCCATGATGCAGATTTAGTTGTTGTAACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTTCTAATCGCATCTAACCCGGTAGACATTTTAACTTATGCGACTTGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCCATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACATGGCGATACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_228 -TATAGCGATTGCCATGATGCGGACTTAGTTGTTGTAACTGCTGGGACTGCACAAAAACCTGGCGAAACTCGTTTAGACTTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTATGCGACTTGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCCATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTATATCCTTGGCGAACATGGCGATACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCC ->ldh_229 -TATAGCGATTGCCATGATGCGGACTTAGTTGTTGTAACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTATGCGACTTGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCCATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTATATCCTTGGCGAACATGGCGATACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_230 -TATAGCGATTGCCATGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGATTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTATGCGACTTGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_231 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTTCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTATATCCTTGGCGAACACGGCGACACAGAGTTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_232 -TATAGCGATTGCCATGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTATGCGACTTGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTATATCCTTGGCGAACATGGCGATACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_233 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAACCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_234 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGTTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_235 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCATTTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_236 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTGCGTAAGTGTTCGTGATGCA ->ldh_237 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_238 -TATAGCGACTGTCACGATGCTGACTTAGTTGTTGTCACTGCAGGAACAGCGCAAAAACCTGGTGAAACTCGTCTAGATTTAGTAAACCGTAATATCAAGATTATGAAAGGTATTGTCGATGAAGTTATGGCAAGCGGTTTTGATGGTATTTTCCTAATTGCCTCAAATCCAGTAGATATTTTGACTTATGCGACATGGAAATTCTCTGGACTTCCTAAAGAACGCGTGATTGGTTCAGGTACAAGTCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTAAAAGTTGATGCTCGTAACGTCCATGGTTATATTCTAGGTGAACACGGTGATACAGAATTCCCGGCTTGGAGCCATACAACAGTCGGCGGTCTTCCAATTACTGAGTGGATTAGCGAAGACGAACAAGGCGCAATGGAAACTATTTTCGTGAGTGTGCGTGATGCT ->ldh_239 -TATAGCGACTGTCACGATGCTGACTTAGTTGTTGTCACTGCAGGAACAGCGCAAAAACCTGGTGAAACTCGTCTAGATTTAGTAAACCGTAATATCAAGATTATGAAAGGTATTGTCGATGAAGTTATGGCAAGCGGTTTTGATGGTATTTTCCTAATTGCATCAAATCCAGTAGATATTTTGACTTATGCGACATGGAAATTCTCTGGACTTCCTAAAGAACGCGTGATTGGTTCAGGTACAAGTCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTGAAAGTCGACGCTCGTAACGTCCATGGCTATATTCTAGGTGAACACGGTGATACAGAATTCCCAGCTTGGAGCCATACAACAGTCGGCGGTCTTCCAATTACTGAGTGGATTAGCGAAGACGAACAAGGCGCAATGGAAACTATTTTCGTGAGTGTGCGTGATGCT ->ldh_240 -TATAGCGACTGCCATGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGATGGCATCTTTTTAATCGCGTCTAACCCAGTAGACATTTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCTGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTTGGCGGCCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_241 -TATAGCGACTGCCATGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGATGGCATCTTTTTAATCGCGTCTAACCCAGTAGACATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAGGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCTTACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGTGACACAGAATTCCCAGCATGGAGCCATACTATAGTCGGCGGCCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_242 -TATAGCGACTGCCATGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGATGGCATCTTTTTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCTGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTTGGCGGCCTTCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_243 -TATAGCGACTGCCATGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGATGGCATCTTTTTAATCGCGTCTAACCCAGTAGACATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAGGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCTGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGTGACACAGAATTCCCAGCATGGAGCCATACTACAGTCGGCGGCCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_244 -TATAGTGATTGCCACGATGCAGACCTTGTTGTTGTAACTGCTGGAACTGCGCAAAAACCTGGCGAAACTCGCTTAGACCTAGTAAACCGTAATATTAAAATCATGAAAGGCATTGTAGATGAAGTTATGGCAAGCGGATTTGACGGTATTTTCCTTATCGCGTCTAACCCAGTCGACATCTTAACTTATGCTACATGGAAATTCTCAGGGCTTCCAAAAGAACGCGTTATCGGTTCCGGAACTAGCCTTGATACCGCTCGTTTCCGCATGTCAATTGCAGACTATTTAAAAGTAGATGCGCGTAATGTTCATGGTTATATCCTTGGCGAACATGGAGATACAGAATTCCCAGCTTGGAGTCATACAACAGTCGGTGGTCTTCCAATCACTGAGTGGATTAACGAAGATGAACAAGGTGCAATGGAAACCATATTCGTAAGTGTACGTGACGCA ->ldh_245 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTAGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_246 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCCGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCTATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_247 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTCGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCGCACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_248 -TATAGCGATTGCCACGATGCAAATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAATAGTCGGCGGCCTTCCAATCGCTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_249 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_250 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACGTCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_251 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_252 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_253 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAATTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_254 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGACCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_255 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_256 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCAAACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_257 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTTTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCCGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_258 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGCAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_259 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACACCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_260 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGGGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_261 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGGGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_262 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_263 -TATAGCGACTGCCATGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGATGGCATCTTTTTAATCGCGTCTAACCCAGTAGACATTTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCTGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGTGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGGGGCCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_264 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTCTCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_265 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAATTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_266 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTGTCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_267 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGACACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_268 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTCTCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_269 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTCCGTAAGTGTTCGTGATGCA ->ldh_270 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAACCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_271 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCAAACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_272 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCCCACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_273 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTCTCGTAAGTGTTCGTGATGCA ->ldh_274 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATATTTTCGTAAGTGTTCGTGATGCA ->ldh_275 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCAAACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_276 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTGTCGTAAGTGTTCGTGATGCA ->ldh_277 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTCGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_278 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGACACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_279 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTTCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_280 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_281 -TATAACGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_282 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACCGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_283 -TATAGCGATTGCCATGATGCGGACTTAGTTGTTGTAACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGCATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTATGCGACTTGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCCATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTATATCCTTGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCC ->ldh_284 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTTTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACACTACATGGAAATTCTCCGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_285 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_286 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCCGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_287 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACCGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_288 -TATAGCGACTGCTACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_289 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_290 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_291 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCTATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_292 -TATAGCGATTGCCATGATGCGGACTTAGTTGTTGTAACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTGGACATTTTAACTTATGCGACTTGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCCATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTATATCCTTGGCGAACATGGCGATACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCC ->ldh_293 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCTGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGTGGCCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCC ->ldh_294 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_295 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTCGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_296 -TATAGCGACTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTACGCTACTTGGAAATTTTCAGGTCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGCTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_297 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCAGTAGACATTTTAACTTACGCTACTTGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGTGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_298 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_299 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTAGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGATACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_300 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCAGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACAGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_301 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGATCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGAGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCTATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_302 -TATAGCGATTGCCATGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCGCAAAAACCTGGCGAAACTCGTTTAGACTTAGTAAACCGTAATATCAAAATCATGAAAGGCATCGTGGACGAAGTTATGGCCAGCGGATTTGACGGCATTTTCCTAATCGCGTCTAACCCAGTGGACATCCTAACTTATGCGACTTGGAAATTCTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACGAGTCTTGATACAGCTCGTTTCCGTATGTCGATTGCCGATTATTTAAAAGTGGATGCTCGTAACGTCCATGGTTACATTCTCGGCGAACACGGTGATACTGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGACGAACAAGGCGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_303 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGTGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_304 -TATAGCGACTGCCATGATGCTGACTTAGTTGTTGTTACAGCAGGTACAGCACAAAAACCTGGCGAGACTCGTTTAGATTTAGTAAATCGTAACATTAAGATTATGAAAGGCATTGTCGATGAAGTAATGGCAAGTGGTTTTGATGGTATTTTCCTAATCGCATCAAACCCTGTGGATATTTTAACTTACGCGACATGGAAATTTTCTGGACTTCCTAAAGAACGAGTTATCGGTTCTGGAACAAGTTTAGATACAGCGCGCTTCCGTATGTCAATTGCCGATTATTTAAAAGTGGATGCTCGTAATGTCCATGGCTATATTCTAGGTGAACACGGTGATACCGAATTCCCAGCTTGGAGACATACAACAGTTGGTGGACTCCCAATTACAGAATGGATTAATGAAGATGAACAAGGCGCAATGGACACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_305 -TATAGCGACTGCCATGATGCTGACTTAGTTGTTGTTACAGCAGGTACAGCACAAAAACCTGGCGAGACTCGTTTAGATTTAGTGAATCGTAATATTAAGATTATGAAAGGCATTGTCGATGAAGTAATGGCAAGTGGTTTTGATGGTATTTTCCTAATCGCATCAAACCCAGTGGATATTTTAACTTACGCGACATGGAAATTCTCTGGACTTCCCAAAGAACGAGTTATCGGTTCTGGAACAAGTTTAGATACAGCGCGTTTCCGTATGTCAATTGCCGATTATTTAAAAGTGGATGCTCGTAATGTCCATGGTTATATTTTAGGTGAACATGGAGATACTGAATTCCCAGCTTGGAGCCATACTACAGTAGGCGGACTACCAATTACAGAATGGATTAATGAAGATGAACAAGGCGCAATGGACACCATTGTCGTAAGCGTTCGCGATGCG ->ldh_306 -TATAGCGACTGCCATGATGCTGACTTAGTTGTTGTTACAGCAGGTACAGCACAAAAACCTGGCGAGACTCGTTTAGATTTAGTGAATCGTAATATTAAGATTATGAAAGGCATTGTCGATGAAGTAATGGCAAGTGGTTTTGATGGTATTTTCCTAATCGCATCAAACCCAGTGGATATTTTAACTTACGCGACATGGAAATTCTCTGGACTTCCCAAAGAACGAGTTATCGGTTCTGGAACAAGTTTAGATACAGCGCGTTTCCGTATGTCAATTGCCGATTATTTAAAAGTGGATGCTCGTAATGTCCATGGTTATATTTTAGGTGAACATGGAGATACTGAATTCCCAGCTTGGAGCCATACTACAGTAGGCGGACTACCAATTACAGAATGGATTAATGAAGATGGACAAGGCGCAATGGACACCATTTTCGTAAGCGTTCGCGATGCG ->ldh_307 -TATAGCGACTGCCATGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCATCTAACCCAGTAGACATTTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTAAAAGTGGATGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTTGGTGGCCTTCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_308 -TATAGCGATTGCCATGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCGCAAAAACCTGGCGAAACTCGTTTAGACTTAGTAAACCGTAATATCAAAATCATGAAAGGCATCGTGGACGAAGTTATGGCCAGCGGATTTGACGGCATTTTCCTAATCGCGTCTAACCCAGTGGACATCCTAACTTATGCGACTTGGAAATTCTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACGAGTCTTGATACAGCTCGTTTCCGTATGTCGATTGCCGATTATTTAAAAGTGGATGCTCGTAACGTCCATGGTTACATTCTCGGCGAACACGGTGATACTGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGACGAACAAGGCGCAATGGATACCATCTTCGTAAGCGTTCGTGATGCA ->ldh_309 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACCGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCTATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_310 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACCGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATAAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCTATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_311 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCCGGCGAAACTCGTTTGGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGTGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGTCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_312 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_313 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_314 -TATAGCGACTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_315 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_316 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCTATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_317 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAACCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACTTGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_318 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_319 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGAGATGCA ->ldh_320 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTTTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCCGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTCGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_321 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGAAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_322 -TATAGCGATTGCGGCGATGCCGATCTAATTGTTATCACAGCAGGTACTGCACAAAAACCTGGCGAAACTCGTCTTGACCTAGTAAACCGCAACATCAAAATTATGAAAGGAATTATTGACGAAGTCATGGCGAGCGGTTTTGATGGGATCTTCCTAATTGCATCAAACCCTGTTGATGTATTAACTTACGCAACTTGGAAATTCTCCGGACTTCCAAAAGAACGTGTTATCGGTTCCGGAACAAGTTTAGATACAGCTCGTTTCCGCTCTTCAATTGCTGACTATGTAAAAGTGGACGCTCGAAATGTGCACGGATATATTCTCGGGGAACACGGCGACACTGAATTCCCAGCTTGGAGCCATACAACTGTTGGTGGACTTCCAATCTCCGAATGGATTAAAGAAGACGAACAAGGAGCGATGAACACGATTTTCGAAAGTGTTCGTGATGC ->ldh_323 -TATAGCGACTGCCATGATGCTGACTTAGTTGTTGTTACAGCAGGTACAGCACAAAAACCTGGCGAGACTCGTTTAGATTTAGTGAATCGTAATATTAAGATTATGAAAGGCATTGTCGATGAAGTAATGGCAAGTGGTTTTGATGGTATTTTCCTAATCGCATCAAACCCAGTGGATATTTTAACTTACGCGACATGGAAATTCTCTGGACTTCCCAAAGAACGAGTTATCGGTTCTGGAACAAGTTTAGATACAGCGCGTTTCCGTATGTCAATTGCCGATTATTTAAAAGTGGATGCTCGTAATGTCCATGGTTATATTTTAGGTGAACATGGAGATACTGAATTCCCAGCTTGGAGCCATACTACAGTAGGCGGACTACCAATTACAGAATGGATTAATGAAGATGAACAAGGCGCAATGGACACCATTTTCGTAAGCGTTCGCGATGCG ->ldh_324 -TACAGCGACTGCAGCGATGCAGATATCGTCGTTGTTACTGCAGGTACTGCTCAAAAACCAGGCGAAACACGATTAGATTTAGTAAACCGCAATATCCGTATCATGAAAGGTATTGTAGATGAAGTAATGGCAAGCGGGTTTGACGGCATCTTCTTGATCGCTTCCAACCCTGTAGACATCTTGACTTACGCAACTTGGAAATTCTCCGGTCTTCCAAAAGAACGCGTTATCGGATCAGGTACAAGCTTAGATACAGCTCGCTTCCGTATGTCGATTGCTGATTATTTGAAAGTAGATGCGCGTAACGTCCATGGCTACATTTTAGGCGAACACGGCGACACTGAATTCCCAGCATGGAGCCATACAACAGTTGGTGGTCTGCCAATTTCCGAATGGATCAACGAAAATGAAAAAGGGGCAATGGACACCATCTTTGTCAGCGTTCGTGATGCC ->ldh_325 -TACAGCGACTGCAGCGATGCAGATATCGTCGTTGTTACTGCAGGTACTGCTCAAAAACCAGGCGAAACACGATTAGATTTAGTAAACCGCAATATCCGTATCATGAAAGGTATTGTAGATGAAGTAATGGCAAGCGGGTTTGACGGCATCTTCTTGATCGCTTCCAATCCTGTAGACATTTTGACTTACGCAACTTGGAAATTCTCCGGTCTTCCAAAAGAACGCGTTATCGGATCAGGTACAAGCTTAGATACAGCTCGCTTCCGTATGTCGATTGCTGATTATTTGAAAGTAGATGCGCGTAACGTCCATGGCTACATTTTAGGCGAACACGGCGACACTGAATTCCCAGCATGGAGCCATACAACAGTTGGTGGTCTGCCAATTTCCGAATGGATCAACGAAAATGAAAAAGGAGCAATGGACACCATCTTTGTCAGCGTTCGTGATGCC ->ldh_326 -TATAGTGATTGCCACGACGCAGATATCGTTGTTGTTACAGCTGGTACGGCACAAAAACCTGGTGAAACTAGACTTGATCTTGTGAGCCGTAATATACGTATTATGAAATCGATTGTGGATGAAATAATGGCAAGTGGTTTTGATGGCATTTTCTTAGTAGCATCAAACCCTGTGGATATTCTTACTTATGCGACTTGGAAATTCTCTGGGTTGCCAAAAGAACGCGTGATTGGTTCTGGTACAAGTCTTGATACGGCACGTTTCCGGATGTCAATTGCCGATTATTTAAAAGTCGATGCGCGTAACGTCCACGGATATATCCTTGGTGAGCATGGCGATACGGAATTCCCTGCCTGGAGCCACACAACTGTTGGTGGACTACCAATCATGGAATGGATTGAGGAAGATGAACAAGGTGCAATGGATACGATTTTCGTAAGTGTTCGTGATGC ->ldh_327 -TATAGCGATTGCCATGACGCAGATTTAGTTGTTGTAACAGCTGGAACTGCGCAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAACCGTAATATCAAAATCATGAAAGGCATCGTGGACGAAGTTATGGCCAGCGGATTTGACGGCATTTTCCTAATCGCGTCTAACCCAGTGGACATCCTAACTTATGCGACTTGGAAATTCTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCTCGTTTCCGTATGTCGATTGCCGATTATTTAAAAGTGGATGCTCGTAACGTCCATGGTTACATTCTTGGCGAACACGGTGATACTGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGACGAACAAGGCGCAATGGATACTATCTTCGTAAGCGTTCGTGATGCA ->ldh_328 -TATAGTGATTGTCACGATGCGGATATCGTTGTTGTTACAGCTGGTACGGCGCAAAAACCTGGTGAAACTAGACTGGATCTTGTGAGCCGTAATATACGTATTATGAAATCGATTGTGGACGAAGTTATGGCGAGCGGATTTGACGGTATTTTCTTGGTAGCGTCGAACCCAGTGGATATTCTTACTTATGCGACTTGGAAATTCTCTGGTCTTCCAAAAGAGCGCGTGATTGGTTCTGGTACGAGTCTTGATACGGCGCGTTTCCGGATGTCGATTGCGGATTTCTTGAAAGTCGATGCGCGTAATGTCCATGGCTATATTTTGGGTGAGCATGGCGATACGGAATTCCCGGCTTGGAGCCACACAACGGTTGGTGGTCTGCCAATTACGGAATGGATCGATGATAGTGAGCAAGGCGCGATGGATACGATTTTCGTGAGTGTTCGTGACGCG ->ldh_329 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTGACAGCTGGTACAGCACAAAAACCAGGCGAAACACGTCTCGATCTTGTAAATCGTAATATAAAAATTATGAAAGGTATTGTTGATGAAGTCATGGCAAGTGGTTTTGATGGCATCTTCTTAATCGCTTCAAATCCAGTCGACATCCTTACTTATGCAACTTGGAAATTCTCCGGCCTACCAAAAGAACGCGTTATCGGTTCAGGTACAAGTCTTGATACAGCACGTTTCCGTATGTCCATCGCTGATTATTTAAAAGTGGATGCCCGTAATGTACATGGGTACATTTTAGGTGAACATGGTGACACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCTATCACAGAATGGATTACAGAAAACGAGCAAGGTGCTATGGATACAATTTTCGTTAGCGTGCGTGACGCA ->ldh_330 -TACAGCGATTGCAGCGATGCTGATCTTATCGTCGTGACAGCCGGAACTGCTCAAAAGCCCGGTGAAACACGTCTTGACCTTGTCAGCCGCAATATAAAAATTATGAAAAGTATTGTTGACCAAGTTATGGCAAGTGGTTTTGATGGTATCTTCTTGATTGCTTCAAATCCAGTTGACATTTTAACTTACGCAACATGGAAGTTCTCAGGTCTTCCAAAAGAACGAGTTATCGGCTCTGGAACAAGCCTTGATACCGCTCGCTTCCGTTCTTCAATTGCCGACTATGTAAAAGTGGATGCACGTAACGTCCATGGTTACATTCTAGGTGAACACGGTGATACAGAATTCCCAGCATGGAGCCATACAACGGTTGGTGGACTTCCAATCACTGAATGGATTAAAGAAAACGAACAAGGTGCGATGGATACAATTTTCGTCAGTGTTCGTGATGCA ->ldh_331 -TATAGCGATTGCGGCGATGCCGATCTAATTGTTATCACAGCAGGTACTGCACAAAAACCTGGTGAAACTCGTCTTGACCTAGTAAACCGTAACATCAAAATCATGAAAGGGATCATCGATGAAGTCATGGCAAGCGGTTTTGATGGGATCTTCCTAATTGCGTCAAACCCTGTTGACGTATTAACTTACGCAACTTGGAAATTCTCTGGACTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGTTTAGATACAGCTCGTTTCCGTTCTTCAATTGCTGACTATGTAAAAGTGGACGCTCGAAATGTGCATGGATATATTCTCGGGGAACACGGCGACACTGAATTCCCAGCTTGGAGCCATACAACTGTTGGTGGACTTCCAATCTCCGAATGGATTAAAGAAGACGAACAAGGAGCAATGGACACGATTTTCGAAAGTGTTCGTGATGC ->ldh_332 -TATAGCGATTGTCATGACGCGGACCTTGTTGTTGTAACAGCGGGTACTGCGCAAAAGCCTGGTGAAACGAGATTGGATCTTGTCAGCCGTAATATACGTATTATGAAAGCCATCGTGGATGAAGTTATGGCAAGTGGTTTTGACGGCATTTTCTTGATTGCGTCGAATCCGGTTGATATTTTGACATATGCGACTTGGAAATTCTCTGGTTTGCCTAAGGAACGTGTTATTGGTTCTGGTACAAGCCTTGATACTGCACGTTTCCGTATGTCGATTGCCGATTATTTGAAAGTGGATGCGCGTAATGTCCATGGTTACATTCTTGGGGAACATGGTGATACGGAATTCCCTGCTTGGAGCCATACAACTGTCGGTGGTTTGCCAATTACAGAATGGATCGAAGAAAACGAACAAGGCGCGATGGACACGATTTTCGTAAGTGTTCGTGATGC ->ldh_333 -TATAGTGATTGTCACGATGCGGATATCGTTGTTGTTACAGCTGGTACGGCGCAAAAACCTGGTGAAACTAGACTTGATCTTGTGAGCCGTAATATACGTATTATGAAATCAATTGTGGACGAAGTGATGGCGAGCGGTTTTGACGGTATTTTCTTAGTTGCGTCGAACCCTGTCGATATTCTTACTTATGCGACTTGGAAATTCTCTGGCCTGCCAAAAGAGCGCGTGATTGGGTCTGGTACGAGTCTTGATACGGCGCGTTTTCGGATGTCGATTGCGGATTTCTTGAAAGTCGATGCGCGTAACGTCCATGGTTACATTCTTGGTGAGCATGGCGATACGGAATTTCCAGCTTGGAGCCACACAACGGTTGGTGGACTGCCAATTACGGAGTGGATCGATGACAGTGAGCAAGGTGCGATGGATACGATTTTCGTGAGTGTTCGTGATGC ->ldh_334 -TATAGTGATTGTCACGATGCGGATATCGTTGTTGTTACGGCTGGTACGGCGCAAAAGCCTGGGGAAACAAGACTTGACTTGGTGAGCCGTAATATACGTATTATGAAATCGATCGTGGATGAGGTTATGGCAAGCGGTTTTGACGGTATCTTCTTGGTTGCATCGAACCCTGTTGATATTCTTACTTATGCGACTTGGAAATTCTCTGGTCTGCCAAAAGAGCGCGTGATTGGTTCTGGTACAAGTCTTGATACGGCGCGTTTCCGGATGTCGATTGCGGATTTCTTGAAGGTCGATGCGCGTAATGTCCATGGTTACATTCTTGGTGAGCATGGCGATACGGAGTTCCCAGCTTGGAGTCATACAACGGTTGGTGGACTGCCAATTATGGAATGGATCGAGGAAGACGAGCAAGGCGCGATGGATACGATTTTTGTTAGCGTTCGTGACGCG ->ldh_335 -TATAGCGACTGTCACGATGCTGACTTAGTTGTTGTCACTGCAGGAACAGCGCAAAAACCTGGTGAAACTCGTCTAGATTTAGTAAACCGTAATATCAAGATTATGAAAGGTATTGTCGATGAAGTTATGGCAAGCGGTTTTGATGGTATTTTCCTAATTGCATCAAATCCAGTAGATATTTTGACTTATGCGACATGGAAATTCTCTGGACTTCCTAAAGAACGAGTGATTGGTTCAGGTACAAGTCTCGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTGAAAGTCGACGCTCGTAACGTCCATGGCTATATTCTAGGTGAACACGGTGATACAGAATTCCCAGCTTGGAGCCATACAACAGTCGGCGGTCTTCCAATTACTGAGTGGATTAGTGAAGACGAACAAGGCGCAATGGAAACTATTTTCGTGAGTGTGCGTGATGCT ->ldh_336 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCAGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_337 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGTCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_338 -TATAGCGACTGCCATGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCGAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTATGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTAAAAGTAGACGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTTGGTGGCCTTCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_339 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_340 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCAGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_341 -TATAGCGACTGCCATGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCGAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTATGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTAAAAGTGGATGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTTGGTGGCCTTCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_342 -TATAGTGATTGTCACGATGCGGATATCGTTGTTGTTACGGCTGGTACGGCGCAAAAACCTGGTGAAACTAGACTTGATCTTGTGAGCCGTAATATACGTATTATGAAATCGATTGTAGACGAAGTGATGGCGAGCGGCTTTGATGGGATTTTCTTAGTGGCTTCGAATCCTGTCGATATTCTTACTTATGCAACTTGGAAATTCTCTGGTTTGCCAAAAGAGCGTGTGATTGGTTCTGGTACGAGTCTTGATACGGCGCGTTTCCGGATGTCGATTGCCGATTATTTGAAAGTCGATGCGCGTAATGTGCATGGTTACATTCTTGGTGAGCATGGCGATACGGAATTCCCGGCTTGGAGCCATACAACGGTTGGTGGACTGCCAATCATGGAATGGATCGAGGAAGACGAGCAAGGCGCGATGGATACGATTTTCGTGAGTGTTCGTGACGC ->ldh_343 -TATAGTGATTGCCACGATGCAGACCTTGTTGTTGTAACTGCTGGAACTGCGCAAAAACCTGGCGAAACTCGCTTAGACCTAGTAAACCGTAATATTAAAATCATGAAAGGCATTGTAGATGAAGTTATGGCAAGCGGATTTGACGGTATTTTCCTTATCGCGTCTAACCCAGTCGACATCTTAACTTATGCTACATGGAAATTCTCAGGGCTTCCAAAAGAACGCGTTATCGGTTCCGGAACAAGCCTTGATACCGCTCGTTTCCGCATGTCAATTGCAGACTATTTAAAAGTAGATGCGCGTAATGTTCATGGTTATATCCTTGGCGAACATGGAGATACAGAATTCCCAGCTTGGAGTCATACAACAGTCGGTGGTCTTCCAATCACTGAGTGGATTAACGAAGATGAACAAGGTGCAATGGAAACCATATTCGTAAGTGTACGTGATGCA ->ldh_344 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCATTTAATGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_345 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCATTTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_346 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGATCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_347 -TATAGCGACTGTCACGATGCTGACTTAGTTGTTGTCACTGCAGGAACAGCGCAAAAACCTGGTGAAACTCGTCTAGATTTAGTAAACCGTAATATCAAGATTATGAAAGGTATTGTCGATGAAGTTATGGCAAGCGGTTTTGATGGTATTTTCCTAATTGCCTCAAATCCAGTAGATATTTTGACTTATGCGACATGGAAATTCTCTGGACTTCCTAAAGAACGCGTGATTGGTTCAGGTACAAGTCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTAAAAGTTGATGCTCGTAACGTCCATGGCTATATTCTAGGTGAACACGGTGATACAGAATTCCCCGCTTGGAGCCATACAACAGTTGGCGGTCTTCCAATTACCGAGTGGATTAGCGAAGACGAACAAGGCGCAATGGAAACTATTTTCGTGAGTGTGCGTGATGCT ->ldh_348 -TATAGTGATTGCCACGATGCAGACCTTGTTGTTGTAACTGCTGGAACTGCGCAAAAACCTGGCGAAACTCGCTTAGACCTAGTAAACCGTAATATTAAAATCATGAAAGGCATTGTAGATGAAGTTATGGCAAGCGGATTTGATGGTATTTTCCTTATCGCGTCTAACCCAGTCGACATCTTAACTTATGCTACATGGAAATTCTCAGGGCTTCCAAAAGAACGCGTTATCGGTTCTGGAACTAGCCTTGATACCGCTCGTTTCCGCATGTCAATTGCAGACTATTTAAAAGTAGATGCGCGTAATGTTCATGGTTATATCCTTGGCGAACATGGAGATACAGAATTCCCAGCTTGGAGTCATACAACAGTCGGTGGTCTTCCAATCACTGAGTGGATTAACGAAGATGAACAAGGTGCAATGGAAACCATATTCGTAAGTGTACGTGACGCA ->ldh_349 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTCGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_350 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCCGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_351 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_352 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCACGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_353 -TATAGCGACTGCCATGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGCGAAACTCGTTTAGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCGAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTAAAAGTAGACGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTTGGTGGCCTTCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_354 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCCGGTGAAACTCGTTTAGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTTCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_355 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCCGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_356 -TATAGCGACTGCCATGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGATGGCATCTTTTTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCTGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGTGACACAGAATTCCCAGCATGGAGCCATACAACAGTTGGTGGCCTTCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_357 -TATAGCGACTGTCACGATGCTGACTTAGTTGTTGTCACTGCAGGAACAGCGCAAAAACCTGGTGAAACTCGTCTAGATTTAGTAAACCGTAATATCAAGATTATGAAAGGTATTGTCGATGAAGTTATGGCAAGCGGTTTTGATGGTATTTTCCTAATTGCCTCAAATCCAGTAGATATTTTGACTTATGCGACATGGAAATTCTCTGGACTTCCTAAAGAACGCGTGATTGGTTCAGGCACAAGTCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTAAAAGTTGACGCTCGTAACGTCCATGGCTATATTCTAGGTGAACACGGAGATACAGAATTCCCCGCTTGGAGCCATACAACAGTCGGCGGTCTTCCAATTACCGAGTGGATTAGCGAAGACGAGCAAGGCGCAATGGAAACTATTTTCGTGAGTGTGCGTGATGCT ->ldh_358 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGCTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_359 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_360 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_361 -TATAGCGACTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGTGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_362 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCGAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGAACGTTTCCGTATGTCGATTGCCGATTACTTAAAAGTGGATGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTTGGTGGCCTTCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_363 -TATAGCGACTGTCACGATGCTGACTTAGTTGTTGTCACTGCAGGAACAGCGCAAAAACCTGGTGAAACTCGCCTAGATTTAGTAAACCGTAATATCAAGATTATGAAAGGTATTGTCGATGAAGTTATGGCAAGCGGTTTTGATGGTATTTTCCTAATTGCATCAAATCCAGTAGATATTTTGACTTATGCGACATGGAAATTCTCTGGACTTCCTAAAGAACGCGTGATTGGTTCAGGTACAAGTCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTGAAAGTCGACGCTCGTAACGTCCATGGCTATATTCTAGGTGAACACGGTGATACAGAATTCCCAGCTTGGAGCCATACAACAGTCGGCGGTCTTCCAATTACTGAGTGGATTAGCGAAGACGAACAAGGCGCAATGGAAACTATTTTCGTGAGTGTGCGTGATGCT ->ldh_364 -TATAGCGACTGTCACGATGCTGACTTAGTTGTTGTCACTGCAGGAACAGCGCAAAAACCTGGTGAAACTCGTCTAGATTTAGTAAACCGTAATATCAAGATTATGAAAGGTATTGTCGATGAAGTTATGGCAAGCGGTTTTGATGGTATTTTCCTAATTGCATCAAATCCAGTAGATATTTTGACTTATGCGACATGGAAATTCTCTGGACTTCCTAAAGAACGCGTGATTGGTTCAGGTACAAGTCTCGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTGAAAGTCGACGCTCGTAACGTCCATGGCTATATTCTAGGTGAACACGGTGATACAGAATTCCCAGCTTGGAGCCATACAACAGTCGGCGGTCTTCCAATTACTGAGTGGATTAGCGAAGACGAACAAGGCGCAATGGAAACTATTTTCGTGAGTGTGCGTGATGCT ->ldh_365 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACTTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAATTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_366 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTATCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_367 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGATCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_368 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCGCACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_369 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCTGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_370 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_371 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACAGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_372 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_373 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTTTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_374 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGCTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_375 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAACATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_376 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGTTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_377 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTCGATATTTTAACTTATGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_378 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTAGTAAGTGTTCGTGATGCA ->ldh_379 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTCGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCAAACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_380 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGCTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_381 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCGCACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_382 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCCGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTACGCTACTTGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_383 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_384 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCTGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_385 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTACGCTACTTGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATTGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_386 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCCGGTGAAACTCGTTTAGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_387 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTACGCTACTTGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_388 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCGATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_389 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCCGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCAGTAGACATTTTAACTTACGCTACTTGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGTGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_390 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_391 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACAGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_392 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATAAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_393 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_394 -TATAGCGATTGCCATGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCGCAAAAACCTGGCGAAACTCGTTTAGACTTAGTAAACCGTAATATCAAAATCATGAAAGGCATCGTGGACGAAGTTATGGCCAGCGGATTTGACGGCATTTTCCTAATCGCGTCCAACCCAGTGGACATCCTAACTTATGCGACTTGGAAATTCTCAGGTCTTCCTAAAGAACGTGTTATCGGTTCCGGAACAAGTCTTGATACAGCTCGTTTCCGTATGTCGATTGCCGATTATTTAAAAGTGGATGCTCGTAACGTCCATGGTTACATTCTCGGCGAACACGGTGATACTGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGACGAACAAGGCGCAATGGATACCATCTTCGTAAGCGTTCGTGATGCA ->ldh_395 -TATAGCGATTGCCATGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCGCAAAAACCTGGCGAAACTCGTTTAGACTTAGTAAACCGTAATATCAAAATCATGAAAGGCATCGTGGACGAAGTTATGGCCAGCGGATTTGACGGCATTTTCCTAATCGCGTCTAACCCAGTGGACATCCTAACTTATGCGACTTGGAAATTCTCAGGTCTTCCTAAAGAACGTGTTATCGGTTCCGGAACAAGTCTTGATACAGCTCGTTTCCGTATGTCGATTGCCGATTATTTAAAAGTGGATGCTCGTAACGTCCATGGTTACATTCTCGGCGAACACGGTGATACTGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGACGAACAAGGCGCAATGGATACCATCTTCGTAAGCGTTCGTGATGCA ->ldh_396 -TATAGCGATTGCCATGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCGCAAAAACCTGGCGAAACTCGTTTAGACTTAGTAAACCGTAATATCAAAATCATGAAAGGCATCGTGGACGAAGTTATGGCCAGCGGATTTGACGGCATTTTCCTAATCGCGTCTAACCCAGTGGACATCCTAACTTATGCGACTTGGAAATTCTCAGGTCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCTCGTTTCCGTATGTCGATTGCCGATTATTTAAAAGTGGATGCTCGTAACGTCCATGGTTACATTCTTGGCGAACACGGTGATACTGAATTCCCAGCATGGAGCCACACAACTGTCGGTGGCCTTCCAATTACTGAATGGATTAGCGAAGACGAACAAGGCGCAATGGATACTATCTTCGTAAGCGTTCGTGATGCA ->ldh_397 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACTGCTGGTACAGCACAAAAACCAGGCGAAACACGTCTTGATCTTGTCAATCGTAATATAAAAATTATGAAAGGGATTGTTGATGAAGTCATGGCAAGTGGTTTTGATGGTATCTTCTTAATCGCTTCAAACCCAGTCGACATCCTTACTTATGCCACATGGAAATTCTCCGGCCTACCAAAAGAACGCGTTATCGGTTCAGGTACAAGCCTTGATACAGCACGTTTCCGTATGTCCATTGCTGATTATTTAAAAGTGGATGCCCGCAATGTACATGGGTACATTCTAGGTGAACATGGTGACACAGAGTTCCCAGCGTGGAGCCACACAACTGTCGGCGGTCTTCCTATTACAGAATGGATTACAGAAAACGAGCAAGGTGCTATGGATACGATTTTCGTCAGCGTGCGTGACGCA ->ldh_398 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATGCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_399 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGGGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_400 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAATTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_401 -TATAGCGACTGCCACGATACGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_402 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGATGGCATCTTTTTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCTGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTAACGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCG ->ldh_403 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCATAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_404 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGCACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_405 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCCGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_406 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCAGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAACGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_407 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTGTCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAATAGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_408 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGTTTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_409 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCCGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTATGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_410 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_411 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGGACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_412 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGGATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_413 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAAAGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_414 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGTGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_415 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGGACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_416 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCAAACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_417 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTTTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCCGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTCTCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_418 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTTTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCCGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_419 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAAGGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_420 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATAAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_421 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCAAACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_422 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGGACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_423 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGGCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_424 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTTTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCCGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGCACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_425 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAAATGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_426 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAACGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_427 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_428 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACGATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_429 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_430 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTTTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCCGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTACGTAAGTGTTCGTGATGCA ->ldh_431 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGGATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_432 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTTCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_433 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCACTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTCGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_434 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGCTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_435 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATTATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_436 -TATAGCGACTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGATCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_437 -TATAGCGACTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGATCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_438 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTTATGCA ->ldh_439 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGTGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_440 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTGTCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_441 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCCTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_442 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCAGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_443 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGATCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_444 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATTGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_445 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTTCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_446 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGTACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_447 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCAACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_448 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAAGGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_449 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCTCATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_450 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCGGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_451 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTCGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCATTCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_452 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAAAGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_453 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCGCACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_454 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGTA ->ldh_455 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCATCTAACCCAGTAGACATTTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAGGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCCGATTACTTAAAAGTGGATGCTCGTAACGTCCATGGTTATATCCTCGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTTGGTGGCCTCCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_456 -TATAGCGACTGCCATGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCGAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTAAAAGTGGATGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTTGGTGGCCTTCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_457 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACTTGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCCGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_458 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCATCTAACCCAGTAGACATTTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAGGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCCGATTACTTAAAAGTGGATGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTTGGTGGCCTCCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_459 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCCCACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_460 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCGAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGGACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTAAAAGTGGATGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTTGGTGGCCTTCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_461 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_462 -TATAGCGACTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_463 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATAAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_464 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAATTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_465 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTGTCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_466 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGGTACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGACACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_467 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCATTTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_468 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTCCGTAAGTGTTCGTGATGCA ->ldh_469 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGGATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_470 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_471 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCCGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_472 -TATAGCGATTGCCACGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAAACTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGATGGCATCTTTTTAATCGCGTCTAACCCAGTAGACATTTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCTGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTTGGTGGCCTTCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_473 -TATAGCGACTGCCATGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCATCTAACCCAGTAGACATCTTAACTTATGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTAAAAGTAGACGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTTGGTGGCCTTCCAATCACTGAATGGATCAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_474 -TATAGCGACTGTCACGATGCTGACTTAGTTGTTGTCACTGCAGGAACAGCGCAAAAACCTGGTGAAACTCGTCTAGATTTAGTAAACCGTAATATCAAGATTATGAAAGGTATTGTCGATGAAGTTATGGCAAGCGGTTTTGATGGTATTTTCCTAATTGCATCAAATCCAGTAGATATTTTGACTTATGCGACATGGAAATTCTCTGGACTTCCTAAAGAACGAGTGATTGGTTCAGGTACAAGTCTCGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTGAAAGTCGACGCTCGTAACGTCCATGGCTATATTCTAGGTGAACACGGTGATACAGAATTCCCAGCTTGGAGCCATACAACAGTCGGAGGTCTTCCAATTACTGAGTGGATTAGTGAAGACGAACAAGGCGCAATGGAAACTATTTTCGTGAGTGTGCGTGATGCT ->ldh_475 -TACAGCGACTGCAGCGATGCAGATATCGTCGTTGTTACTGCAGGTACTGCTCAAAAACCAGGCGAAACACGATTAGATTTAGTAAACCGCAATATCCGTATCATGAAAGGTATTGTAGATGAAGTAATGGCAAGCGGGTTTGACGGCATCTTCTTGATCGCTTCCAATCCTGTAGACATTTTGACTTACGCAACTTGGAAATTCTCCGGTCTTCCAAAAGAACGCGTTATTGGATCAGGTACAAGCTTAGATACAGCTCGCTTCCGTATGTCGATTGCTGATTATTTGAAAGTAGATGCGCGTAACGTCCATGGCTACATTTTAGGCGAACACGGCGACACTGAATTCCCAGCATGGAGCCATACAACAGTTGGTGGTCTGCCAATTTCCGAATGGATCAACGAAAATGAAAAAGGGGCAATGGACACCATCTTTGTCAGCGTTCGTGATGCC ->ldh_476 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGCTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTATGTGTTCGTGATGCA ->ldh_477 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCGAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAGGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCTGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGGGGCCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCATAAGTGTTCGTGATGCA ->ldh_478 -TATAGCGACTGTCACGATGCTGACTTAGTTGTTGTCACTGCAGGAACAGCGCAAAAACCTGGTGAAACTCGTCTAGATTTAGTAAACCGTAATATCAAGATTATGAAAGGTATTGTCGATGAAGTTATGGCAAGCGGTTTTGATGGTATTTTCCTAATTGCATCAAATCCAGTAGATATTTTGACTTATGCGACATGGAAATTCTCTGGACTTCCTAAAGAACGCGTGATTGGTTCAGGTACAAGTCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTGAAAGTCGACGCTCGTAACGTCCATGGCTATATTCTAGGTGAACACGGTGATACAGAATTCCCAGCTTGGAGCCATACAACAGTCGGTGGTCTTCCAATTACTGAGTGGATTAGTGAAGACGAACAAGGCGCAATGGAAACTATTTTCGTGAGTGTGCGTGATGCT ->ldh_479 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCTGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGTGGCCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCC ->ldh_480 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGATGGCATCTTTTTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCTGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_481 -TATAGCGATTGCGGCGATGCCGATCTAATTGTTATCACAGCTGGTACTGCGCAAAAACCTGGCGAAACTCGTCTTGACTTAGTAAACCGTAACATCAAAATCATGAAGGGGATTATTGATGAAGTCATGGCAAGCGGCTTTGACGGCATCTTCTTAATCGCTTCAAACCCAGTAGATATTTTAACTTACGCAACTTGGAAATTCTCTGGACTTCCAAAAGAACGCGTTATTGGTTCTGGAACAAGTTTAGATACAGCTCGTTTCCGTTCTTCGATTGCTGACTATGTTAAAGTCGATGCAAGAAACGTGCACGGATACATTCTTGGTGAACATGGAGACACTGAATTCCCAGCTTGGAGCCATACAACTGTTGGTGGCCTTCCAATCTCCGAATGGATCAAAGAAGACGAACAAGGTGCCATGGATACAATTTTTGAAAGCGTTCGTGATGC ->ldh_482 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGATGGCATCTTTTTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCCGATTACTTAAAAGTGGACGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTTGGTGGCCTTCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_483 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGATGGCATCTTTTTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCTGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACATGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGTGGCCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCC ->ldh_484 -TATAGCGATTGCGGCGATGCCGATCTAATTGTTATCACAGCAGGTACTGCACAAAAACCTGGTGAAACTCGTCTTGACCTAGTAAACCGTAATATCAAAATTATGAAAGGAATTATCGACGAAGTCATGGCGAGCGGTTTTGATGGGATCTTCCTAATTGCATCAAACCCTGTTGATGTATTAACTTACGCAACTTGGAAATTCTCCGGACTTCCAAAAGAACGTGTTATCGGTTCCGGAACAAGTTTAGATACAGCTCGTTTCCGCTCTTCAATTGCTGACTATGTAAAAGTGGACGCTCGAAATGTGCACGGATATATTCTCGGGGAACACGGCGACACTGAATTCCCAGCTTGGAGCCATACAACTGTTGGTGGACTTCCAATCTCCGAATGGATTAAAGAAGACGAACAAGGAGCGATGAACACGATTTTCGAAAGTGTTCGTGATGC ->ldh_485 -TATAGTGATTGTCACGATGCGGATATCGTTGTTGTTACGGCTGGTACGGCGCAAAAACCTGGTGAAACTAGACTTGATCTTGTGAGCCGTAATATACGTATTATGAAATCGATTGTGGACGAGGTTATGGCGAGCGGTTTTGACGGTATTTTCTTGGTGGCGTCGAACCCTGTGGATATTCTTACTTATGCAACTTGGAAATTCTCCGGTTTGCCAAAAGAGCGCGTGATTGGTTCTGGTACGAGTCTTGATACGGCGCGTTTCCGGATGTCGATTGCGGATTACTTGAAAGTCGATGCGCGTAATGTGCATGGTTACATTCTTGGTGAGCATGGCGATACGGAATTCCCGGCTTGGAGCCATACAACGGTTGGTGGACTGCCAATCATGGAATGGATCGAGGAAGACGAGCAAGGCGCGATGGATACGATTTTCGTGAGTGTTCGTGACGC ->ldh_486 -TATAGCGACTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGATCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACTTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGAGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_487 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGTCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_488 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATAAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_489 -TATAGCGATTGCCACAATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCTATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_490 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_491 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_492 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCCGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_493 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTAGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_494 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACCTAGTAAACCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_495 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_496 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCAGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_497 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_498 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCTCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_499 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTAGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_500 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCTATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_501 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTAGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCAGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_502 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_503 -TATAGCGATTGCCAAGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_504 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGGACAAGCCTTGATACAGCACGTTTCCGTATGTCGATCGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_505 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_506 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCTATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_507 -TATAGCAATTGCCACGATGCAGATTTAGTTGTTGTAACTGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTAGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACTTGGAAATTTTCCGGTCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_508 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCCGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACCGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_509 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_510 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_511 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATTGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_512 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTAGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCTATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_513 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_514 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_515 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_516 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_517 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGCGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_518 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCTATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_519 -TATAGCGACTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTACGCTACTTGGAAATTTTCAGGTCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_520 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_521 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACCGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGTGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_522 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGTGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_523 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_524 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTTCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_525 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_526 -TATAGCGATTGCCATGATGCGGACTTAGTTGTTGTAACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTTCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTATATCCTTGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_527 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_528 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_529 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_530 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_531 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGATCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGAGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCTATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_532 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_533 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTACGCTACTTGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGATACATCTTAGGAGAACACGGTGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_534 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_535 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTAGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_536 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGCAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_537 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGTGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGTCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_538 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAATCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_539 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTAGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCTATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_540 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTCGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAATTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_541 -TATAGCGATTGCCATGATGCGGACTTAGTTGTTGTAACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTATGCGACTTGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCCATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTATATCCTTGGCGAACATGGCGATACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCC ->ldh_542 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGTGACACAGAATTCCCAGCATGGAGCCACACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_543 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCCGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTTCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_544 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTACGCTACTTGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_545 -TATAGCGACTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_546 -TATAGCGACTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAATCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_547 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGATCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGAGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCTATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_548 -TATAGCGACTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_549 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACATGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_550 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCCGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACAGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_551 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCAGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGTGACACAGAATTCCCAGCATGGAGCCACACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_552 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACCGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_553 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTTCGCATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_554 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACCGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_555 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_556 -TATAGCGACTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGATCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_557 -TATAGCGACTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_558 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTTCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_559 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_560 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCAGTAGACATTTTAACTTACGCTACTTGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_561 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGGACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_562 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTAGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTGAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_563 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACCGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_564 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCCGGTGAAACTCGTTTAGACTTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGACGCTCGTAACGTTCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_565 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_566 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGGACTGCACAAAAACCCGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_567 -TATAGCGACTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGATCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACTTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGAGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCTATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_568 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAACCTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_569 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTCGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_570 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGTGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_571 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCAGTAGACATTTTAACTTACGCTACTTGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCTATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_572 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_573 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACCTCGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_574 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_575 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTCGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_576 -TATAGCGATTGCGGCGATGCCGATCTAATTGTTATCACAGCTGGTACTGCGCAAAAACCTGGTGAAACTCGTCTTGACCTAGTAAACCGCAATATCAAAATCATGAAGGGAATTATTGATGAAGTCATGGCAAGCGGCTTTGATGGCATCTTCTTAATCGCTTCAAACCCAGTTGATATTTTAACTTACGCAACTTGGAAATTCTCTGGACTTCCAAAAGAACGCGTTATTGGTTCTGGAACAAGTTTAGATACAGCTCGTTTCCGTTCTTCGATTGCTGATTATGTTAAAGTCGATGCAAGAAACGTGCACGGATACATTCTTGGTGAACATGGGGACACTGAATTCCCAGCTTGGAGCCATACAACCGTTGGTGGACTTCCAATCTCCGAATGGATTAAAGAAGACGAACAAGGCGCTATGGATACGATTTTCGAAAGTGTTCGTGATGC ->ldh_577 -TATAGCGACTGCCATGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACATAGTAAATCGTAACATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCATCTAACCCAGTAGACATTTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTAAAAGTGGATGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTTGGTGGCCTTCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_578 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_579 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGACGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_580 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTACGCTACTTGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCCGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGTGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_581 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTATCGTAAGTGTTCGTGATGCA ->ldh_582 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGGGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_583 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTATTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_584 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGAGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_585 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_586 -TATAGCGACTGCCACGATGCGAACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_587 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCAGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGGTTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_588 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_589 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_590 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGATGGCATCTTTTTAATCGCGTCTAACCCAGTAGACATTTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCTGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTTGGTGGCCTTCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_591 -TATAGCGACTGCCATGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGGTTTGATGGCATCTTTTTAATCGCGTCTAACCCAGTAGACATTTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCTGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGTGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGGGGCCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_592 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_593 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_594 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCGTCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_595 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_596 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTACGCTACTTGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTCGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGTGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_597 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCCGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACTTGGAAATTCTCGGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGTGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_598 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCAGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_599 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGAGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_600 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGTGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_601 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_602 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACCGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGTGACACAGAATTCCCAGCATGGAGCCACACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_603 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_604 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTCGATATTTTAACTTATGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGATACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_605 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_606 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTCGATATTTTAACTTATGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCTCGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_607 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTCGATATTTTAACTTATGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGCTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGTGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_608 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_609 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_610 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCCGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_611 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_612 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACATGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_613 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_614 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCCGGTGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_615 -TATAGCGATTGCCACGATGCAGACTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGACGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_616 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCCGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_617 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCCGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATTAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCTATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_618 -TATAGCGATTGCCACGATGCAGACTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_619 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCCGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAATGTCCATGGTTACATCTTAGGAGAACACGGCGATACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_620 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCCGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCTATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_621 -TATAGCGACTGCCATGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCGAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTATGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTAAAAGTAGACGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGTCACACAACAGTTGGTGGCCTTCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_622 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCATTTACTGAATGGATTAGTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_623 -TATAGTGATTGCCACGATGCAGACCTTGTTGTTGTAACTGCTGGAACTGCGCAAAAACCTGGCGAAACTCGCTTAGACCTAGTAAACCGTAATATTAAAATCATGAAAGGCATTGTAGATGAAGTTATGGCAAGCGGATTTGATGGTATTTTCCTTATCGCGTCTAACCCAGTCGACATCTTAACTTATGCTACATGGAAATTCTCAGGGCTTCCAAAAGAACGCGTTATCGGTTCCGGAACTAGCCTTGATACCGCTCGTTTCCGCATGTCAATTGCAGACTATTTAAAAGTAGATGCGCGTAATGTTCATGGTTATATCCTTGGCGAACATGGAGATACAGAATTCCCAGCTTGGAGTCATACAACAGTCGGTGGTCTTCCAATCACTGAGTGGATTAACGAAGATGAACAAGGTGCAATGGAAACCATATTCGTAAGTGTACGTGATGCA ->ldh_624 -TATAGCGATTGCCACGATGCAGACCTTGTTGTTGTAACTGCTGGAACTGCGCAAAAACCTGGCGAAACTCGCTTAGACCTAGTAAACCGTAATATTAAAATCATGAAAGGCATTGTAGATGAAGTTATGGCAAGCGGATTTGATGGTATTTTCCTTATCGCGTCTAACCCAGTCGACATCTTAACTTATGCTACATGGAAATTCTCAGGGCTTCCAAAAGAACGCGTTATCGGTTCCGGAACTAGCCTTGATACCGCTCGTTTCCGCATGTCAATTGCAGACTATTTAAAAGTAGATGCGCGTAATGTTCATGGTTATATCCTTGGCGAACATGGAGATACAGAATTCCCAGCTTGGAGTCATACAACAGTCGGTGGTCTTCCAATCACTGAGTGGATTAACGAAGATGAACAAGGTGCAATGGAAACCATATTCGTAAGTGTACGTGACGCA ->ldh_625 -TATAGCGACTGCCATGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCGAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTAAAAGTAGACGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTTGGTGGCCTTCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_626 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_627 -TTCTGTCACTGCCACGATGCGTACTTAGTTGTTGTAACTGCCGGGACGGCACAAAAACCTGGTGAATCTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_628 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGTCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_629 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGTTAGCGGATTTGACGGTATCTTTTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCCGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_630 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGCATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_631 -TATAGCGACTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_632 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACCTAGTAAACCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTAAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_633 -TATAGCGATTGCCATGATGCGGACTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTATGCGACTTGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCCATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTATATCCTTGGCGAACATGGCGATACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTCCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCC ->ldh_634 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCCGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_635 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_636 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTTGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_637 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTACGCTACTTGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGTGACACAGAATTCCCAGCATGGAGCCACACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_638 -TATAGCGACTGTCACGATGCTGACTTAGTTGTTGTCACTGCAGGAACAGCGCAAAAACCTGGTGAAACTCGTCTAGATTTAGTAAACCGTAATATCAAGATTATGAAAGGTATTGTCGATGAAGTTATGGCAAGCGGTTTTGATGGTATTTTCCTAATTGCATCAAATCCAGTAGATATTTTGACTTATGCGACATGGAAATTCTCTGGACTTCCTAAAGAACGAGTGATTGGTTCAGGCACAAGTCTCGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTGAAAGTCGACGCTCGTAACGTCCATGGCTATATTCTAGGTGAACACGGTGATACAGAATTCCCAGCTTGGAGCCATACAACAGTCGGCGGTCTTCCAATTACTGAGTGGATTAGTGAAGACGAACAAGGCGCAATGGAAACTATTTTCGTGAGTGTGCGTGATGCT ->ldh_639 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAATTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_640 -TATAGCGACTGTCACGATGCTGACTTAGTTGTTGTCACTGCAGGAACAGCGCAAAAACCTGGTGAAACTCGTCTAGATTTAGTAAACCGTAATATCAAGATTATGAAAGGTATTGTCGATGAAGTTATGGCAAGCGGTTTTGATGGTATTTTCCTAATTGCATCAAATCCAGTAGATATTTTGACTTATGCGACATGGAAATTCTCTGGACTTCCTAAAGAACGCGTGATTGGTTCAGGTACAAGTCTCGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTGAAAGTCGACGCTCGTAACGTCCATGGCTATATTCTAGGTGAACACGGTGATACAGAATTCCCAGCTTGGAGCCATACAACAGTCGGTGGTCTTCCAATTACTGAGTGGATTAGTGAAGACGAACAAGGCGCAATGGAAACTATTTTCGTGAGTGTGCGTGATGCT ->ldh_641 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACCAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_642 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCACAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_643 -TATAGCGACTGCCAAGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_644 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGGAAGTGTTCGTGATGCA ->ldh_645 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCTCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_646 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGATGGCATCTTTTTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCTGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCC ->ldh_647 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGATGGCATCTTTTTAATCGCGTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGTTTCCGTATGTCGATTGCTGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCG ->ldh_648 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTGGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_649 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGCGGATTTGACGGCATCTTCTTAATCGCGTCTAACCCAGTAGACATCCTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCTGATTACTTAAAAGTGGATGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_650 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCTAGTGGATTTGACGGCATCTTCTTAATCGCGTCTAACCCAGTAGACATCCTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCTGATTACTTAAAAGTGGATGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGCGTTCGTGATGCA ->ldh_651 -TATAGCGATTGCGGCGATGCCGATCTAATTGTTATCACAGCTGGTACTGCGCAAAAACCTGGTGAAACTCGTCTTGACCTAGTAAACCGCAATATCAAAATCATGAAGGGAATTATTGATGAAGTCATGGCAAGCGGCTTTGATGGCATCTTCTTAATCGCTTCAAACCCAGTTGATATTTTAACTTACGCAACTTGGAAATTCTCTGGACTTCCAAAAGAACGCGTTATTGGTTCTGGAACAAGTTTAGATACAGCTCGTTTCCGTTCTTCGATTGCTGATTATGTTAAAGTCGATGCAAGAAACGTGCACGGATACATTCTTGGTGAACACGGGGACACTGAATTCCCAGCTTGGAGCCATACAACCGTTGGTGGACTTCCAATCTCCGAATGGATTAAAGAAGACGAACAAGGCGCTATGGATACGATTTTCGAAAGTGTTCGTGATGC ->ldh_652 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGCGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_653 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAACCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_654 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAACTGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_655 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGTCCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_656 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_657 -TATAGCGATTGCCATGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTATGCGACTTGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTATATCCTTGGCGAACACGGCGACACAGAGTTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCC ->ldh_658 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACCCAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_659 -TATAGCGATTGCCATGATGCAGATTTAGTTGTTGTAACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTTCTAATCGCATCTAACCCGGTAGACATTTTAACTTATGCGACTTGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCCATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_660 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACCGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_661 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACAGTTGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_662 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGATGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCTATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_663 -TATAGCGATTGCCATGATGCGGACTTAGTTGTTGTAACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTATGCGACTTGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCCATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTATATCCTTGGCGAACATGGCGATACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGACTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCC ->ldh_664 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGCGAAACTCGTTTAGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTACGCTACTTGGAAATTTTCAGGTCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_665 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTATATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_666 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCCGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGTGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_667 -TATAGTGATTGCCACGATGCAGACCTTGTTGTTGTAACTGCTGGAACTGCGCAAAAACCTGGCGAAACTCGCTTAGACCTAGTAAACCGTAATATTAAAATCATGAAAGGCATTGTAGATGAAGTTATGGCAAGCGGATTTGATGGTATTTTCCTTATCGCGTCTAACCCAGTCGACATCTTAACTTATGCTACATGGAAATTCTCAGGGCTTCCAAAAGAACGCGTTATCGGTTCCGGAACTAGCCTTGATACCGCTCGTTTCCGCATGTCAATTGCAGACTATTTAAAAGTAGATGCGCGTAATGTTCATGGTTATATCCTTGGCGAACATGGAGATACAGAATTCCCAGCTTGGAGTCATACAACAGTCGGTGGTCTTCCAATCACTGAGTGGATTAACGAAGATGAACAAGGTGCAATGGAAACCATATTCGTAAGTGTACGTGACGCA ->ldh_668 -TATAGCGACTGTCACGATGCTGACTTAGTTGTTGTCACTGCAGGAACAGCGCAAAAACCTGGTGAAACTCGTCTAGATTTAGTAAACCGTAATATAAAGATTATGAAAGGTATTGTCGATGAAGTTATGGCAAGCGGTTTTGATGGTATTTTCCTAATTGCCTCAAATCCAGTAGATATTTTGACTTATGCGACATGGAAATTCTCTGGACTTCCTAAAGAACGCGTGATTGGTTCAGGTACAAGTCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTAAAAGTTGATGCTCGTAACGTCCATGGCTATATTCTAGGTGAACACGGTGATACAGAATTCCCCGCTTGGAGCCATACAACAGTCGGCGGTCTTCCAATTACCGAGTGGATTAGCGAAGACGAACAAGGCGCAATGGAAACTATTTTCGTGAGTGTGCGTGATGCT ->ldh_669 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_670 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGACTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_671 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGGACTGCACAAAAACCCGGTGAAACTCGTTTAGACTTAGTAAATCGTAACATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTTTCAGGCCTTCCTAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_672 -TATAGCGATTGCCATGATGCGGACTTAGTTGTTGTAACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACTTAGTAAATCGTAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTATGCGACTTGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCCATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTATATCCTTGGCGAACATGGCGATACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCC ->ldh_673 -TATAGCGATTGCCACGATGCAGATTTAGTTGTTGTAACAGCTGGAACCGCACAAAAACCTGGCGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGGTTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGATATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCAGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGCGACACAGAATTCCCAGCATGGAGACACACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACCATTTTCGTAAGTGTTCGTGATGCA ->ldh_674 -TACAGCGACTGCAGCGATGCAGATATCGTCGTTGTTACTGCAGGTACTGCTCAAAAACCAGGCGAAACACGATTAGATTTAGTAAACCGCAATATCCGTATTATGAAAGGCATTGTAGATGAAGTAATGGCAAGCGGGTTCGACGGCATCTTCTTGATTGCTTCCAACCCTGTAGACATCTTGACTTACGCAACTTGGAAATTCTCCGGTCTTCCAAAAGAACGCGTTATTGGATCAGGTACAAGCTTAGATACAGCTCGTTTCCGTATGTCGATCGCTGATTATTTGAAAGTAGATGCGCGTAATGTCCATGGCTACATCTTAGGCGAACATGGCGACACTGAATTCCCAGCATGGAGCCATACAACAGTTGGTGGTCTGCCAATTTCCGAATGGATCAACGAAAATGAAAAAGGCGCAATGGACACCATCTTTGTCAGCGTTCGTGATGCC ->ldh_675 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTGCGTAAGTGTTCGTGATGCA ->ldh_676 -TATAGCGACTGCCACGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTGTGGATGAAGTTATGGCGAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTATATGGAAATTCTCAGGTCTTCCAAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCGATTGCCGATTACTTAAAAGTGGATGCTCGTAACGTCCATGGTTATATCCTCGGCGAACACGGCGACACAGAATTCCCAGCATGGAGCCATACAACAGTTGGTGGCCTTCCAATCACTGAATGGATTAACGAAGACGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_677 -TATAGCGATTGCCACGATGCAGACCTTGTTGTTGTAACTGCTGGAACTGCGCAAAAACCTGGCGAAACTCGCTTAGACCTAGTAAACCGTAATATTAAAATCATGAAAGGCATTGTAGATGAAGTTATGGCAAGCGGATTTGACGGTATTTTCCTTATCGCGTCTAACCCAGTCGACATCTTAACTTATGCTACATGGAAATTCTCAGGGCTTCCAAAAGAACGCGTTATCGGTTCCGGAACTAGCCTTGATACCGCTCGTTTCCGCATGTCAATTGCAGACTATTTAAAAGTAGATGCGCGTAATGTTCATGGTTATATCCTTGGCGAACATGGAGATACAGAATTCCCAGCTTGGAGTCATACAACAGTCGGTGGTCTTCCAATCACTGAGTGGATTAACGAAGATGAACAAGGTGCAATGGAAACCATTTTCGTAAGTGTACGTGATGCA ->ldh_678 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTGTCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_679 -TATAGCGATTGCCATGATGCAGATTTAGTTGTTGTAACAGCTGGAACTGCACAAAAACCTGGTGAAACTCGTTTGGACTTAGTAAACCGCAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGTGGATTTGACGGTATTTTCCTAATCGCATCTAACCCGGTAGACATTTTAACTTATGCGACTTGGAAATTCTCAGGTCTTCCGAAAGAACGCGTTATCGGTTCTGGAACAAGCCTTGATACCGCACGTTTCCGTATGTCCATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTATATCCTTGGCGAACATGGCGATACAGAATTCCCAGCATGGAGCCATACAACAGTCGGCGGCCTTCCAATCACTGAATGGATTACTGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_680 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATTCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_681 -TATAGCGACTGCCATGATGCAGACTTAGTTGTTGTCACTGCTGGGACTGCACAAAAACCTGGTGAAACTCGTTTGGACCTAGTAAACCGCAATATCAAAATCATGAAAGGCATTATGGATGAAGTTATGGCTAGCGGATTTGATGGCATCTTTTTAATCGCGTCTAACCCAGTAGACATTTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAGGAACGCGTTATCGGTTCTGGAACAAGTCTTGATACAGCACGCTTCCGTATGTCGATTGCTGACTACCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCTTAGGAGAACACGGTGACACAGAATTCCCAGCATGGAGCCATACTACAGTCGGCGGCCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_682 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTGCGTAAGTGTTCGTGATGCA ->ldh_683 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_684 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_685 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTATCTGCCGGGACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAAATCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAATAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_686 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCTGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_687 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCAGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGCATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCTGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATCTTCGTAAGTGTTCGTGATGCA ->ldh_688 -TATAGCGACTGCCACGATGCGGACCTAGTTGTTGTAACTGCCGGTACTGCTCAAAAACCAGGTGAAACTCGTTTAGATCTAGTAAATCGTAATATCAAAATCATGAAAGGTATCGTGGATGAAGTTATGGCAAGCGGATTTGATGGTATCTTCTTAATCGCTTCTAACCCAGTAGACATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGCATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAGTTCCCAGCATGGAGCCACACAACTGTCGGCGGCCTTCCAATTACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA ->ldh_689 -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTCGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGAACACGGCGATACAGAATTCCCAGCATGGAACCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCA - ->ldh_690 # INF by Taranis -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGGACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCAGCTTATGAAATTATTAATAAAAAAGGCGCTACATTCTACGGTGTTGCTGCAGCTCTAGCTCGTATTACAAAGGCAATTCTAAATAACGAAAATGCGATCTTACCACTTTCTGTTTATCTAGATGGTCATTATGGTATGAACGACATTTATATCGGCGCTCCCGCAGTCGTTAACCGTCAAGGCGTTCGCCATATTGTTGAAATGAATTTAAACGACAAAGAAAAAGAACAAATGAAAAACTCTGCAGATACACTTAAAAAAGTTCTAGACGACGCAATGAAACAAATCGACTAA - ->ldh_691 # INF by Taranis -TATAGCGACTGCCACGATGCGGACTTAGTTGTTGTAACTGCCGGGACTGCACAAAAACCTGGTGAAACTCGTTTAGATTTAGTAAATCGTAATATTAAAATCATGAAAGGCATCGTGGATGAAGTAATGGCTAGCGGATTTGACGGTATCTTCTTAATCGCTTCTAACCCAGTAGATATCTTAACTTACGCTACATGGAAATTCTCAGGTCTTCCAAAAGAACGTGTTATCGGTTCTGGAACAAGCCTTGATACAGCACGTTTCCGTATGTCAATTGCCGACTATCTAAAAGTAGATGCTCGTAACGTCCATGGTTACATCCTTGGCGGACACGGCGATACAGAATTCCCAGCATGGAGCCACACAACTGTCGGCGGTCTTCCAATCACTGAATGGATTAGCGAAGATGAACAAGGTGCAATGGATACTATTTTCGTAAGTGTTCGTGATGCAGCTTATGAAATTATTAATAAAAAAGGCGCTACATTCTACGGTGTTGCTGCAGCTCTAGCTCGTATTACAAAGGCAATTCTAAATAACGAAAATGCGATCTTACCACTTTCTGTTTATCTAGATGGTCATTATGGTATGAACGACATTTATATCGGCGCTCCCGCAGTCGTTAACCGTCAAGGCGTTCGCCATATTGTTGAAATGAATTTAAACGACAAAGAAAAAGAACAAATGAAAAACTCTGCAGATACACTTAAAAAAGTTCTAGACGACGCAATGAAACAAATCGACTAA diff --git a/test/MLST_listeria/lhkA.fasta b/test/MLST_listeria/lhkA.fasta deleted file mode 100644 index 03ac944..0000000 --- a/test/MLST_listeria/lhkA.fasta +++ /dev/null @@ -1,856 +0,0 @@ ->lhkA_1 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_2 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_3 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_4 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCGCCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_5 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_6 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_7 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_8 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAATCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_9 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATTTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_10 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_11 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTTTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_12 -TATCCTACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_13 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATAATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAACCGAATCAACAAACTACGAGCAGCAACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_14 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTGGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_15 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTAGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_16 -TACCCTACTCAGCTCAATCAACCATTACCAAAAGATTTTTCAATTTCAAAAGATGATAAGAAAAAATTAAAAAGTGGCGAAACAGTTAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTACCAATTATGGATGGCGATAAATTTGTTGGATCCATCGTCTTAAATTCTCCAATTAGCGGGACTGAACAAGTGATTGGAACAATTAATCGTTATATGTTCTATACTATTTTACTATCGATAACGATTGCGCTTATTTTGAGTGCTATTCTGTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCAACAAAAGATGTGATCCAAGGCAATTATAAGGCGCGGCTAAAAGAGAATAATTTTGATGAAATTGGCGCACTTGCCATTGATTTCAACAAAATGACGGAAACGCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_17 -TACCCTACTCAGCTCAATCAACCATTACCAAAAGATTTTTCAATTTCAAAAGATGATAAGAAAAAATTGAAAAGTGGTGAAACAGTTAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTACCTATTATGGATGGCGATAAGTTTGTTGGCTCTATTGTCTTGAATTCTCCAATTAGCGGAACTGAACAAGTGATTGGAACAATTAATCGTTATATGTTCTATACTATTTTACTATCGATAACGATTGCGCTTATTTTGAGTGCTATTCTGTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCAACAAAAGATGTGATCCAAGGCAATTATAAGGCGCGGCTAAAAGAGAATAATTTTGATGAAATTGGCGCACTTGCCATTGATTTCAACAAAATGACGGAAACGCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_18 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCATGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_19 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTCATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_20 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATAATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACAGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAACCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_21 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGTTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_22 -TATCCAACACAGATGAATCAGCCGTTACCAATGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_23 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGAAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_24 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTATATGTTCTATACTATTTTACTCTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_25 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_26 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_27 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_28 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_29 -TATCCAACACAAATGAACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGTGGCGAAACGGTCAGCAAGAAAATAGATAATCGATTTAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACAGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_30 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACAGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACCTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_31 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTTGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAAACCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_32 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAAACCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_33 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGTTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_34 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAAAGACAAGAGAAACGGAGACGC ->lhkA_35 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGTGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_36 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCGCCTATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_37 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_38 -TATCCTACACAAATGGACCAGCCTCTGCCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGTAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTATATGTGCCAATTATGGACGGCGATAAATTTGTTGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGGTACATGTTCTATACTATTTTACTTTCCATAACAGTTGCACTTATTCTTAGTGCAATTTTGTCCAAACTACAAGTGAATCGAATCAACAAATTACGAGCAGCGACGAAAGATGTTATTCAAGGGAACTACAAAGCGCGACTAAAAGAAAATAACTTTGATGAAATTGGGGCGCTTGCGATTGATTTCAATAAGATGACACAAACGCTTGAAATATCGCAAGAAGAAATTGAAAGACAAGAAAAACGTCGCCGT ->lhkA_39 -TATCCTACACAAATGGACCAGCCTCTGCCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGTAAGAAAATAGATAATCGATTCAATAGAGAAATGACTATTGTATATGTGCCAATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGGTACATGTTCTATACTATTTTACTTTCCATAACAGTTGCACTTATTCTTAGTGCAATTTTGTCCAAACTACAAGTGAATCGAATCAACAAATTACGAGCAGCAACGAAAGATGTTATTCAAGGGAACTACAAAGCGCGACTAAAAGAAAATAACTTTGATGAAATTGGGGCGCTTGCGATTGATTTCAATAAGATGACACAAACGCTTGAAATATCGCAAGAAGAAATTGAAAGACAAGAAAAACGTCGCCGT ->lhkA_40 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACTCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_41 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAGGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAACGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_42 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCATCTATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_43 -TATCCAACGCAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_44 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATAATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTCTCCATAACAGTTGCACTTATTCTTAGCGCGATTTTGTCCAAACTACAAGTGAATCGAATCAATAAACTACGAGCAGCAACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_45 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGAGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_46 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_47 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATAATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTCTATACTATTTTACTCTCCATAACAGTTGCACTTATTCTTAGCGCGATTTTGTCCAAACTACAAGTGAATCGAATCAATAAACTACGAGCAGCAACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_48 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_49 -TACCCTACTCAGCTCAATCAGCCATTACCAAAGGATTTTTCTATTTCAAAAGATGATAAGACAAAATTAAAAAGTGGTGAAACAGTTAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTACCAATTATGGATGGCGATAAGTTTGTTGGATCCATCGTCTTGAATTCTCCAATTAGCGGGACTGAACAAGTGATTGGCACAATTAATCGCTATATGTTCTATACTATTTTACTATCGATTACGATTGCGCTTATTTTGAGTGCTATTCTGTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCGACAAAAGATGTAATCCAAGGCAATTATAAGGCGCGGCTAAAAGAGAATAATTTTGATGAGATTGGCGCACTTGCCATCGATTTCAATAAAATGACGGAAACGCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_50 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCGCTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_51 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_52 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTTGAAAGCGGCGAAACGGTTAGTAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_53 -TACCCTACTCAGCTCAATCAACCATTACCAAAAGATTTTTCAATTTCAAAAGATGATAAGAAAAAATTAAAAAGTGGCGAAACAGTTAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTACCAATTATGGATGGCGATAAATTTGTTGGATCCATCGTCTTAAATTCTCCAATTAGCGGGACTGAACAAGTGATTGGAACAATTAATCGTTATATGTTCTATACTATTTTACTATCGATAACGATTGCGCTTATGTTGAGTGCTATTCTGTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCAACAAAAGATGTGATCCAAGGCAATTATAAGGCGCGGCTAAAAGAGAATAATTTTGATGAAATTGGCGCACTTGCCATTGATTTCAACAAAATGACGGAAACACTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_54 -TACCCTACTCAGCTCAATCAGCCATTACCAAAGGATTTTTCTATTTCAAAAGATGATAAGACAAAATTAAAAAGTGGTGAAACAGTTAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTACCAATTATGGATGGCGATAAGTTTGTTGGATCCATCGTCTTGAATTCTCCAATTAGCGGGACTGAACAAGTGATTGGCACAATTAATCGCTATATGTTCTATACTATTTTACTATCGATTACGATTGCGCTTATTTTGAGTGCTATTCTGTCCAAACTACAAGTCAACCGGATTAATAAATTGCGTTCAGCGACAAAAGATGTAATCCAAGGCAATTATAAGGCGCGGCTAAAAGAGAATAATTTTGATGAGATTGGCGCACTTGCCATCGATTTCAATAAAATGACGGAAACGCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_55 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACAGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_56 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_57 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTTCGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_58 -TATCCTACTCAGCTCAATCAGCCATTACCAAAAGATTTTTCAATTTCAAAAGATGATAAGAAAAAATTAAAAAGTGGCGAAACAGTTAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTACCAATTATGGATGGCGATAAATTTGTTGGATCCATCGTCTTAAATTCTCCAATTAGCGGGACTGAACAAGTGATTGGAACAATTAATCGTTATATGTTCTATACTATTTTACTATCGATAACGATTGCGCTTATGTTGAGTGCTATTCTGTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCGACAAAAGATGTGATCCAAGGCAATTATAAGGCGCGGCTAAAAGAGAATAATTTTGATGAAATTGGCGCACTTGCCATTGATTTCAACAAAATGACGGAAACGCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_59 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGATAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_60 -TACCCTACTCAGCTCAATCAACCATTACCAAAAGATTTTTCAATTTCAAAAGATGATAAGAAAAAATTAAAAAGTGGCGAAACAGTTAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTACCTATTATGGATGGCGATAAGTTTGTTGGCTCTATTGTCTTGAATTCTCCAATTAGCGGAACTGAACAAGTGATTGGAACAATTAATCGTTATATGTTCTATACTATTTTACTGTCGATTACGATTGCGCTTATTTTGAGTGCTATTCTGTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCGACAAAAGATGTGATCCAAGGCAATTATAAAGCGCGGCTAAAAGAGAATAATTTTGATGAAATTGGCGCACTTGCCATTGATTTCAACAAAATGACGGAAACACTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_61 -TATCCTACTCAGCTCAATCAGCCATTACCAAAAGATTTTTCAATTTCAAAAGATGATAAGAAAAAATTAAAAAGTGGCGAAACAGTTAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTACCAATTATGGATGGCGATAAGTTTGTTGGCTCTATTGTCTTGAATTCTCCAATTAGCGGGACTGAACAAGTGATTGGAACAATTAATCGTTATATGTTCTATACTATTTTACTGTCGATTACGATTGCGCTTATTTTAAGTGCTATTCTGTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCGACAAAAGATGTGATCCAAGGCAATTATAAAGCGCGGCTAAAAGAGAATAATTTTGATGAAATTGGCGCACTTGCCATTGATTTCAACAAAATGACGGAAACGCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_62 -TATCCGACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_63 -TATCCAACGCAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCGCTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_64 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTATATGTTCTATACTATTTTACTCTCCATAACAGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_65 -TACCCTACTCAGCTCAATCAACCATTACCAAAAGATTTTTCAATTTCAAAAGATGATAAGAAAAAATTGAAAAGTGGTGAAACAGTTAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTACCTATTATGGATGGCGATAAGTTTGTTGGCTCTATTGTCTTGAATTCTCCAATTAGCGGAACTGAACAAGTGATTGGAACAATTAATCGTTATATGTTCTATACTATTTTACTATCGATAACGATTGCGCTTATTTTGAGTGCTATTCTGTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCAACAAAAGATGTGATTCAAGGCAATTATAAGGCGCGGCTAAAAGAGAATAATTTTGATGAAATTGGCGCACTTGCCATTGATTTCAACAAAATGACGGAAACGCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_66 -TATCCAACTCAAATGAATCAGCCCTTACCAAAAGATTTCTCCATTTCTAAAGATGATAAAAAGAAATTAAAAAGCGGCGAAACGGTCAGCAAAAAAATCGATAATCGTTTTAACCGTGAAATGACAATTGTCTATGTGCCAATCATGAACGGGGATAAATTTGTTGGTTCTATCGTCTTAAATTCTCCAATTAGTGGAACAGAGCAAGTGATAGGAACCATAAATCGTTATATGTTCTATACTATTTTACTTTCAATTACGATAGCACTTATTTTGAGTGCCATTCTGTCTAAGCTTCAAGTTAATCGTATCAATAAATTACGATCTGCGACCAAAGACGTTATCCAAGGTAATTATAAAGCACGTCTAAAAGAAAATAATTTTGATGAGATTGGCGCGCTTGCTATTGATTTTAATAAAATGACGCAAACGCTTGAAAAGTCTCAAGAAGAAATCGAACGTCAAGAAAAGCGGCGCCGT ->lhkA_67 -TACCCTACTCAGCTCAATCAACCATTACCAAAAGATTTTTCAATTTCAAAAGATGATAAGAAAAAATTAAAAAGTGGCGAAACAGTTAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTACCAATTATGGATGGCGATAAATTTGTTGGATCCATCGTCTTAAATTCTCCAATTAGCGGGACTGAACAAGTGATTGGAACAATTAATCGTTATATGTTCTATACTATTTTACTATCGATTACGATTGCGCTTATTTTGAGTGCTATTCTGTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCAACAAAAGATGTGATCCAAGGCAATTATAAGGCGCGGCTAAAAGAGAATAATTTTGATGAAATTGGCGCACTTGCCATTGATTTCAACAAAATGACGGAAACGCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_68 -TACCCTACTCAGCTCAATCAACCATTACCAAAAGATTTTTCAATTTCAAAAGATGATAAGAAAAAATTAAAAAGTGGCGAAACAGTTAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTACCAATTATGGATGGCGATAAATTTGTTGGATCCATCGTCTTAAATTCTCCAATTAGCGGGACTGAACAAGTGATTGGAACAATTAATCGTTATATGTTCTATACTATTTTACTATCGATAACGATTGCGCTTATTTTGAGTGCTATTCTGTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCAACAAAAGATGTGATTCAAGGCAATTATAAGGCGCGGCTAAAAGAGAATAATTTTGATGAAATTGGCGCACTTGCCATTGATTTCAACAAAATGACGGAAACGCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_69 -TATCCTACTCAGCTCAATCAGCCATTACCAAAAGATTTTTCGATTTCAAAAGATGATAAGAAAAAATTAAAAAGTGGCGAAACAGTGAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTGCCTATTATGGATGGCGATAAGTTTGTTGGCTCTATTGTCTTGAATTCTCCAATTAGCGGAACTGAACAAGTGATTGGAACAATTAATCGCTATATGTTCTATACTATTTTACTATCGATTACAATTGCACTTATTTTGAGTGCTATTCTGTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCGACAAAAGATGTAATCCAAGGCAATTATAAGGCGCGGCTAAAAGAGAATAATTTTGATGAAATTGGCGCACTTGCCATTGATTTCAACAAAATGACGGAAACGCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_70 -TATCCCACACAAATGAATCAACCTCTTCCAAAAGATTTCTCGATTTCTAGTGAGGATAAGAAAAAATTAGAAAGCGGCAAAACAGTAAGTAAAAAAATCGATAATCGTTTTAACCAAGAAATGACGATTGTTTATGTCCCACTCATGAATGGAGATAATTTTGTTGGTTCCATTGTATTAAACTCACCGATTAGCGGTACAGAGCAAGTGATAGGAACAATAAATCAATATATGTTCTACACTATTTTACTTTCTATTACAGTAGCTCTAATTCTTAGTGCAATCATCTCCAAACTGCAAGTGAATCGGATTAATAAACTACGAGCTGCAACAAAGGATGTTATCCAAGGAAATTATCAAACACGTTTAAAAGAAAATAATTTTGATGAGATTGGCGCACTTGCAATTGACTTCAACAAAATGACTACAACACTAGAAGCCTCTCAAGAAGAAATTGAGCGTCAAGAAAAACGACGGCGT ->lhkA_71 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGACAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_72 -TATCCTACACAAATGGACCAGCCTCTGCCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGTAAGAAAATAGATAATCGATTCAATAGAGAAATGACTATTGTATATGTGCCAATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGGTACATGTTCTATACTATTTTACTTTCCATAACAGTTGCACTTATTCTTAGTGCAATTTTGTCCAAACTACAAGTGAATCGAATCAACAAATTACGAGCAGCGACGAAAGATGTTATTCAAGGGAACTACAAAGCGCGACTAAAAGAAAATAACTTTGATGAAATTGGGGCGCTTGCGATTGATTTCAATAAGATGACACAAACGCTTGAAATATCGCAAGAAGAAATTGAAAGACAAGAAAAACGTCGCCGT ->lhkA_73 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTAGAAAGCGGCGAAACGGTTAGTAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTATATGTGCCAATTATGGACGGCGATAAATTCGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGGTACATGTTCTATACTATTTTACTTTCCATAACAGTTGCACTTATTCTTAGTGCAATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGGAACTACAAAGCGCGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACACAAACGCTTGAAATATCGCAAGAAGAAATTGAAAGACAAGAAAAACGTCGCCGT ->lhkA_74 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTAGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGGTACATGTTCTATACTATTTTACTTTCCATAACAGTTGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAATTACGAGCAGCAACGAAAGATGTTATTCAAGGGAACTACAAAGCGCGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_75 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTAGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTTGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGGTACATGTTCTATACTATTTTACTTTCCATAACAGTTGCACTTATTCTTAGTGCAATTTTGTCCAAACTACAAGTGAATCGAATCAACAAATTACGAGCAGCGACGAAAGATGTTATTCAAGGGAACTACAAAGCGCGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACATTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGTCGCCGT ->lhkA_76 -TATCCTACACAAATGGACCAGCCTCTGCCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGTAAGAAAATAGATAATCGATTCAATAGAGAAATGACTATTGTATATGTGCCAATTATGGACGGCGATAAATTTGTTGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGGTACATGTTCTATACTATTTTACTTTCCATAACAGTTGCACTTATTCTTAGTGCAATTTTGTCCAAACTACAAGTGAATCGAATCAACAAATTACGAGCAGCGACGAAAGATGTTATTCAAGGGAACTACAAAGCGAGACTAAAAGAAAATAACTTTGATGAAATTGGGGCGCTTGCGATTGATTTCAATAAGATGACACAAACGCTTGAAATATCGCAAGAAGAAATTGAAAGACAAGAAAAACGTCGCCGT ->lhkA_77 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTAGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGGTACATGTTCTATACTATTTTACTTTCCATAACAGTTGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_78 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTATGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_79 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCTAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_80 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATAACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_81 -TACCCTACTCAGCTCAATCAACCATTACCAAAAGATTTTTCAATTTCAAAAGATGATAAGAAAAAATTGAAAAGTGGTGAAACAGTTAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTGCCTATTATGGATGGCGATAAGTTTGTTGGCTCTATTGTCTTGAATTCTCCAATTAGCGGAACTGAACAAGTGATTGGAACAATTAATCGTTATATGTTCTATACTATTTTACTATCGATAACGATTGCGCTTATTTTGAGTGCTATTCTGTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCGACAAAAGATGTGATCCAAGGCAATTATAAGGCGCGGCTAAAAGAGAATAATTTTGATGAAATTGGCGCACTTGCCATTGATTTCAACAAAATGACGGAAACGCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_82 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAAATACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_83 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTTAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_84 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGTAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_85 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACAGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_86 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACAAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_87 -TATCCAATACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_88 -TACCCTACTCAGCTCAATCAACCATTACCAAAAGATTTTTCAATTTCAAAAGATGATAAGAAAAAATTAAAAAGTGGCGAAACAGTTAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTACCTATTATGGATGGCGATAAGTTTGTTGGCTCTATTGTCTTGAATTCTCCAATTAGCGGAACTGAACAAGTGATTGGAACAATTAATCGTTATATGTTCTATACTATTTTACTATCGATAACGATTGCGCTTATTTTGAGTGCTATTCTGTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCAACAAAAGATGTGATCCAAGGCAATTATAAGGCGCGGCTAAAAGAGAATAATTTTGATGAAATTGGCGCACTTGCCATTGATTTCAACAAAATGACGGAAACGCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_89 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCATCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_90 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAACAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_91 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGTTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_92 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAACGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_93 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTGGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_94 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTGGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCGCTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_95 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGTGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_96 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTTAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTATATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_97 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGCACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_98 -TATCCAACGCAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGTGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_99 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTAAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_100 -TATCCAACGCAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGTCGT ->lhkA_101 -TATCCCACACAAATGAATCAGCCTCTTCCAAAAGATTTCTCGATTTCTAGTGAAGATAAGAAAAAATTGGAAAGCGGCGAAACAGTGAGTAAAAAAATCGATAATCGTTTTAACCAAGAAATGACGATTGTTTATGTCCCACTCATGAATGGAGATAATTTTGTTGGTTCCATTGTATTGAACTCACCGATTAGCGGTACAGAGCAAGTGATAGGAACAATTAATCAATATATGTTCTACACTATTTTACTTTCTATTACCGTAGCTCTAATTCTTAGTGCAATCATCTCTAAACTGCAAGTGAATCGGATTAATAAACTACGAGCTGCGACAAAGGATGTTATTCATGGAAATTATCAAACACGTTTAAAAGAAAATAATTTTGATGAGATTGGCGCACTTGCAATTGACTTCAACAAAATGACTACAACACTAGAAGCCTCTCAAGAAGAAATTGAGCGTCAAGAAAAACGACGGCGC ->lhkA_102 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATCAATTTGTCGGTTCTATCGTGCTAAATTCACCTATTAGTGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACAGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAACCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_103 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAACGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_104 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGTCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_105 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATCAATTTGTCGGTTCTATCGTGCTAAATTCACCTATTAGTGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACAGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAACCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_106 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATAATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTCTATACTATTTTACTCTCCATAACAGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_107 -TATCCAACTCAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGTGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACAGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_108 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTATCGGCTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_109 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGTGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCGCTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGTGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_110 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_111 -TATCCAACACAAATGAACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTAATTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_112 -TATCCAACTCAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGTGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTAATTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_113 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCAATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCTATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_114 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCAATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCTATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_115 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGTAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_116 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCTGAGACGC ->lhkA_117 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGTAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_118 -GTGTTTCCCACTTCCGACAGACCACTCCCGCCCGATTTTAAAATTTCAAAAAAAGCTTCTAAAAATCTCAAAAAAGGCGAAACCGTCACGATTAAAGTCGATAATCGTTTTAATCAAGAAATGTCTGTTGTGTATGTCCCGATTTTATCAGGCAAAACCTTTCTTGGCTCAGTTATTTTGAACTCTCCGATCAGCGGGACAGAAAAAGTCATCGGAGCAATTAATCGTTACATGTTTTTCACGATTCTTTTATCGGTAATCATCGCGCTTGTTTTAAGTGCTATTTTAGCTAAATTGCAAGTGAATCGAATTAATCGCTTACGTGAAGCAACAAAACGAGTGATTAGTGGGGATTATGAAACGCGATTAAACGAAAATAACTGGGATGAAATTGGCGCACTTTCCTCAGATTTTAATAAAATGACAGAAACCTTAGCCACCTCAAAGGAGGAAATAGAACGTCAAGAAAAACGGCGCCGA ->lhkA_119 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_120 -TATCCAACCCAGCTAAATCAGCCATTACCCAAGGACTTCTCTATTTCATCTGATGATAAGAAAAAATTAGAAAGTGGCGAAACGGTTAGTAAAAAGATTGATAATCGTTTTAATAGAGAAATGACAATTGTGTATGTTCCGATTATGGATGGCAGCAAATTTGTTGGTTCAATCGTGCTGAATTCGCCGATAAGCGGCACCCAGCAAGTCATAGGTACAATTAATCGTTATATGTTCTACACTATTTTGCTTTCTATCACTGTGGCACTTATCCTCAGCGCAATTTTATCCAAGCTGCAAGTCAATCGGATTAATAAACTGCGAGCTGCCACCAAAGATGTTATTCAAGGCAATTACAAAGCCAGACTAAAAGAAAATAACTTCGATGAGATCGGCGCGCTTGCTATTGATTTCAATAAAATGACGCAAACACTCGAAACGTCCCAAGAAGAAATTGAACGTCAAGAAAAACGGCGTCGT ->lhkA_121 -ATTTTTCCGAGTTCAAGTCGTCCACTCCCACCTGATTTTAAAATTTCAGACGCGGATATGAAAAAATTAAAAAAAGGTGAAACCGTGACGATTAAAGTGGACAGTCGTTTTAATCAGGAAATGTCTGTGGTTTATGTGCCTATTATGAACGGCGAAACCTATTTGGGCGCGGTTATTTTAAATGCTCCGATTAGTGGGACTGAAAAAGTGGTTGGGACGATTAATCAGTATATGTTCTATACGATTCTGATTTCCATTCTGGTAGCTTTGATTTTAAGTTTCCTTATGTCGAAGCTTCAAGTCAATCGAATTAATAAACTGCGTGACGCAACGAAGCGTGTGACGCGTGGGGATTATGAGACGCGGCTAAAAGAAAATAATGTGGATGAAATTGGCGAGCTTTCGCACGATTTTAACCTTATGACGGAAAATTTAGCGGAATCACGCGAAGAAATCGAGCGACAAGAGCGCCGCAGACGA ->lhkA_122 -CTATTCCCGGCCTCAAGCCGTCCGCTTCCGCCTGATTTTAAAATCTCAGAAAGCGATACGAAAAAACTGAAGAACGGAAAAACCGTAACCATCAAAGTGGACAACAGGTTTAACCAAGAAATGTCTGTTGTTTACGTCCCACTGATGAACGGTGACACCTACCTCGGTTCAATCATTTTAAACTCGCCGATCAGCGGCACTGAAAAAGTAATCGATGCGATTAACCGCTACATGTTCTTTACGATTCTGCTTTCCATTGCGATTGCGTTGATTCTAAGCGCTATTCTCGCAAAACTGCAAGTCAATCGGATTAATCGTCTACGTGACGCAACGAAACGCGTTATCGCTGGTGACTATGAAACACGCCTAAGAGAAAATAATTGGGATGAAATCGGCGAACTTGCCTCCGACTTTAATCAAATGACAAAAACTCTTTCTGCCTCACGTGAAGAAGTAGAACGGCAAGAAAAAAGGCGGCGT ->lhkA_123 -GTGTTTCCCACTTCCGACAGACCACTCCCGCCCGATTTTAAAATTTCAAAAGAGGCTTCTAAAAATCTCAAAAAAGGCGAAACCGTAACGATTAAAGTCGATAATCGTTTTAATCAAGAAATGTCTGTTGTGTATGTCCCGATTTTATCAGGCAAAACCTTTCTTGGCTCAGTTATTTTGAACTCTCCGATCAGCGGGACAGAAAAAGTCATCGGAGCAATTAATCGTTACATGTTTTTCACGATTCTTTTATCGGTAATCATCGCGCTTGTTTTAAGTGCTATTTTAGCTAAATTGCAAGTGAATCGAATTAATCGCTTACGTGAAGCAACAAAACGAGTGATTAGCGGGGATTATGAAACGCGATTAAACGAAAATAACTGGGATGAAATTGGCGCACTTTCCTCAGATTTTAATAAAATGACAGAAACCTTAGCCACCTCAAAGGAGGAAATAGAACGTCAAGAAAAACGGCGCCGA ->lhkA_124 -TATCCAACACAAATGAATCAACCACTTCCAAAAGATTTCTCGATTTCCGTAGAAGATAAGAAAAAGTTGAAAAGCGGTGAAACGGTAAGTAAAAAAATTGATAATCGTTTTAATCAAGAAATGACCATTGTTTATGTCCCATTGATGAATGGCGATAATTTTGTAGGATCCATCGTTTTGAATTCTCCAATTAGTGGCACTGAACAAGTGATAGGAACAATTAATCAATATATGTTCTATACAATTTTGCTTTCGATCACAGTAGCACTTATTCTTAGCGCAATTATTTCCAAATTACAAGTGAATCGGATAAACAAATTACGCGCCGCAACAAAAGATGTGATCGAAGGAAATTATCAAACTCGCTTAAAAGAAAATAACATTGATGAAATTGGTGCGCTCGCAATCGATTTCAACAAAATGACAACGACGCTTGAAGCATCCCAAGAAGAAATTGAGCGTCAAGAAAAACGGCGGCGT ->lhkA_125 -TATCCAACACAAATGAATCAACCACTTCCAAAAGATTTCTCGATTTCCGCAGAAGATAAGAAAAAGTTGAAAAGTGGTGAAACGGTAAGTAAAAAAATTGATAATCGTTTTAATCAAGAAATGACCATTGTTTATGTCCCGTTGATGAATGGCGATAATTTTGTAGGATCCATCGTTTTGAATTCTCCAATTAGTGGCACTGAACAAGTGATAGGAACAATTAATCAATATATGTTCTATACTATTTTGCTTTCGATCACAGTAGCACTTATTCTTAGCGCAATTATTTCCAAATTACAAGTGAATCGGATAAACAAATTACGCGCCGCAACAAAAGATGTGATCGAAGGAAATTATCAAACTCGCTTAAAAGAAAATAACATTGATGAAATTGGTGCGCTCGCAGTCGATTTCAACAAAATGACAACGACGCTTGAAGCTTCCCAAGAAGAAATTGAGCGTCAAGAAAAACGGCGGCGT ->lhkA_126 -TATCCAACCCAAATGAATCAGCCCTTACCAAAAGATTTCTCCATTTCTAAAGATGATAAAAAGAAATTAAAAAGCGGCGAAACGGTCAGCAAAAAAATCGATAATCGTTTTAACCGTGAAATGACAATTGTCTATATGCCAATTATGAACGGGGATAAATTTGTTGGTTCTATCGTCTTAAATTCTCCAATTAGTGGAACAGAGCAAGTGATAGGAACCATAAATCGTTATATGTTCTATACTATTTTACTTTCAATTACGATAGCACTTATTTTGAGTGCCATTCTGTCTAAGCTTCAAGTTAATCGTATCAATAAATTGCGATCTGCGACCAAAGACGTTATCCAAGGTAATTATAAAGCACGTCTAAAAGAAAATAATTTTGATGAGATTGGCGCGCTTGCTATTGATTTTAATAAAATGACGCAAACGCTTGAAAAGTCTCAAGAAGAAATCGAACGTCAAGAAAAGCGGCGCCGT ->lhkA_127 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGTAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_128 -TATCCAACTCAAATGAATCAGCCCTTACCAAAAGATTTCTCCATTTCTAAAGATGATAAAAAGAAATTAAAAAGCGGCGAAACGGTCAGCAAAAAAATCGATAATCGTTTTAACCGTGAAATGACAATTGTCTATGTGCCAATTATGAACGGGGATAAATTTGTTGGTTCTATCGTCTTAAATTCTCCAATTAGTGGAACAGAGCAAGTGATAGGAACCATAAATCGTTATATGTTCTATACTATTTTACTTTCAATTACGATAGCACTTATTTTGAGTGCCATTCTGTCTAAGCTTCAAGTTAATCGTATCAATAAATTACGATCTGCGACCAAAGACGTTATCCAAGGTAATTATAAAGCACGTCTAAAAGAAAATAATTTTGATGAGATTGGCGCGCTTGCTATTGATTTTAATAAAATGACGCAAACGCTTGAAAAGTCTCAAGAAGAAATCGAACGTCAAGAAAAGCGGCGCCGT ->lhkA_129 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACGCAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_130 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_131 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGGGAAGCGGAGACGC ->lhkA_132 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACCATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_133 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATCAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_134 -TACCCTACTCAGCTCAATCAACCATTACCAAAAGATTTTTCAATTTCAAAAGATGATAAGAAAAAATTGAAAAGTGGTGAAACAGTTAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTACCAATTATGGATGGCGATAAATTTGTTGGATCCATCGTCTTGAATTCTCCAATTAGCGGGACTGAACAAGTGATTGGAACAATTAATCGCTATATGTTCTATACTATTTTACTATCGATTACGATTGCACTTATTTTGAGTGCTATTCTGTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCGACAAAAGATGTGATTCAAGGCAATTATAAGGCGCGGCTAAAAGAGAATAATTTTGATGAAATTGGCGCACTTGCCATTGATTTCAATAAAATGACGGAAACGCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_135 -TATCCAACACAGATGAAACAGCCTTTACCTAAGGATTTCTTTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_136 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAAACCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_137 -TATCCAACTCAAATGAATCAGCCCTTACCAAAAGATTTCTCCATTTCTAAAGATGATAAAAAGAAATTAAAAAGCGGCGAAACGGTCAGCAAAAAAATCGATAATCGTTTTAACCGTGAAATGACAATTGTCTATGTGCCAATTATGAACGGGGATAAATTTGTTGGTTCTATCGTCTTAAATTCTCCAATTAGTGGAACAGAGCAAGTGATAGGAACCATAAATCGTTATATGTTCTATACTATTTTACTTTCAATTACGATAGCACTTATTTTGAGTGCCATTCTGTCTAAGCTTCAAGTTAATCGTATCAATAAATTACGATCTGCGACCAAAGACGTTATCCAAGGTAATTATAAAGCACGTCTAAAAGAAAATAATTTTGATGAGATTGGGGCGCTTGCTATTGATTTTAATAAAATGACGCAAACGCTTGAAAAGTCTCAAGAAGAAATCGAACGTCAAGAAAAGCGGCGCCGT ->lhkA_138 -TACCCTACTCAGCTCAATCAACCATTACCAAAAGATTTTTCAATTTCAAAAGATGATAAGAAAAAATTGAAAAGTGGTGAAACAGTTAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTGCCTATTATGGATGGCGATAAGTTTGTTGGATCTATCGTCTTGAATTCTCCAATTAGCGGAACTGAACAAGTGATTGGAACAATTAATCGTTATATGTTCTATACTATTTTACTGTCGATTACGATTGCGCTTATTTTGAGTGCTATTCTGTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCGACAAAAGATGTGATCCAAGGCAATTATAAAGCGCGGCTAAAAGAGAATAATTTTGATGAAATTGGCGCACTTGCCATTGATTTCAACAAAATGACGGAAACGCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_139 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTATGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_140 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTAGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_141 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCAACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_142 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCCACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATTTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_143 -TATCCAACACAAATGAACCAACCACTTCCAAAAGATTTCTCAATTTCCTCAGAAGATAAGAAAAAATTAAAAAGTGGTGAAACGGTAAGTAAAAAAATTGATAATCGTTTTAATCAAGAAATGACCATTGTTTATGTCCCGTTGATGAATGGCGATAATTTTGTAGGATCCATCGTTTTGAATTCTCCAATTAGCGGCACTGAACAAGTGATAGGAACAATTAATCAATACATGTTCTATACTATTTTGCTTTCGATCACAGTAGCACTTATTCTTAGCGCAATTATTTCCAAATTACAAGTGAATCGGATAAACAAATTACGCGCCGCAACAAAAGATGTGATTGAAGGAAATTATCAAACTCGCTTAAAAGAAAATAACATTGATGAAATTGGCGCGCTCGCAGTCGATTTCAACAAAATGACAACGACGCTTGAAACATCCCAAGAAGAAATTGAGCGTCAAGAAAAACGGCGGCGT ->lhkA_144 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTATATGTTCTATACTATTTTACTCTCCATAACAGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGTGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_145 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGTGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATAATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGAACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCAACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTTGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_146 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACATCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_147 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGTGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_148 -TACCCTACTCAGCTCAATCAACCATTACCAAAAGATTTTTCAATTTCAAAAGATGATAAGAAAAAATTGAAAAGTGGTGAAACAGTTAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTACCAATTATGGATGGCGATAAATTTGTTGGCTCCATCGTCTTGAATTCTCCAATTAGCGGGACTGAACAAGTGATTGGAACAATTAATCGTTATATGTTCTATACTATTTTACTATCGATAACGATTGCGCTTATTTTGAGTGCTATTCTGTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCGACAAAAGATGTGATCCAAGGCAATTATAAGGCGCGGCTAAAAGAGAATAATTTTGATGAAATTGGCGCACTTGCCATTGATTTCAACAAAATGACGGAAACGCTTGAAACATCTCAAGAAGAAATTGAACGTCAAGAAAAACGTCGGCGC ->lhkA_149 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCCGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_150 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGACAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCACTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_151 -TATCCAACACAGATGAATCAGCCGTTACCCAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_152 -TACCCTACTCAGCTCAATCAACCATTACCAAAAGATTTTTCAATTTCAAAAGATGATAAGAAAAAATTAAAAAGTGGCGAAACAGTTAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTACCTATTATGGATGGCGATAAGTTTGTTGGCTCTATTGTCTTGAATTCTCCAATTAGCGGAACTGAACAAGTGATTGGAACAATTAATCGTTATATGTTCTATACTATTTTACTGTCGATTACGATTGCGCTTATTTTGAGTGCTATTCTGTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCGACAAAAGATGTGATCCAAGGCAATTATAAGGCGCGGCTAAAAGAGAATAATTTTGATGAAATTGGCGCACTTGCCATTGATTTCAACAAAATGACGGAAACGCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_153 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_154 -TATCCAACACAGATGAATCAGCCGTTACCAAAAGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCGCCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_155 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACTGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTATATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAATAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_156 -TATCCAACACAAATGAATCAACCACTTCCAAAAGATTTCTCGATTTCCGCAGAAGATAAGAAAAAGTTGAAAAGTGGTGAAACGGTAAGTAAAAAAATTGATAATCGTTTTAATCAAGAAATGACCATTGTTTATGTCCCGTTGATGAATGGCGATAATTTTGTAGGATCCATCGTTTTGAATTCTCCAATTAGTGGCACTGAACAAGTGATAGGAACAATTAATCAATATATGTTCTATACAATTTTGCTTTCGATCACAGTAGCACTTATTCTTAGCGCAATTATTTCCAAATTACAAGTGAATCGGATAAACAAATTACGCGCCGCAACAAAAGATGTGATCGAAGGAAATTATCAAACTCGCTTAAAAGAAAATAACATTGATGAAATTGGTGCGCTCGCAGTCGATTTCAACAAAATGACAACGACGCTTGAAGCTTCCCAAGAAGAAATTGAGCGTCAAGAAAAACGGCGGCGT ->lhkA_157 -TATCCAACACAAATGAATCAACCACTTCCAAAAGATTTCTCGATTTCCGCAGAAGATAAGAAAAAGTTGAAAAGTGGTGAAACGGTAAGTAAAAAAATTGATAATCGTTTTAATCAAGAAATGACCATTGTTTATGTCCCGTTGATGAATGGCGATAATTTTGTAGGATCCATCGTTTTGAATTCTCCAATTAGTGGCACTGAACAAGTGATAGGAACAATTAATCAATATATGTTCTATACTATTTTACTTTCGATCACAGTAGCACTTATTCTTAGCGCAATTATTTCCAAATTACAAGTGAATCGGATAAACAAATTACGCGCCGCAACAAAAGATGTGATCGAAGGAAATTATCAAACTCGCTTAAAAGAAAATAACATTGATGAAATTGGTGCGCTCGCAGTCGATTTCAACAAAATGACAACGACGCTTGAAGCTTCCCAAGAAGAAATTGAGCGTCAAGAAAAACGGCGGCGT ->lhkA_158 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCAGAAGATAAGAAAAAACTGGAAAGTGGCGAAACGGTTAGTAAGAAAATAGATAATCGATTTAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_159 -TATCCTACTCAGCTCAATCAGCCATTACCAAAAGATTTTTCGATTTCAAAAGATGATAAGAAAAAATTAAAAAGTGGCGAAACAGTGAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTGCCTATTATGGATGGCGATAAGTTTGTTGGCTCTATTGTCTTGAATTCTCCAATTAGCGGAACTGAACAAGTGATTGGAACAATTAATCGCTATATGTTCTATACTATTTTACTGTCGATTACAATTGCACTTATTTTGAGTGCTATTCTGTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCGACAAAAGATGTAATCCAAGGCAATTATAAGGCGCGGCTAAAAGAGAATAATTTTGATGAAATTGGCGCACTTGCCATTGATTTCAACAAAATGACGGAAACGCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_160 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGTGGAGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGTAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_161 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGTGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_162 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTTGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_163 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_164 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAACCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_165 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGAACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_166 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACGAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_167 -TATCCAACACAGATGAAACAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_168 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGCAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_169 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATAATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTCTCCATAACAGTTGCACTTATTCTTAGCGCGATTTTGTCCAAACTACAAGTGAATCGAATCAATAAACTACGAGCAGCAACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGGCAAGAAAAAAGGCGCCGT ->lhkA_170 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGCACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_171 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTTGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTATATGTTCTATACTATTTTACTCTCCATAACAGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGTGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_172 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_173 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGACAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTATATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_174 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTATATGTTCTATACTATTTTACTCTCCATAACAGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_175 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGTGGCGAAACGGTCAGCAAGAAAATAGATAATCGATTTAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_176 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTAATTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_177 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGAACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_178 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTAAATTCACCTATTAGTGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACAGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAACCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_179 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATAGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_180 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTATATGTTCTATACTATTTTACTCTCCATAACAGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGTGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_181 -TACCCAACCCAGCTAAATCAGCCATTACCCAAGGACTTCTCTATTTCATCTGATGATAAGAAAAAATTAGAAAGTGGCGAAACGGTAAGTAAAAAGATTGATAATCGTTTTAATAGAGAAATGACGATTGTGTATGTTCCGATTATGGATGGCAGCAAATTTGTTGGTTCAATCGTGCTGAATTCGCCGATAAGCGGCACCCAGCAAGTCATAGGTACAATTAATCGTTATATGTTCTACACTATTTTGCTTTCTATCACTGTGGCACTTATCCTTAGCGCGATTTTATCCAAGCTGCAAGTCAATCGGATTAATAAACTGCGTGCTGCCACCAAAGATGTTATTCAAGGTAATTACAAAGCCAGACTAAAAGAAAATAACTTCGATGAGATCGGCGCGCTTGCTATTGATTTCAATAAAATGACGCAAACACTCGAAACGTCCCAAGAAGAAATTGAACGTCAAGAAAAACGGCGTCGT ->lhkA_182 -TATCCAACCCAGCTAAATCAGCCATTACCCAAGGACTTCTCTATTTCATCTGATGATAAGAAAAAATTAGAAAGTGGCGAAACGGTAAGTAAAAAGATTGATAATCGTTTTAATAGAGAAATGACGATTGTGTATGTTCCGATTATGGATGGCAGCAAATTTGTTGGTTCAATCGTGCTGAATTCGCCGATAAGCGGCACCCAGCAAGTCATAGGTACAATTAATCGTTATATGTTCTACACTATTTTGCTTTCTATCACTGTGGCACTTATCCTTAGCGCGATTTTATCCAAGCTGCAAGTCAATCGGATTAATAAACTGCGTGCTGCCACCAAAGATGTTATTCAAGGTAATTACAAAGCCAGACTAAAAGAAAATAACTTCGATGAGATCGGCGCGCTTGCTATTGATTTCAATAAAATGACGCAAACACTCGAAACGTCCCAAGAAGAAATTGAACGTCAAGAAAAACGGCGTCGT ->lhkA_183 -ATTTATCCGAGTTCAAGTCGTCCACTCCCACCTGATTTTAAAATTTCAGACGCGGATATGAAAAAATTAAAAAAAGGTGAAACCGTGACAATTAAAGTGGACAGTCGTTTTAACCAGGGAATGTCTGTCGTTTATGTGCCTATTATGAACGGGGAAAGCTATTTGGGTGCGGTTATTTTAAATGCTCCGATTAGTGGGACGGAAAAAGTGGTTGGGACGATTAACCAATATATGTTCTATGCGATTCTGATTTCCATTCTGGTAGCTTTGATTTTAAGTTTCCTAATGTCGAAGCTTCAAGTCAATCGAATTAATAAACTGCGGGACGCAACCAAGCGTGTGACGCGCGGGGATTATGAGACGCGGTTAAAAGAAAATAAGGTGGATGAAATTGGCGAACTGGCGCACGATTTTAACCGTATGACGGAAAATTTAGCGGAATCCCGCGAAGAAATTGAACGTCAAGAGCGCCGCAGAAGA ->lhkA_184 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACCTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGAAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_185 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGACAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_186 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAAAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_187 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTATTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_188 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAGAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGTGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_189 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_190 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCGCTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCAACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_191 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGACAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_192 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACATAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_193 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGATGC ->lhkA_194 -TATCCAACGCAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCGCTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_195 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGTTAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_196 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAGGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_197 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGTGGCGAAACGGTTAGTAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_198 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGTGGCGAAACGGTTAGTAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTATATGTTCTATACTATTTTACTCTCCATAACCGTAGCGCTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_199 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGTGGCGAAACGGTTAGTAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTATATGTTCTATACTATTTTACTCTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_200 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAATAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_201 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAAAACGACAAGAGAAGCGGAGACGC ->lhkA_202 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGCGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_203 -TATCCTACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTCTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_204 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGGGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_205 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCGCCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_206 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTGCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_207 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTCGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_208 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGATAAGAGAAACGGAGACGC ->lhkA_209 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACAC ->lhkA_210 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTTGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCGCCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_211 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGCCGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_212 -TATCCAACACAGATGAATCAGCCGCTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_213 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTTTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_214 -TACCCTACTCAGCTCAATCAACCATTACCAAAAGATTTTTCAATTTCAAAAGATGATAAGAAAAAATTGAAAAGTGGTGAAACAGTTAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTACCAATTATGGATGGCGATAAATTTGTTGGATCTATCGTCTTGAATTCTCCAATTAGCGGAACTGAACAAGTGATTGGAACAATTAATCGTTATATGTTCTATACTATTTTACTGTCGATTACGATTGCGCTTATTTTGAGTGCTATTCTGTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCGACAAAAGATGTGATTCAAGGCAATTATAAAGCGCGCCTAAAAGAGAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAACAAAATGACGGAAACGCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_215 -TATCCAACACAAATGAATCAACCACTTCCAAAAGATTTCTCGATTTCCGCAGAAGATAAGAAAAAATTGAAAAGTGGTGAAACGGTAAGTAAAAAAATTGATAATCGTTTTAATCAAGAAATGACCATTGTTTATGTCCCGTTGATGAATGGCGATAATTTTGTAGGATCCATCGTTTTGAATTCTCCAATTAGTGGCACTGAACAAGTGATAGGAACAATTAATCAATATATGTTCTATACAATTTTGCTTTCGATCACAGTAGCACTTATTCTTAGCGCAATTATTTCCAAATTACAAGTGAATCGGATAAACAAATTACGCGCCGCAACAAAAGATGTGATCGAAGGAAATTATCAAACTCGCTTAAAAGAAAATAACATTGATGAAATTGGTGCGCTCGCAGTCGATTTCAACAAAATGACAACGACGCTTGAAGCTTCCCAAGAAGAAATTGAGCGTCAAGAAAAACGGCGGCGT ->lhkA_216 -TACCCTACTCAGCTCAATCAACCATTACCAAAAGATTTTTCAATTTCAAAAGATGATAAGAAAAAATTAAAAAGTGGCGAAACAGTTAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTACCTATTATGGATGGCGATAAGTTTGTTGGCTCTATTGTCTTGAATTCTCCAATTAGCGGAACTGAACAAGTGATTGGAACAATTAATCGTTATATGTTCTATACTATTTTACTGTCGATTACGATTGCGCTTATTTTGAGTGCTATTCTGTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCGACAAAAGATGTGATCCAAGGCAATTATAAAGCGCGGCTAAAAGAGAATAATTTTGATGAAATTGGCGCACTTGCCATTGATTTCAACAAAATGACGGAAACGCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_217 -TATCCAACTCAAATGAACCAGCCCTTACCAAAAGATTTCTCCATTTCTAAAGATGATAAAAAGAAATTAAAAAGCGGCGAAACGGTCAGCAAAAAAATCGATAATCGTTTTAACCGTGAAATGACAATTGTCTATGTGCCAATTATGAACGGGGATAAATTTGTTGGTTCTATCGTCTTAAATTCTCCAATTAGTGGAACAGAGCAAGTGATAGGAACCATAAATCGTTATATGTTCTATACTATTTTACTTTCAATTACGATAGCACTTATTTTGAGTGCCATTCTGTCTAAGCTTCAAGTTAATCGTATCAATAAATTACGATCTGCGACCAAAGACGTTATCCAAGGTAATTATAAAGCACGTCTAAAAGAAAATAATTTTGATGAGATTGGCGCGCTTGCTATTGATTTTAATAAAATGACGCAAACTCTTGAAAAGTCTCAAGAAGAAATCGAACGTCAAGAAAAGCGGCGCCGT ->lhkA_218 -TATCCAACACAAATGAATCAACCACTTCCAAAAGATTTCTCGATTTCCGCAGAAGATAAGAAAAAGTTGAAAAGTGGTGAAACGGTAAGTAAAAAAATTGATAATCGTTTTAATCAAGAAATGACCATTGTTTATGTCCCGTTGATGAATGGCGATAATTTTGTAGGATCCATCGTTTTGAATTCTCCAATTAGTGGCACTGAACAAGTGATAGGAACAATTAATCAATATATGTTCTATACAATTTTGCTTTCGATCACAGTAGCACTTATTCTTAGCGCAATTATTTCCAAATTACAAGTGAATCGGATAAACAAATTACGCGCCGCAACAAAAGATGTGATCGAAGGAAATTATCAAACTCGCTTAAAAGAAAATAACATTGATGAAATTGGTGCGCTCGCAGTCGATTTCAACAAAATGACAACGACGCTTGAAGCTTCCCAAGAAGAAATTGAGCGTCAAGAAAAACGTCGGCGT ->lhkA_219 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACTCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_220 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCATCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_221 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCAGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_222 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGTGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_223 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAGGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_224 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGACAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_225 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCTAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_226 -TATCCAACGCAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTATATGTTCTATACTATTTTACTCTCCATAACAGTTGCACTTATTCTTAGCGCGATTTTGTCCAAACTACAAGTGAATCGAATCAATAAACTACGAGCAGCAACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_227 -TACCCTACTCAGCTCAATCAACCATTACCAAAAGATTTTTCAATTTCAAAAGATGATAAGAAAAAATTGAAAAGTGGTGAAACAGTTAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTACCAATTATGGATGGCGATAAATTTGTTGGATCTATCGTCTTGAATTCTCCAATTAGCGGAACTGAACAAGTGATTGGAACAATTAATCGTTATATGTTCTATACTATTTTACTGTCGATTACGATTGCGCTTATTTTGAGTGCTATTCTGTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCGACAAAAGATGTGATCCAAGGCAATTATAAAGCGCGGCTAAAAGAGAATAATTTTGATGAAATTGGCGCACTTGCCATTGATTTCAACAAAATGACGGAAACGCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_228 -TATCCCACACAAATGAATCAGCCTCTTCCAAAAGATTTCTCGATTTCTAGTGAAGATAAGAAAAAATTGGAAAGCGGCGAAACAGTGAGTAAAAAAATCGATAATCGTTTTAACCAAGAAATGACGATTGTTTATGTCCCACTCATGAATGGAGATAATTTTGTTGGTTCCATTGTATTGAACTCACCGATTAGCGGTACAGAGCAAGTGATAGGAACAATTAATCAATATATGTTCTACACTATTTTACTTTCTATTACCGTAGCTCTAATTCTTAGTGCAATCATCTCTAAACTGCAAGTGAATCGGATTAATAAACTACGAGCTGCGACAAAGGATGTTATTCATGGGAATTATCAAACACGTTTAAAAGAAAATAATTTTGATGAGATTGGCGCACTTGCAATTGACTTCAACAAAATGACTACAACACTAGAAGCCTCTCAAGAAGAAATTGAGCGTCAAGAAAAACGACGGCGC ->lhkA_229 -TATCCAACACAAATGAATCAACCACTTCCAAAAGATTTCTCGATTTCCGCAGAAGATAAGAAAAAGTTGAAAAGTGGTGAAACGGTAAGTAAAAAAATTGATAATCGTTTTAATCAAGAAATGACCATTGTTTATGTCCCGTTAATGAATGGCGATAATTTTGTAGGATCCATCGTTTTGAATTCTCCAATTAGTGGCACTGAACAAGTGATAGGAACAATTAATCAATATATGTTCTATACAATTTTGCTTTCGATCACAGTAGCACTTATTCTTAGCGCAATTATTTCCAAATTACAAGTGAATCGGATAAACAAATTACGCGCCGCAACAAAAGATGTGATCGAAGGAAATTATCAAACTCGCTTAAAAGAAAATAACATTGATGAAATTGGTGCGCTCGCAGTCGATTTCAACAAAATGACAACGACGCTTGAAGCTTCCCAAGAAGAAATTGAGCGTCAAGAAAAACGTCGGCGT ->lhkA_230 -TATCCTACTCAGCTCAATCAGCCATTACCAAAAGATTTTTCGATTTCAAAAGATGATAAGAAAAAATTAAAAAGTGGCGAAACAGTGAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTGCCTATTATGGATGGCGATAAGTTTGTTGGCTCTATTGTCTTGAATTCTCCAATTAGCGGAACTGAACAAGTGATTGGAACAATTAATCGCTATATGTTCTATACTATTTTACTTTCGATTACAATTGCACTTATTTTGAGTGCTATTCTCTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCGACAAAAGATGTAATCCAAGGCAATTATAAGGCGCGGCTAAAAGAGAATAATTTTGATGAAATTGGCGCACTTGCCATTGATTTCAACAAAATGACGGAAACGCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_231 -TATCCTACTCAGTTCAATCAGCCATTACCAAAAGATTTTTCGATTTCAAAAGATGATAAGAAAAAATTAAAAAGTGGCGAAACAGTGAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTGCCTATTATGGATGGCGATAAGTTTGTTGGCTCTATTGTCTTGAATTCTCCAATTAGCGGAACTGAACAAGTGATTGGAACAATTAATCGCTATATGTTCTATACTATTTTACTATCGATTACAATTGCACTTATTTTGAGTGCTATTCTGTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCGACAAAAGATGTAATCCAAGGCAATTATAAGGCGCGGCTAAAAGAGAATAATTTTGATGAAATTGGCGCACTTGCCATTGATTTCAACAAAATGACGGAAACGCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_232 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCTATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_233 -GTGTTTCCCACTTCTAATAGACCACTCCCGCCAGATTTTAAAATTTCAAAAAAGGCTTCTAAAAATCTCAAAAAAGGCGAAACTGTCACGATTAAAGTCGATAATCGTTTTAATCAAGAAATGTCTGTTGTGTATGTCCCTATTTTATCAGGCAAAACTTTTCTTGGCTCGGTCATTTTGAACTCTCCGATCAGCGGGACAGAAAAAGTCATCGGAACAATTAATCGGTACATGTTTTTCACGATTCTTTTATCGGTAATCATCGCGCTTGTTTTAAGTGCAATTTTAGCCAAATTGCAAGTGAATCGAATTAATCGCTTACGTGAAGCAACGAAACGAGTAATTAGTGGAGATTATGAAACTCGTTTAAACGAAAATAACTGGGATGAAATTGGTGCACTTTCCTCAGATTTTAATAAAATGACAGAAACATTAGCCGCCTCTAAGGAGGAAATAGAACGTCAAGAAAAACGACGCCGA ->lhkA_234 -TACCCTACTCAGCTCAATCAACCATTACCAAAAGATTTTTCAATTTCAAAAGATGATAAGAAAAAATTAAAAAGTGGCGAAACAGTTAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTACCAATTATGGATGGCGATAAATTTGTTGGATCCATCGTCTTAAATTCTCCAATTAGCGGGACTGAACAAGTGATTGGAACAATTAATCGTTATATGTTCTATACTATTTTACTATCGATAACGATTGCGCTTATGTTGAGTGCTATTCTGTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCAACAAAAGATGTGATCCAAGGCAATTATAAGGCGCGGCTAAAAGAGAATAATTTTGATGAAATTGGCGCACTTGCCATTGATTTCAACAAAATGACGGAAACGCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_235 -GTGTTTCCCACTTCCGACAGACCACTCCCGCCCGATTTTAAAATTTCAAAAGAGGCTTCTAAAAATCTCAAAAAAGGCGAAACCGTCACGATTAAAGTCGATAATCGTTTTAATCAAGAAATGTCTGTTGTGTATGTCCCGATTTTATCAGGCAAAACCTTTCTTGGCTCAGTTATTTTGAACTCTCCGATCAGCGGGACAGAAAAAGTCATCGGAGCAATTAATCGTTACATGTTTTTCACGATTCTTTTATCGGTAATCATCGCGCTTGTTTTAAGTGCTATTTTAGCTAAATTGCAAGTGAATCGGATTAATCGCTTACGTGAAGCAACAAAACGAGTGATTAGTGGGGATTATGAAACGCGATTAAACGAAAATAACTGGGATGAAATTGGCGCACTTTCCTCAGATTTTAATAAAATGACAGAAACCTTAGCCACCTCAAAGGAGGAAATAGAACGTCAAGAAAAACGGCGCCGA ->lhkA_236 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTTAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGATTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_237 -TATCCTACACAAATGGACCAGCCTCTGCCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGTAAGAAAATAGATAATCGATTCAATAGAGAAATGACTATTGTATATGTGCCAATTATGGACGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGGTACATGTTCTATACTATTTTACTTTCCATAACAGTTGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAATTACGAGCAGCGACGAAAGATGTTATTCAAGGGAACTACAAAGCGCGACTAAAAGAAAATAACTTTGATGAAATTGGGGCGCTTGCGATTGATTTCAATAAGATGACACAAACGCTTGAAATATCGCAAGAAGAAATTGAAAGACAAGAAAAACGTCGCCGT ->lhkA_238 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_239 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGATACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_240 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCAGCTATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_241 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATCTCTTCGGAAGATAAGAAAAAACTGGAAAGTGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_242 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACTGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_243 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCGCTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_244 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGTCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_245 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATCTCTTCGGAAGATAAGAAAAAACTGGAAAGTGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_246 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGTGGCGAAACGGTTAGTAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGTGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_247 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTTAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTAAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCAATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_248 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCAGAAGATAAGAAAAAACTGGAAAGTGGCGAAACGGTTAGTAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_249 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGAACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCTATAACCGTAGCACTTATTCTTAGTGCAATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCACTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_250 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATAATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTCTCCATAACAGTTGCACTTATTCTTAGCGCGATTTTGTCCAAACTACAAGTGAATCGAATCAATAAACTACGAGCAGCAACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_251 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_252 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTAGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_253 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCAATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_254 -TATCCAACACAAATGGACCAGCCTCTACCCAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGTGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_255 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_256 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATCAATTTGTCGGTTCTATCGTGCTAAATTCACCTATTAGTGGGACCGAGCAAGTAATTAGTACGATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACAGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAACCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_257 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGTGAAACGGTTAGTAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_258 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCACTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_259 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACAGTTGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_260 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCACTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_261 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_262 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTCTCCATAACAGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACCTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_263 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAAAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_264 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTAGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_265 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTCAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_266 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTATATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_267 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACAGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_268 -TATCCAACTCAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGTAAGAAAATAGATAATCGATTTAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_269 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGTGGAGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_270 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGTGGCGAAACGGTCAGCAAGAAAATAGATAATCGATTTAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACAGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_271 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCTATAACCGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_272 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCAATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_273 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTCTCCATAACAGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_274 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGGGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_275 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATAATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTCTCCATAACAGTTGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCAACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_276 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCAGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTTAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_277 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGTGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCAATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCTATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_278 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTTAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_279 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCAGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTTAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_280 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTATATGTTCTATACTATTTTACTCTCCATAACCGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGTGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_281 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGGCAAGAAAAAAGGCGCCGT ->lhkA_282 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_283 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_284 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_285 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGGTTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_286 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATCTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_287 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGATATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_288 -TATCCTACACAAATGGACCAGCCTCTGCCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGTAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTATATGTGCCAATTATGGACGGCGATAAATTTGTTGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGGTACATGTTCTATACTATTTTACTTTCCATAACAGTTGCACTTATTCTTAGTGCAATTTTGTCGAAACTACAAGTGAATCGAATCAACAAATTACGAGCAGCAACGAAAGATGTTATTCAAGGGAACTACAAAGCGCGACTAAAAGAAAATAACTTTGATGAAATTGGGGCGCTTGCGATTGATTTCAATAAGATGACACAAACATTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGTCGCCGT ->lhkA_289 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATAATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTCTCCATAACAGTTGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCAACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_290 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTACTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTATATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_291 -TATCCTACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_292 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATAATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTCTCCATAACAGTTGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCAACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGTGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_293 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATAGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCGCTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_294 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGGAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_295 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGAATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_296 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACAATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTATATGTTCTATACTATTTTACTCTCCATAACAGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_297 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTCTCCATAACAGTTGCACTTATTCTTAGCGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGTGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_298 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTTAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCAATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_299 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACAGTTGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_300 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCTATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_301 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTTAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCAATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_302 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGTGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_303 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCGCTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_304 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCGCCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTATATGTTCTATACTATTTTACTCTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_305 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAGAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_306 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACAAAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_307 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAGAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_308 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATAATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTCTCCATAACCGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCAACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_309 -TATCCAACACAGATGAATCAGCCTTTACCTAGGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_310 -GTGTTTCCCACTTCCGACAGACCACTCCCGCCCGATTTTAAAATTTCAAAAGAGGCTTCTAAAAATCTCAAAAAAGGCGAAACCGTAACGATTAAAGTCGATAATCGTTTTAATCAAGAAATGTCTGTTGTGTATGTCCCGATTTTATCAGGCAAAACCTTTCTTGGCTCAGTTATTTTGAACTCTCCGATCAGCGGGACAGAAAAAGTCATCGGAGCAATTAATCGTTACATGTTTTTCACGATTCTTTTATCGGTAATCATCGCGCTTGTTTTAAGTGCTATTTTAGCTAAATTGCAAGTGAATCGAATTAATCGCTTACGTGAAGCAACAAAACGAGTGATTAGCGGGGATTATGAAACGCGATTAAACAAAAATAACTGGGATGAAATTGGCGCCCTTTCCTCAGATTTTAATAAAATGACAGAAACCTTAGCCACCTCAAAGGAGGAAATAGAACGTCAAGAAAAACGGCGCCGA ->lhkA_311 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTTTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_312 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAACAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_313 -TATCCAACTCAAATGAATCAGCCCTTACCAAAAGATTTCTCCATTTCTAAAGATGATAAAAAGAAATTAAAAAGCGGCGAAACGGTTAGCAAAAAAATCGATAATCGTTTTAACCGTGAAATGACAATTGTCTATGTGCCAATTATGAACGGGGATAAATTTGTTGGTTCTATCGTCTTAAATTCTCCAATTAGTGGAACAGAGCAAGTGATAGGAACCATAAATCGTTATATGTTCTATACTATTTTACTTTCAATTACGATAGCACTTATTTTGAGTGCCATTCTGTCTAAGCTTCAAGTTAATCGTATCAATAAATTACGATCTGCGACCAAAGACGTTATCCAAGGTAATTATAAAGCACGTTTAAAAGAAAATAATTTTGATGAGATTGGCGCGCTTGCTATTGATTTTAATAAAATGACGCAAACGCTTGAAAAGTCTCAAGAAGAAATCGAACGTCAAGAAAAGCGGCGCCGT ->lhkA_314 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCCCCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_315 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCTCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAAAGACAAGAGAAACGGAGACGC ->lhkA_316 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTTAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_317 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_318 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGATCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_319 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAAATACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_320 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGACTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_321 -TATCCAACACAGATGAATCAGCCGTTACCATAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_322 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGTGAAACGGTTAGCAAGAAAATAGATAATCGATTTAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTAAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCAATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_323 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTTCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_324 -TATCCAACACAGAGGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_325 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAACAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_326 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACCATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_327 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACAGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAAAGACAAGAGAAACGGAGACGC ->lhkA_328 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTAAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_329 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCGCCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAATGACAAGAGAAGCGGAGACGC ->lhkA_330 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTTAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTATATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_331 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTATATGTTCTATACTATTTTACTTTCCATAACAGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGTGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_332 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGACAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTAAATTCACCTATTAGTGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACAGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAACCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_333 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATAATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTCTCCATAACAGTTGCACTTATTCTTAGCGCGATTTTGTCCAAACTACAAGTGAATCGAATCAATAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGGCAAGAAAAAAGGCGCCGT ->lhkA_334 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTATATGTTCTATACTATTTTACTCTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAGAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGTGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_335 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAGAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCAACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_336 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTATATGTTCTATACTATTTTACTCTCCATAACCGTAGCGCTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGTGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_337 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTATCGGCTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCGCTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_338 -TATCCAACACAGATGAATCAGCCTTTACCTTAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAACGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_339 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTTAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTATATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAACCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_340 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATAATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTCTCCATAACAGTTGCACTTATTCTTAGCGCGATTTTGTCCAAACTACAAGTGAATCGAATCAATAAACTACGAGCAGCAACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGGCAAGAAAAAAGGCGCCGT ->lhkA_341 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATAATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTCTCCATAACAGTTGCACTTATTCTTAGCGCGATTTTGTCCAAACTACAAGTGAATCGAATCAATAAACTACGAGCAGCAACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTAAAAATTTCTCAAGAAGAAATTGAAAGGCAAGAAAAAAGGCGCCGT ->lhkA_342 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATAATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTCTATACTATTTTACTCTCCATAACAGTTGCACTTATTCTTAGCGCGATTTTGTCCAAACTACAAGTGAATCGAATCAATAAACTACGAGCAGCAACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGGCAAGAAAAAAGGCGCCGT ->lhkA_343 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATAATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTCTCCATAACCGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGGGCAGCAACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_344 -TATCCAACGCAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACTGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCGCTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_345 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTAAATTCACCTATTAGTGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACAGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAACCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAAATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_346 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCAACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCACTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_347 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTATATGTTCTATACTATTTTACTCTCCATAACCGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_348 -TATCCAACGCAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATAATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTATATGTTCTATACTATTTTACTCTCCATAACAGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAACCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_349 -TATCCAACTCAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGTGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCGCCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCGCTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_350 -TATCCAACTCAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGTGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCGCCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCGCTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGTGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_351 -TATCCAACACAGATGAATCAGTCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_352 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAGAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_353 -TATCCTACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_354 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTAAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_355 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAGAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCACTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_356 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGCTAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_357 -TATCCAACTCAAATGAATCAGCCCTTACCAAAAGATTTCTCCATTTCTAAAGATGATAAAAAGAAATTAAAAAGCGGCGAAACGGTCAGCAAAAAAATCGATAATCGTTTTAACCGTGAAATGACAATTGTCTATGTGCCAATTATGAACGGGGATAAATTTGTTGGTTCTATCGTCTTAAATTCTCCAATTAGCGGAACAGAGCAAGTGATAGGAACCATAAATCGTTATATGTTCTATACTATTTTACTTTCAATTACGATAGCACTTATTTTGAGTGCCATTCTGTCTAAGCTTCAAGTTAATCGTATCAATAAATTGCGATCTGCGACCAAAGACGTTATCCAAGGTAATTATAAAGCACGTCTAAAAGAAAATAATTTTGATGAGATTGGCGCGCTTGCTATTGATTTTAATAAAATGACGCAAACGCTTGAAAAGTCTCAAGAAGAAATCGAACGTCAAGAAAAGCGGCGCCGT ->lhkA_358 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACATAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_359 -TATCCAACTCAAATGAACCAGCCCTTACCAAAAGATTTCTCCATTTCTAAAGATGATAAAAAGAAATTAAAAAGCGGCGAAACGGTCAGCAAAAAAATCGATAATCGTTTTAACCGTGAAATGACAATTGTCTATGTGCCAATTATGAACGGGGATAAATTTGTTGGTTCTATCGTCTTAAATTCTCCAATTAGTGGAACAGAGCAAGTGATAGGAACCATAAATCGTTATATGTTCTATACTATTTTACTTTCAATTACGATAGCACTTATTTTGAGTGCCATTCTGTCTAAGCTTCAAGTTAATCGTATCAATAAATTACGATCTGCGACCAAAGACGTTATCCAAGGTAATTATAAAGCACGTCTAAAAGAAAATAATTTTGATGAGATTGGGGCGCTTGCTATTGATTTTAATAAAATGACGCAAACGCTTGAAAAGTCTCAAGAAGAAATCGAACGTCAAGAAAAGCGGCGCCGT ->lhkA_360 -TATCCAACTCAAATGAATCAGCCCTTACCAAAAGATTTCTCCATTTCTAAAGATGATAAAAAGAAATTAAAAAGCGGCGAAACGGTCAGCAAAAAAATCGATAATCGTTTTAACCGTGAAATGACAATTGTCTATGTGCCAATCATGAACGGGGATAAATTTGTTGGTTCTATCGTCTTAAATTCTCCAATTAGTGGAACAGAGCAAGTGATAGGAACCATAAATCGTTATATGTTCTATACTATTTTACTTTCAATTACGATAGCACTTATTTTGAGTGCCATTCTGTCTAAGCTTCAAGTTAATCGTATCAATAAATTACGATCTGCGACCAAAGACGTTATCCAAGGTAATTATAAAGCACGTCTAAAAGAAAATAATTTTGATGAGATTGGCGCACTTGCTATTGATTTTAATAAAATGACGCAAACGCTTGAAAAGTCTCAAGAAGAAATCGAACGTCAAGAAAAGCGGCGCCGT ->lhkA_361 -TACCCTACTCAGCTCAATCAACCATTACCAAAAGATTTTTCAATTTCAAAAGATGATAAGAAAAAATTGAAAAGTGGTGAAACAGTTAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTGCCTATTATGGATGGCGATAAGTTTGTTGGCTCTATTGTCTTGAATTCTCCAATTAGCGGAACTGAACAAGTGATTGGAACAATTAATCGTTATATGTTCTATACTATTTTACTATCGATAACGATTGCGCTTATTTTGAGTGCTATTCTGTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCGACAAAAGATGTGATCCAAGGCAATTATAAGGCGCGGCTAAAAGAGAATAATTTTGATGAAATTGGCGCACTTGCCATTGATTTCAACAAAATGACGGAAACACTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_362 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGATATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_363 -TATCCTACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAAAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTATATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_364 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGTTAAACGTCTAGCTTCTGTATCAATGAGTGCT ->lhkA_365 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAATTGACACAAACCCTTGAATAACATTTACTAAACCTTCCGTGAGGCCACTAATCGTTGTGA ->lhkA_366 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAGTCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_367 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGTGAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_368 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACGAGAGAAGCGGAGACGC ->lhkA_369 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCCGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_370 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_371 -TATCCAACACAGATGAATCAGCCGTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_372 -TATCCTACACAAATGGACCAGCCTCTGCCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGTAAGAAAATAGATAATCGATTCAATAGAGAAATGACTATTGTATATGTGCCAATTATGGACGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGGTACATGTTCTATACTATTTTACTTTCCATAACAGTTGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGGAACTACAAAGCGCGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACACAAACGCTTGAAATATCGCAAGAAGAAATTGAAAGACAAGAAAAACGTCGCCGT ->lhkA_373 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGAACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_374 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTAAATTCACCTATTAGTGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACAGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAACCGAATCAACAAACTACGAGCAGCAACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_375 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGTGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_376 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGTGGCGAAACGGTTAGTAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTATATGTTCTATACTATTTTACTCTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCAATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_377 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCCTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_378 -TTTCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_379 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGACAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_380 -TATCCAACACAGATGAATCAGCCGTTACCAAAGAATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_381 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATAACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_382 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACTGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_383 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTTGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_384 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACAAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_385 -TATCCTACTCAGCTCAATCAGCCATTACCAAAGGATTTTTCGATTTCAAAAGATGATAAGACAAAATTAAAAAGTGGTGAAACAGTTAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTACCAATTATGGATGGCGATAAGTTTGTTGGATCCATCGTCTTGAATTCTCCAATTAGCGGGACTGAACAAGTGATTGGCACAATTAATCGCTATATGTTCTATACTATTTTACTATCGATTACGATTGCGCTTATTTTGAGTGCTATTCTGTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCGACAAAAGATGTGATCCAAGGCAATTATAAGGCGCGGCTAAAAGAGAATAATTTTGATGAGATTGGCGCACTTGCCATCGATTTCAATAAAATGACGGAAACGCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_386 -TATCCTACTCAGCTCAATCAGCCATTACCAAAAGATTTTTCAATTTCAAAAGATGATAAGAAAAAATTGAAAAGTGGTGAAACAGTTAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTACCAATTATGGATGGCGATAAGTTTGTTGGATCCATCGTCTTGAATTCTCCAATTAGCGGGACTGAACAAGTGATTGGCACAATTAATCGCTATATGTTCTATACTATTTTACTATCGATTACGATTGCGCTTATTTTGAGTGCTATTCTGTCCAAACTACAAGTCAACCGGATTAATAAATTGCGTTCAGCGACAAAAGATGTAATCCAAGGCAATTATAAGGCGCGGCTAAAAGAGAATAATTTTGATGAGATTGGCGCACTTGCCATCGATTTCAATAAAATGACGGAAACGCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_387 -TACCCTACTCAGCTCAATCAACCATTACCAAAAGATTTTTCAATTTCAAAAGATGATAAGAAAAAATTGAAAAGTGGTGAAACAGTTAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTACCAATTATGGATGGCGATAAATTTGTTGGATCTATCGTCTTGAATTCTCCAATTAGCGGGACTGAACAAGTGATTGGAACAATTAATCGTTATATGTTCTATACTATTTTACTGTCGATTACGATTGCGCTTGTTTTGAGTGCTATTCTGTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCGACAAAAGATGTGATCCAAGGCAATTATAAAGCGCGGCTAAAAGAGAATAATTTTGATGAAATTGGCGCACTTGCCATTGATTTCAACAAAATGACGGAAACGCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_388 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTATACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_389 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_390 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCGCCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTTCGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_391 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCCTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACAGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_392 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTAAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_393 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGACAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCACTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_394 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTAGAAAGCGGCGAAACGGTTAGTAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTATATGTGCCAATTATGGACGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGGTACATGTTCTATACTATTTTACTTTCCATAACAGTTGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGGAACTACAAAGCGCGACTAAAAGAAAATAACTTTGATGAAATTGGGGCGCTTGCGATTGATTTCAATAAGATGACACAAACGCTTGAAATATCGCAAGAAGAAATTGAAAGACAAGAAAAACGTCGCCGT ->lhkA_395 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTTTATACTATTTTACTTTCCATAACCGTAGCGCTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACTCTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_396 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAGACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_397 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTTAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTAAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATACTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_398 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_399 -TATCCTACACAAATGGACCAGCCTCTGCCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGTAAGAAAATAGATAATCGATTCAATAGAGAAATGACTATTGTATATGTGCCAATTATGGACGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGGTACATGTTCTATACTATTTTACTTTCCATAACAGTTGCACTTATTCTTAGTGCAATTTTGTCCAAACTACAAGTGAATCGAATCAACAAATTACGAGCAGCAACGAAAGATGTTATTCAAGGGAACTACAAAGCGCGACTAAAAGAAAATAACTTTGATGAAATTGGGGCGCTTGCGATTGATTTCAATAAGATGACACAAACGCTTGAAATATCGCAAGAAGAAATTGAAAGACAAGAAAAACGTCGCCGT ->lhkA_400 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGTAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACGATTAATCGTTATATGTTCTATACTATTTTACTCTCCATAACCGTAGCGCTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCCGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCAATTGATTTCAATAAGATGACTCAAACACTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_401 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATAATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTATATGTTCTATACTATTTTACTCTCCATAACCGTAGCGCTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGTGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_402 -TATCCAACACAGGTGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_403 -TATCCAACTCAAATGAATCAGCCCTTACCAAAAGATTTCTCCATTTCTAAAGATGATAAAAAGAAATTAAAAAGCGGCGAAACGGTCAGCAAAAAAATCGATAATCGTTTTAACCGTGAAATGACAATTGTCTATGTGCCAATTATGAACGGGGATAAATTTGTTGGTTCTATCGTCTTAAATTCTCCAATTAGTGGAACAGAGCAAGTGATAGGAACCATAAATCGTTATATGTTCTATACTATTTTACTTTCAATTACGATAGCACTTATTTTGAGTGCCATTCTGTCTAAGCTTCAAGTTAATCGTATCAATAAATTGCGATCTGCGACCAAAGACGTTATCCAAGGTAATTATAAAGCACGTCTAAAAGAAAATAATTTTGATGAGATTGGCGCGCTTGCTATTGATTTTAATAAAATGACGCAAACGCTTGAAAAGTCTCAAGAAGAAATCGAACGTCAAGAAAAGCGGCGCCGT ->lhkA_404 -TATCCAACACAAATGAACCAACCACTTCCAAAAGATTTCTCGATTTCCGCAGAAGATAAGAAAAAGTTAAAAAGTGGTGAAACGGTAAGTAAAAAAATTGATAATCGTTTTAATCAAGAAATGACCATTGTTTATGTCCCGTTGATGAATGGCGATAATTTTGTAGGATCCATCGTTTTGAATTCTCCAATTAGCGGCACTGAACAAGTGATAGGAACAATTAATCAATACATGTTCTATACTATTTTGCTTTCGATCACAGTAGCACTTATCCTTAGCGCAATTATTTCCAAATTACAAGTGAATCGGATAAACAAACTACGCGCCGCAACAAAAGATGTGATTGAGGGGAATTATCAAACTCGCTTAAAAGAAAATAACATTGATGAAATTGGCGCGCTCGCAGTCGATTTCAACAAAATGACAACAACACTTGAAGCTTCCCAAGAAGAAATTGAGCGTCAAGAAAAACGGCGGCGT ->lhkA_405 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAGGCGGAGACGC ->lhkA_406 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCTTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_407 -TACCCTACTCAGCTCAATCAACCATTACCAAAAGATTTTTCAATTTCAAAAGATGATAAGAAAAAATTGAAAAGTGGTGAAACAGTTAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTACCAATTATGGATGGCGATAAATTTGTTGGCTCCATCGTCTTGAATTCTCCAATTAGCGGGACTGAACAAGTGATTGGAACAATTAATCGTTATATGTTCTATACTATTTTACTATCGATAACGATTGCGCTTATTTTGAGTGCTATTCTGTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCGACAAAAGATGTGATCCAAGGCAATTATAAAGCGCGGCTAAAAGAGAATAATTTTGATGAAATTGGCGCACTTGCCATTGATTTCAACAAAATGACGGAAACGCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_408 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGTGGCGAAACGGTCAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTACATGTTCTATACTATTTTACTTTCCATAACCGTAGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGTAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_409 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGCGACGC ->lhkA_410 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAACTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCGATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_411 -TATCCTACTCAGCTCAATCAGCCATTACCAAAAGATTTTTCAATTTCAAAAGATGATAAGAAAAAATTAAAAAGTGGCGAAACAGTTAGTAAAAAAATCGACAATCGTTTTAATCGTGAAATGACTATTGTTTATGTACCAATTATGGATGGCGATAAATTTGTTGGATCCATCGTCTTAAATTCTCCAATTAGCGGGACTGAACAAGTGATTGGAACAATTAATCGTTATATGTTCTATACTATTTTACTATCGATAACGATTGCGCTTATGTTGAGTGCTATTCTGTCCAAACTACAAGTCAATCGGATTAATAAATTGCGTTCAGCGACAAAAGATGTGATCCAAGGCAATAATAAGGCGCGGCTAAAAGAGAATAATTTTGATGAAATTGGCGCACTTGCCATTGATTTCAACAAAATGACGGAAACGCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAAAAACGTCGGCGC ->lhkA_412 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAATTCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTTGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_413 -TATCCAACTCAAATGAATCAGCCCTTACCAAAAGATTTCTCCATTTCTAAAGATGATAAAAAGAAATTAAAAAGCGGCGAAACGGTCAGCAAAAAAATCGATAATCGTTTTAACCGTGAAATGACAATTGTCTATGTGCCAATTATGAACGGGGATAAATTTGTTGGTTCTATCGTCTTAAATTCTCCAATTAGTGGAACAGAGCAAGTGATAGGAACCATAAATCGTTATATGTTCTATACTATTTTACTTTCAATTACGATAGCACTTATTTTGAGTGCCATTCTGTCTAAGCTTCAAGTTAATCGTATCAATAAATTACGATCTGCGACCAAAGACGTTATCCAAGGTAATTATAAAGCACGTCTAAAAGAAAATAATTTTGATGAGATTGGGGCGCTTGCTATTGATTTTAATAAAATGACGCAAACGCTTGAAAAGTCTCAAGAAAAAATCGAACGTCAAGAAAAGCGGCGCCGT ->lhkA_414 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGTTATATGTTCTATACTATTTTACTCTCCATAACAGTAGCACTTATTCTTAGTGCGATATTGTCCAAACTACAAGTGAATCGAATCAACAAACTGCGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGTGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_415 -TATCCAACACAGATGAATCAGCCTTTACCTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAATGACGTTATTCAAGGCAATTACAAAGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_416 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAGAAACTGGAAAGCGGCGAAACGGTTAGCAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTTTATGTGCCGATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACAATTAATCGTTATATGTTCTATACTATTTTACTTTCCATAACAGTTGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAACTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACTCAAACACTGGAAATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGGCGCCGT ->lhkA_417 -TGTTTCTAGGATATCTTCATTATTTTCTGTAGAAGTGGCACTATTTTTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_418 -TATCCAACACAAATGGACCAGCCTCTACCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTAGAAAGCGGCGAAACGGTTAGTAAGAAAATAGATAATCGATTCAATAGAGAAATGACTATTGTATATGTGCCAATTATGGACGGCGATAAATTTGTTGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGGTACATGTTCTATACTATTTTACTTTCCATAACAGTTGCACTTATTCTTAGTGCAATTTTGTCCAAACTACAAGTGAATCGAATCAACAAATTACGAGCAGCGACGAAAGATGTTATTCAAGGCAACTACAAAGCCAGACTAAAAGAAAATAACTTTGATGAAATTGGCGCGCTTGCGATTGATTTCAATAAGATGACACAAACATTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGTCGCCGT ->lhkA_419 -TATCCTACACAAATGGACCAGCCTCTGCCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGTAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTATATGTGCCAATTATGGACGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGGTACATGTTCTATACTATTTTACTTTCCATAACAGTTGCACTTATTCTTAGTGCGATTTTGTCCAAACTACAAGTGAATCGAATCAACAAATTACGAGCAGCGACGAAAGATGTTATTCAAGGGAACTACAAAGCGCGACTAAAAGAAAATAACTTTGATGAAATTGGGGCGCTTGCGATTGATTTCAATAAGATGACACAAACGCTTGAAATATCGCAAGAAGAAATTGAAAGACAAGAAAAACGTCGCCGT ->lhkA_420 -TATCCTACACAAATGGACCAGCCTCTGCCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGTAAGAAAATAGATAATCGATTCAATAGAGAAATGACTATTGTATATGTGCCAATTATGGACGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGGTACATGTTCTATACTATTTTACTTTCCATAACAGTTGCACTTATTCTTAGTGCAATTTTGTCGAAACTACAAGTGAATCGAATCAACAAATTACGAGCAGCAACGAAAGATGTTATTCAAGGGAACTACAAAGCGCGACTAAAAGAAAATAACTTTGATGAAATTGGGGCGCTTGCGATTGATTTCAATAAGATGACACAAACATTGGAGATTTCTCAAGAAGAAATTGAAAGACAAGAAAAACGTCGCCGT ->lhkA_421 -TATCCTACACAAATGGACCAGCCTCTGCCTAAAGACTTTTCGATTTCTTCGGAAGATAAGAAAAAACTGGAAAGCGGCGAAACGGTTAGTAAGAAAATAGATAATCGATTCAATAGAGAAATGACGATTGTATATGTGCCAATTATGGATGGTGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCTATTAGCGGGACCGAGCAAGTAATTGGTACTATTAATCGGTACATGTTCTATACTATTTTACTTTCCATAACAGTTGCACTTATTCTTAGTGCAATTTTGTCCAAACTACAAGTGAATCGAATCAACAAATTACGAGCAGCAACGAAAGATGTTATTCAAGGGAACTACAAAGCGCGACTAAAAGAAAATAACTTTGATGAAATTGGGGCGCTTGCGATTGATTTCAATAAGATGACACAAACGCTTGAAATATCGCAAGAAGAAATTGAAAGACAAGAAAAACGTCGCCGT ->lhkA_422 -TATCCAACTCAAATGAATCAGCCCTTACCAAAAGATTTCTCCATTTCTAAAGATGATAAAAAGAAATTAAAAAGCGGCGAAACGGTCAGCAAAAAAATCGATAATCGTTTTAACCGTGAAATGACAATTGTCTATGTGCCAATTATGAACGGGGATAAATTTGTTGGTTCTATCGTCTTAAATTCTCCAATTAGTGGAACAGAGCAAGTGATAGGAACCATAAATCGTTATATGTTCTATACTATTTTACTTTCAATTACGATAGCACTTATTTTGAGTGCCATTCTGTCTAAGCTTCAAGTTAATCGTATCAATAAATTACGATCTGCGACCAAAGACGTTATCCAAGGTAATTATAAAGCACGTTTAAAAGAAAATAATTTTGATGAGATTGGCGCGCTTGCTATTGATTTTAATAAAATGACGCAAACGCTTGAAAAGTCTCAAGAAGAAATCGAACGTCAAGAAAAGCGGCGCCGT ->lhkA_423 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCACCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTAGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_424 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_425 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATTTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_426 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTTCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCGCCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTAGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC ->lhkA_427 -TATCCAACACAGATGAATCAGCCTTTAACTAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGTGAAACGGTTAGTAAGAAAATAGATAATCGCTTTAACAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGATAAATTTGTCGGTTCTATCGTGCTGAATTCACCCATTAGCGGTACGGAGCAAGTAATTGGCACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAACGCTCGCTTGAAGGAAAATAATTTTGATGAAATTGGTGCACTCGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATTGAACGACAAGAGAAACGGAGACGC ->lhkA_428 -TATCCAACACAGATGAATCAGCCGTTACCAAAGGATTTCTCTATTTCTGCGGATGATAAGAAAAAGCTTGAAAGTGGCGAAACAGTTAGTAAGAAAATAGATAATCGCTTTAATAAAGAAATGACAATTGTGTACGTCCCAATAATGAATGGCGACAAATTTGTCGGTTCTATCGTGCTCAATTCGCCTATTAGCGGTACGGAGCAAGTAATTGGTACGATTAACCGCTATATGTTCTACACTATTTTACTTTCTATAACGGTAGCACTTATTCTTAGCGCAATCTTGTCCAAACTACAAGTAAATCGAATCAACAAACTACGAGCAGCGACAAAAGACGTTATTCAAGGCAATTACAAAGCTCGATTGAAGGAAAATAATTTTGATGAAATTGGTGCACTAGCCATTGATTTCAATAAAATGACACAAACCCTTGAAACATCTCAAGAAGAAATAGAACGACAAGAGAAGCGGAGACGC diff --git a/test/MLST_listeria/lm0002.fasta b/test/MLST_listeria/lm0002.fasta new file mode 100644 index 0000000..f98fe82 --- /dev/null +++ b/test/MLST_listeria/lm0002.fasta @@ -0,0 +1,8 @@ +>lmo0002_1 +ATGAAATTTGTTATTGAGCGTGATCGTCTTGTCCAAGCAGTCAATGAAGTTACTCGTGCCATCTCTGCAAGAACAACGATTCCAATTTTAACGGGGATAAAAATAGTCGTAAATGATGAAGGTGTAACACTAACTGGTAGTGATTCCGATATTTCCATCGAAGCATTTATTCCATTAATTGAAAATGATGAAGTAATTGTAGAAGTAGAGAGTTTTGGTGGAATTGTACTTCAATCAAAATACTTTGGCGATATTGTCCGTCGTTTACCAGAAGAAAATGTAGAAATTGAAGTAACGTCTAATTACCAAACCAACATTAGTTCTGGTCAAGCCTCCTTTACACTAAACGGCTTAGATCCAATGGAATATCCTAAATTACCTGAAGTAACAGATGGAAAAACAATTAAAATTCCAATTAATGTACTAAAAAATATTGTTAGACAAACTGTTTTTGCTGTTTCTGCTATTGAAGTTCGTCCAGTACTTACTGGTGTAAACTGGATTATCAAAGAAAATAAACTAAGCGCAGTTGCAACCGATAGTCATCGTCTAGCTTTACGTGAAATTCCACTTGAAACAGACATTGATGAAGAATACAATATTGTTATTCCTGGAAAAAGTTTATCTGAATTAAATAAACTTTTAGATGACGCAAGCGAATCTATTGAAATGACCCTTGCCAACAACCAAATTCTTTTTAAATTAAAAGATTTATTATTTTACTCTCGTTTACTTGAAGGTAGTTACCCAGATACATCTCGATTAATTCCAACTGATACTAAATCAGAATTAGTCATTAATTCCAAAGCATTTTTACAAGCAATTGACCGTGCGTCGCTACTTGCTCGCGAAAATCGTAATAACGTTATTAAATTAATGACGCTGGAAAATGGCCAAGTAGAAGTATCCTCCAATTCTCCGGAAGTTGGGAATGTTTCTGAAAATGTCTTCAGCCAAAGTTTTACTGGCGAAGAAATCAAAATATCTTTTAACGGTAAATACATGATGGATGCCCTACGTGCTTTTGAAGGTGATGATATTCAAATTTCCTTCTCAGGTACTATGAGACCATTTGTACTTCGACCAAAAGATGCAGCCAATCCAAATGAAATTTTACAATTAATCACGCCGGTTAGAACTTACTAA +>lmo0002_2 +ATGAAATTTGTTATTGAGCGTGATCGTCTTGTCCAAGCAGTCAATGAAGTTACTCGTGCCATCTCTGCAAGAACAACGATTCCAATTTTAACGGGGATAAAAATAGTCGTAAATGATGAAGGTGTAACACTAACTGGTAGTGATTCCGATATTTCAATCGAAGCATTTATTCCATTAATTGAAAATGATGAAGTAATTGTAGAAGTGGAGAGTTTTGGTGGAATTGTACTTCAATCAAAATACTTTGGCGATATTGTTCGTCGTTTACCAGAAGAAAATGTAGAAATTGAAGTGACTTCTAACTACCAAACCAACATTAGTTCTGGCCAAGCATCCTTTACATTAAATGGCTTAGATCCAATGGAATATCCTAAATTACCTGAAGTAACAGACGGAAAAACAATTAAAATTCCAATTAATGTACTTAAAAATATTGTTAGACAAACTGTTTTTGCTGTGTCTGCGATTGAAGTTCGTCCAGTACTTACTGGTGTAAACTGGATTATCAAAGAAAATAAACTAAGCGCAGTTGCAACCGATAGTCATCGTCTAGCTTTACGTGAAATACCTCTTGAAACAGACATTGATGAAGAATACAACATTGTTATTCCTGGAAAAAGTTTATCTGAATTAAATAAACTTTTAGATGATGCAAGCGAATCTATTGAAATGACCCTTGCCAACAACCAAATTCTTTTTAAATTAAAAGATTTATTATTTTATTCTCGTTTACTTGAAGGTAGTTATCCAGATACATCTCGATTAATTCCAACTGATACTAAATCAGAATTAGTCATTAATTCCAAAGCATTTTTACAAGCAATTGACCGTGCGTCCCTACTTGCTCGCGAAAATCGTAATAACGTTATTAAATTAATGACGCTAGAAAATGGCCAAGTAGAAGTATCATCCAATTCTCCAGAAGTTGGGAATGTTTCTGAAAATGTCTTCAGCCAAAGTTTTACTGGCGAAGAGATCAAAATATCTTTTAACGGTAAATACATGATGGATGCCCTACGTGCTTTTGAAGGTGATGATATTCAAATTTCCTTCTCCGGTACAATGAGACCATTCGTACTTCGACCAAAAGATGCAGCCAATCCAAATGAAATTTTACAATTAATCACGCCGGTTAGAACTTACTAA +>lmo0002_3 +ATGAAATTTGTTATTGAGCGTGATCGTCTTGTCCAAGCAGTCAATGAAGTTACTCGTGCCATCTCTGCAAGAACAACGATTCCAATTTTAACGGGGATAAAAATAGTCGTAAATGATGAAGGTGTAACACTAACTGGTAGTGATTCCGATATTTCAATCGAAGCATTTATTCCATTAATTGAAAATGATGAAGTAATTGTAGAAGTGGAGAGTTTTGGTGGAATTGTACTTCAATCAAAATACTTTGGTGATATTGTTCGTCGCTTACCAGAAGAAAATGTAGAAATTGAAGTGACTTCTAACTACCAAACCAACATTAGTTCTGGCCAAGCATCCTTTACATTAAATGGCTTAGATCCAATGGAATATCCTAAATTACCTGAAGTAACAGACGGAAAAACAATTAAAATTCCAATTAATGTACTTAAAAATATTGTTAGACAAACTGTTTTTGCTGTGTCTGCGATTGAAGTTCGTCCAGTACTTACTGGTGTAAACTGGATTATCAAAGAAAATAAACTAAGCGCAGTTGCAACCGATAGTCATCGTCTAGCTTTACGTGAAATACCTCTTGAAACAGACATTGATGAAGAATACAACATTGTTATTCCTGGAAAAAGTTTATCTGAATTAAATAAACTTTTAGATGATGCAAGCGAATCTATTGAAATGACCCTTGCCAACAACCAAATTCTTTTTAAATTAAAAGATTTATTATTTTATTCTCGTTTACTTGAAGGTAGTTATCCAGATACATCTCGATTAATTCCAACTGATACTAAATCAGAATTAGTCATTAATTCCAAAGCATTTTTACAAGCAATTGACCGTGCGTCCCTACTTGCTCGCGAAAATCGTAATAACGTTATTAAATTAATGACGCTAGAAAATGGCCAAGTAGAAGTATCATCCAATTCTCCAGAAGTTGGGAATGTTTCTGAAAATGTCTTCAGCCAAAGTTTTACTGGCGAAGAGATCAAAATATCTTTTAACGGTAAATACATGATGGATGCCCTACGTGCTTTTGAAGGTGATGATATTCAAATTTCCTTCTCCGGTACAATGAGACCATTCGTACTTCGACCAAAAGATGCAGCCAATCCAAATGAAATTTTACAATTAATCACGCCGGTTAGAACTTACTAA +>lmo0002_4 +ATGAAATTTGTTATTGAGCGTGATCGTCTTGTCCAAGCAGTCAATGAAGTTACTCGTGCCATCTCTGCAAGAACAACGATTCCAATTTTAACGGGGATAAAAATAGTCGTAAATGATGAAGGTGTAACACTAACTGGTAGTGATTCCGATATTTCCATCGAAGCATTTATTCCATTAATTGAAAATGATGAAGTAATTGTAGAAGTGGAAAGTTTTGGTGGAATTGTACTTCAATCCAAATACTTTGGCGATATTGTTCGTCGTTTACCAGAAGAAAATGTAGAAATTGAAGTAACGTCTAATTACCAAACCAACATTAGTTCTGGTCAAGCCTCCTTTACACTAAACGGCTTAGATCCAATGGAATATCCTAAATTACCTGAAGTAACAGATGGAAAAACAATTAAAATTCCAATTAATGTACTAAAAAATATTGTTAGACAAACTGTTTTTGCTGTTTCTGCTATTGAAGTTCGTCCAGTACTTACTGGTGTAAACTGGATTATCAAAGAAAATAAACTAAGCGCAGTTGCAACCGATAGTCATCGTCTAGCTTTACGTGAAATTCCACTTGAAACAGACATTGATGAAGAATACAACATTGTTATTCCTGGAAAAAGTTTATCTGAATTAAATAAACTTTTAGATGACGCAAGCGAATCTATTGAAATGACCCTTGCCAACAACCAAATTCTTTTTAAATTAAAAGATTTATTATTTTACTCTCGTTTACTTGAAGGTAGTTACCCAGATACATCTCGATTAATTCCAACTGATACTAAATCAGAATTAGTCATTAATTCCAAAGCATTTTTACAAGCAATTGACCGTGCGTCGCTACTTGCTCGCGAAAATCGTAATAACGTTATTAAATTAATGACGCTGGAAAATGGCCAAGTAGAAGTATCCTCCAATTCTCCGGAAGTTGGGAATGTTTCTGAAAATGTCTTCAGCCAAAGTTTTACTGGCGAAGAAATCAAAATATCTTTTAACGGTAAATACATGATGGATGCCCTACGTGCTTTTGAAGGTGATGATATTCAAATTTCCTTCTCAGGTACTATGAGACCATTTGTACTTCGACCAAAAGATGCAGCCAATCCAAATGAAATTTTACAATTAATCACGCCGGTTAGAACTTACTAA diff --git a/test/MLST_listeria/lmo0011.fasta b/test/MLST_listeria/lmo0011.fasta new file mode 100644 index 0000000..709c59c --- /dev/null +++ b/test/MLST_listeria/lmo0011.fasta @@ -0,0 +1,10 @@ +>lmo0011_1 +ATGAAAGCGACAGCCATCGCACACACGAATGTGGCGCTAATTAAATACTGGGGAAAACGCGATGAACACTTGATTCTACCTGCAAACAGTAGTTTATCCTTCACGGTAGATAAATTTTATACAAAGACAACGGTAGAATGGGACGAAAAATTAACCCAAGATACATTTATTTTAAATAATGAACAAAAAACGGATGCAAAAGTAGCTCGTTTTATAGATAAAATGCGGGAGGAATTCGGTATTTCTGCAAAAGCAAAAATAACTTCCGAAAATCACGTTCCAACTGCAGCAGGACTTGCTTCATCTGCTTCTGCATTTGCAGCTCTTGCGCTTGCTGGATCTAATGCAGCTGGTAGAAAAGACACAAAAGAATATATTTCCAGACTGGCTCGTTTCGGGTCTGGTTCTGCTTCTCGTTCCGTTTTCGGTGATTTTGTCATTTGGGAAAAAGGTGAGCTCGCGGATGGTAGTGATTCTTTTGCCGTTCCATTCACGAATAAATTATGTGACAAAATGTCCCTTGTAGTCGCAGTCGTTTCAGACAAAGAAAAGAAAGTTTCCAGTCGTGATGGAATGCGTCTAACCGTTGAAACATCTCCTTTTTTCGAAAACTGGGTTTCTGCTGCTGAAATAGACTTGGAAGAAATGAAACAAGCCATTTTAGACGAAGATTTCATCAAAGTTGGCGAAATTACAGAACGAAACGGAATGAAAATGCATGCGACAACACTTGGTGCAGAGCCGCCATTTACTTATTTTCAACCACAGTCACTCGAAATAATGGATGCTGTTAGAGAACTACGCGAAAATGGTATACCGGCCTATTTTACAATGGATGCTGGACCAAATGTTAAAGTTATTTGTGAGCGTGCAAATGAAAATATCGTAGCAGAGAAGTTGTCAGGTTTGGCTAAAAACGTTCTAATTTGCCACGCTGGTAAGGAAGCGAGTGTTGTATCAGATGAAAAATAA +>lmo0011_2 +ATGAGAGCGACAGCCATCGCACACACGAATGTGGCGCTAATTAAATACTGGGGAAAACGCGATGAACACTTGATTCTACCTGCAAACAGTAGTTTATCCTTCACGGTAGATAAATTTTATACAAAAACAACGGTAGAATGGGACGAAAACTTAGCCCAGGATACATTTATTCTAAATAATGAACAAAAAACGGATGCAAAAGTAGCTCGTTTTATAGATAAAATGCGTGAAGAATTCGGTATTTCAGCAAAAGCAAAAATTACTTCCGAAAATCACGTTCCAACTGCGGCCGGGCTTGCTTCATCGGCTTCTGCATTTGCAGCTCTTGCACTTGCTGGATCTAGCGCTGCTGGCAGAAAAGACACAAAAGAATATATTTCCAGACTGGCTCGTTTCGGGTCTGGTTCTGCTTCTCGTTCCGTTTTCGGAGATTTTGTCATTTGGGAAAAAGGCGAACTCGCGGACGGTAGTGATTCATTTGCAGTACCTTTCACCAACAAATTATGTGACAAAATGTCTCTTGTAGTCGCAGTCGTTTCGGATAAAGAAAAGAAAGTTTCTAGTCGGGATGGAATGCGCCTAACTGTTGAAACATCACCGTTTTTCGAAAAATGGGTTTCTGCTGCTGAAACAGACTTGGAAGAAATGAAACAAGCTATTTTGGATGAAGATTTCATCAAAGTGGGCGAAATCACAGAACGAAACGGAATGAAAATGCATGCGACAACGCTTGGTGCCGAGCCTCCATTTACTTATTTTCAACCGAAGTCCCTTGAAATAATGGATGCTGTTAGAGAATTACGAGAAAATGGTATACCGGCCTATTTTACAATGGATGCTGGTCCAAATGTTAAAGTTATTTGTGAGCGTGAAAATGAAAATATCGTAGCAGATAAGTTGTCAGGTTTGGCTAAAAACGTTCTAATTTGCCACGCTGGTAAGGAAGCGAGTGTTGTATCAGATGAAAAATAA +>lmo0011_3 +ATGAGAGCGACAGCCATCGCACACACGAATGTGGCGCTAATTAAATACTGGGGAAAACGCGATGAACACTTGATTCTACCTGCAAACAGTAGTTTATCCTTCACGGTAGATAAATTTTATACAAAAACAACGGTAGAATGGGACGGAAATTTAGCCCAAGATACATTTATTCTAAATAATGAACACAAAACGGATGCAAAAGTAGCTCGTTTTATAGATAAAATGCGTGAAGAATTCGGTATTTCAGCAAAAGCAAAAATCACTTCCGAAAATCACGTTCCAACTGCAGCCGGGCTTGCTTCATCGGCTTCTGCATTTGCAGCTCTTGCGCTTGCTGGATCTAGCGCTGCTGGCAGAAAAGACACAAAAGAATATATTTCCAGACTGGCTCGTTTCGGGTCTGGTTCTGCTTCTCGTTCCGTTTTCGGAGATTTTGTCATTTGGGAAAAAGGCGAACTCGCGGACGGTAGTGATTCATTTGCAGTACCTTTTACCAACAAATTATGTGACAAAATGTCTCTTGTAGTCGCAGTCGTTTCTGATAAAGAAAAGAAAGTTTCTAGTCGGGATGGAATGCGTTTAACCGTTGAAACATCACCATTTTTTGAAAAATGGGTCTCTGCGGCTGAAACGGATTTAGAAGAAATGAAACAAGCTATTTTGGATGAAAATTTCATCAAAGTTGGCGAAATCACTGAACGAAATGGAATGAAAATGCATGCGACAACGCTTGGTGCCGAGCCGCCATTTACTTATTTTCAACCGAAGTCCCTTGAAATAATGGATGCTGTTAGAGAATTACGAGAAAATGGTATACCGGCCTATTTCACAATGGATGCTGGTCCGAATGTTAAAGTTATTTGTGAGCGTGAAAATGAAAATATCGTAGCAGATAAGTTGTCAGGTTTGGCTAAAAACGTTCTAATTTGCCACGCTGGTAAGGAAGCGAGTGTTGTATCAGATGAAAAATAA +>lmo0011_4 +ATGAAAGCGACAGCCATCGCACACACGAATGTGGCGCTAATTAAATACTGGGGAAAACGCGATGAACACTTGATTCTACCTGCAAACAGTAGTTTATCCTTCACGGTAGATAAATTTTATACAAAAACAACGGTAGAATGGGACGGAAATTTAGCCCAAGATACATTTATTCTAAATAATGAACACAAAACGGATGCAAAAGTAGCTCGTTTTATAGATAAAATGCGTGAAGAATTCGGTATTTCAGCAAAAGCAAAAATCACTTCCGAAAATCACGTTCCAACTGCGGCCGGGCTTGCTTCATCGGCTTCTGCATTTGCAGCTCTTGCACTTGCTGGATCTAGCGCTGCTGGCAGAAAAGACACAAAAGAATATATTTCCAGACTGGCTCGTTTCGGGTCTGGTTCTGCTTCTCGTTCCGTTTTCGGAGATTTTGTCATTTGGGAAAAAGGCGAACTCGCGGACGGTAGTGATTCATTTGCAGTACCTTTCACCAACAAATTATGTGATAAAATGTCTCTTGTAGTCGCAGTCGTTTCGGATAAAGAAAAGAAAGTTTCTAGTCGGGATGGAATGCGCCTAACTGTTGAAACATCACCGTTTTTCGAAAAATGGGTTTCTGCTGCTGAAACAGACTTGGAAGAAATGAAACAAGCTATTTTGGATGAAGATTTCATCAAAGTGGGCGAAATCACAGAACGAAACGGAATGAAAATGCATGCGACAACGCTTGGTGCCGAGCCTCCATTTACTTATTTTCAACCGAAGTCCCTTGAAATAATGGATGCTGTTAGAGAATTACGAGAAAATGGTATACCGGCCTATTTTACAATGGATGCTGGTCCAAATGTTAAAGTTATTTGTGAGCGTGAAAATGAAAATATCGTAGCAGATAAGTTGTCAGGTTTGGCTAAAAACGTTCTAATTTGCCACGCTGGTAAGGAAGCGAGTGTTGTATCAGATGAAAAATAA +>lmo0011_5 +ATGAGAGCGACAGCCATCGCACACACGAATGTGGCGCTAATTAAATACTGGGGAAAACGCGATGAACACTTGATTCTACCTGCAAACAGTAGTTTATCCTTCACGGTAGATAAATTTTATACAAAAACAACGGTAGAATGGGACGGAAATTTAGCCCAAGATACATTTATTCTAAATAATGAACACAAAACGGATGCAAAAGTAGCTCGTTTTATAGATAAAATGCGTGAAGAATTCGGTATTTCAGCAAAAGCAAAAATCACTTCCGAAAATCACGTTCCAACTGCAGCCGGGCTTGCTTCATCGGCTTCTGCATTTGCAGCTCTTGCGCTTGCTGGATCTAGCGCTGCTGGCAGAAAAGACACAAAAGAATATATTTCCAGACTGGCTCGTTTCGGGTCTGGTTCTGCTTCTCGTTCCGTTTTCGGAGATTTTGTCATTTGGGAAAAAGGCGAACTCGCGGACGGTAGTGATTCATTTGCAGTACCTTTCACCAACAAATTATGTGATAAAATGTCTCTTGTAGTCGCAGTCGTTTCGGATAAAGAAAAGAAAGTTTCTAGTCGGGATGGAATGCGCCTAACTGTTGAAACATCACCGTTTTTCGAAAAATGGGTTTCTGCTGCTGAAACAGACTTGGAAGAAATGAAACAAGCTATTTTGGATGAAGATTTCATCAAAGTGGGCGAAATCACAGAACGAAACGGAATGAAAATGCATGCGACAACGCTTGGTGCCGAGCCTCCATTTACTTATTTTCAACCGAAGTCCCTTGAAATAATGGATGCTGTTAGAGAATTACGAGAAAATGGTATACCGGCCTATTTTACAATGGATGCTGGTCCAAATGTTAAAGTTATTTGTGAGCGTGAAAATGAAAATATCGTAGCAGATAAGTTGTCAGGTTTGGCTAAAAACGTTCTAATTTGCCACGCTGGTAAGGAAGCGAGTGTTATATCAGATGAAAAATAA diff --git a/test/MLST_listeria/lmo0019.fasta b/test/MLST_listeria/lmo0019.fasta new file mode 100644 index 0000000..426971f --- /dev/null +++ b/test/MLST_listeria/lmo0019.fasta @@ -0,0 +1,10 @@ +>lmo0019_1 +ATGGGGACTTTTTTTAGTAAATGGGGGAAGTGGATACTTGTCCTTGGATTAGTGTTCAGTGTTTTTAGTGTTTCTACAGCTGGTCAGGCGGCGGCAAAAGAGACTGTGATTAACAAGCAAATGGTAACAACTGCAAGCCTTAATGTTCGTTCAACTAATTCGACTTCTGGGAAAGTTATTGGCTGGCTTAAGAACAATACAAAGTTCAAAGCGATTGCAAAAACATCGAACAACTGGTATCGCTTTAGCTTTAAAGGGAAAAACGGCTACGTATCTGGGAAATATGTAAAAGCCGCAACTGCAACTCCGACTCCAAAACCTCCAACGCCAAAAATTGTGCAAATGAACGTGCCATTAATCGTTCAGCGTCCACAATTACCAACAGGTTGCGAGATTACAAACATTGCGATGATGCTGCGCTATGCTGGAAAAAATGTTGATAAAGTAAAACTTGCCAAAGAAATGAAGCGTCATAAATCCAATCCAAATTATGGTTTTGTCGGGAATCCATTTTCTAAGAGCGGTTGGACGATTTATCCACCGGCTTTAGTTAATCAAGTTAAAAAATATACTGGGAGCGCGAAGAATATGACTGGAACAAATTTAGCTGGTATTAAAAATCAGTTGAATAAAAAACGTCCAGTTGTAGCTTGGGTGAGTAAATTCCACGGTTTTTCCGTTCACGCAATCACCATTACCGGTTATGATAAAAATAATTTTTACTACAATGACAGCTGGTCTGGTCAAAAAAATGCACGAATTTCGCAAAGTTACTTTAATACATGTTGGAGCAAACAAGCAAAACGCGCGATTTCGTATTAA +>lmo0019_2 +ATGGGGAATTCTTTTAGTAAATGGGGGAAATGGATACTTGTCCTTGGGTTAATGTTCAGCGTTTTTAGTGTTTCTACAGCTGGTCAGGCGGCGGCAAAAGAGACTGTGATTAACAAGCAAATGGTAACAACTGCAAGCCTTAATGTTCGTTCAACTAATTCGACTTCTGGGAAAGTTATTGGTTGGCTTAAGAGCAATACAAAGTTCAAAGCGATTGCTAAAACATCGAATAACTGGTATCGATTTAGTTTTAAAGGAAAAAATGGCTACGTATCTGGGAAATACGTCAAGGCCGCAACTGCCGCGCCGGCACCAAAACCTTCAACGCCAAAAATTGTGCAAATGAATGTGCCTTTAATCGTTCAGCGTCCACAATTGCCAACTGGCTGCGAGATTACAAACATTGCGATGATGTTGCGCTACGCTGGGAAAAATGTCGATAAAGTCAAACTTGCCAAAGAAATGAAACGTCATAAATCCAATCCGAATTATGGTTTTGTTGGAAATCCATTCTCTAAGAGTGGCTGGACGATTTATCCACCGGCTTTAGTTAATCAAGTTAAAAAATATACTGGGAGCGCGAAGAATATGACTGGAACAAATTTAGCTGGTATTAAAAATCAGTTGAATAAAAAACGTCCAGTTGTAGCTTGGGTTAGTAATTTCCATGGTTTTTCCGTTCATGCGATTACTATTACAGGTTATGATAAAAATAATTTTTACTATAACGACAGCTGGTCCGGTCAAAAAAATGCACGAATTTCGCAAAGTTATTTTAATACTTGTTGGAGCAAACAAGCAAAACGCTCGATTTCATATTAA +>lmo0019_3 +ATGGGGAATTCTTTTAGTAAATGGGGGAAATGGATACTTGTCCTTGGGTTAATGTTCAGCGTTTTTAGTGTTTCTACAGCTGGTCAGGCGGCGGCAAAAGAGACTGTGATTAACAAGCAAATGGTAACAACTGCAAGCCTTAATGTTCGTTCAACTAATTCGACTTCTGGGAAAGTTATTGGTTGGCTTAAGAGCAATACAAAGTTCAAAGCGATTGCTAAAACATCGAATAACTGGTATCGATTTAGTTTTAAAGGAAAAAATGGCTACGTATCTGGGAAATACGTCAAGGCCGCAACTGCCGCGCCGGCACCAAAACCTTCAACACCAAAAATTGTGCAAATGAATGTGCCTTTAATCGTTCAGCGTCCACAATTGCCAACTGGCTGCGAGATTACAAACATTGCGATGATGCTGCGCTACGCTGGGAAAAATGTCGATAAAGTCAAACTTGCCAAAGAAATGAAACGTCATAAATCCAATCCGAATTATGGTTTTGTTGGAAATCCATTCTCTAAGAGTGGCTGGACGATTTATCCACCGGCTTTAGTTAATCAAGTTAAAAAATATACTGGGAGCGCGAAGAATATGACTGGAACAAATTTAGCTGGTATTAAAAATCAGTTGAATAAAAAACGTCCAGTTGTAGCTTGGGTTAGTAATTTCCATGGTTTTTCCGTTCATGCGATTACTATTACAGGTTATGATAAAAATAATTTTTACTATAACGACAGCTGGTCCGGTCAAAAAAATGCACGAATTTCGCAAAGTTATTTTAATACTTGTTGGAGCAAACAAGCAAAACGCGCGATTTCATATTAA +>lmo0019_4 +ATGGGGAATTCTTTTAGTAAATGGGGGAAATGGATACTTGTCCTTGGGTTAATGTTCAGCGTTTTTAGTGTTTCTACAGCTGGTCAGGCGGCGGCAAAAGAGACTGTGATTAACAAGCAAATGGTAACAACTGCAAGCCTAAATGTTCGTTCAACTAATTCGACTTCTGGGAAAGTTATTGGTTGGCTTAAGAGCAATACAAAGTTCAAAGCGATTGCTAAAACATCGAATAACTGGTATCGATTTAGTTTGAAAGGAAAAAATGGCTACGTATCTGGGAAATACGTCAAGGCCGCAACTGCCGCGCCGGCACCAAAACCTTCAACGCCAAAAATTGTGCAAATGAATGTGCCTTTAATCGTTCAGCGTCCACAATTGCCAACTGGCTGCGAGATTACAAACATTGCGATGATGCTGCGCTACGCTGGGAAAAATGTCGATAAAGTCAAACTTGCCAAAGAAATGAAACGTCATAAATCCAATCCGAATTATGGTTTTGTTGGAAATCCATTCTCTAAGAGTGGCTGGACGATTTATCCACCGGCTTTAGTTAATCAAGTTAAAAAATATACTGGGAGCGCGAAGAATATGACTGGAACAAATTTAGCTGGTATTAAAAATCAGTTGAATAAAAAACGTCCAGTTGTAGCTTGGGTTAGTAATTTCCATGGTTTTTCCGTTCATGCGATTACTATTACAGGTTATGATAAAAATAATTTTTACTATAACAACAGCTGGTCCGGTCAAAAAAATGCACGAATTTCGCAAAGTTATTTTAATACTTGTTGGAGCAAACAAGCAAAACGCGCGATTTCATATTAA +>lmo0019_5 +ATGGGGACTTTTTTTAGTAAATGGGGGAAGTGGATACTTGTCCTTGGATTAGTATTCAGTGTTTTTAGTGTTTCTACAGCTGGTCAGGCGGCGGCAAAAGAGACTGTGATTAACAAGCAAATGGTAACAACTGCAAGCCTTAATGTTCGTTCAACTAATTCGACTTCTGGGAAAGTTATTGGCTGGCTTAAGAACAATACAAAGTTCAAAGCGATTGCAAAAACATCGAACAACTGGTATCGCTTTAGCTTTAAAGGGAAAAACGGCTACGTATCTGGGAAATATGTAAAAGCCGCAACTGCAACTCCGACTCCAAAACCTCCAACGCCAAAAATTGTGCAAATGAACGTGCCATTAATCGTTCAGCGTCCACAATTACCAACAGGTTGCGAGATTACAAACATTGCGATGATGCTGCGCTATGCTGGAAAAAATGTTGATAAAGTAAAACTTGCCAAAGAAATGAAGCGTCATAAATCCAATCCAAATTATGGTTTTGTCGGGAATCCATTTTCTAAGAGCGGTTGGACGATTTATCCACCGGCTTTAGTTAATCAAGTTAAAAAATATACTGGGAGCGCGAAGAATATGACTGGAACAAATTTAGCTGGTATTAAAAATCAGTTGAATAAAAAACGTCCAGTTGTAGCTTGGGTGAGTAAATTCCACGGTTTTTCCGTTCACGCAATCACCATTACCGGTTATGATAAAAATAATTTTTACTACAACGACAGCTGGTCTGGTCAAAAAAATGCACGAATTTCGCAAAGTTACTTTAATACATGTTGGAGCAAACAAGCAAAACGCGCGATTTCGTATTAA diff --git a/test/test.sh b/test/test.sh index f9ba660..f80a206 100755 --- a/test/test.sh +++ b/test/test.sh @@ -129,12 +129,13 @@ echo "Assemblies: $assemblies" echo "Schema: $schema" echo "$PWD" cd -$script_dir/../taranis.py analyze_schema -inputdir $script_dir/MLST_listeria -outputdir analyze_schema_test +echo "Executing taranis analyze_schema" +$script_dir/../taranis.py analyze_schema -i $script_dir/MLST_listeria -o analyze_schema_test --output-allele-annot --cpus 1 -$script_dir/../taranis.py reference_alleles -coregenedir $script_dir/MLST_listeria -outputdir reference_alleles_test +# $script_dir/../taranis.py reference_alleles -coregenedir $script_dir/MLST_listeria -outputdir reference_alleles_test -$script_dir/../taranis.py allele_calling -coregenedir $script_dir/$schema -inputdir $script_dir/$assemblies -refgenome $script_dir/$refgenome -outputdir allele_calling_test -percentlength 20 -refalleles reference_alleles_test -profile $script_dir/$profile +# $script_dir/../taranis.py allele_calling -coregenedir $script_dir/$schema -inputdir $script_dir/$assemblies -refgenome $script_dir/$refgenome -outputdir allele_calling_test -percentlength 20 -refalleles reference_alleles_test -profile $script_dir/$profile -$script_dir/../taranis.py distance_matrix -alleles_matrix allele_calling_test/result.tsv -outputdir distance_matrix_test +# $script_dir/../taranis.py distance_matrix -alleles_matrix allele_calling_test/result.tsv -outputdir distance_matrix_test -echo "ALL DONE. TEST COMPLETED SUCCESSFULLY YOUR INSTALLATION SHOULD BE CORRECT." +echo "ALL DONE. TEST COMPLETED SUCCESSFULLY." From e6a81fb3a486665638f0d5a7f13b77bdba196a7e Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 22 Jan 2024 14:54:07 +0100 Subject: [PATCH 024/214] fixing liting and error testing --- taranis/__main__.py | 1 - taranis/allele_calling.py | 2 +- 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index a8bcc94..a55690a 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -10,7 +10,6 @@ import sys import time -import taranis.prediction import taranis.utils import taranis.analyze_schema import taranis.reference_alleles diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index cee068a..00108d4 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -84,7 +84,7 @@ def assign_allele_type(self, query_seq, allele_name, sample_contig, schema_gene) def search_alleles(self, ref_allele): allele_name = Path(ref_allele).stem schema_gene = os.path.join(self.schema, allele_name + ".fasta") - allele_name = Path(ref_allele).stem + # allele_name = Path(ref_allele).stem # run blast with sample as db and reference allele as query sample_blast_match = self.sample_blast.run_blast(ref_allele) if len(sample_blast_match) > 0: From ccbe7c6851a890d14fd89932413fce37db81a102 Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 22 Jan 2024 15:02:29 +0100 Subject: [PATCH 025/214] Again trying to fix liting and testing --- .github/workflows/tests.yml | 2 +- taranis/analyze_schema.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 9f7d3e6..58d9da5 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -24,4 +24,4 @@ jobs: run: pip install . - name: test analyze schema - run: taranis analyze-schema -i $script_dir/MLST_listeria -o analyze_schema_test -cpus 1 \ No newline at end of file + run: taranis analyze-schema -i $script_dir/MLST_listeria -o analyze_schema_test --cpus 1 \ No newline at end of file diff --git a/taranis/analyze_schema.py b/taranis/analyze_schema.py index 984de3a..ed9e078 100644 --- a/taranis/analyze_schema.py +++ b/taranis/analyze_schema.py @@ -149,7 +149,7 @@ def check_allele_quality(self, prokka_annotation: dict) -> OrderedDict: a_quality[rec_id]["reason"] = "Duplicate allele" if self.remove_duplicated: bad_quality_record.append(rec_id) - # check if sequence is a sub allele + # check if sequence is a sub allele for rec_id, seq_value in allele_seq.items(): unique_seq.remove(seq_value) if seq_value in unique_seq: From 844a2ea369747d6008ceb35310d0a0083f516cc1 Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 22 Jan 2024 16:09:05 +0100 Subject: [PATCH 026/214] modified schema input parameter --- .github/workflows/tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 58d9da5..337b013 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -24,4 +24,4 @@ jobs: run: pip install . - name: test analyze schema - run: taranis analyze-schema -i $script_dir/MLST_listeria -o analyze_schema_test --cpus 1 \ No newline at end of file + run: taranis analyze-schema -i $script_dir/taranis/test/MLST_listeria -o analyze_schema_test --cpus 1 \ No newline at end of file From 7235f223f0d0cbb559ce60c1a72cc9ecbfcfe685 Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 22 Jan 2024 16:37:15 +0100 Subject: [PATCH 027/214] correcting wrong path of schema --- .github/workflows/tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 337b013..ddcd0da 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -24,4 +24,4 @@ jobs: run: pip install . - name: test analyze schema - run: taranis analyze-schema -i $script_dir/taranis/test/MLST_listeria -o analyze_schema_test --cpus 1 \ No newline at end of file + run: taranis analyze-schema -i $script_dir/test/MLST_listeria -o analyze_schema_test --cpus 1 \ No newline at end of file From 70f451e2cc6b04526ee0fb0c62ac0e822c3c5ed3 Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 22 Jan 2024 16:43:09 +0100 Subject: [PATCH 028/214] including echo ls to know which is the working path --- .github/workflows/tests.yml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index ddcd0da..f704bdd 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -23,5 +23,8 @@ jobs: - name: Install taranis run: pip install . + - name: testing directory + run: echo ls + - name: test analyze schema run: taranis analyze-schema -i $script_dir/test/MLST_listeria -o analyze_schema_test --cpus 1 \ No newline at end of file From dd2429c0c1cf268bf0fc818964379679e21ed163 Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 22 Jan 2024 16:47:33 +0100 Subject: [PATCH 029/214] including ls to know which is the working path --- .github/workflows/tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index f704bdd..9c2eb08 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -24,7 +24,7 @@ jobs: run: pip install . - name: testing directory - run: echo ls + run: ls - name: test analyze schema run: taranis analyze-schema -i $script_dir/test/MLST_listeria -o analyze_schema_test --cpus 1 \ No newline at end of file From d4002f6d12a40e025f7ff2bb8d82d90b5c843b1d Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 22 Jan 2024 16:52:07 +0100 Subject: [PATCH 030/214] removing variable --- .github/workflows/tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 9c2eb08..647d40c 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -27,4 +27,4 @@ jobs: run: ls - name: test analyze schema - run: taranis analyze-schema -i $script_dir/test/MLST_listeria -o analyze_schema_test --cpus 1 \ No newline at end of file + run: taranis analyze-schema -i test/MLST_listeria -o analyze_schema_test --cpus 1 \ No newline at end of file From a77d859c2e38ee75ea821fee1ccacf0481f623e6 Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 22 Jan 2024 17:11:31 +0100 Subject: [PATCH 031/214] activate conda environment --- .github/workflows/tests.yml | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 647d40c..03c2e9a 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -17,14 +17,11 @@ jobs: - name: Install dependencies run: $CONDA/bin/conda env update --file environment.yml --name base - # - name: install python packages - # run: pip install -r requirements.txt + - name: activate conda + run: conda activate base - name: Install taranis run: pip install . - - name: testing directory - run: ls - - name: test analyze schema run: taranis analyze-schema -i test/MLST_listeria -o analyze_schema_test --cpus 1 \ No newline at end of file From 85c579a725f9099fa7f79d142c450cb7e7197162 Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 22 Jan 2024 17:16:15 +0100 Subject: [PATCH 032/214] added conda init before activate conda base --- .github/workflows/tests.yml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 03c2e9a..02c77d0 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -17,6 +17,9 @@ jobs: - name: Install dependencies run: $CONDA/bin/conda env update --file environment.yml --name base + - name: init conda + run: conda init + - name: activate conda run: conda activate base From aa893aeb1a52d13d2669d5851e42199d258851b8 Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 22 Jan 2024 18:19:29 +0100 Subject: [PATCH 033/214] activte conda env with the source command and the activate --- .github/workflows/tests.yml | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 02c77d0..ed118a0 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -15,13 +15,10 @@ jobs: uses: actions/checkout@v4 - name: Install dependencies - run: $CONDA/bin/conda env update --file environment.yml --name base - - - name: init conda - run: conda init + run: $CONDA/bin/conda env update --file environment.yml --name taranis_env - name: activate conda - run: conda activate base + run: source $CONDA/etc/profile.d/conda.sh && conda activate taranis_env - name: Install taranis run: pip install . From 38bb76149461dfd3ba5e4ff0422dc05634f0e144 Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 22 Jan 2024 18:48:44 +0100 Subject: [PATCH 034/214] testing how to run prokka --- .github/workflows/tests.yml | 24 +++++++++++------------- 1 file changed, 11 insertions(+), 13 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index ed118a0..1fd9bf0 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -11,17 +11,15 @@ jobs: runs-on: ubuntu-latest steps: - - name: Check out pipeline code - uses: actions/checkout@v4 - - name: Install dependencies - run: $CONDA/bin/conda env update --file environment.yml --name taranis_env - - - name: activate conda - run: source $CONDA/etc/profile.d/conda.sh && conda activate taranis_env - - - name: Install taranis - run: pip install . - - - name: test analyze schema - run: taranis analyze-schema -i test/MLST_listeria -o analyze_schema_test --cpus 1 \ No newline at end of file + - uses: actions/checkout@v4 + - uses: conda-incubator/setup-miniconda@v3 + with: + activate-environment: anaconda-client-env + environment-file: etc/example-environment.yml + python-version: 3.5 + condarc-file: etc/example-condarc.yml + auto-activate-base: false + - run: | + conda list + prokka -h \ No newline at end of file From c56816281cf38209f2b3756fe271fb2ffd9fc556 Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 22 Jan 2024 18:55:57 +0100 Subject: [PATCH 035/214] testing how to run prokka_2 --- .github/workflows/tests.yml | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 1fd9bf0..d217fee 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -16,9 +16,7 @@ jobs: - uses: conda-incubator/setup-miniconda@v3 with: activate-environment: anaconda-client-env - environment-file: etc/example-environment.yml - python-version: 3.5 - condarc-file: etc/example-condarc.yml + environment-file: environment.yml auto-activate-base: false - run: | conda list From cfdf752d11ad45717a8bc4724b595cf13a463349 Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 22 Jan 2024 19:13:18 +0100 Subject: [PATCH 036/214] testing 1 --- .github/workflows/tests.yml | 2 +- environment.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index d217fee..f69cb4a 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -15,7 +15,7 @@ jobs: - uses: actions/checkout@v4 - uses: conda-incubator/setup-miniconda@v3 with: - activate-environment: anaconda-client-env + activate-environment: taranis_env environment-file: environment.yml auto-activate-base: false - run: | diff --git a/environment.yml b/environment.yml index 05b18db..74c2ba4 100644 --- a/environment.yml +++ b/environment.yml @@ -1,4 +1,4 @@ -name: taranis +name: taranis_env channels: - conda-forge - bioconda From 32242b1b3e6ab993b6530f158e891714e08c5990 Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 22 Jan 2024 19:38:05 +0100 Subject: [PATCH 037/214] test2 --- environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index 74c2ba4..f4e5e64 100644 --- a/environment.yml +++ b/environment.yml @@ -4,7 +4,7 @@ channels: - bioconda - defaults dependencies: - - conda-forge::python>=3.10 + - python=3.10 - bioconda::prokka>=1.14 - bioconda::blast>=2.9 - bioconda::mash>=2 From 10c21dd49a5782890429119a3f118da533092611 Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 22 Jan 2024 21:10:53 +0100 Subject: [PATCH 038/214] test3 --- environment.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/environment.yml b/environment.yml index f4e5e64..4892d3e 100644 --- a/environment.yml +++ b/environment.yml @@ -10,5 +10,4 @@ dependencies: - bioconda::mash>=2 - bioconda::prodigal=2.6.3 - pip - - pip : - - -r requirements.txt + From c280e14884d6663d8dbdd312c8deb3e5ff772fe3 Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 22 Jan 2024 21:21:31 +0100 Subject: [PATCH 039/214] test4 --- .github/workflows/tests.yml | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index f69cb4a..671e012 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -11,13 +11,14 @@ jobs: runs-on: ubuntu-latest steps: - - - uses: actions/checkout@v4 - uses: conda-incubator/setup-miniconda@v3 with: activate-environment: taranis_env environment-file: environment.yml auto-activate-base: false - - run: | + - name: Activate Conda environment + run: source $CONDA/etc/profile.d/conda.sh && conda activate taranis_env + - name: check conda + run: | conda list prokka -h \ No newline at end of file From 29248163d7671e90546a752077d089e8cd1702b9 Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 22 Jan 2024 21:24:13 +0100 Subject: [PATCH 040/214] test5 --- .github/workflows/tests.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 671e012..9110019 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -11,6 +11,7 @@ jobs: runs-on: ubuntu-latest steps: + - uses: actions/checkout@v3 - uses: conda-incubator/setup-miniconda@v3 with: activate-environment: taranis_env From 2ae8222cffd5ffc3b6365ff4fa3d93e2983e8b6f Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 22 Jan 2024 21:33:07 +0100 Subject: [PATCH 041/214] test5 --- .github/workflows/tests.yml | 28 ++++++++++++++++------------ 1 file changed, 16 insertions(+), 12 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 9110019..a1e5173 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -6,20 +6,24 @@ on: push: jobs: - test_ci: - name: Code testing + create-conda-env: runs-on: ubuntu-latest - + steps: - - uses: actions/checkout@v3 - - uses: conda-incubator/setup-miniconda@v3 + - name: Checkout repository + uses: actions/checkout@v2 + + - name: Set up Miniconda + uses: conda-incubator/setup-miniconda@v2 with: - activate-environment: taranis_env + activate-environment: myenv environment-file: environment.yml - auto-activate-base: false - - name: Activate Conda environment - run: source $CONDA/etc/profile.d/conda.sh && conda activate taranis_env - - name: check conda + + - name: Verify Conda environment + run: conda env list + + - name: Run your script run: | - conda list - prokka -h \ No newline at end of file + source $CONDA/etc/profile.d/conda.sh + conda activate myenv + prokka -h \ No newline at end of file From c17163576f9b32b9c9c469a9c5d5038dd12491f0 Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 22 Jan 2024 22:07:12 +0100 Subject: [PATCH 042/214] test6 --- .github/workflows/tests.yml | 4 +++- environment.yml | 3 ++- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index a1e5173..8f842c5 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -26,4 +26,6 @@ jobs: run: | source $CONDA/etc/profile.d/conda.sh conda activate myenv - prokka -h \ No newline at end of file + pip install . + taranis analyze-schema -i test/MLST_listeria -o analyze_schema_test --cpus 1 + \ No newline at end of file diff --git a/environment.yml b/environment.yml index 4892d3e..f4e5e64 100644 --- a/environment.yml +++ b/environment.yml @@ -10,4 +10,5 @@ dependencies: - bioconda::mash>=2 - bioconda::prodigal=2.6.3 - pip - + - pip : + - -r requirements.txt From 97003eac79a9661ad90c8abca43efeaa88beb89c Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 22 Jan 2024 22:36:43 +0100 Subject: [PATCH 043/214] added kaleido package --- requirements.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index a6540f6..2165712 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,4 +4,5 @@ click questionary bio scikit-learn -plotly \ No newline at end of file +plotly +kaleido \ No newline at end of file From a81f27d93c5d9defd9db1d686b83b62548700ee1 Mon Sep 17 00:00:00 2001 From: luissian Date: Tue, 23 Jan 2024 19:17:58 +0100 Subject: [PATCH 044/214] Udpated code with latest comment in PR --- .github/workflows/tests.yml | 4 +-- taranis/__main__.py | 2 +- taranis/analyze_schema.py | 9 +++---- taranis/blast.py | 52 ++++++++++++++++++------------------- 4 files changed, 33 insertions(+), 34 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 8f842c5..d04729d 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -16,7 +16,7 @@ jobs: - name: Set up Miniconda uses: conda-incubator/setup-miniconda@v2 with: - activate-environment: myenv + activate-environment: taranis_env environment-file: environment.yml - name: Verify Conda environment @@ -25,7 +25,7 @@ jobs: - name: Run your script run: | source $CONDA/etc/profile.d/conda.sh - conda activate myenv + conda activate taranis_env pip install . taranis analyze-schema -i test/MLST_listeria -o analyze_schema_test --cpus 1 \ No newline at end of file diff --git a/taranis/__main__.py b/taranis/__main__.py index a55690a..c8fb719 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -381,7 +381,7 @@ def allele_calling( try: os.makedirs(output) except OSError as e: - log.info("Unable to create folder at %s because %s", output, e) + log.info("Unable to create folder at %s with error %s", output, e) stderr.print("[red] ERROR. Unable to create folder " + output) sys.exit(1) # Filter fasta files from reference folder diff --git a/taranis/analyze_schema.py b/taranis/analyze_schema.py index ed9e078..331ac29 100644 --- a/taranis/analyze_schema.py +++ b/taranis/analyze_schema.py @@ -8,7 +8,7 @@ from Bio import SeqIO -from collections import OrderedDict +from collections import OrderedDict, defaultdict import taranis.utils @@ -140,11 +140,10 @@ def check_allele_quality(self, prokka_annotation: dict) -> OrderedDict: # get the unique sequences and compare the length with all sequences unique_seq = list(set(list(allele_seq.values()))) if len(unique_seq) < len(allele_seq): - tmp_dict = {} + value_to_keys = defaultdict(list) for rec_id, seq_value in allele_seq.items(): - if seq_value not in tmp_dict: - tmp_dict[seq_value] = 0 - else: + value_to_keys[seq_value].append(rec_id) + if len(value_to_keys[seq_value]) > 1: a_quality[rec_id]["quality"] = "Bad quality" a_quality[rec_id]["reason"] = "Duplicate allele" if self.remove_duplicated: diff --git a/taranis/blast.py b/taranis/blast.py index db97836..39e948e 100644 --- a/taranis/blast.py +++ b/taranis/blast.py @@ -6,7 +6,6 @@ from pathlib import Path from Bio.Blast.Applications import NcbiblastnCommandline -import pdb log = logging.getLogger(__name__) stderr = rich.console.Console( @@ -54,36 +53,37 @@ def create_blastdb(self, file_name, blast_dir): def run_blast( self, - query, - evalue=0.001, - perc_identity=90, - reward=1, - penalty=-2, - gapopen=1, - gapextend=1, - max_target_seqs=10, - max_hsps=10, - num_threads=1, - ): - """_summary_ - blastn -outfmt "6 , qseqid , sseqid , pident , qlen , length , mismatch , gapopen , evalue , bitscore , sstart , send , qstart , qend , sseq , qseq" -query /media/lchapado/Reference_data/proyectos_isciii/taranis/documentos_antiguos/pasteur_schema/lmo0002.fasta -db /media/lchapado/Reference_data/proyectos_isciii/taranis/test/blastdb/RA-L2073_R1/RA-L2073_R1 -evalue 0.001 -penalty -2 -reward 1 -gapopen 1 -gapextend 1 -perc_identity 100 > /media/lchapado/Reference_data/proyectos_isciii/taranis/test/blast_sample_locus002.txt + query: str, + evalue: float = 0.001, + perc_identity: int = 90, + reward: int = 1, + penalty: int = -2, + gapopen: int = 1, + gapextend: int = 1, + max_target_seqs: int = 1000, + max_hsps: int = 10, + num_threads: int = 1, + ) -> list: + """blast command is executed, returning a list of each match found Args: - query (_type_): _description_ - evalue (float, optional): _description_. Defaults to 0.001. - perc_identity (int, optional): _description_. Defaults to 90. - reward (int, optional): _description_. Defaults to 1. - penalty (int, optional): _description_. Defaults to -2. - gapopen (int, optional): _description_. Defaults to 1. - gapextend (int, optional): _description_. Defaults to 1. - max_target_seqs (int, optional): _description_. Defaults to 10. - max_hsps (int, optional): _description_. Defaults to 10. - num_threads (int, optional): _description_. Defaults to 1. + query (str): file path which contains the fasta sequence to query + evalue (float, optional): filtering results on e-value. Defaults to 0.001. + perc_identity (int, optional): percentage of identity. Defaults to 90. + reward (int, optional): value for rewardin. Defaults to 1. + penalty (int, optional): penalty value. Defaults to -2. + gapopen (int, optional): value for gap open. Defaults to 1. + gapextend (int, optional): value for gap extended. Defaults to 1. + max_target_seqs (int, optional): max target to output. Defaults to 1000. + max_hsps (int, optional): max hsps. Defaults to 10. + num_threads (int, optional): number of threads. Defaults to 1. + + Returns: + list: list of strings containing blast results """ blast_parameters = '"6 , qseqid , sseqid , pident , qlen , length , mismatch , gapopen , evalue , bitscore , sstart , send , qstart , qend , sseq , qseq"' - pdb.set_trace() - # db=self.blast_dir, evalue=evalue, perc_identity=perc_identity_ref, reward=reward, penalty=penalty, gapopen=gapopen, gapextend=gapextend, outfmt=blast_parameters, max_target_seqs=max_target_seqs, max_hsps=max_hsps, num_threads=num_threads, query=core_reference_allele_path) cline = NcbiblastnCommandline( + task="blastn", db=self.out_blast_dir, evalue=evalue, perc_identity=perc_identity, From 7a0aea5dfe268b5fa307fce59abe3b6b7ad94eb5 Mon Sep 17 00:00:00 2001 From: luissian Date: Wed, 24 Jan 2024 20:52:57 +0100 Subject: [PATCH 045/214] replace deprecate setup.py for pyproject.toml --- pyproject.toml | 27 +++++++++++++++++++++++++++ setup.py | 36 ------------------------------------ 2 files changed, 27 insertions(+), 36 deletions(-) create mode 100644 pyproject.toml delete mode 100644 setup.py diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..0c1156b --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,27 @@ +[build-system] +requires = ["setuptools"] +build-backend = "setuptools.build_meta" + +[project] +name = "taranis" +version = "3.0.0" +dynamic = ["dependencies"] +requires-python = ">=3.10" + +authors = [ + {name = "Sara Monzon", email = "smonzon@isciii.es"}, + {name = "Luis Chapado", email = "lchapado@externos.isciii.es"}, +] +maintainers = [ + {name = "Luis Chapado", email = "lchapado@externos.isciii.es"} +] +description = "Tools for gene-by-gene allele calling analysis" +readme = "README.md" +license = {file = "LICENSE"} + + +[tool.setuptools.dynamic] +dependencies = {file = ["requirements.txt"]} + +[tool.setuptools.packages.find] +exclude = ["img", "virtualenv"] \ No newline at end of file diff --git a/setup.py b/setup.py deleted file mode 100644 index 14646b6..0000000 --- a/setup.py +++ /dev/null @@ -1,36 +0,0 @@ -#!/usr/bin/env python - -from setuptools import setup, find_packages - -version = "3.0.0" - -with open("README.md") as f: - readme = f.read() - -with open("requirements.txt") as f: - required = f.read().splitlines() - -setup( - name="taranis", - version=version, - description="Tools for gene-by-gene allele calling analysis", - long_description=readme, - long_description_content_type="text/markdown", - keywords=[ - "buisciii", - "bioinformatics", - "pipeline", - "sequencing", - "NGS", - "next generation sequencing", - ], - author="Sara Monzon", - author_email="smonzon@isciii.es", - url="https://github.com/BU-ISCIII/taranis", - license="GNU GENERAL PUBLIC LICENSE v.3", - entry_points={"console_scripts": ["taranis=taranis.__main__:run_taranis"]}, - install_requires=required, - packages=find_packages(exclude=("docs")), - include_package_data=True, - zip_safe=False, -) From 96eb877053108417d170842275f8cca1a237935f Mon Sep 17 00:00:00 2001 From: luissian Date: Thu, 25 Jan 2024 17:01:33 +0100 Subject: [PATCH 046/214] reduce the 2 loop for checking the duplicated and sub allele --- taranis/analyze_schema.py | 28 +++++++++++++++------------- test/MLST_listeria/lmo0011.fasta | 2 ++ test/MLST_listeria/lmo0019.fasta | 2 ++ 3 files changed, 19 insertions(+), 13 deletions(-) diff --git a/taranis/analyze_schema.py b/taranis/analyze_schema.py index 331ac29..4977a43 100644 --- a/taranis/analyze_schema.py +++ b/taranis/analyze_schema.py @@ -139,18 +139,20 @@ def check_allele_quality(self, prokka_annotation: dict) -> OrderedDict: # check if there are duplicated alleles # get the unique sequences and compare the length with all sequences unique_seq = list(set(list(allele_seq.values()))) - if len(unique_seq) < len(allele_seq): - value_to_keys = defaultdict(list) - for rec_id, seq_value in allele_seq.items(): - value_to_keys[seq_value].append(rec_id) - if len(value_to_keys[seq_value]) > 1: - a_quality[rec_id]["quality"] = "Bad quality" - a_quality[rec_id]["reason"] = "Duplicate allele" - if self.remove_duplicated: - bad_quality_record.append(rec_id) - # check if sequence is a sub allele + value_to_keys = defaultdict(list) for rec_id, seq_value in allele_seq.items(): - unique_seq.remove(seq_value) + value_to_keys[seq_value].append(rec_id) + # Check if sequence is already duplicate + if len(value_to_keys[seq_value]) > 1: + a_quality[rec_id]["quality"] = "Bad quality" + a_quality[rec_id]["reason"] = "Duplicate allele" + if self.remove_duplicated: + bad_quality_record.append(rec_id) + # check if sequence is a sub allele + try: + unique_seq.remove(seq_value) + except ValueError: + log.warning("Already deleted same sequence as for record id %s" , record.id) if seq_value in unique_seq: a_quality[rec_id]["quality"] = "Bad quality" a_quality[rec_id]["reason"] = "Sub set allele" @@ -293,8 +295,8 @@ def stats_graphics(stats_folder: str) -> None: # create graphic for alleles/number of genes group_alleles_df = stats_df.groupby( - pd.cut(stats_df["num_alleles"], allele_range) - ).count() + pd.cut(stats_df["num_alleles"], allele_range) + ).count() _ = taranis.utils.create_graphic( graphic_folder, "num_genes_per_allele.png", diff --git a/test/MLST_listeria/lmo0011.fasta b/test/MLST_listeria/lmo0011.fasta index 709c59c..30a2300 100644 --- a/test/MLST_listeria/lmo0011.fasta +++ b/test/MLST_listeria/lmo0011.fasta @@ -8,3 +8,5 @@ ATGAGAGCGACAGCCATCGCACACACGAATGTGGCGCTAATTAAATACTGGGGAAAACGCGATGAACACTTGATTCTACC ATGAAAGCGACAGCCATCGCACACACGAATGTGGCGCTAATTAAATACTGGGGAAAACGCGATGAACACTTGATTCTACCTGCAAACAGTAGTTTATCCTTCACGGTAGATAAATTTTATACAAAAACAACGGTAGAATGGGACGGAAATTTAGCCCAAGATACATTTATTCTAAATAATGAACACAAAACGGATGCAAAAGTAGCTCGTTTTATAGATAAAATGCGTGAAGAATTCGGTATTTCAGCAAAAGCAAAAATCACTTCCGAAAATCACGTTCCAACTGCGGCCGGGCTTGCTTCATCGGCTTCTGCATTTGCAGCTCTTGCACTTGCTGGATCTAGCGCTGCTGGCAGAAAAGACACAAAAGAATATATTTCCAGACTGGCTCGTTTCGGGTCTGGTTCTGCTTCTCGTTCCGTTTTCGGAGATTTTGTCATTTGGGAAAAAGGCGAACTCGCGGACGGTAGTGATTCATTTGCAGTACCTTTCACCAACAAATTATGTGATAAAATGTCTCTTGTAGTCGCAGTCGTTTCGGATAAAGAAAAGAAAGTTTCTAGTCGGGATGGAATGCGCCTAACTGTTGAAACATCACCGTTTTTCGAAAAATGGGTTTCTGCTGCTGAAACAGACTTGGAAGAAATGAAACAAGCTATTTTGGATGAAGATTTCATCAAAGTGGGCGAAATCACAGAACGAAACGGAATGAAAATGCATGCGACAACGCTTGGTGCCGAGCCTCCATTTACTTATTTTCAACCGAAGTCCCTTGAAATAATGGATGCTGTTAGAGAATTACGAGAAAATGGTATACCGGCCTATTTTACAATGGATGCTGGTCCAAATGTTAAAGTTATTTGTGAGCGTGAAAATGAAAATATCGTAGCAGATAAGTTGTCAGGTTTGGCTAAAAACGTTCTAATTTGCCACGCTGGTAAGGAAGCGAGTGTTGTATCAGATGAAAAATAA >lmo0011_5 ATGAGAGCGACAGCCATCGCACACACGAATGTGGCGCTAATTAAATACTGGGGAAAACGCGATGAACACTTGATTCTACCTGCAAACAGTAGTTTATCCTTCACGGTAGATAAATTTTATACAAAAACAACGGTAGAATGGGACGGAAATTTAGCCCAAGATACATTTATTCTAAATAATGAACACAAAACGGATGCAAAAGTAGCTCGTTTTATAGATAAAATGCGTGAAGAATTCGGTATTTCAGCAAAAGCAAAAATCACTTCCGAAAATCACGTTCCAACTGCAGCCGGGCTTGCTTCATCGGCTTCTGCATTTGCAGCTCTTGCGCTTGCTGGATCTAGCGCTGCTGGCAGAAAAGACACAAAAGAATATATTTCCAGACTGGCTCGTTTCGGGTCTGGTTCTGCTTCTCGTTCCGTTTTCGGAGATTTTGTCATTTGGGAAAAAGGCGAACTCGCGGACGGTAGTGATTCATTTGCAGTACCTTTCACCAACAAATTATGTGATAAAATGTCTCTTGTAGTCGCAGTCGTTTCGGATAAAGAAAAGAAAGTTTCTAGTCGGGATGGAATGCGCCTAACTGTTGAAACATCACCGTTTTTCGAAAAATGGGTTTCTGCTGCTGAAACAGACTTGGAAGAAATGAAACAAGCTATTTTGGATGAAGATTTCATCAAAGTGGGCGAAATCACAGAACGAAACGGAATGAAAATGCATGCGACAACGCTTGGTGCCGAGCCTCCATTTACTTATTTTCAACCGAAGTCCCTTGAAATAATGGATGCTGTTAGAGAATTACGAGAAAATGGTATACCGGCCTATTTTACAATGGATGCTGGTCCAAATGTTAAAGTTATTTGTGAGCGTGAAAATGAAAATATCGTAGCAGATAAGTTGTCAGGTTTGGCTAAAAACGTTCTAATTTGCCACGCTGGTAAGGAAGCGAGTGTTATATCAGATGAAAAATAA +>lm00011_5_dup +ATGAGAGCGACAGCCATCGCACACACGAATGTGGCGCTAATTAAATACTGGGGAAAACGCGATGAACACTTGATTCTACCTGCAAACAGTAGTTTATCCTTCACGGTAGATAAATTTTATACAAAAACAACGGTAGAATGGGACGGAAATTTAGCCCAAGATACATTTATTCTAAATAATGAACACAAAACGGATGCAAAAGTAGCTCGTTTTATAGATAAAATGCGTGAAGAATTCGGTATTTCAGCAAAAGCAAAAATCACTTCCGAAAATCACGTTCCAACTGCAGCCGGGCTTGCTTCATCGGCTTCTGCATTTGCAGCTCTTGCGCTTGCTGGATCTAGCGCTGCTGGCAGAAAAGACACAAAAGAATATATTTCCAGACTGGCTCGTTTCGGGTCTGGTTCTGCTTCTCGTTCCGTTTTCGGAGATTTTGTCATTTGGGAAAAAGGCGAACTCGCGGACGGTAGTGATTCATTTGCAGTACCTTTCACCAACAAATTATGTGATAAAATGTCTCTTGTAGTCGCAGTCGTTTCGGATAAAGAAAAGAAAGTTTCTAGTCGGGATGGAATGCGCCTAACTGTTGAAACATCACCGTTTTTCGAAAAATGGGTTTCTGCTGCTGAAACAGACTTGGAAGAAATGAAACAAGCTATTTTGGATGAAGATTTCATCAAAGTGGGCGAAATCACAGAACGAAACGGAATGAAAATGCATGCGACAACGCTTGGTGCCGAGCCTCCATTTACTTATTTTCAACCGAAGTCCCTTGAAATAATGGATGCTGTTAGAGAATTACGAGAAAATGGTATACCGGCCTATTTTACAATGGATGCTGGTCCAAATGTTAAAGTTATTTGTGAGCGTGAAAATGAAAATATCGTAGCAGATAAGTTGTCAGGTTTGGCTAAAAACGTTCTAATTTGCCACGCTGGTAAGGAAGCGAGTGTTATATCAGATGAAAAATAA diff --git a/test/MLST_listeria/lmo0019.fasta b/test/MLST_listeria/lmo0019.fasta index 426971f..ee1b8a0 100644 --- a/test/MLST_listeria/lmo0019.fasta +++ b/test/MLST_listeria/lmo0019.fasta @@ -8,3 +8,5 @@ ATGGGGAATTCTTTTAGTAAATGGGGGAAATGGATACTTGTCCTTGGGTTAATGTTCAGCGTTTTTAGTGTTTCTACAGC ATGGGGAATTCTTTTAGTAAATGGGGGAAATGGATACTTGTCCTTGGGTTAATGTTCAGCGTTTTTAGTGTTTCTACAGCTGGTCAGGCGGCGGCAAAAGAGACTGTGATTAACAAGCAAATGGTAACAACTGCAAGCCTAAATGTTCGTTCAACTAATTCGACTTCTGGGAAAGTTATTGGTTGGCTTAAGAGCAATACAAAGTTCAAAGCGATTGCTAAAACATCGAATAACTGGTATCGATTTAGTTTGAAAGGAAAAAATGGCTACGTATCTGGGAAATACGTCAAGGCCGCAACTGCCGCGCCGGCACCAAAACCTTCAACGCCAAAAATTGTGCAAATGAATGTGCCTTTAATCGTTCAGCGTCCACAATTGCCAACTGGCTGCGAGATTACAAACATTGCGATGATGCTGCGCTACGCTGGGAAAAATGTCGATAAAGTCAAACTTGCCAAAGAAATGAAACGTCATAAATCCAATCCGAATTATGGTTTTGTTGGAAATCCATTCTCTAAGAGTGGCTGGACGATTTATCCACCGGCTTTAGTTAATCAAGTTAAAAAATATACTGGGAGCGCGAAGAATATGACTGGAACAAATTTAGCTGGTATTAAAAATCAGTTGAATAAAAAACGTCCAGTTGTAGCTTGGGTTAGTAATTTCCATGGTTTTTCCGTTCATGCGATTACTATTACAGGTTATGATAAAAATAATTTTTACTATAACAACAGCTGGTCCGGTCAAAAAAATGCACGAATTTCGCAAAGTTATTTTAATACTTGTTGGAGCAAACAAGCAAAACGCGCGATTTCATATTAA >lmo0019_5 ATGGGGACTTTTTTTAGTAAATGGGGGAAGTGGATACTTGTCCTTGGATTAGTATTCAGTGTTTTTAGTGTTTCTACAGCTGGTCAGGCGGCGGCAAAAGAGACTGTGATTAACAAGCAAATGGTAACAACTGCAAGCCTTAATGTTCGTTCAACTAATTCGACTTCTGGGAAAGTTATTGGCTGGCTTAAGAACAATACAAAGTTCAAAGCGATTGCAAAAACATCGAACAACTGGTATCGCTTTAGCTTTAAAGGGAAAAACGGCTACGTATCTGGGAAATATGTAAAAGCCGCAACTGCAACTCCGACTCCAAAACCTCCAACGCCAAAAATTGTGCAAATGAACGTGCCATTAATCGTTCAGCGTCCACAATTACCAACAGGTTGCGAGATTACAAACATTGCGATGATGCTGCGCTATGCTGGAAAAAATGTTGATAAAGTAAAACTTGCCAAAGAAATGAAGCGTCATAAATCCAATCCAAATTATGGTTTTGTCGGGAATCCATTTTCTAAGAGCGGTTGGACGATTTATCCACCGGCTTTAGTTAATCAAGTTAAAAAATATACTGGGAGCGCGAAGAATATGACTGGAACAAATTTAGCTGGTATTAAAAATCAGTTGAATAAAAAACGTCCAGTTGTAGCTTGGGTGAGTAAATTCCACGGTTTTTCCGTTCACGCAATCACCATTACCGGTTATGATAAAAATAATTTTTACTACAACGACAGCTGGTCTGGTCAAAAAAATGCACGAATTTCGCAAAGTTACTTTAATACATGTTGGAGCAAACAAGCAAAACGCGCGATTTCGTATTAA +>lmo0019_4_sub_seq +ATGGGGAATTCTTTTAGTAAATGGGGGAAATGGATACTTGTCCTTGGGTTAATGTTCAGCGTTTTTAGTGTTTCTACAGCTGGTCAGGCGGCGGCAAAAGAGACTGTGATTAACAAGCAAATGGTAACAACTGCAAGCCTAAATGTTCGTTCAACTAATTCGACTTCTGGGAAAGTTATTGGTTGGCTTAAGAGCAATACAAAGTTCAAAGCGATTGCTAAAACATCGAATAACTGGTATCGATTTAGTTTGAAAGGAAAAAATGGCTACGTATCTGGGAAATACGTCAAGGCCGCAACTGCCGCGCCGGCACCAAAACCTTCAACGCCAAAAATTGTGCAAATGAATGTGCCTTTAATCGTTCAGCGTCCACAATTGCCAACTGGCTGCGAGATTACAAACATTGCGATGATGCTGCGCTACGCTGGGAAAAATGTCGATAAAGTCAAACTTGCCAAAGAAATGAAACGTCATAAATCCAATCCGAATTATGGTTTTGTTGGAAATCCATTCTCTAAGAGTGGCTGGACGATTTATCCACCGGCTTTAGTTAATCAAGTTAAAAAATATACTGGGAGCGCGAAGAATATGACTGGAACAAATTTAGCTGGTATTAAAAATCAGTTGAATAAAAAAGTAGCTTGGGTTAGTAATTTCCATGGTTTTTCCGTTCATGCGATTACTATTACAGGTTATGATAAAAATAATTTTTACTATAACAACAGCTGGTCCGGTCAAAAAAATGCACGAATTTCGCAAAGTTATTTTAATACTTGTTGGAGCAAACAAGCAAAACGCGCGATTTCATATTAA From 30844af6efa3a0f1fbc0b3746c5b1df4da45ff9c Mon Sep 17 00:00:00 2001 From: luissian Date: Thu, 25 Jan 2024 17:02:36 +0100 Subject: [PATCH 047/214] remove unnecesary comments --- taranis/__main__.py | 32 ++++---------------------------- 1 file changed, 4 insertions(+), 28 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index c8fb719..da1ed40 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -28,7 +28,6 @@ def run_taranis(): rich.traceback.install(console=stderr, width=200, word_wrap=True, extra_lines=1) # Print taranis header - # stderr.print("\n[green]{},--.[grey39]/[green],-.".format(" " * 42), highlight=False) stderr.print( "[blue] ______ ___ ___ ", highlight=False, @@ -55,7 +54,6 @@ def run_taranis(): stderr.print( "\n" "[grey39] Taranis version {}".format(__version__), highlight=False ) - # Lanch the click cli taranis_cli() @@ -92,7 +90,6 @@ def decorator(f): cmd = super(CustomHelpOrder, self).command(*args, **kwargs)(f) help_priorities[cmd.name] = help_priority return cmd - return decorator @@ -123,13 +120,6 @@ def taranis_cli(verbose, log_file): ) log.addHandler(log_fh) - -# Analyze schema -# taranis analyze-schema -i /media/lchapado/Reference_data/proyectos_isciii/taranis/documentos_antiguos/pasteur_schema -o /media/lchapado/Reference_data/proyectos_isciii/taranis/test/analyze_schema -# testing data for analyze schema -# taranis analyze-schema -i /media/lchapado/Reference_data/proyectos_isciii/taranis/taranis_testing_data/listeria_testing_schema -o /media/lchapado/Reference_data/proyectos_isciii/taranis/test/analyze_schema - - @taranis_cli.command(help_priority=1) @click.option( "-i", @@ -211,16 +201,6 @@ def analyze_schema( ): schema_files = taranis.utils.get_files_in_folder(inputdir, "fasta") - """ TODO.DELETE CODE - schema_analyze = [] - for schema_file in schema_files: - schema_obj = taranis.analyze_schema.AnalyzeSchema(schema_file, output, remove_subset, remove_duplicated, remove_no_cds, genus, species, usegenus) - schema_analyze.append(schema_obj.analyze_allele_in_schema()) - import pdb; pdb.set_trace() - _ = taranis.analyze_schema.collect_statistics(schema_analyze, output, output_allele_annot) - sys.exit(0) - # for schema_file in schema_files: - """ results = [] start = time.perf_counter() with concurrent.futures.ProcessPoolExecutor(max_workers=cpus) as executor: @@ -242,6 +222,9 @@ def analyze_schema( for future in concurrent.futures.as_completed(futures): results.append(future.result()) _ = taranis.analyze_schema.collect_statistics(results, output, output_allele_annot) + + _ = taranis.analyze_schema.collect_statistics(schema_analyze, output, output_allele_annot) + finish = time.perf_counter() print(f"Schema analyze finish in {round((finish-start)/60, 2)} minutes") @@ -268,8 +251,7 @@ def reference_alleles( schema: str, output: str, ): - # taranis reference-alleles -s ../../documentos_antiguos/datos_prueba/schema_1_locus/ -o ../../new_taranis_result_code - # taranis reference-alleles -s /media/lchapado/Reference_data/proyectos_isciii/taranis/taranis_testing_data/listeria_testing_schema/ -o /media/lchapado/Reference_data/proyectos_isciii/taranis/test/reference_alleles + schema_files = taranis.utils.get_files_in_folder(schema, "fasta") # Check if output folder exists @@ -296,12 +278,6 @@ def reference_alleles( _ = ref_alleles.create_ref_alleles() -# Allele calling -# taranis -l ../../test/taranis.log allele-calling -s ../../documentos_antiguos/datos_prueba/schema_test/ -r ../../documentos_antiguos/datos_prueba/reference_alleles/ -g ../../taranis_data/listeria_genoma_referencia/listeria.fasta -a ../../taranis_data/listeria_sampled/RA-L2073_R1.fasta -o ../../test/ -# taranis allele-calling -s ../../documentos_antiguos/datos_prueba/schema_test/ -r ../../documentos_antiguos/datos_prueba/reference_alleles/ -g ../../taranis_data/listeria_genoma_referencia/listeria.fasta -a ../../taranis_data/listeria_sampled/RA-L2073_R1.fasta -o ../../test/ -# taranis allele-calling -s ../../documentos_antiguos/datos_prueba/schema_test/ -r ../../documentos_antiguos/datos_prueba/reference_alleles/ -g ../../taranis_data/listeria_genoma_referencia/listeria.fasta -a ../../taranis_data/muestras_listeria_servicio_fasta/3789/assembly.fasta -o ../../test/ - - @taranis_cli.command(help_priority=3) @click.option( "-s", From fe8f691e681bfb2f25c3a5993d7756d4fe1f1e79 Mon Sep 17 00:00:00 2001 From: luissian Date: Thu, 25 Jan 2024 17:19:23 +0100 Subject: [PATCH 048/214] testing new pyproject.toml file --- .github/workflows/tests.yml | 4 +++- environment.yml | 1 + pyproject.toml | 29 ++++++++++------------------- 3 files changed, 14 insertions(+), 20 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index d04729d..9a8095c 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -6,7 +6,7 @@ on: push: jobs: - create-conda-env: + test_analyze_schema: runs-on: ubuntu-latest steps: @@ -26,6 +26,8 @@ jobs: run: | source $CONDA/etc/profile.d/conda.sh conda activate taranis_env + - name: testung analyze schema + run: pip install . taranis analyze-schema -i test/MLST_listeria -o analyze_schema_test --cpus 1 \ No newline at end of file diff --git a/environment.yml b/environment.yml index f4e5e64..a2ef128 100644 --- a/environment.yml +++ b/environment.yml @@ -5,6 +5,7 @@ channels: - defaults dependencies: - python=3.10 + - conda-forge::poetry=1.7.1 - bioconda::prokka>=1.14 - bioconda::blast>=2.9 - bioconda::mash>=2 diff --git a/pyproject.toml b/pyproject.toml index 0c1156b..2389545 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,27 +1,18 @@ -[build-system] -requires = ["setuptools"] -build-backend = "setuptools.build_meta" - -[project] +[tool.poetry] name = "taranis" version = "3.0.0" -dynamic = ["dependencies"] -requires-python = ">=3.10" - -authors = [ - {name = "Sara Monzon", email = "smonzon@isciii.es"}, - {name = "Luis Chapado", email = "lchapado@externos.isciii.es"}, -] -maintainers = [ - {name = "Luis Chapado", email = "lchapado@externos.isciii.es"} -] description = "Tools for gene-by-gene allele calling analysis" readme = "README.md" -license = {file = "LICENSE"} +authors = ["Sara Monzon "] +license = "GNU-3.0" +[tool.poetry.dependencies] +python = "^3.10" + +[tool.poetry.scripts] +taranis = "taranis.__main__:run_taranis" -[tool.setuptools.dynamic] -dependencies = {file = ["requirements.txt"]} [tool.setuptools.packages.find] -exclude = ["img", "virtualenv"] \ No newline at end of file +exclude = ["img", "virtualenv"] + From 792e3d49c4ac18657500ad1a29613a24b2311c03 Mon Sep 17 00:00:00 2001 From: luissian Date: Thu, 25 Jan 2024 17:27:04 +0100 Subject: [PATCH 049/214] fixing liting and update test --- .github/workflows/tests.yml | 4 ++-- taranis/__main__.py | 5 ++--- taranis/analyze_schema.py | 8 +++++--- 3 files changed, 9 insertions(+), 8 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 9a8095c..d8bb01c 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -28,6 +28,6 @@ jobs: conda activate taranis_env - name: testung analyze schema run: - pip install . - taranis analyze-schema -i test/MLST_listeria -o analyze_schema_test --cpus 1 + poetry install + taranis analyze-schema -i test/MLST_listeria -o analyze_schema_test --cpus 1 --output-allele-annot --remove-no-cds --remove-duplicated --remove-subset \ No newline at end of file diff --git a/taranis/__main__.py b/taranis/__main__.py index da1ed40..6243bba 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -90,6 +90,7 @@ def decorator(f): cmd = super(CustomHelpOrder, self).command(*args, **kwargs)(f) help_priorities[cmd.name] = help_priority return cmd + return decorator @@ -120,6 +121,7 @@ def taranis_cli(verbose, log_file): ) log.addHandler(log_fh) + @taranis_cli.command(help_priority=1) @click.option( "-i", @@ -223,8 +225,6 @@ def analyze_schema( results.append(future.result()) _ = taranis.analyze_schema.collect_statistics(results, output, output_allele_annot) - _ = taranis.analyze_schema.collect_statistics(schema_analyze, output, output_allele_annot) - finish = time.perf_counter() print(f"Schema analyze finish in {round((finish-start)/60, 2)} minutes") @@ -251,7 +251,6 @@ def reference_alleles( schema: str, output: str, ): - schema_files = taranis.utils.get_files_in_folder(schema, "fasta") # Check if output folder exists diff --git a/taranis/analyze_schema.py b/taranis/analyze_schema.py index 4977a43..6cb4206 100644 --- a/taranis/analyze_schema.py +++ b/taranis/analyze_schema.py @@ -152,7 +152,9 @@ def check_allele_quality(self, prokka_annotation: dict) -> OrderedDict: try: unique_seq.remove(seq_value) except ValueError: - log.warning("Already deleted same sequence as for record id %s" , record.id) + log.warning( + "Already deleted same sequence as for record id %s", record.id + ) if seq_value in unique_seq: a_quality[rec_id]["quality"] = "Bad quality" a_quality[rec_id]["reason"] = "Sub set allele" @@ -295,8 +297,8 @@ def stats_graphics(stats_folder: str) -> None: # create graphic for alleles/number of genes group_alleles_df = stats_df.groupby( - pd.cut(stats_df["num_alleles"], allele_range) - ).count() + pd.cut(stats_df["num_alleles"], allele_range) + ).count() _ = taranis.utils.create_graphic( graphic_folder, "num_genes_per_allele.png", From b6d5841729df912478bdc0a2bc8d9c1297a4af3e Mon Sep 17 00:00:00 2001 From: luissian Date: Thu, 25 Jan 2024 17:36:14 +0100 Subject: [PATCH 050/214] update installation taranis for testing --- .github/workflows/tests.yml | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index d8bb01c..24cea8c 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -22,12 +22,13 @@ jobs: - name: Verify Conda environment run: conda env list - - name: Run your script + - name: Activate env and install taranis run: | source $CONDA/etc/profile.d/conda.sh conda activate taranis_env - - name: testung analyze schema - run: poetry install + + - name: testing analyze schema + run: taranis analyze-schema -i test/MLST_listeria -o analyze_schema_test --cpus 1 --output-allele-annot --remove-no-cds --remove-duplicated --remove-subset \ No newline at end of file From 09732f6481cea3be1d50d7c0839051efb73df2f1 Mon Sep 17 00:00:00 2001 From: luissian Date: Thu, 25 Jan 2024 17:40:02 +0100 Subject: [PATCH 051/214] include all command in the same run --- .github/workflows/tests.yml | 3 --- 1 file changed, 3 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 24cea8c..dad0a2c 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -27,8 +27,5 @@ jobs: source $CONDA/etc/profile.d/conda.sh conda activate taranis_env poetry install - - - name: testing analyze schema - run: taranis analyze-schema -i test/MLST_listeria -o analyze_schema_test --cpus 1 --output-allele-annot --remove-no-cds --remove-duplicated --remove-subset \ No newline at end of file From 742aed3c0717c2f6bbe08c4ad2ff804c3dc12f44 Mon Sep 17 00:00:00 2001 From: luissian Date: Thu, 25 Jan 2024 21:03:11 +0100 Subject: [PATCH 052/214] Split the number of cpus used for prokka and app --- taranis/__main__.py | 10 +++++++++- taranis/analyze_schema.py | 20 +++++++++++++++----- taranis/utils.py | 5 +++++ 3 files changed, 29 insertions(+), 6 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index 6243bba..facde63 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -204,8 +204,15 @@ def analyze_schema( schema_files = taranis.utils.get_files_in_folder(inputdir, "fasta") results = [] + max_cpus = taranis.utils.cpus_available() + if cpus > max_cpus: + stderr.print("[red] Number of CPUs bigger than the CPUs available") + stderr.print("Running code with ", max_cpus) + cpus = max_cpus + # Keeping 3 threads for running prokka for each parallel process + using_cpus, prokka_cpus = [cpus // 3, 3] if cpus // 3 >= 1 else [1, 1] start = time.perf_counter() - with concurrent.futures.ProcessPoolExecutor(max_workers=cpus) as executor: + with concurrent.futures.ThreadPoolExecutor(max_workers=using_cpus) as executor: futures = [ executor.submit( taranis.analyze_schema.parallel_execution, @@ -217,6 +224,7 @@ def analyze_schema( genus, species, usegenus, + prokka_cpus, ) for schema_file in schema_files ] diff --git a/taranis/analyze_schema.py b/taranis/analyze_schema.py index 6cb4206..25ae5bf 100644 --- a/taranis/analyze_schema.py +++ b/taranis/analyze_schema.py @@ -33,6 +33,7 @@ def __init__( genus: str, species: str, usegenus: str, + prokka_cpus: int, ) -> "AnalyzeSchema": """AnalyzeSchema instance creation @@ -46,6 +47,7 @@ def __init__( genus (str): Genus name for Prokka schema genes annotation species (str): Species name for Prokka schema genes annotation usegenus (str): genus-specific BLAST databases for Prokka + prokka_cpus (int): number of cpus used in prokka Returns: AnalyzeSchema: Instance of the created class @@ -59,6 +61,7 @@ def __init__( self.genus = genus self.species = species self.usegenus = usegenus + self.prokka_cpus = prokka_cpus def check_allele_quality(self, prokka_annotation: dict) -> OrderedDict: """Each allele in the locus file is analyzed its quality by checking @@ -76,6 +79,7 @@ def check_allele_quality(self, prokka_annotation: dict) -> OrderedDict: Returns: OrderedDict: Quality information for each allele """ + log.debug("Processing allele quality for %s", self.allele_name) a_quality = OrderedDict() allele_seq = {} bad_quality_record = [] @@ -135,7 +139,7 @@ def check_allele_quality(self, prokka_annotation: dict) -> OrderedDict: and a_quality[record.id]["quality"] == "Bad quality" ): bad_quality_record.append(record.id) - + log.debug("Checking bad quality of alleles for %s", self.allele_name) # check if there are duplicated alleles # get the unique sequences and compare the length with all sequences unique_seq = list(set(list(allele_seq.values()))) @@ -188,7 +192,7 @@ def fetch_statistics_from_alleles(self, a_quality: dict) -> dict: Returns: dict: statistics information for all alleles """ - # POSIBLE_BAD_QUALITY = ["not a start codon", "not a stop codon", "Extra in frame stop codon", "is not a multiple of three", "Duplicate allele", "Sub set allele"] + stderr.print("Processing quality statistics") record_data = {} bad_quality_reason = {} a_length = [] @@ -232,11 +236,13 @@ def analyze_allele_in_schema(self) -> list[dict, dict]: list[dict, dict]: _description_ """ allele_data = {} + log.info("Analizing allele %s", self.allele_name) # run annotations prokka_folder = os.path.join(self.output, "prokka", self.allele_name) anotation_files = taranis.utils.create_annotation_files( - self.schema_allele, prokka_folder, self.allele_name + self.schema_allele, prokka_folder, self.allele_name, cpus=self.prokka_cpus ) + log.info("Fetching anotation information for %s", self.allele_name) prokka_annotation = taranis.utils.read_annotation_file(anotation_files + ".gff") # Perform quality @@ -254,6 +260,7 @@ def parallel_execution( genus: str, species: str, usegenus: str, + prokka_cpus: int = 3, ) -> list[dict, dict]: """_summary_ @@ -266,6 +273,7 @@ def parallel_execution( genus (str): Genus name for Prokka schema genes annotation species (str): Species name for Prokka schema genes annotation usegenus (str): genus-specific BLAST databases for Prokka + prokka_cpus (int): number of cpus used to execute prokka. Default 3 Returns: list[dict, dict]:: _description_ @@ -279,6 +287,7 @@ def parallel_execution( genus, species, usegenus, + prokka_cpus, ) return schema_obj.analyze_allele_in_schema() @@ -291,13 +300,15 @@ def stats_graphics(stats_folder: str) -> None: Args: stats_folder (str): folder path to store graphic """ + stderr.print("Creating graphics") + log.info("Creating graphics") allele_range = [0, 300, 600, 1000, 1500] graphic_folder = os.path.join(stats_folder, "graphics") _ = taranis.utils.create_new_folder(graphic_folder) # create graphic for alleles/number of genes group_alleles_df = stats_df.groupby( - pd.cut(stats_df["num_alleles"], allele_range) + pd.cut(stats_df["num_alleles"], allele_range), observed=False ).count() _ = taranis.utils.create_graphic( graphic_folder, @@ -385,7 +396,6 @@ def stats_graphics(stats_folder: str) -> None: + ",".join(data_field) + "\n" ) - _ = taranis.utils.write_data_to_compress_filed( out_folder, "allele_annotation.csv", ann_data ) diff --git a/taranis/utils.py b/taranis/utils.py index bbe6961..8f9c6c4 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -6,6 +6,7 @@ import glob import io import logging +import multiprocessing import numpy as np import pandas as pd import plotly.graph_objects as go @@ -62,6 +63,10 @@ def rich_force_colors(): ] +def cpus_available() -> int: + return multiprocessing.cpu_count() + + def get_seq_direction(allele_sequence): # check direction if ( From 999f80b54d47aa2b5a6613ee56fb0cfbed6830dc Mon Sep 17 00:00:00 2001 From: luissian Date: Sun, 28 Jan 2024 16:10:48 +0100 Subject: [PATCH 053/214] commit to start with reference allele feature --- .github/workflows/tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index dad0a2c..05c1b3a 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -11,7 +11,7 @@ jobs: steps: - name: Checkout repository - uses: actions/checkout@v2 + uses: actions/checkout@v4 - name: Set up Miniconda uses: conda-incubator/setup-miniconda@v2 From d2b4c2a3aeda8d7bfaff2c9bf9b55898042860c6 Mon Sep 17 00:00:00 2001 From: luissian Date: Sun, 28 Jan 2024 16:17:21 +0100 Subject: [PATCH 054/214] Including poetry.lock in gitignore --- .gitignore | 1 + taranis/reference_alleles.py | 74 ++++++++++++++++++++++++++++++++++-- 2 files changed, 72 insertions(+), 3 deletions(-) diff --git a/.gitignore b/.gitignore index 423e48b..56f7103 100644 --- a/.gitignore +++ b/.gitignore @@ -29,6 +29,7 @@ wheels/ .installed.cfg *.egg MANIFEST +poetry.lock # PyInstaller # Usually these files are written by a python script from a template diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index b22697e..1358103 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -9,8 +9,12 @@ # from Bio import SeqIO from Bio.Seq import Seq +from Bio import SeqIO from Bio.Blast.Applications import NcbiblastnCommandline +from collections import OrderedDict +from pathlib import Path import taranis.utils +import taranis.blast import pdb @@ -75,6 +79,7 @@ def check_locus_quality(self): def create_matrix_distance(self): # f_name = os.path.basename(self.fasta_file).split('.')[0] f_name = os.path.basename(self.fasta_file) + allele_name = Path(self.fasta_file).stem mash_folder = os.path.join(self.output, "mash") # _ = taranis.utils.write_fasta_file(mash_folder, self.selected_locus, multiple_files=True, extension=False) # save directory to return after mash @@ -90,22 +95,84 @@ def create_matrix_distance(self): ) # Get pairwise allele sequences mash distances # mash_distance_command = ["mash", "dist", sketch_path, sketch_path] - mash_distance_command = ["mash", "triangle", "-i", "reference.msh"] + mash_distance_command = ["mash", "triangle", "-i", self.fasta_file] mash_distance_result = subprocess.Popen( mash_distance_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE ) - # pdb.set_trace() out, err = mash_distance_result.communicate() with open("matrix_distance.tsv", "w") as fo: # adding alleles to create a heading # the value are not required to be in order, just only any name and the right length fo.write("alleles\t" + "\t".join(list(self.selected_locus.keys())) + "\n") fo.write(out.decode("UTF-8")) + + # Create the blast distance. database is the locus and the query is each + # allele in the locus file + # Create blast db with sample file + sample_blast_obj = taranis.blast.Blast("nucl") + blast_dir = os.path.join(self.output, f_name) + _ = sample_blast_obj.create_blastdb(self.fasta_file, blast_dir) + + # create all fasta files for query + fasta_folder = os.path.join(self.output, "fasta", allele_name) + tmp_blast_matrix = {} + allele_list = [] + counter = 0 + with open(self.fasta_file) as fh: + for record in SeqIO.parse(fh, "fasta"): + # pdb.set_trace() + blast_allele = {} + query_file = taranis.utils.write_fasta_file( + fasta_folder, record.id, record.id, str(record.seq) + ) + # query_file = os.path.join(fasta_folder,record.id) + seq_blast_match = sample_blast_obj.run_blast( + query_file, + perc_identity=10, + evalue=1, + max_target_seqs=10000, + num_threads=4, + ) + q_allele = seq_blast_match[0].split("\t")[0] + allele_list.append(q_allele) + for seq_blast in seq_blast_match: + b_line = seq_blast.split("\t") + if not b_line[1] in blast_allele: + blast_allele[b_line[1]] = b_line[2] + else: + if blast_allele[b_line[1]] < b_line[2]: + blast_allele[b_line[1]] = b_line[2] + tmp_blast_matrix[q_allele] = blast_allele + counter += 1 + if counter % 25 == 0: + print("processing allele number ", counter) + blast_matrix = OrderedDict() + allele_list_2 = allele_list.copy() + pdb.set_trace() + for main_allele in allele_list: + identity_value = [] + for sub_allele in allele_list_2: + if sub_allele in tmp_blast_matrix[main_allele]: + identity_value.append(tmp_blast_matrix[main_allele][sub_allele]) + else: + identity_value.append(0) + blast_matrix[main_allele] = identity_value + import pandas as pd + + matrix_df = pd.DataFrame(blast_matrix) + blast_matrix_path = os.path.join( + self.output, "blast", allele_name + "_blast_matrix.csv" + ) + matrix_df.to_csv(blast_matrix_path, sep=",") + pdb.set_trace() + + """ import pandas as pd locus_num = len(self.selected_locus) # pdb.set_trace() matrix_df = pd.read_csv("matrix_distance.tsv", sep="\t").fillna(value=0) + # remove the first line of the matrix that contain only the number of alleles matrix_df = matrix_df.drop(0) locus_list = matrix_df.iloc[0:locus_num, 0] @@ -383,10 +450,11 @@ def create_matrix_distance(self): # import numpy as np # X = np.array([[0, 2, 3], [2, 0, 3], [3, 3, 0]]) # clustering = AgglomerativeClustering(affinity="precomputed").fit(X) + """ def create_ref_alleles(self): self.records = taranis.utils.read_fasta_file(self.fasta_file) - _ = self.check_locus_quality() + # _ = self.check_locus_quality() # pdb.set_trace() # Prepare data to use mash to create the distance matrix _ = self.create_matrix_distance() From 6a8a78a85b2a28afbe1a29d8a97390bc2ff9eea4 Mon Sep 17 00:00:00 2001 From: luissian Date: Sun, 28 Jan 2024 16:20:31 +0100 Subject: [PATCH 055/214] Fixing liting --- taranis/reference_alleles.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index 1358103..bbee5a7 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -1,5 +1,5 @@ import logging -import numpy as np +# import numpy as np import os import re import rich.console @@ -10,7 +10,7 @@ # from Bio import SeqIO from Bio.Seq import Seq from Bio import SeqIO -from Bio.Blast.Applications import NcbiblastnCommandline +# from Bio.Blast.Applications import NcbiblastnCommandline from collections import OrderedDict from pathlib import Path import taranis.utils @@ -172,7 +172,7 @@ def create_matrix_distance(self): locus_num = len(self.selected_locus) # pdb.set_trace() matrix_df = pd.read_csv("matrix_distance.tsv", sep="\t").fillna(value=0) - + # remove the first line of the matrix that contain only the number of alleles matrix_df = matrix_df.drop(0) locus_list = matrix_df.iloc[0:locus_num, 0] From 54fc96fc63d135c8849a8d34ee8100097b270c00 Mon Sep 17 00:00:00 2001 From: luissian Date: Sun, 28 Jan 2024 16:22:27 +0100 Subject: [PATCH 056/214] Fixing liting --- taranis/reference_alleles.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index bbee5a7..88912a9 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -1,4 +1,5 @@ import logging + # import numpy as np import os import re @@ -10,6 +11,7 @@ # from Bio import SeqIO from Bio.Seq import Seq from Bio import SeqIO + # from Bio.Blast.Applications import NcbiblastnCommandline from collections import OrderedDict from pathlib import Path From ee9d3c2333fdc986423458f451e37acbadecfb37 Mon Sep 17 00:00:00 2001 From: luissian Date: Sun, 4 Feb 2024 18:52:00 +0100 Subject: [PATCH 057/214] created distance matrix class --- taranis/distance.py | 76 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 76 insertions(+) create mode 100644 taranis/distance.py diff --git a/taranis/distance.py b/taranis/distance.py new file mode 100644 index 0000000..bc75105 --- /dev/null +++ b/taranis/distance.py @@ -0,0 +1,76 @@ +import io +import logging +import pandas as pd +import subprocess +import rich +import sys +from pathlib import Path +import taranis.utils + +log = logging.getLogger(__name__) +stderr = rich.console.Console( + stderr=True, + style="dim", + highlight=False, + force_terminal=taranis.utils.rich_force_colors(), +) + + +class DistanceMatrix: + def __init__( + self, + file_path: str, + ) -> "DistanceMatrix": + """DistanceMatrix instance creation + + Args: + file_path (str): Locus file path + + Returns: + DistanceMatrix: created instance + """ + self.file_path = file_path + + def create_matrix(self) -> pd.DataFrame: + """Create distance matrix using external program called mash + + Returns: + pd.DataFrame: Triangular distance matrix as panda DataFrame + """ + allele_name = Path(self.file_path).stem + mash_distance_command = [ + "mash", + "triangle", + "-i", + self.file_path, + "-k", + "17", + "-s", + "2000", + ] + try: + mash_distance_result = subprocess.Popen( + mash_distance_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE + ) + out, _ = mash_distance_result.communicate() + log.debug(f"calculate mash distance for {allele_name}") + except Exception as e: + log.error(f"Unable to create distance matrix for {self.file_path}. {e}") + stderr.print( + f"[red] Error when creating distance matrix for {self.file_path}" + ) + stderr.print(f"{e}") + sys.exit(1) + + out_data = out.decode("UTF-8").split("\n") + allele_names = [item.split("\t")[0] for item in out_data[1:-1]] + # create file in memory to increase speed + dist_matrix = io.StringIO() + dist_matrix.write("alleles\t" + "\t".join(allele_names) + "\n") + dist_matrix.write("\n".join(out_data[1:])) + dist_matrix.seek(0) + matrix_pd = pd.read_csv(dist_matrix, sep="\t", index_col="alleles").fillna(0) + # Close object and discard memory buffer + dist_matrix.close() + log.debug(f"create distance for {allele_name}") + return matrix_pd From 72d2395aab6d64471a6973e082074f2a2c369ca0 Mon Sep 17 00:00:00 2001 From: luissian Date: Sun, 4 Feb 2024 22:42:24 +0100 Subject: [PATCH 058/214] saving work. Clustering sequences is done, pending convert to allele and fetch cluster allele representative --- requirements.txt | 2 + taranis/clustering.py | 79 +++++++++++++++++++++++ taranis/reference_alleles.py | 121 +++++++++++++++-------------------- 3 files changed, 131 insertions(+), 71 deletions(-) create mode 100644 taranis/clustering.py diff --git a/requirements.txt b/requirements.txt index 2165712..6ea2be1 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,8 @@ biopython +igraph rich click +leidenalg questionary bio scikit-learn diff --git a/taranis/clustering.py b/taranis/clustering.py new file mode 100644 index 0000000..62ceb82 --- /dev/null +++ b/taranis/clustering.py @@ -0,0 +1,79 @@ +import igraph as ig +import leidenalg +import logging +import numpy as np +import pdb +import rich.console + +import taranis.utils + +log = logging.getLogger(__name__) +stderr = rich.console.Console( + stderr=True, + style="dim", + highlight=False, + force_terminal=taranis.utils.rich_force_colors(), +) + + + +class ClusterDistance: + def __init__(self, dist_matrix:np.array, ref_seq_name: str): + self.dist_matrix = dist_matrix + self.num_seq = dist_matrix.shape[0] + self.ref_seq_name = ref_seq_name + self.seed = None + self.res_param = 0.9 + + + def calculate_mean_cluster(self, src_cluster_row_idxs): + + src_cluster_col_idxs = src_cluster_row_idxs + src_cluster_mtrx_idxs = np.ix_(src_cluster_row_idxs, src_cluster_col_idxs) + row_idx_pos = np.argwhere(src_cluster_row_idxs).flatten() + col_idx_pos = np.argwhere(src_cluster_col_idxs).flatten() + num_of_diag_elements = np.intersect1d(row_idx_pos, col_idx_pos).size + num_of_non_diag_elements = row_idx_pos.size * col_idx_pos.size - num_of_diag_elements + if num_of_non_diag_elements == 0: + return 1 + return (np.sum(self.dist_matrix[src_cluster_mtrx_idxs]) - num_of_diag_elements) / num_of_non_diag_elements + + def convert_to_seq_clusters(seq_cluster_ptrs, seq_id_to_seq_name_map): + output_seq_clusters = list() + + for cluster_id in range(np.max(seq_cluster_ptrs) + 1): + seq_cluster = list() + for seq_id in np.argwhere(seq_cluster_ptrs == cluster_id).flatten(): + seq_cluster.append('{}{}'.format(seq_id_to_seq_name_map[str(seq_id)], os.linesep)) + + output_seq_clusters.append(seq_cluster) + import pdb; pdb.set_trace() + return output_seq_clusters + + def verify_cluster_quality(self , src_cluster_ptrs): + log.debug(f"Verifying cluster for {self.ref_seq_name}") + avg_clusters = {} + for cluster_id in range(np.max(src_cluster_ptrs) + 1): + log.debug(f"calculating mean for cluster number {cluster_id}") + src_cluster_bool_ptrs = (src_cluster_ptrs == cluster_id) + avg_clusters[cluster_id] = self.calculate_mean_cluster(src_cluster_bool_ptrs) + + return avg_clusters + + + + def create_clusters(self): + comm_graph = ig.Graph.Weighted_Adjacency(self.dist_matrix.tolist(), mode=1, loops=False) + graph_clusters = leidenalg.find_partition(comm_graph, leidenalg.CPMVertexPartition, weights='weight', n_iterations=-1, resolution_parameter=self.res_param, seed=self.seed) + cluster_ptrs = np.array(graph_clusters.membership) + pdb.set_trace() + + avg_clusters = self.verify_cluster_quality(cluster_ptrs) + # check that cluste average values are upper than 0.9 + pdb.set_trace() + if not all(x > 0.9 for x in list(avg_clusters.values())): + log.warning(f"There are some cluster below average of 0.9 in locus {self.ref_seq_name} ") + stderr.print(f"[red]There are some cluster below average of 0.9 in locus {self.ref_seq_name}") + return cluster_ptrs + + diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index 88912a9..d0b7499 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -4,19 +4,17 @@ import os import re import rich.console +import pandas as pd +from pathlib import Path # import sys import subprocess -# from Bio import SeqIO -from Bio.Seq import Seq -from Bio import SeqIO +from scipy.sparse import coo_matrix -# from Bio.Blast.Applications import NcbiblastnCommandline -from collections import OrderedDict -from pathlib import Path import taranis.utils -import taranis.blast +import taranis.distance +import taranis.clustering import pdb @@ -32,85 +30,66 @@ class ReferenceAlleles: def __init__(self, fasta_file, output): self.fasta_file = fasta_file + self.locus_name = Path(fasta_file).stem self.output = output - self.records = None - self.locus_quality = {} self.selected_locus = {} - def check_locus_quality(self): - # START_CODONS_FORWARD = ['ATG', 'ATA', 'ATT', 'GTG', 'TTG', 'CTG'] - # START_CODONS_REVERSE = ['CAT', 'TAT', 'AAT', 'CAC', 'CAA', 'CAG'] - - STOP_CODONS_FORWARD = ["TAA", "TAG", "TGA"] - STOP_CODONS_REVERSE = ["TTA", "CTA", "TCA"] - for record in self.records: - # Check if start condon forward - seq = str(record.seq) - s_codon_f = re.match(r"^(ATG|ATA|ATT|GTG|TTG|CTG).+(\w{3})$", seq) - if s_codon_f: - # Check if last 3 characters are codon stop forward - if s_codon_f.group(2) in STOP_CODONS_FORWARD: - # Check if multiple stop codon by translating to protein and - # comparing length - locus_prot = Seq(record.seq).translate() - if len(locus_prot) == int(len(seq) / 3): - self.locus_quality[record.id] = "good quality" - self.selected_locus[record.id] = seq - else: - self.locus_quality[record.id] = "bad quality: multiple_stop" - else: - self.locus_quality[record.id] = "bad quality: no_stop" - else: - # Check if start codon reverse - s_codon_r = re.match(r"^(\w{3}).+ (CAT|TAT|AAT|CAC|CAA|CAG)$", seq) - if s_codon_r: - # Matched reverse start codon - if s_codon_f.group(1) in STOP_CODONS_REVERSE: - locus_prot = Seq(record.seq).reverse_complement().translate() - if len(locus_prot) == int(len(record.seq) / 3): - self.locus_quality[record.id] = "good quality" - self.selected_locus[record.id] = seq - else: - self.locus_quality[record.id] = "bad quality: multiple_stop" - else: - self.locus_quality[record.id] = "bad quality: no_stop" - else: - self.locus_quality[record.id] = "bad_quality: no_start" - return - def create_matrix_distance(self): + def create_cluster_alleles(self): + log.debug("Processing distance matrix for $s", self.fasta_file) + distance_obj = taranis.distance.DistanceMatrix(self.fasta_file) + mash_distance_df = distance_obj.create_matrix() + # fetch the allele position into array + postition_to_allele = {x: mash_distance_df.columns[x] for x in range(len(mash_distance_df.columns))} + log.debug(f"Created distance matrix for {self.fasta_file}") + position_to_allele = {x: mash_distance_df.columns[x] } + # convert the triangle matrix into full data matrix + matrix_np = mash_distance_df.to_numpy() + t_matrix_np = matrix_np.transpose() + matrix_np = t_matrix_np + matrix_np + # At this point minimal distance is 0. For clustering requires to be 1 + # the oposite. + dist_matrix_np = (matrix_np -1) * -1 + """ in alfaclust TO DELETE + sparse_edge_weight_mtrx = coo_matrix(global_edge_weight_mtrx, shape=global_edge_weight_mtrx.shape) + """ + # create a sparse matrix used for summary + sparse_edge_weight_mtrx = coo_matrix(matrix_np, shape=matrix_np.shape) + pdb.set_trace() + cluster_seq = taranis.clustering.ClusterDistance(dist_matrix_np, self.locus_name) + cluster_ptrs = cluster_seq.create_clusters() + cluster_seq.convert_to_seq_clusters(cluster_ptrs, postition_to_allele) # f_name = os.path.basename(self.fasta_file).split('.')[0] - f_name = os.path.basename(self.fasta_file) - allele_name = Path(self.fasta_file).stem - mash_folder = os.path.join(self.output, "mash") + + # mash_folder = os.path.join(self.output, "mash") # _ = taranis.utils.write_fasta_file(mash_folder, self.selected_locus, multiple_files=True, extension=False) # save directory to return after mash # working_dir = os.getcwd() - os.chdir(mash_folder) + # os.chdir(mash_folder) # run mash sketch command - sketch_file = "reference.msh" - mash_sketch_command = ["mash", "sketch", "-i", "-o", sketch_file, f_name] + # sketch_file = "reference.msh" + # mash_sketch_command = ["mash", "sketch", "-i", "-o", sketch_file, f_name] + # mash_sketch_command = ["mash", "sketch", "-i", f_name] + """ mash command used by alfatclust + dist -C -i -v 0.0001 -d 0.30000000000000004 -p 16 /media/lchapado/Reference_data/proyectos_isciii/taranis/documentos_antiguos/pasteur_schema/lmo0002.fasta /media/lchapado/Reference_data/proyectos_isciii/taranis/documentos_antiguos/pasteur_schema/lmo0002.fasta -k 17 -s 2000' + """ # mash sketch -i -o prueba.msh lmo0003.fasta # mash_sketch_command += list(self.selected_locus.keys()) - _ = subprocess.run( - mash_sketch_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE - ) + # _ = subprocess.run( + # mash_sketch_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE + # ) # Get pairwise allele sequences mash distances # mash_distance_command = ["mash", "dist", sketch_path, sketch_path] - mash_distance_command = ["mash", "triangle", "-i", self.fasta_file] - mash_distance_result = subprocess.Popen( - mash_distance_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE - ) - out, err = mash_distance_result.communicate() - with open("matrix_distance.tsv", "w") as fo: - # adding alleles to create a heading - # the value are not required to be in order, just only any name and the right length - fo.write("alleles\t" + "\t".join(list(self.selected_locus.keys())) + "\n") - fo.write(out.decode("UTF-8")) + + + + pdb.set_trace() + # Create the blast distance. database is the locus and the query is each # allele in the locus file # Create blast db with sample file + """ sample_blast_obj = taranis.blast.Blast("nucl") blast_dir = os.path.join(self.output, f_name) _ = sample_blast_obj.create_blastdb(self.fasta_file, blast_dir) @@ -168,7 +147,7 @@ def create_matrix_distance(self): matrix_df.to_csv(blast_matrix_path, sep=",") pdb.set_trace() - """ + import pandas as pd locus_num = len(self.selected_locus) @@ -459,6 +438,6 @@ def create_ref_alleles(self): # _ = self.check_locus_quality() # pdb.set_trace() # Prepare data to use mash to create the distance matrix - _ = self.create_matrix_distance() + _ = self.create_cluster_alleles() pass From bdb1b3ae1e58e2603aeb920b42c8c89f9e520e9b Mon Sep 17 00:00:00 2001 From: luissian Date: Sun, 4 Feb 2024 22:44:22 +0100 Subject: [PATCH 059/214] commit unsaved changes --- taranis/clustering.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/taranis/clustering.py b/taranis/clustering.py index 62ceb82..f91284a 100644 --- a/taranis/clustering.py +++ b/taranis/clustering.py @@ -35,7 +35,7 @@ def calculate_mean_cluster(self, src_cluster_row_idxs): num_of_diag_elements = np.intersect1d(row_idx_pos, col_idx_pos).size num_of_non_diag_elements = row_idx_pos.size * col_idx_pos.size - num_of_diag_elements if num_of_non_diag_elements == 0: - return 1 + return 1 return (np.sum(self.dist_matrix[src_cluster_mtrx_idxs]) - num_of_diag_elements) / num_of_non_diag_elements def convert_to_seq_clusters(seq_cluster_ptrs, seq_id_to_seq_name_map): From e03691da607afbff50098f343ff041627b39e1a1 Mon Sep 17 00:00:00 2001 From: luissian Date: Sun, 4 Feb 2024 22:46:09 +0100 Subject: [PATCH 060/214] formating files --- taranis/clustering.py | 56 +++++++++++++++++++++--------------- taranis/reference_alleles.py | 18 ++++++------ 2 files changed, 42 insertions(+), 32 deletions(-) diff --git a/taranis/clustering.py b/taranis/clustering.py index f91284a..74c935c 100644 --- a/taranis/clustering.py +++ b/taranis/clustering.py @@ -2,7 +2,6 @@ import leidenalg import logging import numpy as np -import pdb import rich.console import taranis.utils @@ -16,27 +15,28 @@ ) - class ClusterDistance: - def __init__(self, dist_matrix:np.array, ref_seq_name: str): + def __init__(self, dist_matrix: np.array, ref_seq_name: str): self.dist_matrix = dist_matrix self.num_seq = dist_matrix.shape[0] self.ref_seq_name = ref_seq_name self.seed = None self.res_param = 0.9 - def calculate_mean_cluster(self, src_cluster_row_idxs): - src_cluster_col_idxs = src_cluster_row_idxs src_cluster_mtrx_idxs = np.ix_(src_cluster_row_idxs, src_cluster_col_idxs) row_idx_pos = np.argwhere(src_cluster_row_idxs).flatten() col_idx_pos = np.argwhere(src_cluster_col_idxs).flatten() num_of_diag_elements = np.intersect1d(row_idx_pos, col_idx_pos).size - num_of_non_diag_elements = row_idx_pos.size * col_idx_pos.size - num_of_diag_elements + num_of_non_diag_elements = ( + row_idx_pos.size * col_idx_pos.size - num_of_diag_elements + ) if num_of_non_diag_elements == 0: return 1 - return (np.sum(self.dist_matrix[src_cluster_mtrx_idxs]) - num_of_diag_elements) / num_of_non_diag_elements + return ( + np.sum(self.dist_matrix[src_cluster_mtrx_idxs]) - num_of_diag_elements + ) / num_of_non_diag_elements def convert_to_seq_clusters(seq_cluster_ptrs, seq_id_to_seq_name_map): output_seq_clusters = list() @@ -44,36 +44,46 @@ def convert_to_seq_clusters(seq_cluster_ptrs, seq_id_to_seq_name_map): for cluster_id in range(np.max(seq_cluster_ptrs) + 1): seq_cluster = list() for seq_id in np.argwhere(seq_cluster_ptrs == cluster_id).flatten(): - seq_cluster.append('{}{}'.format(seq_id_to_seq_name_map[str(seq_id)], os.linesep)) + seq_cluster.append( + "{}{}".format(seq_id_to_seq_name_map[str(seq_id)], os.linesep) + ) output_seq_clusters.append(seq_cluster) - import pdb; pdb.set_trace() return output_seq_clusters - def verify_cluster_quality(self , src_cluster_ptrs): + def verify_cluster_quality(self, src_cluster_ptrs): log.debug(f"Verifying cluster for {self.ref_seq_name}") avg_clusters = {} for cluster_id in range(np.max(src_cluster_ptrs) + 1): - log.debug(f"calculating mean for cluster number {cluster_id}") - src_cluster_bool_ptrs = (src_cluster_ptrs == cluster_id) - avg_clusters[cluster_id] = self.calculate_mean_cluster(src_cluster_bool_ptrs) + log.debug(f"calculating mean for cluster number {cluster_id}") + src_cluster_bool_ptrs = src_cluster_ptrs == cluster_id + avg_clusters[cluster_id] = self.calculate_mean_cluster( + src_cluster_bool_ptrs + ) return avg_clusters - - def create_clusters(self): - comm_graph = ig.Graph.Weighted_Adjacency(self.dist_matrix.tolist(), mode=1, loops=False) - graph_clusters = leidenalg.find_partition(comm_graph, leidenalg.CPMVertexPartition, weights='weight', n_iterations=-1, resolution_parameter=self.res_param, seed=self.seed) + comm_graph = ig.Graph.Weighted_Adjacency( + self.dist_matrix.tolist(), mode=1, loops=False + ) + graph_clusters = leidenalg.find_partition( + comm_graph, + leidenalg.CPMVertexPartition, + weights="weight", + n_iterations=-1, + resolution_parameter=self.res_param, + seed=self.seed, + ) cluster_ptrs = np.array(graph_clusters.membership) - pdb.set_trace() avg_clusters = self.verify_cluster_quality(cluster_ptrs) # check that cluste average values are upper than 0.9 - pdb.set_trace() if not all(x > 0.9 for x in list(avg_clusters.values())): - log.warning(f"There are some cluster below average of 0.9 in locus {self.ref_seq_name} ") - stderr.print(f"[red]There are some cluster below average of 0.9 in locus {self.ref_seq_name}") + log.warning( + f"There are some cluster below average of 0.9 in locus {self.ref_seq_name} " + ) + stderr.print( + f"[red]There are some cluster below average of 0.9 in locus {self.ref_seq_name}" + ) return cluster_ptrs - - diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index d0b7499..e475a37 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -34,29 +34,32 @@ def __init__(self, fasta_file, output): self.output = output self.selected_locus = {} - def create_cluster_alleles(self): log.debug("Processing distance matrix for $s", self.fasta_file) distance_obj = taranis.distance.DistanceMatrix(self.fasta_file) mash_distance_df = distance_obj.create_matrix() # fetch the allele position into array - postition_to_allele = {x: mash_distance_df.columns[x] for x in range(len(mash_distance_df.columns))} + postition_to_allele = { + x: mash_distance_df.columns[x] for x in range(len(mash_distance_df.columns)) + } log.debug(f"Created distance matrix for {self.fasta_file}") - position_to_allele = {x: mash_distance_df.columns[x] } + position_to_allele = {x: mash_distance_df.columns[x]} # convert the triangle matrix into full data matrix matrix_np = mash_distance_df.to_numpy() t_matrix_np = matrix_np.transpose() matrix_np = t_matrix_np + matrix_np # At this point minimal distance is 0. For clustering requires to be 1 # the oposite. - dist_matrix_np = (matrix_np -1) * -1 + dist_matrix_np = (matrix_np - 1) * -1 """ in alfaclust TO DELETE sparse_edge_weight_mtrx = coo_matrix(global_edge_weight_mtrx, shape=global_edge_weight_mtrx.shape) """ # create a sparse matrix used for summary sparse_edge_weight_mtrx = coo_matrix(matrix_np, shape=matrix_np.shape) pdb.set_trace() - cluster_seq = taranis.clustering.ClusterDistance(dist_matrix_np, self.locus_name) + cluster_seq = taranis.clustering.ClusterDistance( + dist_matrix_np, self.locus_name + ) cluster_ptrs = cluster_seq.create_clusters() cluster_seq.convert_to_seq_clusters(cluster_ptrs, postition_to_allele) # f_name = os.path.basename(self.fasta_file).split('.')[0] @@ -80,12 +83,9 @@ def create_cluster_alleles(self): # ) # Get pairwise allele sequences mash distances # mash_distance_command = ["mash", "dist", sketch_path, sketch_path] - - - pdb.set_trace() - + # Create the blast distance. database is the locus and the query is each # allele in the locus file # Create blast db with sample file From fe8fb8ead8ebf915041704d1815d923b6e247dd3 Mon Sep 17 00:00:00 2001 From: luissian Date: Sun, 4 Feb 2024 22:55:27 +0100 Subject: [PATCH 061/214] fixing liting --- taranis/reference_alleles.py | 382 +---------------------------------- 1 file changed, 2 insertions(+), 380 deletions(-) diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index e475a37..77a11cb 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -1,15 +1,8 @@ import logging -# import numpy as np -import os -import re import rich.console -import pandas as pd from pathlib import Path -# import sys -import subprocess - from scipy.sparse import coo_matrix import taranis.utils @@ -38,12 +31,11 @@ def create_cluster_alleles(self): log.debug("Processing distance matrix for $s", self.fasta_file) distance_obj = taranis.distance.DistanceMatrix(self.fasta_file) mash_distance_df = distance_obj.create_matrix() + log.debug(f"Created distance matrix for {self.fasta_file}") # fetch the allele position into array postition_to_allele = { x: mash_distance_df.columns[x] for x in range(len(mash_distance_df.columns)) } - log.debug(f"Created distance matrix for {self.fasta_file}") - position_to_allele = {x: mash_distance_df.columns[x]} # convert the triangle matrix into full data matrix matrix_np = mash_distance_df.to_numpy() t_matrix_np = matrix_np.transpose() @@ -55,383 +47,13 @@ def create_cluster_alleles(self): sparse_edge_weight_mtrx = coo_matrix(global_edge_weight_mtrx, shape=global_edge_weight_mtrx.shape) """ # create a sparse matrix used for summary - sparse_edge_weight_mtrx = coo_matrix(matrix_np, shape=matrix_np.shape) + # sparse_edge_weight_mtrx = coo_matrix(matrix_np, shape=matrix_np.shape) pdb.set_trace() cluster_seq = taranis.clustering.ClusterDistance( dist_matrix_np, self.locus_name ) cluster_ptrs = cluster_seq.create_clusters() cluster_seq.convert_to_seq_clusters(cluster_ptrs, postition_to_allele) - # f_name = os.path.basename(self.fasta_file).split('.')[0] - - # mash_folder = os.path.join(self.output, "mash") - # _ = taranis.utils.write_fasta_file(mash_folder, self.selected_locus, multiple_files=True, extension=False) - # save directory to return after mash - # working_dir = os.getcwd() - # os.chdir(mash_folder) - # run mash sketch command - # sketch_file = "reference.msh" - # mash_sketch_command = ["mash", "sketch", "-i", "-o", sketch_file, f_name] - # mash_sketch_command = ["mash", "sketch", "-i", f_name] - """ mash command used by alfatclust - dist -C -i -v 0.0001 -d 0.30000000000000004 -p 16 /media/lchapado/Reference_data/proyectos_isciii/taranis/documentos_antiguos/pasteur_schema/lmo0002.fasta /media/lchapado/Reference_data/proyectos_isciii/taranis/documentos_antiguos/pasteur_schema/lmo0002.fasta -k 17 -s 2000' - """ - # mash sketch -i -o prueba.msh lmo0003.fasta - # mash_sketch_command += list(self.selected_locus.keys()) - # _ = subprocess.run( - # mash_sketch_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE - # ) - # Get pairwise allele sequences mash distances - # mash_distance_command = ["mash", "dist", sketch_path, sketch_path] - - pdb.set_trace() - - # Create the blast distance. database is the locus and the query is each - # allele in the locus file - # Create blast db with sample file - """ - sample_blast_obj = taranis.blast.Blast("nucl") - blast_dir = os.path.join(self.output, f_name) - _ = sample_blast_obj.create_blastdb(self.fasta_file, blast_dir) - - # create all fasta files for query - fasta_folder = os.path.join(self.output, "fasta", allele_name) - tmp_blast_matrix = {} - allele_list = [] - counter = 0 - with open(self.fasta_file) as fh: - for record in SeqIO.parse(fh, "fasta"): - # pdb.set_trace() - blast_allele = {} - query_file = taranis.utils.write_fasta_file( - fasta_folder, record.id, record.id, str(record.seq) - ) - # query_file = os.path.join(fasta_folder,record.id) - seq_blast_match = sample_blast_obj.run_blast( - query_file, - perc_identity=10, - evalue=1, - max_target_seqs=10000, - num_threads=4, - ) - q_allele = seq_blast_match[0].split("\t")[0] - allele_list.append(q_allele) - for seq_blast in seq_blast_match: - b_line = seq_blast.split("\t") - if not b_line[1] in blast_allele: - blast_allele[b_line[1]] = b_line[2] - else: - if blast_allele[b_line[1]] < b_line[2]: - blast_allele[b_line[1]] = b_line[2] - tmp_blast_matrix[q_allele] = blast_allele - counter += 1 - if counter % 25 == 0: - print("processing allele number ", counter) - blast_matrix = OrderedDict() - allele_list_2 = allele_list.copy() - pdb.set_trace() - for main_allele in allele_list: - identity_value = [] - for sub_allele in allele_list_2: - if sub_allele in tmp_blast_matrix[main_allele]: - identity_value.append(tmp_blast_matrix[main_allele][sub_allele]) - else: - identity_value.append(0) - blast_matrix[main_allele] = identity_value - import pandas as pd - - matrix_df = pd.DataFrame(blast_matrix) - blast_matrix_path = os.path.join( - self.output, "blast", allele_name + "_blast_matrix.csv" - ) - matrix_df.to_csv(blast_matrix_path, sep=",") - pdb.set_trace() - - - import pandas as pd - - locus_num = len(self.selected_locus) - # pdb.set_trace() - matrix_df = pd.read_csv("matrix_distance.tsv", sep="\t").fillna(value=0) - - # remove the first line of the matrix that contain only the number of alleles - matrix_df = matrix_df.drop(0) - locus_list = matrix_df.iloc[0:locus_num, 0] - matrix_np = matrix_df.iloc[:, 1:].to_numpy() - # convert the triangular matrix to mirror up triangular part - t_matrix_np = matrix_np.transpose() - matrix_np = t_matrix_np + matrix_np - # values_np = matrix_df.iloc[:,2].to_numpy() - - # matrix_np = values_np.reshape(locus_num, locus_num) - # out = out.decode('UTF-8').split('\n') - from sklearn.cluster import AgglomerativeClustering - - clusterer = AgglomerativeClustering( - n_clusters=7, - metric="precomputed", - linkage="average", - distance_threshold=None, - ) - clusters = clusterer.fit_predict(matrix_np) - # clustering = AgglomerativeClustering(affinity="precomputed").fit(matrix_np) - mean_distance = np.mean(matrix_np, 0) - # std = np.std(matrix_np) - min_mean = min(mean_distance) - mean_all_alleles = np.mean(mean_distance) - max_mean = max(mean_distance) - # buscar el indice que tiene el minimo valor de media - min_mean_idx = np.where(mean_distance == float(min_mean))[0][0] - # create fasta file with the allele - min_allele = self.selected_locus[locus_list[min_mean_idx]] - - record_allele_folder = os.path.join(os.getcwd(), f_name.split(".")[0]) - min_allele_file = taranis.utils.write_fasta_file( - record_allele_folder, min_allele, locus_list[min_mean_idx] - ) - # pdb.set_trace() - # busca el indice que tiene el valor de la media - mean_all_closser_value = taranis.utils.find_nearest_numpy_value( - mean_distance, mean_all_alleles - ) - mean_all_alleles_idx = np.where(mean_distance == float(mean_all_closser_value))[ - 0 - ][0] - # create fasta file with the allele - mean_allele = self.selected_locus[locus_list[mean_all_alleles_idx]] - # record_allele_folder = os.path.join(mash_folder, f_name) - mean_allele_file = taranis.utils.write_fasta_file( - record_allele_folder, mean_allele, locus_list[mean_all_alleles_idx] - ) - - # busca el indice con la mayor distancia - max_mean_idx = np.where(mean_distance == float(max_mean))[0][0] - # create fasta file with the allele - max_allele = self.selected_locus[locus_list[max_mean_idx]] - max_allele_file = taranis.utils.write_fasta_file( - record_allele_folder, max_allele, locus_list[max_mean_idx] - ) - - # Elijo un outlier lmo0002_185 para ver la distancia - outlier_allele = self.selected_locus[locus_list[184]] - outlier_allele_file = taranis.utils.write_fasta_file( - record_allele_folder, outlier_allele, locus_list[184] - ) - - # elijo un segundo outlier lmo0002_95 que tiene como cluster =1 - outlier2_allele = self.selected_locus[locus_list[95]] - outlier2_allele_file = taranis.utils.write_fasta_file( - record_allele_folder, outlier2_allele, locus_list[95] - ) - - # elijo un tercer outlier lmo0002_185 que tiene como cluster =4 - outlier3_allele = self.selected_locus[locus_list[185]] - outlier3_allele_file = taranis.utils.write_fasta_file( - record_allele_folder, outlier3_allele, locus_list[185] - ) - - # saca una lista de cuantas veces se repite un valor - np.bincount(clusters) - blast_parameters = '"6 , qseqid , sseqid , pident , qlen , length , mismatch , gapopen , evalue , bitscore , sstart , send , qstart , qend , sseq , qseq"' - - # Create local BLAST database for all alleles in the locus - db_name = "/media/lchapado/Reference_data/proyectos_isciii/taranis/new_taranis_result_code/blast/locus_db" - # db_name = os.path.join("blast", 'locus_blastdb') - # fasta_file = "/media/lchapado/Reference_data/proyectos_isciii/taranis/documentos_antiguos/datos_prueba/schema_1_locus/lmo0002.fasta" - # pdb.set_trace() - # blast_command = ['makeblastdb' , '-in' , fasta_file , '-parse_seqids', '-dbtype', "nucl", '-out' , db_name] - # blast_result = subprocess.run(blast_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) - # taranis.utils.create_blastdb(fasta_file, db_name, 'nucl', logger): - # locus_db_name = os.path.join(db_name, f_name[0], f_name[0]) - # query_data= self.selected_locus["lmo0002_1"] - # All alleles in locus VS reference allele chosen (centroid) BLAST - - # ref_query_file="/media/lchapado/Reference_data/proyectos_isciii/taranis/new_taranis_result_code/mash/lmo0002/query.fasta" - # cline = NcbiblastnCommandline(db=db_name, evalue=0.001, perc_identity=100, reward=1, penalty=-2, gapopen=1, gapextend=1, outfmt=blast_parameters, max_target_seqs=0, max_hsps=0, num_threads=4, query=ref_query_file) - - # minima distancia . - # min_dist_file="/media/lchapado/Reference_data/proyectos_isciii/taranis/new_taranis_result_code/mash/lmo0002/lmo0002_610" - # pdb.set_trace() - min_dist_file = os.path.join(record_allele_folder, min_allele_file) - cline = NcbiblastnCommandline( - db=db_name, - evalue=0.001, - perc_identity=90, - reward=1, - penalty=-2, - gapopen=1, - gapextend=1, - outfmt=blast_parameters, - max_target_seqs=1100, - max_hsps=1000, - num_threads=4, - query=min_dist_file, - ) - out, err = cline() - min_dist_lines = out.splitlines() - min_dist_alleles = [] - for min_dist in min_dist_lines: - min_dist_alleles.append(min_dist.split("\t")[1]) - min_np = np.array(min_dist_alleles) - # pdb.set_trace() - print("matches con min distancia: ", len(min_dist_lines)) - print("Not coverage using as reference", np.setdiff1d(locus_list, min_np)) - # distancia media. Sale 133 matches - # mean_dist_file="/media/lchapado/Reference_data/proyectos_isciii/taranis/new_taranis_result_code/mash/lmo0002/lmo0002_870" - mean_dist_file = os.path.join(record_allele_folder, mean_allele_file) - cline = NcbiblastnCommandline( - db=db_name, - evalue=0.001, - perc_identity=90, - reward=1, - penalty=-2, - gapopen=1, - gapextend=1, - outfmt=blast_parameters, - max_target_seqs=1100, - max_hsps=1000, - num_threads=4, - query=mean_dist_file, - ) - out, err = cline() - mean_dist_lines = out.splitlines() - mean_dist_alleles = [] - for mean_dist in mean_dist_lines: - mean_dist_alleles.append(mean_dist.split("\t")[1]) - mean_np = np.array(mean_dist_alleles) - print("matches con distancia media: ", len(mean_dist_lines)) - print("Not coverage using as reference", np.setdiff1d(locus_list, mean_np)) - - # maxima distancia, - # ref_query_file="/media/lchapado/Reference_data/proyectos_isciii/taranis/new_taranis_result_code/mash/lmo0002/lmo0002_216" - max_dist_file = os.path.join(record_allele_folder, max_allele_file) - cline = NcbiblastnCommandline( - db=db_name, - evalue=0.001, - perc_identity=90, - reward=1, - penalty=-2, - gapopen=1, - gapextend=1, - outfmt=blast_parameters, - max_target_seqs=1100, - max_hsps=1000, - num_threads=4, - query=max_dist_file, - ) - out, err = cline() - max_dist_lines = out.splitlines() - max_dist_alleles = [] - for max_dist in max_dist_lines: - max_dist_alleles.append(max_dist.split("\t")[1]) - max_np = np.array(max_dist_alleles) - print("matches con max distancia: ", len(max_dist_lines)) - print("Not coverage using as reference", np.setdiff1d(locus_list, max_np)) - - # eligiendo uno de los outliers , - # outlier_file="/media/lchapado/Reference_data/proyectos_isciii/taranis/new_taranis_result_code/mash/lmo0002/lmo0002_183" - outlier_file = os.path.join(record_allele_folder, outlier_allele_file) - cline = NcbiblastnCommandline( - db=db_name, - evalue=0.001, - perc_identity=90, - reward=1, - penalty=-2, - gapopen=1, - gapextend=1, - outfmt=blast_parameters, - max_target_seqs=1100, - max_hsps=1000, - num_threads=4, - query=outlier_file, - ) - out, err = cline() - outlier_lines = out.splitlines() - outlier_alleles = [] - for outlier_line in outlier_lines: - outlier_alleles.append(outlier_line.split("\t")[1]) - outlier_np = np.array(outlier_alleles) - print("matches con outliers distancia: ", len(outlier_lines)) - - print("Alleles added using outlier as reference", outlier_np) - new_ref_np = np.unique(np.concatenate((min_np, outlier_np), axis=0)) - print("\n", "remaining alleles ", np.setdiff1d(locus_list, new_ref_np)) - - # eligiendo el segundo de los outliers , - # outlier_file="/media/lchapado/Reference_data/proyectos_isciii/taranis/new_taranis_result_code/mash/lmo0002/lmo0002_183" - outlier2_file = os.path.join(record_allele_folder, outlier2_allele_file) - cline = NcbiblastnCommandline( - db=db_name, - evalue=0.001, - perc_identity=90, - reward=1, - penalty=-2, - gapopen=1, - gapextend=1, - outfmt=blast_parameters, - max_target_seqs=1100, - max_hsps=1000, - num_threads=4, - query=outlier2_file, - ) - out, _ = cline() - outlier2_lines = out.splitlines() - outlier2_alleles = [] - for outlier2_line in outlier2_lines: - outlier2_alleles.append(outlier2_line.split("\t")[1]) - outlier2_np = np.array(outlier2_alleles) - print("matches con second outliers distance: ", len(outlier2_lines)) - # print("Alleles added using second outlier as reference" , outlier2_np) - upd_new_ref_np = np.unique(np.concatenate((new_ref_np, outlier2_np), axis=0)) - print( - "\n", - "remaining alleles after second outlier", - np.setdiff1d(locus_list, upd_new_ref_np), - ) - - # eligiendo el tercero de los outliers , - # outlier_file="/media/lchapado/Reference_data/proyectos_isciii/taranis/new_taranis_result_code/mash/lmo0002/lmo0002_183" - outlier3_file = os.path.join(record_allele_folder, outlier3_allele_file) - cline = NcbiblastnCommandline( - db=db_name, - evalue=0.001, - perc_identity=90, - reward=1, - penalty=-2, - gapopen=1, - gapextend=1, - outfmt=blast_parameters, - max_target_seqs=1100, - max_hsps=1000, - num_threads=4, - query=outlier3_file, - ) - out, _ = cline() - outlier3_lines = out.splitlines() - outlier3_alleles = [] - for outlier3_line in outlier3_lines: - outlier3_alleles.append(outlier3_line.split("\t")[1]) - outlier3_np = np.array(outlier3_alleles) - print("matches con third outliers distance: ", len(outlier3_lines)) - # print("Alleles added using second outlier as reference" , outlier2_np) - upd2_new_ref_np = np.unique( - np.concatenate((upd_new_ref_np, outlier3_np), axis=0) - ) - print( - "\n", - "remaining alleles after second outlier", - np.setdiff1d(locus_list, upd2_new_ref_np), - ) - - print("\n Still missing ", len(np.setdiff1d(locus_list, upd2_new_ref_np))) - - pdb.set_trace() - - # from sklearn.cluster import AgglomerativeClustering - # import numpy as np - # X = np.array([[0, 2, 3], [2, 0, 3], [3, 3, 0]]) - # clustering = AgglomerativeClustering(affinity="precomputed").fit(X) - """ def create_ref_alleles(self): self.records = taranis.utils.read_fasta_file(self.fasta_file) From fd12a42cf44940674f20713c8a7af990e2183643 Mon Sep 17 00:00:00 2001 From: luissian Date: Sun, 4 Feb 2024 22:57:42 +0100 Subject: [PATCH 062/214] fixing liting 2 --- taranis/clustering.py | 2 +- taranis/reference_alleles.py | 4 +--- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/taranis/clustering.py b/taranis/clustering.py index 74c935c..10fbdca 100644 --- a/taranis/clustering.py +++ b/taranis/clustering.py @@ -3,7 +3,7 @@ import logging import numpy as np import rich.console - +import os import taranis.utils log = logging.getLogger(__name__) diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index 77a11cb..e313714 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -9,8 +9,6 @@ import taranis.distance import taranis.clustering -import pdb - log = logging.getLogger(__name__) stderr = rich.console.Console( stderr=True, @@ -47,7 +45,7 @@ def create_cluster_alleles(self): sparse_edge_weight_mtrx = coo_matrix(global_edge_weight_mtrx, shape=global_edge_weight_mtrx.shape) """ # create a sparse matrix used for summary - # sparse_edge_weight_mtrx = coo_matrix(matrix_np, shape=matrix_np.shape) + _ = coo_matrix(matrix_np, shape=matrix_np.shape) pdb.set_trace() cluster_seq = taranis.clustering.ClusterDistance( dist_matrix_np, self.locus_name From aa591c05a4ba343d5d978ac9e9efee7abb58fb31 Mon Sep 17 00:00:00 2001 From: luissian Date: Sun, 4 Feb 2024 22:59:40 +0100 Subject: [PATCH 063/214] fixing liting 3 --- taranis/reference_alleles.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index e313714..2107dad 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -46,7 +46,7 @@ def create_cluster_alleles(self): """ # create a sparse matrix used for summary _ = coo_matrix(matrix_np, shape=matrix_np.shape) - pdb.set_trace() + cluster_seq = taranis.clustering.ClusterDistance( dist_matrix_np, self.locus_name ) From 48b954f071c9c4c630519b2d1cd1035522712e86 Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 5 Feb 2024 13:47:12 +0100 Subject: [PATCH 064/214] updating package files --- pyproject.toml | 27 ++++++++++++++++++--------- setup.py | 36 ++++++++++++++++++++++++++++++++++++ 2 files changed, 54 insertions(+), 9 deletions(-) create mode 100644 setup.py diff --git a/pyproject.toml b/pyproject.toml index 2389545..13f2f7a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,18 +1,27 @@ -[tool.poetry] +[build-system] +requires = ["setuptools"] +build-backend = "setuptools.build_meta" + +[project] name = "taranis" version = "3.0.0" +dynamic = ["dependencies"] +requires-python = ">=3.10" + +authors = [ + {name = "Sara Monzon", email = "smonzon@isciii.es"}, + {name = "Luis Chapado", email = "lchapado@externos.isciii.es"}, +] +maintainers = [ + {name = "Luis Chapado", email = "lchapado@externos.isciii.es"} +] description = "Tools for gene-by-gene allele calling analysis" readme = "README.md" -authors = ["Sara Monzon "] -license = "GNU-3.0" +license = {file = "LICENSE"} -[tool.poetry.dependencies] -python = "^3.10" - -[tool.poetry.scripts] -taranis = "taranis.__main__:run_taranis" +[tool.setuptools.dynamic] +dependencies = {file = ["requirements.txt"]} [tool.setuptools.packages.find] exclude = ["img", "virtualenv"] - diff --git a/setup.py b/setup.py new file mode 100644 index 0000000..14646b6 --- /dev/null +++ b/setup.py @@ -0,0 +1,36 @@ +#!/usr/bin/env python + +from setuptools import setup, find_packages + +version = "3.0.0" + +with open("README.md") as f: + readme = f.read() + +with open("requirements.txt") as f: + required = f.read().splitlines() + +setup( + name="taranis", + version=version, + description="Tools for gene-by-gene allele calling analysis", + long_description=readme, + long_description_content_type="text/markdown", + keywords=[ + "buisciii", + "bioinformatics", + "pipeline", + "sequencing", + "NGS", + "next generation sequencing", + ], + author="Sara Monzon", + author_email="smonzon@isciii.es", + url="https://github.com/BU-ISCIII/taranis", + license="GNU GENERAL PUBLIC LICENSE v.3", + entry_points={"console_scripts": ["taranis=taranis.__main__:run_taranis"]}, + install_requires=required, + packages=find_packages(exclude=("docs")), + include_package_data=True, + zip_safe=False, +) From 8e1c65d5bc2c5aa6432f536ab572cb635f1a69a0 Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 5 Feb 2024 16:03:28 +0100 Subject: [PATCH 065/214] Checking if conflicts are gone --- taranis/reference_alleles.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index 2107dad..04a8f88 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -26,6 +26,8 @@ def __init__(self, fasta_file, output): self.selected_locus = {} def create_cluster_alleles(self): + """_summary_ + """ log.debug("Processing distance matrix for $s", self.fasta_file) distance_obj = taranis.distance.DistanceMatrix(self.fasta_file) mash_distance_df = distance_obj.create_matrix() From e079dbc1cdc66c6f1a48519a2501013a89c0ffaa Mon Sep 17 00:00:00 2001 From: luissian Date: Tue, 6 Feb 2024 00:29:19 +0100 Subject: [PATCH 066/214] Added closest distance --- taranis/clustering.py | 88 ++++++++++++++++++++++-------------- taranis/reference_alleles.py | 6 +-- 2 files changed, 56 insertions(+), 38 deletions(-) diff --git a/taranis/clustering.py b/taranis/clustering.py index 10fbdca..36ce62b 100644 --- a/taranis/clustering.py +++ b/taranis/clustering.py @@ -3,7 +3,6 @@ import logging import numpy as np import rich.console -import os import taranis.utils log = logging.getLogger(__name__) @@ -23,11 +22,19 @@ def __init__(self, dist_matrix: np.array, ref_seq_name: str): self.seed = None self.res_param = 0.9 - def calculate_mean_cluster(self, src_cluster_row_idxs): - src_cluster_col_idxs = src_cluster_row_idxs - src_cluster_mtrx_idxs = np.ix_(src_cluster_row_idxs, src_cluster_col_idxs) - row_idx_pos = np.argwhere(src_cluster_row_idxs).flatten() - col_idx_pos = np.argwhere(src_cluster_col_idxs).flatten() + def calculate_closest_index( + self, cluster_mtrx_idxs: tuple, cluster_mean: float + ) -> list: + cluster_matrix = self.dist_matrix[cluster_mtrx_idxs] + cluster_flat = cluster_matrix.flatten() + closest_index = np.argmin(np.abs(cluster_flat - cluster_mean)) + return [np.unravel_index(closest_index, cluster_matrix.shape)] + + def calculate_mean_cluster(self, cluster_mtrx_idxs: tuple, row_idx_pos: np.ndarray): + col_idx_pos = row_idx_pos + # src_cluster_mtrx_idxs = np.ix_(src_cluster_row_idxs, src_cluster_col_idxs) + # row_idx_pos = np.argwhere(src_cluster_row_idxs).flatten() + # col_idx_pos = np.argwhere(src_cluster_col_idxs).flatten() num_of_diag_elements = np.intersect1d(row_idx_pos, col_idx_pos).size num_of_non_diag_elements = ( row_idx_pos.size * col_idx_pos.size - num_of_diag_elements @@ -35,33 +42,39 @@ def calculate_mean_cluster(self, src_cluster_row_idxs): if num_of_non_diag_elements == 0: return 1 return ( - np.sum(self.dist_matrix[src_cluster_mtrx_idxs]) - num_of_diag_elements + np.sum(self.dist_matrix[cluster_mtrx_idxs]) - num_of_diag_elements ) / num_of_non_diag_elements - def convert_to_seq_clusters(seq_cluster_ptrs, seq_id_to_seq_name_map): - output_seq_clusters = list() - - for cluster_id in range(np.max(seq_cluster_ptrs) + 1): - seq_cluster = list() - for seq_id in np.argwhere(seq_cluster_ptrs == cluster_id).flatten(): - seq_cluster.append( - "{}{}".format(seq_id_to_seq_name_map[str(seq_id)], os.linesep) - ) + def convert_to_seq_clusters( + self, cluster_ids: np.array, id_to_seq_name: dict + ) -> dict: + out_clusters = {} + for cluster_id in range(np.max(cluster_ids) + 1): + alleles_in_cluster = [] + out_clusters[cluster_id] = [ + id_to_seq_name[seq_id] + for seq_id in np.argwhere(cluster_ids == cluster_id).flatten() + ] - output_seq_clusters.append(seq_cluster) - return output_seq_clusters + return out_clusters - def verify_cluster_quality(self, src_cluster_ptrs): - log.debug(f"Verifying cluster for {self.ref_seq_name}") - avg_clusters = {} + def collect_data_cluster(self, src_cluster_ptrs): + log.debug(f"Collecting data for cluster {self.ref_seq_name}") + cluster_data = {} for cluster_id in range(np.max(src_cluster_ptrs) + 1): + cluster_data[cluster_id] = {} log.debug(f"calculating mean for cluster number {cluster_id}") - src_cluster_bool_ptrs = src_cluster_ptrs == cluster_id - avg_clusters[cluster_id] = self.calculate_mean_cluster( - src_cluster_bool_ptrs + cluster_bool_ptrs = src_cluster_ptrs == cluster_id + cluster_mtrx_idxs = np.ix_(cluster_bool_ptrs, cluster_bool_ptrs) + row_idx_pos = np.argwhere(cluster_bool_ptrs).flatten() + # col_idx_pos = np.argwhere(cluster_bool_ptrs).flatten() + cluster_mean = self.calculate_mean_cluster(cluster_mtrx_idxs, row_idx_pos) + # pdb.set_trace() + cluster_data[cluster_id]["avg"] = cluster_mean + cluster_data[cluster_id]["closest_idx"] = self.calculate_closest_index( + cluster_mtrx_idxs, cluster_mean ) - - return avg_clusters + return cluster_data def create_clusters(self): comm_graph = ig.Graph.Weighted_Adjacency( @@ -76,14 +89,19 @@ def create_clusters(self): seed=self.seed, ) cluster_ptrs = np.array(graph_clusters.membership) - - avg_clusters = self.verify_cluster_quality(cluster_ptrs) + # Convert the partition to a DataFrame + # df_clusters = pd.DataFrame({'Node': range(len(graph_clusters.membership)), 'Cluster': graph_clusters.membership}) + # Calculate the centroid of each cluster + # cluster_centers = df_clusters.groupby('Cluster').apply(lambda x: np.mean(self.dist_matrix[x['Node']], axis=0)).values + clusters_data = self.collect_data_cluster(cluster_ptrs) # check that cluste average values are upper than 0.9 - if not all(x > 0.9 for x in list(avg_clusters.values())): - log.warning( - f"There are some cluster below average of 0.9 in locus {self.ref_seq_name} " - ) - stderr.print( - f"[red]There are some cluster below average of 0.9 in locus {self.ref_seq_name}" - ) + for value in clusters_data.values(): + if value["avg"] < 0.9 : + log.warning( + f"There are some cluster below average of 0.9 in locus {self.ref_seq_name} " + ) + stderr.print( + f"[red]There are some cluster below average of 0.9 in locus {self.ref_seq_name}" + ) + return cluster_ptrs diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index 04a8f88..9cc5fa1 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -26,8 +26,6 @@ def __init__(self, fasta_file, output): self.selected_locus = {} def create_cluster_alleles(self): - """_summary_ - """ log.debug("Processing distance matrix for $s", self.fasta_file) distance_obj = taranis.distance.DistanceMatrix(self.fasta_file) mash_distance_df = distance_obj.create_matrix() @@ -53,7 +51,9 @@ def create_cluster_alleles(self): dist_matrix_np, self.locus_name ) cluster_ptrs = cluster_seq.create_clusters() - cluster_seq.convert_to_seq_clusters(cluster_ptrs, postition_to_allele) + alleles_in_cluster = cluster_seq.convert_to_seq_clusters( + cluster_ptrs, postition_to_allele + ) def create_ref_alleles(self): self.records = taranis.utils.read_fasta_file(self.fasta_file) From bf974c4047c89dadeb2ced955f5f35dfd9e71b7f Mon Sep 17 00:00:00 2001 From: luissian Date: Tue, 6 Feb 2024 14:54:13 +0100 Subject: [PATCH 067/214] Implemented cluster center --- taranis/clustering.py | 37 +++++++++++++++++++++++++++--------- taranis/reference_alleles.py | 19 +++++++++++++----- 2 files changed, 42 insertions(+), 14 deletions(-) diff --git a/taranis/clustering.py b/taranis/clustering.py index 36ce62b..70f0706 100644 --- a/taranis/clustering.py +++ b/taranis/clustering.py @@ -4,6 +4,7 @@ import numpy as np import rich.console import taranis.utils +import pdb log = logging.getLogger(__name__) stderr = rich.console.Console( @@ -22,13 +23,12 @@ def __init__(self, dist_matrix: np.array, ref_seq_name: str): self.seed = None self.res_param = 0.9 - def calculate_closest_index( + def calculate_cluster_center( self, cluster_mtrx_idxs: tuple, cluster_mean: float - ) -> list: + ) -> int: cluster_matrix = self.dist_matrix[cluster_mtrx_idxs] - cluster_flat = cluster_matrix.flatten() - closest_index = np.argmin(np.abs(cluster_flat - cluster_mean)) - return [np.unravel_index(closest_index, cluster_matrix.shape)] + row_means = np.mean(cluster_matrix, axis=1) + return cluster_mtrx_idxs[0][np.argmin(np.abs(row_means - cluster_mean))][0] def calculate_mean_cluster(self, cluster_mtrx_idxs: tuple, row_idx_pos: np.ndarray): col_idx_pos = row_idx_pos @@ -50,7 +50,6 @@ def convert_to_seq_clusters( ) -> dict: out_clusters = {} for cluster_id in range(np.max(cluster_ids) + 1): - alleles_in_cluster = [] out_clusters[cluster_id] = [ id_to_seq_name[seq_id] for seq_id in np.argwhere(cluster_ids == cluster_id).flatten() @@ -69,14 +68,18 @@ def collect_data_cluster(self, src_cluster_ptrs): row_idx_pos = np.argwhere(cluster_bool_ptrs).flatten() # col_idx_pos = np.argwhere(cluster_bool_ptrs).flatten() cluster_mean = self.calculate_mean_cluster(cluster_mtrx_idxs, row_idx_pos) - # pdb.set_trace() + # get the closest distance coordenates to cluster mean value cluster_data[cluster_id]["avg"] = cluster_mean - cluster_data[cluster_id]["closest_idx"] = self.calculate_closest_index( + cluster_data[cluster_id]["center_id"] = self.calculate_cluster_center( cluster_mtrx_idxs, cluster_mean ) + log.debug(f"Get the closest distance to culster mean for {cluster_id}") + # get the number of sequences for the cluster + cluster_data[cluster_id]["n_seq"] = len(cluster_mtrx_idxs[0]) return cluster_data def create_clusters(self): + # pdb.set_trace() comm_graph = ig.Graph.Weighted_Adjacency( self.dist_matrix.tolist(), mode=1, loops=False ) @@ -89,6 +92,22 @@ def create_clusters(self): seed=self.seed, ) cluster_ptrs = np.array(graph_clusters.membership) + """ + cluster_centers = [] + for cluster_id in np.unique(cluster_ptrs): + # Get the indices of data points in the current cluster + cluster_indices = np.where(cluster_ptrs == cluster_id)[0] + + # Compute the centroid (mean) of the data points in the current cluster + cluster_distances = self.dist_matrix[np.ix_(cluster_indices, cluster_indices)] + average_distances = np.mean(cluster_distances, axis=1) + centroid_index = cluster_indices[np.argmin(average_distances)] + centroid = self.dist_matrix[centroid_index] + cluster_centers.append(centroid) + for i, center in enumerate(cluster_centers): + print(f"Cluster {i+1}: {center}") + pdb.set_trace() + """ # Convert the partition to a DataFrame # df_clusters = pd.DataFrame({'Node': range(len(graph_clusters.membership)), 'Cluster': graph_clusters.membership}) # Calculate the centroid of each cluster @@ -104,4 +123,4 @@ def create_clusters(self): f"[red]There are some cluster below average of 0.9 in locus {self.ref_seq_name}" ) - return cluster_ptrs + return cluster_ptrs, clusters_data diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index 9cc5fa1..18f0cad 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -2,9 +2,10 @@ import rich.console from pathlib import Path +import os from scipy.sparse import coo_matrix - +import pdb import taranis.utils import taranis.distance import taranis.clustering @@ -47,19 +48,27 @@ def create_cluster_alleles(self): # create a sparse matrix used for summary _ = coo_matrix(matrix_np, shape=matrix_np.shape) - cluster_seq = taranis.clustering.ClusterDistance( + cluster_obj = taranis.clustering.ClusterDistance( dist_matrix_np, self.locus_name ) - cluster_ptrs = cluster_seq.create_clusters() - alleles_in_cluster = cluster_seq.convert_to_seq_clusters( + cluster_ptrs, cluster_data = cluster_obj.create_clusters() + alleles_in_cluster = cluster_obj.convert_to_seq_clusters( cluster_ptrs, postition_to_allele ) + cluster_file = os.path.join(self.output, "cluster_alleles_" + self.locus_name + ".txt") + pdb.set_trace() + with open(cluster_file, "w") as fo: + for cluster_id, alleles in alleles_in_cluster.items(): + fo.write("Cluster number" + str(cluster_id + 1) + "\n") + fo.write("\n".join(alleles) + "\n") + pdb.set_trace() + return cluster_data def create_ref_alleles(self): self.records = taranis.utils.read_fasta_file(self.fasta_file) # _ = self.check_locus_quality() # pdb.set_trace() # Prepare data to use mash to create the distance matrix - _ = self.create_cluster_alleles() + cluster_data = self.create_cluster_alleles() pass From d4d8df2790d71897d80cf238ded597b4d63c08d7 Mon Sep 17 00:00:00 2001 From: luissian Date: Tue, 6 Feb 2024 17:21:03 +0100 Subject: [PATCH 068/214] Completed saved reference alleles to file --- taranis/clustering.py | 26 +++---------------------- taranis/reference_alleles.py | 37 ++++++++++++++++++++++++++++-------- taranis/utils.py | 4 ---- 3 files changed, 32 insertions(+), 35 deletions(-) diff --git a/taranis/clustering.py b/taranis/clustering.py index 70f0706..7dec9bc 100644 --- a/taranis/clustering.py +++ b/taranis/clustering.py @@ -4,7 +4,6 @@ import numpy as np import rich.console import taranis.utils -import pdb log = logging.getLogger(__name__) stderr = rich.console.Console( @@ -73,7 +72,7 @@ def collect_data_cluster(self, src_cluster_ptrs): cluster_data[cluster_id]["center_id"] = self.calculate_cluster_center( cluster_mtrx_idxs, cluster_mean ) - log.debug(f"Get the closest distance to culster mean for {cluster_id}") + log.debug(f"Get the cluster center for {cluster_id}") # get the number of sequences for the cluster cluster_data[cluster_id]["n_seq"] = len(cluster_mtrx_idxs[0]) return cluster_data @@ -92,30 +91,11 @@ def create_clusters(self): seed=self.seed, ) cluster_ptrs = np.array(graph_clusters.membership) - """ - cluster_centers = [] - for cluster_id in np.unique(cluster_ptrs): - # Get the indices of data points in the current cluster - cluster_indices = np.where(cluster_ptrs == cluster_id)[0] - - # Compute the centroid (mean) of the data points in the current cluster - cluster_distances = self.dist_matrix[np.ix_(cluster_indices, cluster_indices)] - average_distances = np.mean(cluster_distances, axis=1) - centroid_index = cluster_indices[np.argmin(average_distances)] - centroid = self.dist_matrix[centroid_index] - cluster_centers.append(centroid) - for i, center in enumerate(cluster_centers): - print(f"Cluster {i+1}: {center}") - pdb.set_trace() - """ - # Convert the partition to a DataFrame - # df_clusters = pd.DataFrame({'Node': range(len(graph_clusters.membership)), 'Cluster': graph_clusters.membership}) - # Calculate the centroid of each cluster - # cluster_centers = df_clusters.groupby('Cluster').apply(lambda x: np.mean(self.dist_matrix[x['Node']], axis=0)).values + clusters_data = self.collect_data_cluster(cluster_ptrs) # check that cluste average values are upper than 0.9 for value in clusters_data.values(): - if value["avg"] < 0.9 : + if value["avg"] < 0.9: log.warning( f"There are some cluster below average of 0.9 in locus {self.ref_seq_name} " ) diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index 18f0cad..83583bc 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -9,6 +9,7 @@ import taranis.utils import taranis.distance import taranis.clustering +from Bio import SeqIO log = logging.getLogger(__name__) stderr = rich.console.Console( @@ -42,9 +43,7 @@ def create_cluster_alleles(self): # At this point minimal distance is 0. For clustering requires to be 1 # the oposite. dist_matrix_np = (matrix_np - 1) * -1 - """ in alfaclust TO DELETE - sparse_edge_weight_mtrx = coo_matrix(global_edge_weight_mtrx, shape=global_edge_weight_mtrx.shape) - """ + # create a sparse matrix used for summary _ = coo_matrix(matrix_np, shape=matrix_np.shape) @@ -52,23 +51,45 @@ def create_cluster_alleles(self): dist_matrix_np, self.locus_name ) cluster_ptrs, cluster_data = cluster_obj.create_clusters() + # convert the center pointer to allele name and create list to get + # sequences + reference_alleles = [] + for cluster_id, values in cluster_data.items(): + center_allele = postition_to_allele[values["center_id"]] + values["center_id"] = center_allele + reference_alleles.append(center_allele) alleles_in_cluster = cluster_obj.convert_to_seq_clusters( cluster_ptrs, postition_to_allele ) - cluster_file = os.path.join(self.output, "cluster_alleles_" + self.locus_name + ".txt") + cluster_file = os.path.join( + self.output, "cluster_alleles_" + self.locus_name + ".txt" + ) pdb.set_trace() with open(cluster_file, "w") as fo: for cluster_id, alleles in alleles_in_cluster.items(): fo.write("Cluster number" + str(cluster_id + 1) + "\n") fo.write("\n".join(alleles) + "\n") pdb.set_trace() - return cluster_data + + return cluster_data, reference_alleles + + def save_reference_alleles(self, reference_alleles: list) -> None: + record_seq = {} + with open(self.fasta_file) as fh: + for record in SeqIO.parse(fh, "fasta"): + if record.id in reference_alleles: + record_seq[record.id] = str(record.seq) + ref_allele_file = os.path.join(self.output, self.locus_name + ".fa") + with open(ref_allele_file, "w") as fo: + for r_id, r_seq in record_seq.items(): + fo.write(r_id + "\n") + fo.write(r_seq + "\n") + return def create_ref_alleles(self): self.records = taranis.utils.read_fasta_file(self.fasta_file) - # _ = self.check_locus_quality() - # pdb.set_trace() # Prepare data to use mash to create the distance matrix - cluster_data = self.create_cluster_alleles() + cluster_data, reference_alleles = self.create_cluster_alleles() + _ = self.save_reference_alleles(reference_alleles) pass diff --git a/taranis/utils.py b/taranis/utils.py index 8f9c6c4..1381d52 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -1,8 +1,4 @@ #!/usr/bin/env python -""" -Common utility function used for relecov_tools package. -""" - import glob import io import logging From e568594801cf79921aeadccf9795d205c8782453 Mon Sep 17 00:00:00 2001 From: luissian Date: Tue, 6 Feb 2024 22:03:31 +0100 Subject: [PATCH 069/214] Modified project.toml to run script --- pyproject.toml | 5 ++++- setup.py | 1 + 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 13f2f7a..c2e6af9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,5 +1,5 @@ [build-system] -requires = ["setuptools"] +requires = ["setuptools", "wheel"] build-backend = "setuptools.build_meta" [project] @@ -23,5 +23,8 @@ license = {file = "LICENSE"} [tool.setuptools.dynamic] dependencies = {file = ["requirements.txt"]} +[project.scripts] +taranis = "taranis.__main__:run_taranis" + [tool.setuptools.packages.find] exclude = ["img", "virtualenv"] diff --git a/setup.py b/setup.py index 14646b6..d6ecd35 100644 --- a/setup.py +++ b/setup.py @@ -29,6 +29,7 @@ url="https://github.com/BU-ISCIII/taranis", license="GNU GENERAL PUBLIC LICENSE v.3", entry_points={"console_scripts": ["taranis=taranis.__main__:run_taranis"]}, + python_requires=">=3.9, <4", install_requires=required, packages=find_packages(exclude=("docs")), include_package_data=True, From a34912b93365952895a725b93e8cc313db71068b Mon Sep 17 00:00:00 2001 From: luissian Date: Tue, 6 Feb 2024 22:31:32 +0100 Subject: [PATCH 070/214] remove poetry when installed taranis --- .github/workflows/tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 05c1b3a..555dc2e 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -26,6 +26,6 @@ jobs: run: | source $CONDA/etc/profile.d/conda.sh conda activate taranis_env - poetry install + python -m pip install . taranis analyze-schema -i test/MLST_listeria -o analyze_schema_test --cpus 1 --output-allele-annot --remove-no-cds --remove-duplicated --remove-subset \ No newline at end of file From f57b881aaddfac24e380329d3517a5866f1cac22 Mon Sep 17 00:00:00 2001 From: luissian Date: Wed, 7 Feb 2024 21:11:58 +0100 Subject: [PATCH 071/214] added documentation for each function --- taranis/__main__.py | 6 ++- taranis/clustering.py | 68 ++++++++++++++++++++++++---- taranis/reference_alleles.py | 86 +++++++++++++++++++++++++++++++----- 3 files changed, 139 insertions(+), 21 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index facde63..b504f5d 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -259,6 +259,7 @@ def reference_alleles( schema: str, output: str, ): + start = time.perf_counter() schema_files = taranis.utils.get_files_in_folder(schema, "fasta") # Check if output folder exists @@ -282,7 +283,10 @@ def reference_alleles( """Create the reference alleles from the schema """ for f_file in schema_files: ref_alleles = taranis.reference_alleles.ReferenceAlleles(f_file, output) - _ = ref_alleles.create_ref_alleles() + results = ref_alleles.create_ref_alleles() + _ = taranis.reference_alleles.collect_statistics([results], output) + finish = time.perf_counter() + print(f"Reference alleles finish in {round((finish-start)/60, 2)} minutes") @taranis_cli.command(help_priority=3) diff --git a/taranis/clustering.py b/taranis/clustering.py index 7dec9bc..0b9b693 100644 --- a/taranis/clustering.py +++ b/taranis/clustering.py @@ -16,6 +16,12 @@ class ClusterDistance: def __init__(self, dist_matrix: np.array, ref_seq_name: str): + """ClusterDistance instance creation + + Args: + dist_matrix (np.array): distance matrix + ref_seq_name (str): locus name + """ self.dist_matrix = dist_matrix self.num_seq = dist_matrix.shape[0] self.ref_seq_name = ref_seq_name @@ -25,15 +31,35 @@ def __init__(self, dist_matrix: np.array, ref_seq_name: str): def calculate_cluster_center( self, cluster_mtrx_idxs: tuple, cluster_mean: float ) -> int: + """Get the center allele for the cluster by selecting the allele closest + value to cluster mean + + Args: + cluster_mtrx_idxs (tuple): tuple with the filter indexes to create + submatrix for each cluster + cluster_mean (float): cluster mean value to compare + + Returns: + int: index of the allele which is the center of cluster + """ cluster_matrix = self.dist_matrix[cluster_mtrx_idxs] row_means = np.mean(cluster_matrix, axis=1) return cluster_mtrx_idxs[0][np.argmin(np.abs(row_means - cluster_mean))][0] - def calculate_mean_cluster(self, cluster_mtrx_idxs: tuple, row_idx_pos: np.ndarray): + def calculate_mean_cluster( + self, cluster_mtrx_idxs: tuple, row_idx_pos: np.ndarray + ) -> float: + """Calculate the mean of cluster distance values + + Args: + cluster_mtrx_idxs (tuple): tuple with the filter indexes to create + submatrix for each cluster + row_idx_pos (np.ndarray): indexes of matrix belongs to cluster + + Returns: + float: mean of the cluster + """ col_idx_pos = row_idx_pos - # src_cluster_mtrx_idxs = np.ix_(src_cluster_row_idxs, src_cluster_col_idxs) - # row_idx_pos = np.argwhere(src_cluster_row_idxs).flatten() - # col_idx_pos = np.argwhere(src_cluster_col_idxs).flatten() num_of_diag_elements = np.intersect1d(row_idx_pos, col_idx_pos).size num_of_non_diag_elements = ( row_idx_pos.size * col_idx_pos.size - num_of_diag_elements @@ -47,6 +73,17 @@ def calculate_mean_cluster(self, cluster_mtrx_idxs: tuple, row_idx_pos: np.ndarr def convert_to_seq_clusters( self, cluster_ids: np.array, id_to_seq_name: dict ) -> dict: + """convert the index into the alleale names + + Args: + cluster_ids (np.array): index of matrix belongs to cluster + id_to_seq_name (dict): having the index as key and allele name in + value + + Returns: + dict: where key is the cluster number and value is the list of + alleles belongs to the cluster + """ out_clusters = {} for cluster_id in range(np.max(cluster_ids) + 1): out_clusters[cluster_id] = [ @@ -56,7 +93,17 @@ def convert_to_seq_clusters( return out_clusters - def collect_data_cluster(self, src_cluster_ptrs): + def collect_data_cluster(self, src_cluster_ptrs: np.ndarray) -> dict: + """Collect the mean, index allele center and number of alleles in + cluster for each cluster + + Args: + src_cluster_ptrs (np.ndarray): cluster matrix + + Returns: + dict: where key is the cluster number and value a list of the + statistic data + """ log.debug(f"Collecting data for cluster {self.ref_seq_name}") cluster_data = {} for cluster_id in range(np.max(src_cluster_ptrs) + 1): @@ -77,8 +124,13 @@ def collect_data_cluster(self, src_cluster_ptrs): cluster_data[cluster_id]["n_seq"] = len(cluster_mtrx_idxs[0]) return cluster_data - def create_clusters(self): - # pdb.set_trace() + def create_clusters(self) -> list[dict]: + """main method to create clustering using the Leiden algorithm + + Returns: + list: two dictionaries are returned first with the cluster and the + matrix indexes adn second the statistics data for each cluster + """ comm_graph = ig.Graph.Weighted_Adjacency( self.dist_matrix.tolist(), mode=1, loops=False ) @@ -103,4 +155,4 @@ def create_clusters(self): f"[red]There are some cluster below average of 0.9 in locus {self.ref_seq_name}" ) - return cluster_ptrs, clusters_data + return [cluster_ptrs, clusters_data] diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index 83583bc..05bc4e5 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -4,7 +4,6 @@ from pathlib import Path import os -from scipy.sparse import coo_matrix import pdb import taranis.utils import taranis.distance @@ -21,13 +20,31 @@ class ReferenceAlleles: - def __init__(self, fasta_file, output): + def __init__(self, fasta_file: str, output: str): + """ReferenceAlleles instance creation + + Args: + fasta_file (str): file name included path for locus + output (str): output folder + """ self.fasta_file = fasta_file self.locus_name = Path(fasta_file).stem self.output = output self.selected_locus = {} - def create_cluster_alleles(self): + def create_cluster_alleles(self) -> list: + """Alleles in fasta file are clustering by using two additional classes: + DistanceMatrix which creates a matrix of distance using the allele + sequences, and ClusterDistance which get the matrix and group the + alleles in clusters. As per result of ClusterDistance methods, the + reference alleles are saved to file and statistics information is + returned + + Returns: + list: two dictionaires are returned, cluster_data having statistics + and reference_alleles, where keys are cluster number and value + the reference allele for the cluster + """ log.debug("Processing distance matrix for $s", self.fasta_file) distance_obj = taranis.distance.DistanceMatrix(self.fasta_file) mash_distance_df = distance_obj.create_matrix() @@ -44,9 +61,6 @@ def create_cluster_alleles(self): # the oposite. dist_matrix_np = (matrix_np - 1) * -1 - # create a sparse matrix used for summary - _ = coo_matrix(matrix_np, shape=matrix_np.shape) - cluster_obj = taranis.clustering.ClusterDistance( dist_matrix_np, self.locus_name ) @@ -61,19 +75,24 @@ def create_cluster_alleles(self): alleles_in_cluster = cluster_obj.convert_to_seq_clusters( cluster_ptrs, postition_to_allele ) + cluster_folder = os.path.join(self.output, "Clusters") + _ = taranis.utils.create_new_folder(cluster_folder) cluster_file = os.path.join( - self.output, "cluster_alleles_" + self.locus_name + ".txt" + cluster_folder, "cluster_alleles_" + self.locus_name + ".txt" ) - pdb.set_trace() with open(cluster_file, "w") as fo: for cluster_id, alleles in alleles_in_cluster.items(): fo.write("Cluster number" + str(cluster_id + 1) + "\n") fo.write("\n".join(alleles) + "\n") - pdb.set_trace() - return cluster_data, reference_alleles + return [cluster_data, reference_alleles] def save_reference_alleles(self, reference_alleles: list) -> None: + """From the input list it fetch the allele squence and save it as fasta + + Args: + reference_alleles (list): list having the allele ids + """ record_seq = {} with open(self.fasta_file) as fh: for record in SeqIO.parse(fh, "fasta"): @@ -86,10 +105,53 @@ def save_reference_alleles(self, reference_alleles: list) -> None: fo.write(r_seq + "\n") return - def create_ref_alleles(self): + def create_ref_alleles(self) -> dict: + """Main method to create the reference alleles + + Returns: + dict: statistics information for each cluster + """ self.records = taranis.utils.read_fasta_file(self.fasta_file) # Prepare data to use mash to create the distance matrix cluster_data, reference_alleles = self.create_cluster_alleles() _ = self.save_reference_alleles(reference_alleles) + return cluster_data + + +def collect_statistics(data_alleles: list, out_folder: str) -> None: + """Collect the individual statistics for each locus to create graphics + + Args: + data_alleles (list): list having the indiviual statistics data + out_folder (str): folder to save graphics + """ + + def stats_graphics(stats_folder: str, cluster_alleles: dict) -> None: + """Create the statistics graphics. Bar graphic for number of cluster + per alleles + + Args: + stats_folder (str): folder path to store graphic + cluster_alleles (dict): contain number of clusters as key and number + of alleles having the same cluster number as value + """ + stderr.print("Creating graphics") + log.info("Creating graphics") + graphic_folder = os.path.join(stats_folder, "graphics") + _ = taranis.utils.create_new_folder(graphic_folder) + cluster, alleles = zip(*cluster_alleles.items()) + _ = taranis.utils.create_graphic( + graphic_folder, + "num_genes_per_allele.png", + "bar", + cluster, + alleles, + ["Number of clusters", "number of genes"], + "Number of cluster per gene", + ) - pass + cluster_alleles = {} + for d_allele in data_alleles: + cluster_alleles[len(d_allele)] = cluster_alleles.get(len(d_allele), 0) + 1 + _ = stats_graphics(out_folder, cluster_alleles) + return From 019ae42d270bbad6c033c544f41be68afe83a285 Mon Sep 17 00:00:00 2001 From: luissian Date: Wed, 7 Feb 2024 21:16:00 +0100 Subject: [PATCH 072/214] Fixed liting --- taranis/reference_alleles.py | 1 - 1 file changed, 1 deletion(-) diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index 05bc4e5..29744d9 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -4,7 +4,6 @@ from pathlib import Path import os -import pdb import taranis.utils import taranis.distance import taranis.clustering From a8a8b949a0149900fa8acb56037bf97b466e3491 Mon Sep 17 00:00:00 2001 From: luissian Date: Sat, 10 Feb 2024 23:57:03 +0100 Subject: [PATCH 073/214] implemented evaluation cluster --- taranis/__main__.py | 9 ++- taranis/blast.py | 30 ++++++-- taranis/eval_cluster.py | 137 +++++++++++++++++++++++++++++++++++ taranis/reference_alleles.py | 40 +++++++--- 4 files changed, 200 insertions(+), 16 deletions(-) create mode 100644 taranis/eval_cluster.py diff --git a/taranis/__main__.py b/taranis/__main__.py index b504f5d..8a6d008 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -255,9 +255,16 @@ def analyze_schema( type=click.Path(), help="Output folder to save reference alleles", ) +@click.option( + "--eval-cluster/--no-eval-cluster", + required=False, + default=False, + help="Evaluate if the reference alleles match all alleles with a 90% identity", +) def reference_alleles( schema: str, output: str, + eval_cluster: bool, ): start = time.perf_counter() schema_files = taranis.utils.get_files_in_folder(schema, "fasta") @@ -282,7 +289,7 @@ def reference_alleles( sys.exit(1) """Create the reference alleles from the schema """ for f_file in schema_files: - ref_alleles = taranis.reference_alleles.ReferenceAlleles(f_file, output) + ref_alleles = taranis.reference_alleles.ReferenceAlleles(f_file, output, eval_cluster) results = ref_alleles.create_ref_alleles() _ = taranis.reference_alleles.collect_statistics([results], output) finish = time.perf_counter() diff --git a/taranis/blast.py b/taranis/blast.py index 39e948e..4b627e6 100644 --- a/taranis/blast.py +++ b/taranis/blast.py @@ -17,10 +17,21 @@ class Blast: - def __init__(self, db_type): + def __init__(self, db_type: str): + """Blast instance creation + + Args: + db_type (str): type of blast database (nucleotide or protein) + """ self.db_type = db_type - def create_blastdb(self, file_name, blast_dir): + def create_blastdb(self, file_name: str, blast_dir: str) -> None: + """Create blast database and store it at blast dir + + Args: + file_name (str): Fasta file from generate the database + blast_dir (str): directory to store blast database files + """ self.f_name = Path(file_name).stem db_dir = os.path.join(blast_dir, self.f_name) self.out_blast_dir = os.path.join(db_dir, self.f_name) @@ -63,6 +74,7 @@ def run_blast( max_target_seqs: int = 1000, max_hsps: int = 10, num_threads: int = 1, + query_type: str = "file", ) -> list: """blast command is executed, returning a list of each match found @@ -77,10 +89,13 @@ def run_blast( max_target_seqs (int, optional): max target to output. Defaults to 1000. max_hsps (int, optional): max hsps. Defaults to 10. num_threads (int, optional): number of threads. Defaults to 1. - + query_type (str, optional): format of query (either file or string) Returns: list: list of strings containing blast results """ + if query_type == "stdin": + stdin_query = query + query = "-" blast_parameters = '"6 , qseqid , sseqid , pident , qlen , length , mismatch , gapopen , evalue , bitscore , sstart , send , qstart , qend , sseq , qseq"' cline = NcbiblastnCommandline( task="blastn", @@ -98,10 +113,13 @@ def run_blast( query=query, ) try: - out, _ = cline() + if query_type == "stdin": + out, _ = cline(stdin=stdin_query) + else: + out, _ = cline() except Exception as e: - log.error("Unable to run blast for %s ", self.out_blast_dir) - log.error(e) + # log.error("Unable to run blast for %s ", self.out_blast_dir) + # log.error(e) stderr.print(f"[red] Unable to run blast {self.out_blast_dir}") exit(1) return out.splitlines() diff --git a/taranis/eval_cluster.py b/taranis/eval_cluster.py new file mode 100644 index 0000000..ae099ad --- /dev/null +++ b/taranis/eval_cluster.py @@ -0,0 +1,137 @@ +import io +import logging +import numpy as np +import rich.console +import os +import taranis.utils +import taranis.blast +from Bio import SeqIO +import pdb + +log = logging.getLogger(__name__) +stderr = rich.console.Console( + stderr=True, + style="dim", + highlight=False, + force_terminal=taranis.utils.rich_force_colors(), +) + + +class EvaluateCluster: + def __init__(self, locus_path: str, locus_name: str, output: str): + self.locus_path = locus_path + self.locus_name = locus_name + + self.output = os.path.join(output, "evaluate_cluster") + taranis.utils.create_new_folder(self.output) + # locus_blast_dir = os.path.join(self.output, locus_name) + self.blast_obj = taranis.blast.Blast("nucl") + _ = self.blast_obj.create_blastdb(locus_path, self.output) + return + + def find_cluster_from_ref_allele(self, cluster_ref_alleles: dict) -> dict: + return dict( + [(value["center_id"], c_id) for c_id, value in cluster_ref_alleles.items()] + ) + + def summary(self, cluster_data: dict) -> list: + summary_table = [ + "Locus name", + "result", + "alleles not found", + "alleles not in cluster", + ] + sorted_cluster = sorted(cluster_data.keys()) + for cluster_id in sorted_cluster: + row_data = [self.locus_name, str(cluster_id)] + row_data.append(cluster_data[cluster_id]["result"]) + row_data.append( + ";".join(cluster_data[cluster_id]["alleles_not_found"]) + if "alleles_not_found" in cluster_data[cluster_id] + else "-" + ) + row_data.append( + ";".join(cluster_data[cluster_id]["alleles_not_in_cluster"]) + if "alleles_not_in_culster" in cluster_data[cluster_id] + else "-" + ) + summary_table.append(",".join(row_data)) + return summary_table + + def validate_cluster(self, blast_result: dict, cluster_data: list) -> dict: + """For cluster validation, the sequence id matched in blast are compared + with the cluster sequences. Return False validation if there are + difference between them. + + Args: + blast_result (dict): _description_ + cluster_data (list): _description_ + + Returns: + dict: _description_ + """ + # index of sequence id + sseqid = 1 + blast_alleles = [] + alleles_not_in_cluster = [] + for match in blast_result: + blast_allele = match.split("\t")[sseqid] + if blast_allele in cluster_data: + blast_alleles.append(blast_allele) + else: + alleles_not_in_cluster.append(blast_allele) + + if len(cluster_data) == len(blast_alleles): + return {"validation": True} + result = {"validation": False} + # convert list to numpy array to find out differences + c_alleles_np = np.array(list(cluster_data)) + blast_alleles_np = np.array(blast_alleles) + result["alleles_not_found"] = np.setdiff1d( + c_alleles_np, blast_alleles_np + ).tolist() + result["alleles_not_in_cluster"] = np.setdiff1d( + blast_alleles_np, c_alleles_np + ).tolist() + return result + + def evaluate_clusters( + self, cluster_alleles: dict, cluster_ref_alleles, ref_alleles_file: str + ): + reference_alleles = {} + evaluation_alleles = {} + ref_allele_in_cluster = self.find_cluster_from_ref_allele(cluster_ref_alleles) + with open(ref_alleles_file, "r") as fh: + for record in SeqIO.parse(fh, "fasta"): + reference_alleles[record.id] = str(record.seq) + + for r_id, r_seq in reference_alleles.items(): + # create file in memory to increase speed + query_file = io.StringIO() + query_file.write(">" + r_id + "\n" + r_seq) + query_file.seek(0) + blast_result = self.blast_obj.run_blast( + query_file.read(), perc_identity=90, query_type="stdin" + ) + # Close object and discard memory buffer + query_file.close() + + cluster_id = ref_allele_in_cluster[r_id] + # pdb.set_trace() + result_eval = self.validate_cluster( + blast_result, cluster_alleles[cluster_id] + ) + evaluation_alleles[cluster_id] = {} + if result_eval["validation"] is False: + evaluation_alleles[cluster_id]["result"] = "NOK" + if len(result_eval["alleles_not_found"]) > 0: + evaluation_alleles[cluster_id]["alleles_not_found"] = result_eval[ + "alleles_not_found" + ] + if len(result_eval["alleles_not_in_cluster"]) > 0: + evaluation_alleles[cluster_id][ + "alleles_not_in_cluster" + ] = result_eval["alleles_not_in_cluster"] + else: + evaluation_alleles[cluster_id]["result"] = "OK" + return self.summary(evaluation_alleles) diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index 29744d9..6ffb1f4 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -7,7 +7,9 @@ import taranis.utils import taranis.distance import taranis.clustering +import taranis.eval_cluster from Bio import SeqIO +import pdb log = logging.getLogger(__name__) stderr = rich.console.Console( @@ -19,19 +21,21 @@ class ReferenceAlleles: - def __init__(self, fasta_file: str, output: str): + def __init__(self, fasta_file: str, output: str, eval_cluster: bool): """ReferenceAlleles instance creation Args: fasta_file (str): file name included path for locus output (str): output folder + eval_cluster (bool): True if cluster evaluation must be done """ self.fasta_file = fasta_file self.locus_name = Path(fasta_file).stem self.output = output + self.eval_cluster = eval_cluster self.selected_locus = {} - def create_cluster_alleles(self) -> list: + def create_cluster_alleles(self) -> dict: """Alleles in fasta file are clustering by using two additional classes: DistanceMatrix which creates a matrix of distance using the allele sequences, and ClusterDistance which get the matrix and group the @@ -84,13 +88,20 @@ def create_cluster_alleles(self) -> list: fo.write("Cluster number" + str(cluster_id + 1) + "\n") fo.write("\n".join(alleles) + "\n") - return [cluster_data, reference_alleles] + return { + "cluster_data": cluster_data, + "reference_alleles": reference_alleles, + "alleles_in_cluster": alleles_in_cluster, + } - def save_reference_alleles(self, reference_alleles: list) -> None: + def save_reference_alleles(self, reference_alleles: list) -> str: """From the input list it fetch the allele squence and save it as fasta Args: reference_alleles (list): list having the allele ids + + Returns: + str: file path of the reference alleles """ record_seq = {} with open(self.fasta_file) as fh: @@ -100,9 +111,9 @@ def save_reference_alleles(self, reference_alleles: list) -> None: ref_allele_file = os.path.join(self.output, self.locus_name + ".fa") with open(ref_allele_file, "w") as fo: for r_id, r_seq in record_seq.items(): - fo.write(r_id + "\n") + fo.write(">" + r_id + "\n") fo.write(r_seq + "\n") - return + return ref_allele_file def create_ref_alleles(self) -> dict: """Main method to create the reference alleles @@ -112,9 +123,20 @@ def create_ref_alleles(self) -> dict: """ self.records = taranis.utils.read_fasta_file(self.fasta_file) # Prepare data to use mash to create the distance matrix - cluster_data, reference_alleles = self.create_cluster_alleles() - _ = self.save_reference_alleles(reference_alleles) - return cluster_data + allele_data = self.create_cluster_alleles() + ref_fasta_file = self.save_reference_alleles(allele_data["reference_alleles"]) + if self.eval_cluster: + stderr.print(f"Evaluating clusters") + evaluation_obj = taranis.eval_cluster.EvaluateCluster( + self.fasta_file, self.locus_name, self.output + ) + evaluation_obj.evaluate_clusters( + allele_data["alleles_in_cluster"], + allele_data["cluster_data"], + ref_fasta_file, + ) + + return allele_data["cluster_data"] def collect_statistics(data_alleles: list, out_folder: str) -> None: From 0faf7da45406cda736fba6cb230a5ca0b79c91f9 Mon Sep 17 00:00:00 2001 From: luissian Date: Sat, 17 Feb 2024 19:05:33 +0100 Subject: [PATCH 074/214] Implemented parallel and dinamic clustering --- taranis/__main__.py | 81 ++++++++++++- taranis/blast.py | 2 +- taranis/clustering.py | 36 +++--- taranis/distance.py | 13 +- taranis/eval_cluster.py | 79 ++++++++++--- taranis/reference_alleles.py | 223 +++++++++++++++++++++++++++++------ taranis/utils.py | 50 +++++--- 7 files changed, 391 insertions(+), 93 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index 8a6d008..8b9a920 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -258,15 +258,64 @@ def analyze_schema( @click.option( "--eval-cluster/--no-eval-cluster", required=False, - default=False, - help="Evaluate if the reference alleles match all alleles with a 90% identity", + default=True, + help="Evaluate if the reference alleles match against blast with a 90% identity", +) +@click.option( + "-k", + "--kmer-size", + required=False, + type=int, + default=21, + help="Mash parameter for K-mer size.", +) +@click.option( + "-S", + "--sketch-size", + required=False, + type=int, + default=2000, + help="Mash parameter for Sketch size", +) +@click.option( + "-r", + "--cluster-resolution", + required=False, + type=float, + default=0.92, + help="Resolution value used for clustering.", +) +@click.option( + "--seed", + required=False, + type=int, + default=None, + help="Seed value for clustering", +) +@click.option( + "--cpus", + required=False, + multiple=False, + type=int, + default=1, + help="Number of cpus used for execution", ) def reference_alleles( schema: str, output: str, eval_cluster: bool, + kmer_size: int, + sketch_size: int, + cluster_resolution: float, + seed: int, + cpus: int, ): start = time.perf_counter() + max_cpus = taranis.utils.cpus_available() + if cpus > max_cpus: + stderr.print("[red] Number of CPUs bigger than the CPUs available") + stderr.print("Running code with ", max_cpus) + cpus = max_cpus schema_files = taranis.utils.get_files_in_folder(schema, "fasta") # Check if output folder exists @@ -288,10 +337,32 @@ def reference_alleles( stderr.print("[red] ERROR. Unable to create folder " + output) sys.exit(1) """Create the reference alleles from the schema """ + results = [] + """ for f_file in schema_files: - ref_alleles = taranis.reference_alleles.ReferenceAlleles(f_file, output, eval_cluster) - results = ref_alleles.create_ref_alleles() - _ = taranis.reference_alleles.collect_statistics([results], output) + + ref_alleles = taranis.reference_alleles.ReferenceAlleles(f_file, output, eval_cluster, kmer_size, sketch_size) + c_data = ref_alleles.create_ref_alleles() + results.append(c_data) + """ + with concurrent.futures.ThreadPoolExecutor(max_workers=cpus) as executor: + futures = [ + executor.submit( + taranis.reference_alleles.parallel_execution, + f_file, + output, + eval_cluster, + kmer_size, + sketch_size, + cluster_resolution, + seed, + ) + for f_file in schema_files + ] + # import pdb; pdb.set_trace() + for future in concurrent.futures.as_completed(futures): + results.append(future.result()) + _ = taranis.reference_alleles.collect_statistics(results, eval_cluster, output) finish = time.perf_counter() print(f"Reference alleles finish in {round((finish-start)/60, 2)} minutes") diff --git a/taranis/blast.py b/taranis/blast.py index 4b627e6..7054913 100644 --- a/taranis/blast.py +++ b/taranis/blast.py @@ -71,7 +71,7 @@ def run_blast( penalty: int = -2, gapopen: int = 1, gapextend: int = 1, - max_target_seqs: int = 1000, + max_target_seqs: int = 2000, max_hsps: int = 10, num_threads: int = 1, query_type: str = "file", diff --git a/taranis/clustering.py b/taranis/clustering.py index 0b9b693..5b8b437 100644 --- a/taranis/clustering.py +++ b/taranis/clustering.py @@ -15,18 +15,26 @@ class ClusterDistance: - def __init__(self, dist_matrix: np.array, ref_seq_name: str): + def __init__( + self, + dist_matrix: np.array, + ref_seq_name: str, + resolution: float = 0.92, + seed: int = None, + ): """ClusterDistance instance creation Args: dist_matrix (np.array): distance matrix ref_seq_name (str): locus name + resolution (float): resolution value for the clustering + seed (int): seed for the clustering """ self.dist_matrix = dist_matrix self.num_seq = dist_matrix.shape[0] self.ref_seq_name = ref_seq_name - self.seed = None - self.res_param = 0.9 + self.seed = seed + self.resolution = resolution def calculate_cluster_center( self, cluster_mtrx_idxs: tuple, cluster_mean: float @@ -102,12 +110,12 @@ def collect_data_cluster(self, src_cluster_ptrs: np.ndarray) -> dict: Returns: dict: where key is the cluster number and value a list of the - statistic data + statistics data """ log.debug(f"Collecting data for cluster {self.ref_seq_name}") cluster_data = {} for cluster_id in range(np.max(src_cluster_ptrs) + 1): - cluster_data[cluster_id] = {} + cluster_data[cluster_id] = {"locus_name": self.ref_seq_name} log.debug(f"calculating mean for cluster number {cluster_id}") cluster_bool_ptrs = src_cluster_ptrs == cluster_id cluster_mtrx_idxs = np.ix_(cluster_bool_ptrs, cluster_bool_ptrs) @@ -124,13 +132,17 @@ def collect_data_cluster(self, src_cluster_ptrs: np.ndarray) -> dict: cluster_data[cluster_id]["n_seq"] = len(cluster_mtrx_idxs[0]) return cluster_data - def create_clusters(self) -> list[dict]: + def create_clusters(self, resolution) -> list[dict]: """main method to create clustering using the Leiden algorithm + Args: + resolution (float): resolution value for the clustering + Returns: list: two dictionaries are returned first with the cluster and the matrix indexes adn second the statistics data for each cluster """ + self.resolution = resolution comm_graph = ig.Graph.Weighted_Adjacency( self.dist_matrix.tolist(), mode=1, loops=False ) @@ -139,20 +151,10 @@ def create_clusters(self) -> list[dict]: leidenalg.CPMVertexPartition, weights="weight", n_iterations=-1, - resolution_parameter=self.res_param, + resolution_parameter=self.resolution, seed=self.seed, ) cluster_ptrs = np.array(graph_clusters.membership) clusters_data = self.collect_data_cluster(cluster_ptrs) - # check that cluste average values are upper than 0.9 - for value in clusters_data.values(): - if value["avg"] < 0.9: - log.warning( - f"There are some cluster below average of 0.9 in locus {self.ref_seq_name} " - ) - stderr.print( - f"[red]There are some cluster below average of 0.9 in locus {self.ref_seq_name}" - ) - return [cluster_ptrs, clusters_data] diff --git a/taranis/distance.py b/taranis/distance.py index bc75105..428f596 100644 --- a/taranis/distance.py +++ b/taranis/distance.py @@ -18,18 +18,21 @@ class DistanceMatrix: def __init__( - self, - file_path: str, + self, file_path: str, k_mer_value: str = "21", sketch_size: str = "2000" ) -> "DistanceMatrix": """DistanceMatrix instance creation Args: file_path (str): Locus file path + k_mer_value (str, optional): Hashes will be based on strings of this many nucleotides. Defaults to "21". + sketch_size (str, optional): Each sketch will have at most this many non-redundant min-hashes. Defaults to "2000". Returns: - DistanceMatrix: created instance + DistanceMatrix: created distance """ self.file_path = file_path + self.k_mer_value = k_mer_value + self.sketch_size = sketch_size def create_matrix(self) -> pd.DataFrame: """Create distance matrix using external program called mash @@ -44,9 +47,9 @@ def create_matrix(self) -> pd.DataFrame: "-i", self.file_path, "-k", - "17", + str(self.k_mer_value), "-s", - "2000", + str(self.sketch_size), ] try: mash_distance_result = subprocess.Popen( diff --git a/taranis/eval_cluster.py b/taranis/eval_cluster.py index ae099ad..04d6c59 100644 --- a/taranis/eval_cluster.py +++ b/taranis/eval_cluster.py @@ -19,6 +19,13 @@ class EvaluateCluster: def __init__(self, locus_path: str, locus_name: str, output: str): + """EvaluateCluster instance creation + + Args: + locus_path (str): path of the locus + locus_name (str): locus name + output (str): folder to store results + """ self.locus_path = locus_path self.locus_name = locus_name @@ -29,18 +36,39 @@ def __init__(self, locus_path: str, locus_name: str, output: str): _ = self.blast_obj.create_blastdb(locus_path, self.output) return + def delete_blast_db_folder(self): + """Delete blast db folder""" + taranis.utils.delete_folder(os.path.join(self.output, self.locus_name)) + def find_cluster_from_ref_allele(self, cluster_ref_alleles: dict) -> dict: + """Create a dictionary to map de cluster belongs to the reference allele + + Args: + cluster_ref_alleles (dict): values collected for statistics with the + cluster id and the reference allele name + + Returns: + dict: relation between reference allele name and the cluster + """ return dict( [(value["center_id"], c_id) for c_id, value in cluster_ref_alleles.items()] ) - def summary(self, cluster_data: dict) -> list: - summary_table = [ - "Locus name", - "result", - "alleles not found", - "alleles not in cluster", - ] + def summary(self, cluster_data: dict) -> dict: + """Create the summary information from the individual result for each + cluster + + Args: + cluster_data (dict): cluster evaluation + + Returns: + dict: summary table for getting nice presentation of evaluation data + and global result for the locus + """ + summary_table = [] + summary_data = {"result": "OK"} + # heading = "Locus name,result,alleles not found,alleles not in cluster" + # summary_table.append(heading) sorted_cluster = sorted(cluster_data.keys()) for cluster_id in sorted_cluster: row_data = [self.locus_name, str(cluster_id)] @@ -55,20 +83,24 @@ def summary(self, cluster_data: dict) -> list: if "alleles_not_in_culster" in cluster_data[cluster_id] else "-" ) + if cluster_data[cluster_id]["result"] == "NOK": + summary_data["result"] = "NOK" summary_table.append(",".join(row_data)) - return summary_table + # pdb.set_trace() + summary_data["individual"] = summary_table + return summary_data - def validate_cluster(self, blast_result: dict, cluster_data: list) -> dict: + def validate_cluster(self, blast_result: list, cluster_data: list) -> dict: """For cluster validation, the sequence id matched in blast are compared with the cluster sequences. Return False validation if there are difference between them. Args: - blast_result (dict): _description_ - cluster_data (list): _description_ + blast_result (list): blast matches results + cluster_data (list): allele names for the cluster to evaluate Returns: - dict: _description_ + dict: result of the evaluation """ # index of sequence id sseqid = 1 @@ -81,7 +113,8 @@ def validate_cluster(self, blast_result: dict, cluster_data: list) -> dict: else: alleles_not_in_cluster.append(blast_allele) - if len(cluster_data) == len(blast_alleles): + if len(cluster_data) == len(set(blast_alleles)): + # pdb.set_trace() return {"validation": True} result = {"validation": False} # convert list to numpy array to find out differences @@ -96,8 +129,23 @@ def validate_cluster(self, blast_result: dict, cluster_data: list) -> dict: return result def evaluate_clusters( - self, cluster_alleles: dict, cluster_ref_alleles, ref_alleles_file: str - ): + self, cluster_alleles: dict, cluster_ref_alleles: dict, ref_alleles_file: str + ) -> list: + """Perform clusted evaluation comparing for each clusted defined in + previous step with searching the matches that blast found running + witha 90% of percentage of identity + + Args: + cluster_alleles (dict): contains the cluster id as dict and the list + of allele names as value + cluster_ref_alleles (dict): statistics information for each cluster + to fetch the reference allele for each cluster + ref_alleles_file (str): reference alleles to get the seqence for the + reference allele + + Returns: + list: evaluation imformation for each cluster + """ reference_alleles = {} evaluation_alleles = {} ref_allele_in_cluster = self.find_cluster_from_ref_allele(cluster_ref_alleles) @@ -117,7 +165,6 @@ def evaluate_clusters( query_file.close() cluster_id = ref_allele_in_cluster[r_id] - # pdb.set_trace() result_eval = self.validate_cluster( blast_result, cluster_alleles[cluster_id] ) diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index 6ffb1f4..ded1221 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -1,5 +1,5 @@ import logging - +import numpy as np import rich.console from pathlib import Path import os @@ -21,25 +21,67 @@ class ReferenceAlleles: - def __init__(self, fasta_file: str, output: str, eval_cluster: bool): + def __init__( + self, + fasta_file: str, + output: str, + eval_cluster: bool, + kmer_size: int, + sketch_size: int, + cluster_resolution: float = 0.92, + seed: int = None, + ): """ReferenceAlleles instance creation Args: fasta_file (str): file name included path for locus output (str): output folder eval_cluster (bool): True if cluster evaluation must be done + kmer_size (int): kmer size for mash distance + sketch_size (int): sketch size for mash distance + cluster_resolution (float): resolution for clustering + seed (int): seed for random number generator """ self.fasta_file = fasta_file self.locus_name = Path(fasta_file).stem self.output = output self.eval_cluster = eval_cluster + self.kmer_size = kmer_size + self.sketch_size = sketch_size + self.cluster_resolution = cluster_resolution + self.seed = seed self.selected_locus = {} + self.cluster_obj = None - def create_cluster_alleles(self) -> dict: - """Alleles in fasta file are clustering by using two additional classes: - DistanceMatrix which creates a matrix of distance using the allele - sequences, and ClusterDistance which get the matrix and group the - alleles in clusters. As per result of ClusterDistance methods, the + def create_distance_matrix(self) -> list: + """Create the distance matrix for the alleles in the fasta file + + Returns: + np.array: distance matrix + dict: position to allele name + """ + log.debug("Processing distance matrix for $s", self.fasta_file) + distance_obj = taranis.distance.DistanceMatrix( + self.fasta_file, self.kmer_size, self.sketch_size + ) + mash_distance_df = distance_obj.create_matrix() + log.debug(f"Created distance matrix for {self.fasta_file}") + postition_to_allele = { + x: mash_distance_df.columns[x] for x in range(len(mash_distance_df.columns)) + } + # convert the triangle matrix into full data matrix + matrix_np = mash_distance_df.to_numpy() + t_matrix_np = matrix_np.transpose() + matrix_np = t_matrix_np + matrix_np + # At this point minimal distance is 0. For clustering requires to be 1 + # the oposite. + dist_matrix_np = (matrix_np - 1) * -1 + return dist_matrix_np, postition_to_allele + + def processing_cluster_data( + self, cluster_data: np.array, cluster_ptrs: np.array, position_to_allele: dict + ) -> dict: + """As per result of ClusterDistance methods, the reference alleles are saved to file and statistics information is returned @@ -48,11 +90,16 @@ def create_cluster_alleles(self) -> dict: and reference_alleles, where keys are cluster number and value the reference allele for the cluster """ - log.debug("Processing distance matrix for $s", self.fasta_file) - distance_obj = taranis.distance.DistanceMatrix(self.fasta_file) - mash_distance_df = distance_obj.create_matrix() - log.debug(f"Created distance matrix for {self.fasta_file}") + """ + # dist_matrix_np, postition_to_allele = self.create_distance_matrix() + + + # log.debug("Processing distance matrix for $s", self.fasta_file) + # distance_obj = taranis.distance.DistanceMatrix(self.fasta_file, self.kmer_size, self.sketch_size) + # mash_distance_df = distance_obj.create_matrix() + # log.debug(f"Created distance matrix for {self.fasta_file}") # fetch the allele position into array + postition_to_allele = { x: mash_distance_df.columns[x] for x in range(len(mash_distance_df.columns)) } @@ -63,20 +110,18 @@ def create_cluster_alleles(self) -> dict: # At this point minimal distance is 0. For clustering requires to be 1 # the oposite. dist_matrix_np = (matrix_np - 1) * -1 + """ - cluster_obj = taranis.clustering.ClusterDistance( - dist_matrix_np, self.locus_name - ) - cluster_ptrs, cluster_data = cluster_obj.create_clusters() # convert the center pointer to allele name and create list to get # sequences + reference_alleles = [] for cluster_id, values in cluster_data.items(): - center_allele = postition_to_allele[values["center_id"]] + center_allele = position_to_allele[values["center_id"]] values["center_id"] = center_allele reference_alleles.append(center_allele) - alleles_in_cluster = cluster_obj.convert_to_seq_clusters( - cluster_ptrs, postition_to_allele + alleles_in_cluster = self.cluster_obj.convert_to_seq_clusters( + cluster_ptrs, position_to_allele ) cluster_folder = os.path.join(self.output, "Clusters") _ = taranis.utils.create_new_folder(cluster_folder) @@ -116,34 +161,104 @@ def save_reference_alleles(self, reference_alleles: list) -> str: return ref_allele_file def create_ref_alleles(self) -> dict: - """Main method to create the reference alleles + """Alleles in fasta file are clustering by using two additional classes: + DistanceMatrix which creates a matrix of distance using the allele + sequences, and ClusterDistance which get the matrix and group the + alleles in clusters. Returns: - dict: statistics information for each cluster + dict: containg statistics information for each cluster, and + optionally a list of evaluation cluster results """ self.records = taranis.utils.read_fasta_file(self.fasta_file) - # Prepare data to use mash to create the distance matrix - allele_data = self.create_cluster_alleles() - ref_fasta_file = self.save_reference_alleles(allele_data["reference_alleles"]) - if self.eval_cluster: - stderr.print(f"Evaluating clusters") + dist_matrix_np, postition_to_allele = self.create_distance_matrix() + self.cluster_obj = taranis.clustering.ClusterDistance( + dist_matrix_np, + self.locus_name, + ) + # pdb.set_trace() + for resolution in np.arange(self.cluster_resolution, 1, 0.025): + cluster_ptrs, cluster_data = self.cluster_obj.create_clusters( + round(resolution, 3) + ) + + allele_data = self.processing_cluster_data( + cluster_data, cluster_ptrs, postition_to_allele + ) + ref_fasta_file = self.save_reference_alleles( + allele_data["reference_alleles"] + ) + + # evaluate clusters aginst blast results + stderr.print(f"Evaluating clusters for {self.locus_name}") evaluation_obj = taranis.eval_cluster.EvaluateCluster( self.fasta_file, self.locus_name, self.output ) - evaluation_obj.evaluate_clusters( + # pdb.set_trace() + evaluation_result = evaluation_obj.evaluate_clusters( allele_data["alleles_in_cluster"], allele_data["cluster_data"], ref_fasta_file, ) + # pdb.set_trace() + if evaluation_result["result"] == "OK" or resolution >= 1: + # delete blast database used for evaluation + _ = evaluation_obj.delete_blast_db_folder() + break + stderr.print( + f"[yellow]{self.locus_name} resolution {resolution} not good enough. Increasing resolution" + ) + log.info( + "%s resolution %s not good enough. Increasing resolution", + self.locus_name, + resolution, + ) + + return { + "cluster_data": allele_data["cluster_data"], + "evaluation": evaluation_result, + } - return allele_data["cluster_data"] +def parallel_execution( + fasta_file: str, + output: str, + eval_cluster: bool, + kmer_size: int, + sketch_size: int, + cluster_resolution: float, + seed: int, +): + """Parallel execution of the reference alleles creation + + Args: + fasta_file (str): file name included path for locus + output (str): output folder + eval_cluster (bool): True if cluster evaluation must be done + kmer_size (int): kmer size for mash distance + sketch_size (int): sketch size for mash distance + cluster_resolution (float): resolution for clustering + seed (int): seed for random number generator + """ + ref_alleles_obj = taranis.reference_alleles.ReferenceAlleles( + fasta_file, + output, + eval_cluster, + kmer_size, + sketch_size, + cluster_resolution, + seed, + ) + return ref_alleles_obj.create_ref_alleles() -def collect_statistics(data_alleles: list, out_folder: str) -> None: + +def collect_statistics(data_alleles: list, eval_cluster: bool, out_folder: str) -> None: """Collect the individual statistics for each locus to create graphics Args: - data_alleles (list): list having the indiviual statistics data + data_alleles (list): list having two dictionaries, cluster_data for + information and evalluation for the result of evaluating + eval_cluser (bool): True if evaluation data exists to dump this info out_folder (str): folder to save graphics """ @@ -167,12 +282,54 @@ def stats_graphics(stats_folder: str, cluster_alleles: dict) -> None: "bar", cluster, alleles, - ["Number of clusters", "number of genes"], + ["Gene", "Number of clusters"], "Number of cluster per gene", ) - cluster_alleles = {} + # split into cluster_data and evaluation_data + cluster_data = [] + eval_data = [] + cluster_data_graph = {} + clusters_list = [] + # split the data into cluster and evaluation for d_allele in data_alleles: - cluster_alleles[len(d_allele)] = cluster_alleles.get(len(d_allele), 0) + 1 - _ = stats_graphics(out_folder, cluster_alleles) + cluster_data.append(d_allele["cluster_data"]) + eval_data.append(d_allele["evaluation"]) + # collect the number of clusters for each allele + for c_data in cluster_data: + cluster_number = len(c_data) + # get data for graphic + cluster_data_graph[cluster_number] = ( + cluster_data_graph.get(cluster_number, 0) + 1 + ) + # collect cluster information + for c_idx, c_value in dict(sorted(c_data.items())).items(): + clusters_list.append( + c_value["locus_name"] + + "," + + str(c_idx) + + "," + + str(round(c_value["avg"], 2)) + + "," + + c_value["center_id"] + + "," + + str(c_value["n_seq"]) + ) + heading = "Locus name,cluster number,average,center allele,number of sequences" + summary_file = os.path.join(out_folder, "evaluate_cluster", "cluster_summary.csv") + with open(summary_file, "w") as fo: + fo.write(heading + "\n") + fo.write("\n".join(clusters_list) + "\n") + + _ = stats_graphics(out_folder, cluster_data_graph) + if eval_cluster: + heading = "Locus name,cluster number,result,alleles not match in blast,alleles not found in cluster" + eval_file = os.path.join( + out_folder, "evaluate_cluster", "cluster_evaluation.csv" + ) + with open(eval_file, "w") as fo: + fo.write(heading + "\n") + for eval in eval_data: + fo.write("\n".join(eval["individual"]) + "\n") + return diff --git a/taranis/utils.py b/taranis/utils.py index 1381d52..39c8179 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -4,10 +4,11 @@ import logging import multiprocessing import numpy as np +import os import pandas as pd import plotly.graph_objects as go import questionary -import os +import shutil import re import rich.console @@ -170,10 +171,12 @@ def create_graphic( fig = go.Figure() if mode == "lines": fig.add_trace(go.Scatter(x=x_data, y=y_data, mode=mode, name=title)) + fig.update_layout(xaxis_title=labels[0], yaxis_title=labels[1]) elif mode == "pie": fig.add_trace(go.Pie(labels=labels, values=x_data)) elif mode == "bar": fig.add_trace(go.Bar(x=x_data, y=y_data)) + fig.update_layout(xaxis_title=labels[0], yaxis_title=labels[1]) elif mode == "box": fig.add_trace(go.Box(y=y_data)) @@ -182,25 +185,19 @@ def create_graphic( return -def get_files_in_folder(folder: str, extension: str = None) -> list[str]: - """get the list of files, filtered by extension in the input folder. If - extension is not set, then all files in folder are returned +def delete_folder(folder_to_delete: str) -> None: + """Delete the input folder Args: - folder (str): Folder path - extension (str, optional): Extension for filtering. Defaults to None. - - Returns: - list[str]: list of files which match the condition + folder_to_delete (str): folder path to be deleted """ - if not folder_exists(folder): - log.error("Folder %s does not exists", folder) - stderr.print("[red] Schema folder does not exist. " + folder + "!") + try: + shutil.rmtree(folder_to_delete) + except Exception as e: + log.error("Folder %s can not be deleted %s", folder_to_delete, e) + stderr.print("[red] Folder does not have any file which match your request") sys.exit(1) - if extension is None: - extension = "*" - folder_files = os.path.join(folder, "*." + extension) - return glob.glob(folder_files) + return def file_exists(file_to_check): @@ -237,6 +234,27 @@ def folder_exists(folder_to_check): return False +def get_files_in_folder(folder: str, extension: str = None) -> list[str]: + """get the list of files, filtered by extension in the input folder. If + extension is not set, then all files in folder are returned + + Args: + folder (str): Folder path + extension (str, optional): Extension for filtering. Defaults to None. + + Returns: + list[str]: list of files which match the condition + """ + if not folder_exists(folder): + log.error("Folder %s does not exists", folder) + stderr.print("[red] Schema folder does not exist. " + folder + "!") + sys.exit(1) + if extension is None: + extension = "*" + folder_files = os.path.join(folder, "*." + extension) + return glob.glob(folder_files) + + def prompt_text(msg): source = questionary.text(msg).unsafe_ask() return source From 7138e251860dbc56de10871cfb77e1c49243fba7 Mon Sep 17 00:00:00 2001 From: luissian Date: Sat, 17 Feb 2024 19:48:31 +0100 Subject: [PATCH 075/214] fixed liting --- taranis/__main__.py | 7 ------- taranis/blast.py | 4 ++-- taranis/eval_cluster.py | 1 - taranis/reference_alleles.py | 38 +----------------------------------- 4 files changed, 3 insertions(+), 47 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index 8b9a920..f7655e1 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -338,13 +338,6 @@ def reference_alleles( sys.exit(1) """Create the reference alleles from the schema """ results = [] - """ - for f_file in schema_files: - - ref_alleles = taranis.reference_alleles.ReferenceAlleles(f_file, output, eval_cluster, kmer_size, sketch_size) - c_data = ref_alleles.create_ref_alleles() - results.append(c_data) - """ with concurrent.futures.ThreadPoolExecutor(max_workers=cpus) as executor: futures = [ executor.submit( diff --git a/taranis/blast.py b/taranis/blast.py index 7054913..a51d2ad 100644 --- a/taranis/blast.py +++ b/taranis/blast.py @@ -118,8 +118,8 @@ def run_blast( else: out, _ = cline() except Exception as e: - # log.error("Unable to run blast for %s ", self.out_blast_dir) - # log.error(e) + log.error("Unable to run blast for %s ", self.out_blast_dir) + log.error(e) stderr.print(f"[red] Unable to run blast {self.out_blast_dir}") exit(1) return out.splitlines() diff --git a/taranis/eval_cluster.py b/taranis/eval_cluster.py index 04d6c59..5f30820 100644 --- a/taranis/eval_cluster.py +++ b/taranis/eval_cluster.py @@ -6,7 +6,6 @@ import taranis.utils import taranis.blast from Bio import SeqIO -import pdb log = logging.getLogger(__name__) stderr = rich.console.Console( diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index ded1221..5646455 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -9,7 +9,6 @@ import taranis.clustering import taranis.eval_cluster from Bio import SeqIO -import pdb log = logging.getLogger(__name__) stderr = rich.console.Console( @@ -81,39 +80,6 @@ def create_distance_matrix(self) -> list: def processing_cluster_data( self, cluster_data: np.array, cluster_ptrs: np.array, position_to_allele: dict ) -> dict: - """As per result of ClusterDistance methods, the - reference alleles are saved to file and statistics information is - returned - - Returns: - list: two dictionaires are returned, cluster_data having statistics - and reference_alleles, where keys are cluster number and value - the reference allele for the cluster - """ - """ - # dist_matrix_np, postition_to_allele = self.create_distance_matrix() - - - # log.debug("Processing distance matrix for $s", self.fasta_file) - # distance_obj = taranis.distance.DistanceMatrix(self.fasta_file, self.kmer_size, self.sketch_size) - # mash_distance_df = distance_obj.create_matrix() - # log.debug(f"Created distance matrix for {self.fasta_file}") - # fetch the allele position into array - - postition_to_allele = { - x: mash_distance_df.columns[x] for x in range(len(mash_distance_df.columns)) - } - # convert the triangle matrix into full data matrix - matrix_np = mash_distance_df.to_numpy() - t_matrix_np = matrix_np.transpose() - matrix_np = t_matrix_np + matrix_np - # At this point minimal distance is 0. For clustering requires to be 1 - # the oposite. - dist_matrix_np = (matrix_np - 1) * -1 - """ - - # convert the center pointer to allele name and create list to get - # sequences reference_alleles = [] for cluster_id, values in cluster_data.items(): @@ -176,7 +142,7 @@ def create_ref_alleles(self) -> dict: dist_matrix_np, self.locus_name, ) - # pdb.set_trace() + for resolution in np.arange(self.cluster_resolution, 1, 0.025): cluster_ptrs, cluster_data = self.cluster_obj.create_clusters( round(resolution, 3) @@ -194,13 +160,11 @@ def create_ref_alleles(self) -> dict: evaluation_obj = taranis.eval_cluster.EvaluateCluster( self.fasta_file, self.locus_name, self.output ) - # pdb.set_trace() evaluation_result = evaluation_obj.evaluate_clusters( allele_data["alleles_in_cluster"], allele_data["cluster_data"], ref_fasta_file, ) - # pdb.set_trace() if evaluation_result["result"] == "OK" or resolution >= 1: # delete blast database used for evaluation _ = evaluation_obj.delete_blast_db_folder() From 5f2785fcc04dcac7e9fe62dec5d343a16152ea34 Mon Sep 17 00:00:00 2001 From: luissian Date: Sat, 17 Feb 2024 19:51:04 +0100 Subject: [PATCH 076/214] liting for eval_cluster --- taranis/eval_cluster.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/taranis/eval_cluster.py b/taranis/eval_cluster.py index 5f30820..eba5f88 100644 --- a/taranis/eval_cluster.py +++ b/taranis/eval_cluster.py @@ -171,9 +171,9 @@ def evaluate_clusters( if result_eval["validation"] is False: evaluation_alleles[cluster_id]["result"] = "NOK" if len(result_eval["alleles_not_found"]) > 0: - evaluation_alleles[cluster_id]["alleles_not_found"] = result_eval[ - "alleles_not_found" - ] + evaluation_alleles[cluster_id]["alleles_not_found"] = ( + result_eval["alleles_not_found"] + ) if len(result_eval["alleles_not_in_cluster"]) > 0: evaluation_alleles[cluster_id][ "alleles_not_in_cluster" From c917d049da7160333e42c7abb0bea6b48833d5d7 Mon Sep 17 00:00:00 2001 From: luissian Date: Sat, 17 Feb 2024 19:57:21 +0100 Subject: [PATCH 077/214] liting for eval_cluster 2 --- taranis/eval_cluster.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/taranis/eval_cluster.py b/taranis/eval_cluster.py index eba5f88..5f30820 100644 --- a/taranis/eval_cluster.py +++ b/taranis/eval_cluster.py @@ -171,9 +171,9 @@ def evaluate_clusters( if result_eval["validation"] is False: evaluation_alleles[cluster_id]["result"] = "NOK" if len(result_eval["alleles_not_found"]) > 0: - evaluation_alleles[cluster_id]["alleles_not_found"] = ( - result_eval["alleles_not_found"] - ) + evaluation_alleles[cluster_id]["alleles_not_found"] = result_eval[ + "alleles_not_found" + ] if len(result_eval["alleles_not_in_cluster"]) > 0: evaluation_alleles[cluster_id][ "alleles_not_in_cluster" From 9efcc58e903fcd8883c69d7758d2d758b0dd1970 Mon Sep 17 00:00:00 2001 From: luissian Date: Sat, 17 Feb 2024 20:01:38 +0100 Subject: [PATCH 078/214] liting for eval_cluster 3 --- taranis/eval_cluster.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/taranis/eval_cluster.py b/taranis/eval_cluster.py index 5f30820..be2e041 100644 --- a/taranis/eval_cluster.py +++ b/taranis/eval_cluster.py @@ -175,9 +175,9 @@ def evaluate_clusters( "alleles_not_found" ] if len(result_eval["alleles_not_in_cluster"]) > 0: - evaluation_alleles[cluster_id][ - "alleles_not_in_cluster" - ] = result_eval["alleles_not_in_cluster"] + evaluation_alleles[cluster_id]["alleles_not_in_cluster"] = ( + result_eval["alleles_not_in_cluster"] + ) else: evaluation_alleles[cluster_id]["result"] = "OK" return self.summary(evaluation_alleles) From ba544c647b9feaf7f5146718c80aba2f404dc42e Mon Sep 17 00:00:00 2001 From: luissian Date: Fri, 1 Mar 2024 16:39:17 +0100 Subject: [PATCH 079/214] adding files for docs and testing with pytest --- .github/workflows/pytest.yml | 53 ++++++++++++++++++++++++++++++++++++ .readthedocs.yaml | 19 +++++++++++++ docs/requirements.txt | 4 +++ pytest.ini | 6 ++++ test/__init__.py | 0 5 files changed, 82 insertions(+) create mode 100644 .github/workflows/pytest.yml create mode 100644 .readthedocs.yaml create mode 100644 docs/requirements.txt create mode 100644 pytest.ini create mode 100644 test/__init__.py diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml new file mode 100644 index 0000000..efdfc2b --- /dev/null +++ b/.github/workflows/pytest.yml @@ -0,0 +1,53 @@ +name: Python tests +# This workflow is triggered on pushes and PRs to the repository. +# Only run if we changed a Python file +on: + push: + branches: + - dev + paths-ignore: + - "docs/**" + - "CHANGELOG.md" + pull_request: + paths-ignore: + - "docs/**" + - "CHANGELOG.md" + release: + types: [published] + workflow_dispatch: + inputs: + runners: + description: "Runners to test on" + type: choice + options: + - "ubuntu-latest" + - "self-hosted" + default: "self-hosted" + +# Cancel if a newer run with the same workflow name is queued +concurrency: + group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} + cancel-in-progress: true + +jobs: + + test: + runs-on: ubuntu-latest + + steps: + - name: Checkout repository + uses: actions/checkout@v2 + + - name: Set up Python 3.10 + uses: actions/setup-python@v2 + with: + python-version: 3.10 + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -r requirements.txt + + - name: Run tests + run: | + pytest \ No newline at end of file diff --git a/.readthedocs.yaml b/.readthedocs.yaml new file mode 100644 index 0000000..b67bdf0 --- /dev/null +++ b/.readthedocs.yaml @@ -0,0 +1,19 @@ +# Read the Docs configuration file for MkDocs projects +# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details + +# Required +version: 2 + +# Set the version of Python and other tools you might need +build: + os: ubuntu-22.04 + tools: + python: "3.12" + +mkdocs: + configuration: mkdocs.yml + +# Optionally declare the Python requirements required to build your docs +python: + install: + - requirements: docs/requirements.txt \ No newline at end of file diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 0000000..08a0916 --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1,4 @@ +Sphinx>=3.3.1 +sphinxcontrib-napoleon +sphinx_rtd_theme>=0.5.0 +myst-parser \ No newline at end of file diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 0000000..86585b5 --- /dev/null +++ b/pytest.ini @@ -0,0 +1,6 @@ +[pytest] +filterwarnings = + ignore::pytest.PytestRemovedIn8Warning:_pytest.nodes:140 +testpaths = + tests +python_files = test_*.py \ No newline at end of file diff --git a/test/__init__.py b/test/__init__.py new file mode 100644 index 0000000..e69de29 From ac191aec0f331602140620e63d138ca834cf5d82 Mon Sep 17 00:00:00 2001 From: luissian Date: Thu, 7 Mar 2024 00:03:18 +0100 Subject: [PATCH 080/214] working on allele calling, exact match --- taranis/__main__.py | 72 +++++++--------- taranis/allele_calling.py | 163 ++++++++++++++++++----------------- taranis/reference_alleles.py | 2 +- taranis/utils.py | 22 +++++ 4 files changed, 138 insertions(+), 121 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index f7655e1..4798a5b 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -22,6 +22,18 @@ stderr=True, force_terminal=taranis.utils.rich_force_colors() ) +def expand_wildcards(ctx, param, value): + if value: + expanded_paths = [] + for path in value: + # Check if path contains wildcard + if '*' in path: + # Expand wildcard + expanded_paths.extend(glob.glob(path)) + else: + expanded_paths.append(path) + return expanded_paths + return None def run_taranis(): # Set up the rich traceback @@ -352,7 +364,6 @@ def reference_alleles( ) for f_file in schema_files ] - # import pdb; pdb.set_trace() for future in concurrent.futures.as_completed(futures): results.append(future.result()) _ = taranis.reference_alleles.collect_statistics(results, eval_cluster, output) @@ -366,7 +377,7 @@ def reference_alleles( "--schema", required=True, multiple=False, - type=click.Path(), + type=click.Path(exists=True), help="Directory where the schema with the core gene files are located. ", ) @click.option( @@ -374,25 +385,9 @@ def reference_alleles( "--reference", required=True, multiple=False, - type=click.Path(), + type=click.Path(exists=True), help="Directory where the schema reference allele files are located. ", ) -@click.option( - "-g", - "--genome", - required=True, - multiple=False, - type=click.Path(), - help="Genome reference file", -) -@click.option( - "-a", - "--sample", - required=True, - multiple=False, - type=click.Path(), - help="Sample location file in fasta format. ", -) @click.option( "-o", "--output", @@ -401,27 +396,17 @@ def reference_alleles( type=click.Path(), help="Output folder to save reference alleles", ) +@click.argument("assemblies", callback=expand_wildcards, nargs=-1, required=True, type=click.Path(exists=True)) def allele_calling( schema, reference, - genome, - sample, + assemblies, output, ): - folder_to_check = [schema, reference] - for folder in folder_to_check: - if not taranis.utils.folder_exists(folder): - log.error("folder %s does not exists", folder) - stderr.print("[red] Folder does not exist. " + folder + "!") - sys.exit(1) - if not taranis.utils.file_exists(sample): - log.error("file %s does not exists", sample) - stderr.print("[red] File does not exist. " + sample + "!") - sys.exit(1) - schema_files = taranis.utils.get_files_in_folder(schema, "fasta") - if len(schema_files) == 0: - log.error("Schema folder %s does not have any fasta file", schema) - stderr.print("[red] Schema folder does not have any fasta file") + schema_ref_files = taranis.utils.get_files_in_folder(reference, "fasta") + if len(schema_ref_files) == 0: + log.error("Referenc allele folder %s does not have any fasta file", schema) + stderr.print("[red] reference allele folder does not have any fasta file") sys.exit(1) # Check if output folder exists @@ -440,19 +425,24 @@ def allele_calling( os.makedirs(output) except OSError as e: log.info("Unable to create folder at %s with error %s", output, e) - stderr.print("[red] ERROR. Unable to create folder " + output) + stderr.print("[red] ERROR. Unable to create {output} folder" ) sys.exit(1) # Filter fasta files from reference folder - ref_alleles = glob.glob(os.path.join(reference, "*.fasta")) + # ref_alleles = glob.glob(os.path.join(reference, "*.fasta")) # Create predictions + + """ pred_out = os.path.join(output, "prediction") pred_sample = taranis.prediction.Prediction(genome, sample, pred_out) pred_sample.training() pred_sample.prediction() + """ """Analyze the sample file against schema to identify outbreakers """ - sample_allele = taranis.allele_calling.AlleleCalling( - pred_sample, sample, schema, ref_alleles, output - ) - sample_allele.analyze_sample() + results = [] + for assembly_file in assemblies: + results.append(taranis.allele_calling.parallel_execution(assembly_file, schema, schema_ref_files, output)) + + + # sample_allele_obj.analyze_sample() diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 00108d4..8c7b2c6 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -1,3 +1,4 @@ +import io import logging import os import rich.console @@ -7,8 +8,11 @@ import taranis.blast # import numpy +from collections import OrderedDict import pandas as pd from pathlib import Path +from Bio import SeqIO + import pdb @@ -23,105 +27,106 @@ class AlleleCalling: - def __init__(self, prediction, sample_file, schema, reference_alleles, out_folder): - self.prediction = prediction + def __init__(self, sample_file: str, schema: str, reference_alleles: list, out_folder:str): + # self.prediction = prediction self.sample_file = sample_file self.schema = schema self.ref_alleles = reference_alleles self.out_folder = out_folder self.s_name = Path(sample_file).stem self.blast_dir = os.path.join(out_folder, "blastdb") - self.blast_sample = os.path.join(self.blast_dir, self.s_name) - self.blast_heading = [ - "qseqid", - "sseqid", - "pident", - "qlen", - "length", - "mismatch", - "gapopen", - "evalue", - "bitscore", - "sstart", - "send", - "qstart", - "qend", - "sseq", - "qseq", - ] - - def assign_allele_type(self, query_seq, allele_name, sample_contig, schema_gene): - """_summary_ - - Args: - query_seq (_type_): _description_ - allele_name (_type_): _description_ - sample_contig (_type_): _description_ - schema_gene (_type_): _description_ - """ - s_alleles_blast = taranis.blast.Blast("nucl") - ref_allele_blast_dir = os.path.join(self.blast_dir, "ref_alleles") - query_path = os.path.join(self.out_folder, "tmp", allele_name) - # Write to file the sequence to find out the loci name that fully match - f_name = taranis.utils.write_fasta_file(query_path, query_seq, allele_name) - query_file = os.path.join(query_path, f_name) - _ = s_alleles_blast.create_blastdb(schema_gene, ref_allele_blast_dir) - # Blast with sample sequence to find the allele in the schema - seq_blast_match = s_alleles_blast.run_blast(query_file, perc_identity=100) - pdb.set_trace() - if len(seq_blast_match) >= 1: + # create blast for sample file + self.blast_obj = taranis.blast.Blast("nucl") + _ = self.blast_obj.create_blastdb(sample_file, self.blast_dir) + + + def assign_allele_type(self, blast_result: list, allele_file: str)->list: + def get_blast_details(blast_result: list, allele_name: str)->list: + # get blast details + blast_details = [ + blast_result[0].split("_")[0], # Core gene + self.s_name, + "gene annotation", + "product annotation", + allele_name, + "allele quality", + blast_result[1], # contig + blast_result[3], # query length + blast_result[9], # contig start + blast_result[10], # contig end + blast_result[13], # contig sequence + ] + return blast_details + + if len(blast_result) > 1: # allele is named as NIPHEM # Hacer un blast con la query esta secuencia y la database del alelo # Create blast db with sample file pass - elif len(seq_blast_match) == 1: - pass + elif len(blast_result) == 1: + column_blast_res = blast_result[0].split("\t") + sequence = column_blast_res[13].replace("-", "") + + grep_result = taranis.utils.grep_execution(allele_file, sequence, "-b1") + # check if sequence match alleles in schema + if len(grep_result) > 0: + allele_name = grep_result[0].split(">")[1] + allele_details = get_blast_details(column_blast_res, allele_name) + # allele is labled as EXACT + return ["EXACT", allele_name, allele_details] else: pass - - def search_alleles(self, ref_allele): - allele_name = Path(ref_allele).stem - schema_gene = os.path.join(self.schema, allele_name + ".fasta") - # allele_name = Path(ref_allele).stem - # run blast with sample as db and reference allele as query - sample_blast_match = self.sample_blast.run_blast(ref_allele) - if len(sample_blast_match) > 0: - pd_lines = pd.DataFrame([item.split("\t") for item in sample_blast_match]) - pd_lines.columns = self.blast_heading - pd_lines["pident"] = pd_lines["pident"].apply(pd.to_numeric) - sel_max = pd_lines.loc[pd_lines["pident"].idxmax()] - query_seq = sel_max["qseq"] - # np_lines = numpy.array(s_lines) - # convert to float the perc_identity to find out the max value - # max_val = numpy.max(np_lines[:,2].astype(float)) - # mask = np_lines[:, 2] ==str(max_val) - # Select rows that match the percent identity. Index 2 in blast results - # sel_row = np_lines[mask, :] = np_lines[mask, :] - # query_seq = sel_row[0,14] - sample_contig = sel_max["sseqid"] - _ = self.assign_allele_type( - query_seq, allele_name, sample_contig, schema_gene - ) - else: - # Sample does not have a reference allele to be matched - # Keep LNF info - # ver el codigo de espe - # lnf_tpr_tag() - pass pdb.set_trace() - def analyze_sample(self): + + def search_match_allele(self): # Create blast db with sample file - self.sample_blast = taranis.blast.Blast("nucl") - _ = self.sample_blast.create_blastdb(self.sample_file, self.blast_dir) - result = {} + + result = {"allele_type":{}, "allele_match":{}, "allele_details":{}} # pdb.set_trace() for ref_allele in self.ref_alleles: # schema_alleles = os.path.join(self.schema, ref_allele) # parallel in all CPUs in cluster node - result[ref_allele] = self.search_alleles(ref_allele) + alleles = OrderedDict() + match_found = False + with open(ref_allele, "r") as fh: + for record in SeqIO.parse(fh, "fasta"): + alleles[record.id] = str(record.seq) + + for r_id, r_seq in alleles.items(): + # create file in memory to increase speed + query_file = io.StringIO() + query_file.write(">" + r_id + "\n" + r_seq) + query_file.seek(0) + blast_result = self.blast_obj.run_blast( + query_file.read(), perc_identity=90, query_type="stdin" + ) + if len(blast_result) > 0: + match_found = True + break + # Close object and discard memory buffer + query_file.close() + if match_found: + allele_file = os.path.join(self.schema, os.path.basename(ref_allele)) + # blast_result = self.blast_obj.run_blast(q_file,perc_identity=100) + allele_name = Path(allele_file).stem + pdb.set_trace() + result["allele_type"][allele_name], result["allele_match"][allele_name], result["allele_details"][allele_name] = self.assign_allele_type(blast_result, allele_file) + pdb.set_trace() + else: + # Sample does not have a reference allele to be matched + # Keep LNF info + # ver el codigo de espe + # lnf_tpr_tag() + pass + pdb.set_trace() - return + return result + +def parallel_execution(sample_file: str, schema: str, reference_alleles: list, out_folder: str): + + allele_obj = AlleleCalling(sample_file, schema, reference_alleles, out_folder) + return allele_obj.search_match_allele() diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index 5646455..7e25ba4 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -119,7 +119,7 @@ def save_reference_alleles(self, reference_alleles: list) -> str: for record in SeqIO.parse(fh, "fasta"): if record.id in reference_alleles: record_seq[record.id] = str(record.seq) - ref_allele_file = os.path.join(self.output, self.locus_name + ".fa") + ref_allele_file = os.path.join(self.output, self.locus_name + ".fasta") with open(ref_allele_file, "w") as fo: for r_id, r_seq in record_seq.items(): fo.write(">" + r_id + "\n") diff --git a/taranis/utils.py b/taranis/utils.py index 39c8179..bef5662 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -254,6 +254,28 @@ def get_files_in_folder(folder: str, extension: str = None) -> list[str]: folder_files = os.path.join(folder, "*." + extension) return glob.glob(folder_files) +def grep_execution(input_file: str, pattern: str, parameters: str) -> list: + """_summary_ + + Args: + input_file (str): _description_ + pattern (str): _description_ + parmeters (str): _description_ + + Returns: + list: _description_ + """ + try: + result = subprocess.run( + ["grep", parameters, pattern, input_file] , + capture_output=True, + check=True, + text=True, + ) + except subprocess.CalledProcessError as e: + log.error("Unable to run grep. Error message: %s ", e.stderr.decode()) + return [] + return result.stdout.split("\n") def prompt_text(msg): source = questionary.text(msg).unsafe_ask() From cf06950d4302dbf4c6b8b8843dc084145b5db067 Mon Sep 17 00:00:00 2001 From: luissian Date: Sat, 9 Mar 2024 14:02:59 +0100 Subject: [PATCH 081/214] fixed issue when reading on pandas long files --- taranis/__main__.py | 16 ++++++++++++---- taranis/distance.py | 8 +++++++- taranis/reference_alleles.py | 8 +++++--- 3 files changed, 24 insertions(+), 8 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index 4798a5b..f88b998 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -15,6 +15,9 @@ import taranis.reference_alleles import taranis.allele_calling +from pathlib import Path +import pdb + log = logging.getLogger() # Set up rich stderr console @@ -364,8 +367,12 @@ def reference_alleles( ) for f_file in schema_files ] - for future in concurrent.futures.as_completed(futures): - results.append(future.result()) + for future in concurrent.futures.as_completed(futures): + try: + results.append(future.result()) + except Exception as e: + print(e) + continue _ = taranis.reference_alleles.collect_statistics(results, eval_cluster, output) finish = time.perf_counter() print(f"Reference alleles finish in {round((finish-start)/60, 2)} minutes") @@ -442,7 +449,8 @@ def allele_calling( """ results = [] for assembly_file in assemblies: - results.append(taranis.allele_calling.parallel_execution(assembly_file, schema, schema_ref_files, output)) - + assembly_name = Path(assembly_file).stem + results.append({assembly_name: taranis.allele_calling.parallel_execution(assembly_file, schema, schema_ref_files, output)}) + pdb.set_trace() # sample_allele_obj.analyze_sample() diff --git a/taranis/distance.py b/taranis/distance.py index 428f596..872275d 100644 --- a/taranis/distance.py +++ b/taranis/distance.py @@ -72,7 +72,13 @@ def create_matrix(self) -> pd.DataFrame: dist_matrix.write("alleles\t" + "\t".join(allele_names) + "\n") dist_matrix.write("\n".join(out_data[1:])) dist_matrix.seek(0) - matrix_pd = pd.read_csv(dist_matrix, sep="\t", index_col="alleles").fillna(0) + file_test = "/media/lchapado/Reference_data/proyectos_isciii/taranis/test/reference_alleles_testing_full_schema_17a/error.csv" + #with open(file_test, "w") as fo: + # fo.write("alleles\t" + "\t".join(allele_names) + "\n") + # fo.write("\n".join(out_data[1:])) + + #import pdb; pdb.set_trace() + matrix_pd = pd.read_csv(dist_matrix, sep="\t", index_col="alleles", engine="python").fillna(0) # Close object and discard memory buffer dist_matrix.close() log.debug(f"create distance for {allele_name}") diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index 7e25ba4..ee23750 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -121,9 +121,9 @@ def save_reference_alleles(self, reference_alleles: list) -> str: record_seq[record.id] = str(record.seq) ref_allele_file = os.path.join(self.output, self.locus_name + ".fasta") with open(ref_allele_file, "w") as fo: - for r_id, r_seq in record_seq.items(): - fo.write(">" + r_id + "\n") - fo.write(r_seq + "\n") + for ref_allele in reference_alleles: + fo.write(">" + ref_allele + "\n") + fo.write(record_seq[ref_allele] + "\n") return ref_allele_file def create_ref_alleles(self) -> dict: @@ -255,6 +255,8 @@ def stats_graphics(stats_folder: str, cluster_alleles: dict) -> None: eval_data = [] cluster_data_graph = {} clusters_list = [] + stderr.print("Process starts for collecting statistics") + log.info("Process starts for collecting statistics") # split the data into cluster and evaluation for d_allele in data_alleles: cluster_data.append(d_allele["cluster_data"]) From 31500882c934740c748714481c177a5622bc0add Mon Sep 17 00:00:00 2001 From: luissian Date: Sat, 9 Mar 2024 14:03:41 +0100 Subject: [PATCH 082/214] added classification --- taranis/allele_calling.py | 55 +++++++++++++++++++++++++++++---------- taranis/blast.py | 2 +- taranis/utils.py | 4 ++- 3 files changed, 45 insertions(+), 16 deletions(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 8c7b2c6..8936a5e 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -40,7 +40,7 @@ def __init__(self, sample_file: str, schema: str, reference_alleles: list, out_ _ = self.blast_obj.create_blastdb(sample_file, self.blast_dir) - def assign_allele_type(self, blast_result: list, allele_file: str)->list: + def assign_allele_type(self, blast_result: list, allele_file: str, allele_name: str)->list: def get_blast_details(blast_result: list, allele_name: str)->list: # get blast details blast_details = [ @@ -63,21 +63,48 @@ def get_blast_details(blast_result: list, allele_name: str)->list: # Hacer un blast con la query esta secuencia y la database del alelo # Create blast db with sample file - - pass + pdb.set_trace() + elif len(blast_result) == 1: column_blast_res = blast_result[0].split("\t") - sequence = column_blast_res[13].replace("-", "") + column_blast_res[13] = column_blast_res[13].replace("-", "") + allele_details = get_blast_details(column_blast_res, allele_name) - grep_result = taranis.utils.grep_execution(allele_file, sequence, "-b1") + grep_result = taranis.utils.grep_execution(allele_file, column_blast_res[13], "-b1") # check if sequence match alleles in schema if len(grep_result) > 0: allele_name = grep_result[0].split(">")[1] - allele_details = get_blast_details(column_blast_res, allele_name) + # allele is labled as EXACT - return ["EXACT", allele_name, allele_details] - else: - pass + pdb.set_trace() + return ["EXC", allele_name, allele_details] + # check if contig is shorter than allele + pdb.set_trace() + if int(column_blast_res[3]) > int(column_blast_res[4]): + # check if sequence is shorter because it starts or ends at the contig + if ( + column_blast_res[9] == 1 # check at contig start + or column_blast_res[14] == column_blast_res[10] # check at contig end + or column_blast_res[10] == 1 # check reverse at contig end + or column_blast_res[9] == column_blast_res[15] # check reverse at contig start + ): + # allele is labled as PLOT + pdb.set_trace() + return ["PLOT", allele_name, allele_details] + # allele is labled as ASM + pdb.set_trace() + return ["ASM", allele_name, allele_details] + # check if contig is longer than allele + if int(column_blast_res[3]) < int(column_blast_res[4]): + # allele is labled as ALM + pdb.set_trace() + return ["ALM", allele_name, allele_details] + if int(column_blast_res[3]) == int(column_blast_res[4]): + # allele is labled as INF + pdb.set_trace() + return ["INF", allele_name, allele_details] + + pdb.set_trace() @@ -112,18 +139,18 @@ def search_match_allele(self): allele_file = os.path.join(self.schema, os.path.basename(ref_allele)) # blast_result = self.blast_obj.run_blast(q_file,perc_identity=100) allele_name = Path(allele_file).stem - pdb.set_trace() - result["allele_type"][allele_name], result["allele_match"][allele_name], result["allele_details"][allele_name] = self.assign_allele_type(blast_result, allele_file) - pdb.set_trace() + # pdb.set_trace() + result["allele_type"][allele_name], result["allele_match"][allele_name], result["allele_details"][allele_name] = self.assign_allele_type(blast_result, allele_file, allele_name) + # pdb.set_trace() else: # Sample does not have a reference allele to be matched # Keep LNF info # ver el codigo de espe # lnf_tpr_tag() - pass + pdb.set_trace() - pdb.set_trace() + return result def parallel_execution(sample_file: str, schema: str, reference_alleles: list, out_folder: str): diff --git a/taranis/blast.py b/taranis/blast.py index a51d2ad..981454c 100644 --- a/taranis/blast.py +++ b/taranis/blast.py @@ -96,7 +96,7 @@ def run_blast( if query_type == "stdin": stdin_query = query query = "-" - blast_parameters = '"6 , qseqid , sseqid , pident , qlen , length , mismatch , gapopen , evalue , bitscore , sstart , send , qstart , qend , sseq , qseq"' + blast_parameters = '"6 , qseqid , sseqid , pident , qlen , length , mismatch , gapopen , evalue , bitscore , sstart , send , qstart , qend , sseq , slen"' cline = NcbiblastnCommandline( task="blastn", db=self.out_blast_dir, diff --git a/taranis/utils.py b/taranis/utils.py index bef5662..4572013 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -21,6 +21,7 @@ from pathlib import Path from Bio import SeqIO +import pdb log = logging.getLogger(__name__) @@ -273,7 +274,8 @@ def grep_execution(input_file: str, pattern: str, parameters: str) -> list: text=True, ) except subprocess.CalledProcessError as e: - log.error("Unable to run grep. Error message: %s ", e.stderr.decode()) + pdb.set_trace() + log.error("Unable to run grep. Error message: %s ", e) return [] return result.stdout.split("\n") From b9361eeaf52c42d5b753efce183371197eefa767 Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 11 Mar 2024 10:29:09 +0100 Subject: [PATCH 083/214] liting --- taranis/__main__.py | 24 ++++++++++--- taranis/allele_calling.py | 66 ++++++++++++++++++++---------------- taranis/distance.py | 10 ++---- taranis/eval_cluster.py | 6 ++-- taranis/reference_alleles.py | 1 - taranis/utils.py | 5 ++- 6 files changed, 66 insertions(+), 46 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index f88b998..911b521 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -25,12 +25,13 @@ stderr=True, force_terminal=taranis.utils.rich_force_colors() ) + def expand_wildcards(ctx, param, value): if value: expanded_paths = [] for path in value: # Check if path contains wildcard - if '*' in path: + if "*" in path: # Expand wildcard expanded_paths.extend(glob.glob(path)) else: @@ -38,6 +39,7 @@ def expand_wildcards(ctx, param, value): return expanded_paths return None + def run_taranis(): # Set up the rich traceback rich.traceback.install(console=stderr, width=200, word_wrap=True, extra_lines=1) @@ -403,7 +405,13 @@ def reference_alleles( type=click.Path(), help="Output folder to save reference alleles", ) -@click.argument("assemblies", callback=expand_wildcards, nargs=-1, required=True, type=click.Path(exists=True)) +@click.argument( + "assemblies", + callback=expand_wildcards, + nargs=-1, + required=True, + type=click.Path(exists=True), +) def allele_calling( schema, reference, @@ -432,7 +440,7 @@ def allele_calling( os.makedirs(output) except OSError as e: log.info("Unable to create folder at %s with error %s", output, e) - stderr.print("[red] ERROR. Unable to create {output} folder" ) + stderr.print("[red] ERROR. Unable to create {output} folder") sys.exit(1) # Filter fasta files from reference folder # ref_alleles = glob.glob(os.path.join(reference, "*.fasta")) @@ -450,7 +458,13 @@ def allele_calling( results = [] for assembly_file in assemblies: assembly_name = Path(assembly_file).stem - results.append({assembly_name: taranis.allele_calling.parallel_execution(assembly_file, schema, schema_ref_files, output)}) + results.append( + { + assembly_name: taranis.allele_calling.parallel_execution( + assembly_file, schema, schema_ref_files, output + ) + } + ) pdb.set_trace() - + # sample_allele_obj.analyze_sample() diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 8936a5e..c9e11f8 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -14,7 +14,6 @@ from Bio import SeqIO - import pdb log = logging.getLogger(__name__) @@ -27,7 +26,9 @@ class AlleleCalling: - def __init__(self, sample_file: str, schema: str, reference_alleles: list, out_folder:str): + def __init__( + self, sample_file: str, schema: str, reference_alleles: list, out_folder: str + ): # self.prediction = prediction self.sample_file = sample_file self.schema = schema @@ -39,22 +40,23 @@ def __init__(self, sample_file: str, schema: str, reference_alleles: list, out_ self.blast_obj = taranis.blast.Blast("nucl") _ = self.blast_obj.create_blastdb(sample_file, self.blast_dir) - - def assign_allele_type(self, blast_result: list, allele_file: str, allele_name: str)->list: - def get_blast_details(blast_result: list, allele_name: str)->list: + def assign_allele_type( + self, blast_result: list, allele_file: str, allele_name: str + ) -> list: + def get_blast_details(blast_result: list, allele_name: str) -> list: # get blast details blast_details = [ - blast_result[0].split("_")[0], # Core gene + blast_result[0].split("_")[0], # Core gene self.s_name, "gene annotation", "product annotation", - allele_name, + allele_name, "allele quality", - blast_result[1], # contig - blast_result[3], # query length - blast_result[9], # contig start - blast_result[10], # contig end - blast_result[13], # contig sequence + blast_result[1], # contig + blast_result[3], # query length + blast_result[9], # contig start + blast_result[10], # contig end + blast_result[13], # contig sequence ] return blast_details @@ -64,17 +66,19 @@ def get_blast_details(blast_result: list, allele_name: str)->list: # Hacer un blast con la query esta secuencia y la database del alelo # Create blast db with sample file pdb.set_trace() - + elif len(blast_result) == 1: column_blast_res = blast_result[0].split("\t") column_blast_res[13] = column_blast_res[13].replace("-", "") allele_details = get_blast_details(column_blast_res, allele_name) - - grep_result = taranis.utils.grep_execution(allele_file, column_blast_res[13], "-b1") + + grep_result = taranis.utils.grep_execution( + allele_file, column_blast_res[13], "-b1" + ) # check if sequence match alleles in schema if len(grep_result) > 0: allele_name = grep_result[0].split(">")[1] - + # allele is labled as EXACT pdb.set_trace() return ["EXC", allele_name, allele_details] @@ -83,10 +87,12 @@ def get_blast_details(blast_result: list, allele_name: str)->list: if int(column_blast_res[3]) > int(column_blast_res[4]): # check if sequence is shorter because it starts or ends at the contig if ( - column_blast_res[9] == 1 # check at contig start - or column_blast_res[14] == column_blast_res[10] # check at contig end - or column_blast_res[10] == 1 # check reverse at contig end - or column_blast_res[9] == column_blast_res[15] # check reverse at contig start + column_blast_res[9] == 1 # check at contig start + or column_blast_res[14] + == column_blast_res[10] # check at contig end + or column_blast_res[10] == 1 # check reverse at contig end + or column_blast_res[9] + == column_blast_res[15] # check reverse at contig start ): # allele is labled as PLOT pdb.set_trace() @@ -103,15 +109,13 @@ def get_blast_details(blast_result: list, allele_name: str)->list: # allele is labled as INF pdb.set_trace() return ["INF", allele_name, allele_details] - - - pdb.set_trace() + pdb.set_trace() def search_match_allele(self): # Create blast db with sample file - - result = {"allele_type":{}, "allele_match":{}, "allele_details":{}} + + result = {"allele_type": {}, "allele_match": {}, "allele_details": {}} # pdb.set_trace() for ref_allele in self.ref_alleles: # schema_alleles = os.path.join(self.schema, ref_allele) @@ -140,7 +144,11 @@ def search_match_allele(self): # blast_result = self.blast_obj.run_blast(q_file,perc_identity=100) allele_name = Path(allele_file).stem # pdb.set_trace() - result["allele_type"][allele_name], result["allele_match"][allele_name], result["allele_details"][allele_name] = self.assign_allele_type(blast_result, allele_file, allele_name) + ( + result["allele_type"][allele_name], + result["allele_match"][allele_name], + result["allele_details"][allele_name], + ) = self.assign_allele_type(blast_result, allele_file, allele_name) # pdb.set_trace() else: # Sample does not have a reference allele to be matched @@ -148,12 +156,12 @@ def search_match_allele(self): # ver el codigo de espe # lnf_tpr_tag() pdb.set_trace() - - return result -def parallel_execution(sample_file: str, schema: str, reference_alleles: list, out_folder: str): +def parallel_execution( + sample_file: str, schema: str, reference_alleles: list, out_folder: str +): allele_obj = AlleleCalling(sample_file, schema, reference_alleles, out_folder) return allele_obj.search_match_allele() diff --git a/taranis/distance.py b/taranis/distance.py index 872275d..df19477 100644 --- a/taranis/distance.py +++ b/taranis/distance.py @@ -72,13 +72,9 @@ def create_matrix(self) -> pd.DataFrame: dist_matrix.write("alleles\t" + "\t".join(allele_names) + "\n") dist_matrix.write("\n".join(out_data[1:])) dist_matrix.seek(0) - file_test = "/media/lchapado/Reference_data/proyectos_isciii/taranis/test/reference_alleles_testing_full_schema_17a/error.csv" - #with open(file_test, "w") as fo: - # fo.write("alleles\t" + "\t".join(allele_names) + "\n") - # fo.write("\n".join(out_data[1:])) - - #import pdb; pdb.set_trace() - matrix_pd = pd.read_csv(dist_matrix, sep="\t", index_col="alleles", engine="python").fillna(0) + matrix_pd = pd.read_csv( + dist_matrix, sep="\t", index_col="alleles", engine="python" + ).fillna(0) # Close object and discard memory buffer dist_matrix.close() log.debug(f"create distance for {allele_name}") diff --git a/taranis/eval_cluster.py b/taranis/eval_cluster.py index be2e041..5f30820 100644 --- a/taranis/eval_cluster.py +++ b/taranis/eval_cluster.py @@ -175,9 +175,9 @@ def evaluate_clusters( "alleles_not_found" ] if len(result_eval["alleles_not_in_cluster"]) > 0: - evaluation_alleles[cluster_id]["alleles_not_in_cluster"] = ( - result_eval["alleles_not_in_cluster"] - ) + evaluation_alleles[cluster_id][ + "alleles_not_in_cluster" + ] = result_eval["alleles_not_in_cluster"] else: evaluation_alleles[cluster_id]["result"] = "OK" return self.summary(evaluation_alleles) diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index ee23750..04f8fa1 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -80,7 +80,6 @@ def create_distance_matrix(self) -> list: def processing_cluster_data( self, cluster_data: np.array, cluster_ptrs: np.array, position_to_allele: dict ) -> dict: - reference_alleles = [] for cluster_id, values in cluster_data.items(): center_allele = position_to_allele[values["center_id"]] diff --git a/taranis/utils.py b/taranis/utils.py index 4572013..51148aa 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -22,6 +22,7 @@ from Bio import SeqIO import pdb + log = logging.getLogger(__name__) @@ -255,6 +256,7 @@ def get_files_in_folder(folder: str, extension: str = None) -> list[str]: folder_files = os.path.join(folder, "*." + extension) return glob.glob(folder_files) + def grep_execution(input_file: str, pattern: str, parameters: str) -> list: """_summary_ @@ -268,7 +270,7 @@ def grep_execution(input_file: str, pattern: str, parameters: str) -> list: """ try: result = subprocess.run( - ["grep", parameters, pattern, input_file] , + ["grep", parameters, pattern, input_file], capture_output=True, check=True, text=True, @@ -279,6 +281,7 @@ def grep_execution(input_file: str, pattern: str, parameters: str) -> list: return [] return result.stdout.split("\n") + def prompt_text(msg): source = questionary.text(msg).unsafe_ask() return source From f6a3ea1dadf725542cd5c4bc836e0380495dd83b Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 11 Mar 2024 10:36:30 +0100 Subject: [PATCH 084/214] correcting liting --- taranis/allele_calling.py | 1 - taranis/eval_cluster.py | 6 +++--- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index c9e11f8..694ef3e 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -9,7 +9,6 @@ # import numpy from collections import OrderedDict -import pandas as pd from pathlib import Path from Bio import SeqIO diff --git a/taranis/eval_cluster.py b/taranis/eval_cluster.py index 5f30820..be2e041 100644 --- a/taranis/eval_cluster.py +++ b/taranis/eval_cluster.py @@ -175,9 +175,9 @@ def evaluate_clusters( "alleles_not_found" ] if len(result_eval["alleles_not_in_cluster"]) > 0: - evaluation_alleles[cluster_id][ - "alleles_not_in_cluster" - ] = result_eval["alleles_not_in_cluster"] + evaluation_alleles[cluster_id]["alleles_not_in_cluster"] = ( + result_eval["alleles_not_in_cluster"] + ) else: evaluation_alleles[cluster_id]["result"] = "OK" return self.summary(evaluation_alleles) From 7503ea9339085e4dfff46cd5ec0f1eaa2271491d Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 11 Mar 2024 16:42:06 +0100 Subject: [PATCH 085/214] Fixing issues described in PR's comments --- .github/workflows/pytest.yml | 53 ----- .github/workflows/tests.yml | 6 +- taranis/__main__.py | 2 - taranis/allele_calling.py | 17 +- taranis/eval_cluster.py | 2 - taranis/pruebas.py | 6 +- taranis/utils.py | 3 - .../{ => analyze_schema}/lm0002.fasta | 0 .../{ => analyze_schema}/lmo0011.fasta | 0 .../{ => analyze_schema}/lmo0019.fasta | 0 .../reference_allele/lmo1762.fasta | 224 ++++++++++++++++++ .../reference_allele/lmo1784.fasta | 126 ++++++++++ 12 files changed, 358 insertions(+), 81 deletions(-) delete mode 100644 .github/workflows/pytest.yml rename test/MLST_listeria/{ => analyze_schema}/lm0002.fasta (100%) rename test/MLST_listeria/{ => analyze_schema}/lmo0011.fasta (100%) rename test/MLST_listeria/{ => analyze_schema}/lmo0019.fasta (100%) create mode 100644 test/MLST_listeria/reference_allele/lmo1762.fasta create mode 100644 test/MLST_listeria/reference_allele/lmo1784.fasta diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml deleted file mode 100644 index efdfc2b..0000000 --- a/.github/workflows/pytest.yml +++ /dev/null @@ -1,53 +0,0 @@ -name: Python tests -# This workflow is triggered on pushes and PRs to the repository. -# Only run if we changed a Python file -on: - push: - branches: - - dev - paths-ignore: - - "docs/**" - - "CHANGELOG.md" - pull_request: - paths-ignore: - - "docs/**" - - "CHANGELOG.md" - release: - types: [published] - workflow_dispatch: - inputs: - runners: - description: "Runners to test on" - type: choice - options: - - "ubuntu-latest" - - "self-hosted" - default: "self-hosted" - -# Cancel if a newer run with the same workflow name is queued -concurrency: - group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }} - cancel-in-progress: true - -jobs: - - test: - runs-on: ubuntu-latest - - steps: - - name: Checkout repository - uses: actions/checkout@v2 - - - name: Set up Python 3.10 - uses: actions/setup-python@v2 - with: - python-version: 3.10 - - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install -r requirements.txt - - - name: Run tests - run: | - pytest \ No newline at end of file diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 555dc2e..d8a7d08 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -27,5 +27,9 @@ jobs: source $CONDA/etc/profile.d/conda.sh conda activate taranis_env python -m pip install . - taranis analyze-schema -i test/MLST_listeria -o analyze_schema_test --cpus 1 --output-allele-annot --remove-no-cds --remove-duplicated --remove-subset + taranis analyze-schema -i test/MLST_listeria/analyze_schema -o analyze_schema_test --cpus 1 --output-allele-annot --remove-no-cds --remove-duplicated --remove-subset + + - name: Testing Reference allele + run: | + taranis reference-allele -s test/MLST_listeria/reference_allele -o reference_allele_test --cpus 1 \ No newline at end of file diff --git a/taranis/__main__.py b/taranis/__main__.py index 911b521..af9dbc4 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -16,7 +16,6 @@ import taranis.allele_calling from pathlib import Path -import pdb log = logging.getLogger() @@ -465,6 +464,5 @@ def allele_calling( ) } ) - pdb.set_trace() # sample_allele_obj.analyze_sample() diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 694ef3e..d7baf37 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -13,8 +13,6 @@ from Bio import SeqIO -import pdb - log = logging.getLogger(__name__) stderr = rich.console.Console( stderr=True, @@ -64,7 +62,7 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: # Hacer un blast con la query esta secuencia y la database del alelo # Create blast db with sample file - pdb.set_trace() + pass elif len(blast_result) == 1: column_blast_res = blast_result[0].split("\t") @@ -79,10 +77,8 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: allele_name = grep_result[0].split(">")[1] # allele is labled as EXACT - pdb.set_trace() return ["EXC", allele_name, allele_details] # check if contig is shorter than allele - pdb.set_trace() if int(column_blast_res[3]) > int(column_blast_res[4]): # check if sequence is shorter because it starts or ends at the contig if ( @@ -94,28 +90,21 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: == column_blast_res[15] # check reverse at contig start ): # allele is labled as PLOT - pdb.set_trace() return ["PLOT", allele_name, allele_details] # allele is labled as ASM - pdb.set_trace() return ["ASM", allele_name, allele_details] # check if contig is longer than allele if int(column_blast_res[3]) < int(column_blast_res[4]): # allele is labled as ALM - pdb.set_trace() return ["ALM", allele_name, allele_details] if int(column_blast_res[3]) == int(column_blast_res[4]): # allele is labled as INF - pdb.set_trace() return ["INF", allele_name, allele_details] - pdb.set_trace() - def search_match_allele(self): # Create blast db with sample file result = {"allele_type": {}, "allele_match": {}, "allele_details": {}} - # pdb.set_trace() for ref_allele in self.ref_alleles: # schema_alleles = os.path.join(self.schema, ref_allele) # parallel in all CPUs in cluster node @@ -142,19 +131,17 @@ def search_match_allele(self): allele_file = os.path.join(self.schema, os.path.basename(ref_allele)) # blast_result = self.blast_obj.run_blast(q_file,perc_identity=100) allele_name = Path(allele_file).stem - # pdb.set_trace() ( result["allele_type"][allele_name], result["allele_match"][allele_name], result["allele_details"][allele_name], ) = self.assign_allele_type(blast_result, allele_file, allele_name) - # pdb.set_trace() else: # Sample does not have a reference allele to be matched # Keep LNF info # ver el codigo de espe # lnf_tpr_tag() - pdb.set_trace() + pass return result diff --git a/taranis/eval_cluster.py b/taranis/eval_cluster.py index be2e041..2fc2c19 100644 --- a/taranis/eval_cluster.py +++ b/taranis/eval_cluster.py @@ -85,7 +85,6 @@ def summary(self, cluster_data: dict) -> dict: if cluster_data[cluster_id]["result"] == "NOK": summary_data["result"] = "NOK" summary_table.append(",".join(row_data)) - # pdb.set_trace() summary_data["individual"] = summary_table return summary_data @@ -113,7 +112,6 @@ def validate_cluster(self, blast_result: list, cluster_data: list) -> dict: alleles_not_in_cluster.append(blast_allele) if len(cluster_data) == len(set(blast_alleles)): - # pdb.set_trace() return {"validation": True} result = {"validation": False} # convert list to numpy array to find out differences diff --git a/taranis/pruebas.py b/taranis/pruebas.py index 39331c5..cac4ea4 100644 --- a/taranis/pruebas.py +++ b/taranis/pruebas.py @@ -5,7 +5,7 @@ import subprocess # import taranis.utils -import pdb + import random """ @@ -35,7 +35,6 @@ break locus_list.append(line) -# import pdb; pdb.set_trace() rand_locus = random.choice(locus_list) schema_file = "/media/lchapado/Reference_data/proyectos_isciii/taranis/taranis_testing_data/listeria_testing_schema/lmo0002.fasta" new_schema_file = ( @@ -72,7 +71,6 @@ ) blast_parameters = '"6 , qseqid , sseqid , pident , qlen , length , mismatch , gapopen , evalue , bitscore , sstart , send , qstart , qend , sseq , qseq"' -# pdb.set_trace() # db=self.blast_dir, evalue=evalue, perc_identity=perc_identity_ref, reward=reward, penalty=penalty, gapopen=gapopen, gapextend=gapextend, outfmt=blast_parameters, max_target_seqs=max_target_seqs, max_hsps=max_hsps, num_threads=num_threads, query=core_reference_allele_path) cline = NcbiblastnCommandline( db=db_name, @@ -93,8 +91,6 @@ out, _ = cline() except Exception as e: print(e) - pdb.set_trace() b_lines = out.splitlines() print("longitud del cluster = ", len(locus_list)) print("numero de matches = ", len(b_lines)) -# pdb.set_trace() diff --git a/taranis/utils.py b/taranis/utils.py index 51148aa..57c881c 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -21,8 +21,6 @@ from pathlib import Path from Bio import SeqIO -import pdb - log = logging.getLogger(__name__) @@ -276,7 +274,6 @@ def grep_execution(input_file: str, pattern: str, parameters: str) -> list: text=True, ) except subprocess.CalledProcessError as e: - pdb.set_trace() log.error("Unable to run grep. Error message: %s ", e) return [] return result.stdout.split("\n") diff --git a/test/MLST_listeria/lm0002.fasta b/test/MLST_listeria/analyze_schema/lm0002.fasta similarity index 100% rename from test/MLST_listeria/lm0002.fasta rename to test/MLST_listeria/analyze_schema/lm0002.fasta diff --git a/test/MLST_listeria/lmo0011.fasta b/test/MLST_listeria/analyze_schema/lmo0011.fasta similarity index 100% rename from test/MLST_listeria/lmo0011.fasta rename to test/MLST_listeria/analyze_schema/lmo0011.fasta diff --git a/test/MLST_listeria/lmo0019.fasta b/test/MLST_listeria/analyze_schema/lmo0019.fasta similarity index 100% rename from test/MLST_listeria/lmo0019.fasta rename to test/MLST_listeria/analyze_schema/lmo0019.fasta diff --git a/test/MLST_listeria/reference_allele/lmo1762.fasta b/test/MLST_listeria/reference_allele/lmo1762.fasta new file mode 100644 index 0000000..580d9cf --- /dev/null +++ b/test/MLST_listeria/reference_allele/lmo1762.fasta @@ -0,0 +1,224 @@ +>lmo1762_1 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGCACCGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAACGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_2 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTCATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_3 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGCACCGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCCATTGGAACGGCAATTGGCCTCAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_4 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGTACCGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAACGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_5 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCATTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACGCTTGCTAATGTTAGAGAAAAACGAGCCATTGGAACGGCAATTGGCCTCAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_6 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGACTTGATTTTTCATTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACGCTTGCTAACGTTAAAGAAAAACGAGCAATTGGAACGGCGATTGGCCTAAGTGGAATCTTGATTTGTGTGACAAGTATTGTCCTGATGGCAATATAA +>lmo1762_7 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCATTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACGCTTGCTAATGTTAGAGAAAAACGAGCCATTGGAACGGCAATTGGCCTCAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTCATGGCAATATAA +>lmo1762_8 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCATTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACGCTTGCTAATGTTAGAGAAAAACGAGCAATTGGAACGGCAATTGGCCTCAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_9 +TTGAAAAAGTTTAATAGTAAAACTTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGTACCGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAACGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_10 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGTACCGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTACTAACGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_11 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACAGGGCTTGATTTTTCATTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACGCTTGCTAATGTTAGAGAAAAACGAGCCATTGGAACGGCAATTGGCCTCAGTGGAATCTTGATTTGTGTAACAAGCATTGTTCTTATGGCAATATAA +>lmo1762_12 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTCATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGACACGGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_13 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCATTACTGTGGCACTGGGTCTTTATTATTTGTTTAATTTTCACGACGCTTGCTAATGTTAGAGAAAAACGAGCAATTGGAACGGCAATTGGCCTCAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_14 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTCATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCGCTGGCACGGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_15 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTAATCATCTCTATTCTTGCGCTTGCGGTTATTTATTTTGTGATTAATATGATTTCCACTGGAACTGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATCTTCACGACGCTTGCTAATGTTAGAGAAAAACGAGCAATTGGAACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTGACAAGTATCGTCCTGATGGCAATATAA +>lmo1762_16 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACAGGGCTTGATTTTTCATTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACGCTTGCTAATGTTAGAGAAAAACGAGCCATTGGAACGGCAATTGGCCTCAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_17 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTCATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCATTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_18 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTAATTAATATGATTTCCACTGGCACCGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAACGTTAGAGAAAAACGAGCCATTGGAACGGCAATTGGCCTCAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_19 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGACTTGATTTTTCATTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAACGTTAGAGAAAAACGAGCAATTGGAACGGCGATTGGCCTAAGTGGAATCTTGATTTGTGTGACAAGTATTGTCCTGATGGCAATATAA +>lmo1762_20 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTAATCATCTCTATTCTTGCGCTTGCGGTTATTTATTTTGTGATTAATATGATTTCCACTGGAACTGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACGCTTGCTAATGTTAGAGAAAAACGAGCAATTGGAACGGCAATTGGCCTCAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_21 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGACACGGGACTTGATTTTTCATTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACGCTTGCTAACGTTAGAGAAAAACGAGCAATTGGAACGGCGATTGGCCTAAGTGGAATCTTGATTTGTGTGACAAGTATTGTCCTGATGGCAATATAA +>lmo1762_22 +TTGAAGAAATGGAATAGTAAGGCCTATCAGCTTGTCATTATTTCTATTCTTGCCATTGCAGTTATCTATTTTATCATTAACATGGTTGCAACCGGCGTAGGACTTGAATTTTCACTATTGTGGCATTGGGTCTTCATTATATGTTTTATTTTCACGACACTTGCCAATGTAAAAGAAAAACGAGCAATTGGAACAGCAATTGGCTTAAGTGGCATATTAATTTGTGTAACAAGCATTGTACTTATGGCAATATAA +>lmo1762_23 +TTGAAGAAATTGAATAGTAAGACCTATCAACTTATCATTATTTCTATTCTTGCCATTGCAGTTATCTATTTTATCATTAATATGATTGCAACCGGTATAGGACTTGAATTTTCACTATTATGGCATTGGGTCTTCATTATCTGTTTTATTTTCACGACACTTGCCAATGTAAAAGAAAAACGAGCAATTGGAACAGCAATTGGTCTAAGTGGCATATTAATTTGTGTAACAAGCATCGTACTTATGGCAATATAA +>lmo1762_24 +TTGAAAAAATTTAATAGTAAAACCTATCAGATTGTGATCATTTCTATTCTTGCACTTGCAGTTATTTACTTTGTGATTAATATGATTTCCACTGGCACCGGGCTTGATTTTTCTTTACTTTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCCATTGGAACGGCAATTGGCCTCAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_25 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGCACCGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAACGTTAGAGAAAAACGAGCAATTGGTAAGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_26 +TTGAAGAAATCTAATAGTAAGACCTATCAACTTGTCATTATTTCTATTCTTGCAATTGCGGTTATTTATTTTATCATTAATATGGTTTTAACCGGTGTAGGACTTGATTTCTCTTTGTTATGGCATTGGGTCTTTATTATCTGTTTTATTTTCACAACACTTGCCAATGTAAAAGAAAAACGAGCAATTGGAACAGCAATTGGTCTAAGCGGCATCCTCATTTGTGTAACAAGCATCGTACTTATGGCAATTTAA +>lmo1762_27 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTAATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGAACTGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACGCTTGCTAATGTTAGAGAAAAACGAGCAATTGGAACGGCGATTGGCCTAAGTGGAATCCTGATTTGTGTGACAAGTATTGTACTGATGGCAATATAA +>lmo1762_28 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTCATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTACTAATGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_29 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGCACCGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAACGTTAGAGAAAAACGAGCAATTGGAACGGCAATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_30 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTCATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTAGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_31 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTCATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCAAGACACTTGCTAATGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_32 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTAATCATCTCTATTCTTGCTCTTGCAGCTATTTATTTTGTGATTAATATGATTTCCACTGGAACTGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACGCTTGCTAATGTTAGAGAAAAACGAGCAATTGGAACGGCGATTGGCCTGAGTGGAATCCTGATTTGTGTGACAAGTATCGTGCTGATGGCAATATAA +>lmo1762_33 +TTGAAAAAATCTAATAGTAAGACCTATCAACTTGTCATTATTTCTATTCTTGCAATTGCGGTTATTTATTTTATCATTAATATGGTTTTAACCGGTGTAGGTCTTGATTTCTCTTTGTTATGGCATTGGGTCTTTATTATCTGTTTTATTTTCACAACACTTGCCAATGTAAAAGAAAAAAGAGCAATTGGAACAGCAATTGGTCTAAGTGGCATCCTCATTTGTGTAACAAGCATCGTACTTATGGCAATTTAA +>lmo1762_34 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTGATCATCTCTATTCTTGCTGTTGCAGTTATTTATTTTGTGATTAATATGTTTACTACTGGCACAGGACTTGATTTTTCGTTACTTTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACAACACTTGCAAATGTGAGAGAAAAGCGAGCAATTGGAACGACAATTGGCCTTAGTGGAATCCTGATTTGTGTAGCTAGTATCGTCCTTATGGCAATATAA +>lmo1762_35 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCACTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCATTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTTACGACGCTTGCTAATGTTAGAGAAAAACGAGCAATTGGAACGGCAATTGGCCTCAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_36 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCAAGGGGCTTGATTTTTCATTACTGTGGCACTGGATCTTTATTATTTGTTTTATTTTCACGACGCTTGCTAATGTTAGAGAAAAACGAGCCATTGGAACGGCAATTGGCCTCAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_37 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTAATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTAGAACTGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATCTTCACGACGCTTGCTAATGTTAGAGAAAAACGAGCAATTGGAACGGCGATTGGCCTAAGTGGAATCCTGATTTGTGTGACAAGTATCGTACTGATGGCAATATAA +>lmo1762_38 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTAATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGAACTGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATCTTCACGACGCTTGCTAATGTTAGAGAAAAACGAGCAATTGGAACGGCGATTGGCCTAAGTGGAATCCTGATTTGTGTGACAAGTATCGTACTGATGGCAATATAA +>lmo1762_39 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTAATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGAACTGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACGCTTGCTAATGTTAGAGAAAAACGAGCAATTGGAACGGCGATTGGCCTAAGTGGAATCCTGATTTGTGTGACAAGTATCGTACTGATGGCAATATAA +>lmo1762_40 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTAATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGAACTGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATCTTCACGACGCTTGCTAATGTTAGAGAAAAACGAGCAATTGGAACGGCGATTGGCCTAAGTGGAATCCTGATTTGTGTGACAAGTATTGTACTGATGGCAATATAA +>lmo1762_41 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGCACCGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAACGTTAGAGAAAAACGAGCAATTGGTACCGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_42 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCATTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACGCTTGCTAATGTTAGAGAAAAACGAGCAATTGGAACGGCAATTGGCCTCAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTGATGGCAATATAA +>lmo1762_43 +TTGAAAAAATCTAATAGTAAGACCTATCAACTTGTCATTATTTCTATTCTTGCAATTGCGGTTATTTATTTTATCATTAATATGGTTTTAACCGGTGTAGGTCTTGATTTCTCTTTGTTATGGCATTGGGTCTTTATTATCTGTTTTATTTTCACAACACTTGCCAATGTAAAAGAAAAACGAGCAATTGGAACAGCAATTGGTCTAAGCGGCATCCTCATTTGTGTAACAAGCATCGTACTTATGGCAATTTAA +>lmo1762_44 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTAATCATCTCTATTCTTGCTCTTGCAGTAATTTATTTTGTGATTAATATGATTTCCACTGGAACTGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATCTTCACGACGCTTGCTAATGTTAGAGAAAAACGAGCAATTGGAACGGCGATTGGCCTAAGTGGAATCCTGATTTGTGTGACAAGTATCGTACTGATGGCAATATAA +>lmo1762_45 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGCACCGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACATGCTAACGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_46 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTCATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCAATTGGTACGGCGGTTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_47 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGCACCGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAACGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAATGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_48 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTCATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGCGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_49 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTCATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGGGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_50 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTCATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGTAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_51 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTCATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCCGTTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_52 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTCATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATTTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_53 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTCATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGATAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_54 +TTGAAAAAGTTTAATAGTAAAACCTATAAGATTGTCATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_55 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGACTTGATTTTTCATTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACGCTTGCTAACGTTAGAGAAAAACGAGCAATTGGAACGGCGATTGGCCTAAGTGGAATCTTGATTTGTGTGACAAGTATTGTCCTGATGGCAATATAA +>lmo1762_56 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGCACCGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAACGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAGTCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_57 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGCACCGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAACGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATAGCAATATAA +>lmo1762_58 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTCATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTACCACTGGCACGGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_59 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCATTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTTACGACGCTTGCTAATGTTAGAGAAAAACGAGCCATTGGAACGGCAATTGGCCTCAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_60 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGCACCGGACTTGTTTTTTTTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAACGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_61 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGTGCTTGATTTTTCATTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACGCTTGCTAATGTTAGAGAAAAACGAGCAATTGGAACGGCAATTGGCCTCAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_62 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGTACTGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAACGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_63 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTCATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGCGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_64 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTCATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCAATTGGTACGGTGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_65 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGCACCGGACTTGATTTTTCTTTACTATGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAACGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_66 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTCATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGGAATATAA +>lmo1762_67 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTAGCACGGGGCTTGATTTTTCATTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACGCTTGCTAATGTTAGAGAAAAACGAGCCATTGGAACGGCAATTGGCCTCAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_68 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTCATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGTCACGGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_69 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTCATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGATACTTGCTAATGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_70 +TTGAAAAAGTTTAATAGGAAAACCTATCAGATTGTCATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_71 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGCACCGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCCATTGGAACGGCAATTGGCCTCAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTGTGGCAATATAA +>lmo1762_72 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGCACGGGGCTTGATTTTTCATTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACGCTTGCTAATGTTAGAGAAAAACGAGCAATTGGAACGGCAATTGGCCTCAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_73 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGCACCGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCAAACGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_74 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGCACGGGGCTTGATTTTTCATTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCCATTGGAACGGCAATTGGCCTCAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_75 +TTGAAAAAATTTAATAGTAAAACCTATCAGATTGTGATCATTTCTATTCTTGCACTTGCAGTTATTTACTTTGTGATTAATATGATTTCCACTGGCACCGGGCTTGATTTTTCTTTACTTTGGCACTGGATCTTTATCATTTGTTTTATTTTCACGACACTGGTAAATGTAAAAGAAAAACGAGCGATTGGTACGGCAATTGGCCTCAGTGGAATTTTGATTTGTGTAACAAGCATTGTCTTGATGGCAATATAA +>lmo1762_76 +TTGAAAAAATTTAATAGTAAAACCTATCAGATTGTGATCATTTCTATTCTTGCACTTGCAGTTATTTACTTTGTGATTAATATGATTTCCACTGGCACCGGGCTTGATTTTTCTTTACTTTGGCACTGGGTCTTTATCATTTGTTTTATTTTTACGACACTGGTAAACGTAAAAGAAAAACGAGCGATTGGCACGGCAATTGGCCTCAGTGGAATTTTGATTTGTGTAACAAGCATTGTCTTGATGGCAATATAA +>lmo1762_77 +ATGAAAAAATTTAATAGTAAAACCTATCAGATTGTGATCATTTCTATTCTTGCGCTTGCAGTTCTTTACTTTGTGATTAATATGATTTCCACTGGCACCGGGCTTGATTTTTCTTTACTTTGGCACTGGGTCTTTATCATTTGTTTTATTTTCACGACACTAGTAAACGTAAAAGAAAAACGGGCGATTGGTACGGCAATTGGCCTCAGTGGAATTTTGATTTGTGTAACAAGCATTGTCTTGATGGCAATATAA +>lmo1762_78 +ATGAAAAAATTTAATAGTAAAACCTATCAGATTGTGATCATTTCTATTCTTGCGCTTGCAGTTCTTTACTTTGTGATTAATATGATTTCCACTGGCACCGGGCTTGATTTTTCTTTACTTTGGCACTGGGTCTTTATCATTTGTTTTATTTTCACGACACTGGTAAATGTAAAAGAAAAACGGGCGATTGGTACGGCAATTGGCCTCAGTGGAATTTTGATTTGTGTAACAAGCATTGTCTTGATGGCAATATAA +>lmo1762_79 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGCACCGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAACGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGCGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_80 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTCATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGTAATATAA +>lmo1762_81 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTCATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCTTTATGGCAATATAA +>lmo1762_82 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGCACCGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAACGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGACTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_83 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGTCACCGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAACGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_84 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGCACCGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAACGTTAGAGAAAAACGAGCCATTGGAACGGCAATTGGCCTCAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_85 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGCACTGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCCATTGGAACGGCAATTGGCCTCAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_86 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGCACCGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAACGTTAGAGAAAAACGAGCCATTGGAACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_87 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGCTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGCACCGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAACGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_88 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTCATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCTTTACTGTGGCACTGGATCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_89 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCATTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGTATTGTCCTTATGGCAATATAA +>lmo1762_90 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACAGGGCTTGATTTTTCATTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACGCTTGCTAATGTTAGAGAAAAACGAGCAATTGGGACGGCAATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGTATTGTACTTATGGCAATATAA +>lmo1762_91 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGACACCGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCCATTGGAACGGCAATTGGCCTCAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_92 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTAATCATCTCTATTCTTGCGCTTGCGGTTATTTATTTTGTGATTAATATGATTTCCACTGGCACGGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATCTTCACGACGCTTGCTAATGTTAGAGAAAAACGAGCAATTGGAACGGCAATTGGCCTCAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_93 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGCACCGGACTTGATTTTTCTATACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAACGTTAGGGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_94 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGCACCGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCCATTGGAACGGCAATTGGCCTCAGTGGAATCTTGATTTGTGTAACAAGCGTTGTCCTTATGGCAATATAA +>lmo1762_95 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGCACCGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAACGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTTCTTATGGCAATATAA +>lmo1762_96 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCATTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTTACGACGCTTGCTAATGTTAGAGAAAAACGAGCCATTGGAACGGCAATTGGTCTCAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_97 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACAGGGCTTGATTTTTCATTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACGCTTGCTAATGTTAGAGAAAAACGAGCAATTGGGACGGCAATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_98 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTCATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCAATTGTTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_99 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTCATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTGGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_100 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGCACCGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCCATTGGAACGGCAATTGGCCTCAGTGGAATCTTGATTTGTGTAACAAGCATTGTCTTTATGGCAATATAA +>lmo1762_101 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTCATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCTTTACTGTGGTACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_102 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCATTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACGCTTGCTAATGTTAGAGAAAAACGAGCAATTGGGACGGCAATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_103 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTCGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGACTTGATTTTTCATTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACGCTTGCTAACGTTAAAGAAAAACGAGCAATTGGAACGGCGATTGGCCTAAGTGGAATCTTGATTTGTGTGACAAGTATTGTCCTGATGGCAATATAA +>lmo1762_104 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATTATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCATTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACGCTTGCTAATGTTAGAGAAAAACGAGCAATTGGAACGGCAATTGGCCTCAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_105 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGACTTGATTTTTCATTATTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACGCTTGCTAACGTTAAAGAAAAACGAGCAATTGGAACGGCGATTGGCCTAAGTGGAATCTTGATTTGTGTGACAAGTATTGTCCTGATGGCAATATAA +>lmo1762_106 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACAGGGCTTGATTTTTCATTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACGCTTGCTAATGTTAGAGAAAAACGAGCAATTGGGACGGCAATTGGCCTCAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_107 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCATTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACGCTTGCTAATGTTAGAGAAAAACGAGCAATTGGGACGGCAATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGTATTGTACTTATGGCAATATAA +>lmo1762_108 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCGCTCGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCATTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACGCTTGCTAATGTTAGAGAAAAACGAGCAATTGGAACGGCAATTGGCCTCAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_109 +GTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGCACCGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAACGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_110 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTTATCATCTCTATTCTTGCTCTTGCAGTTATTTATTTTGTGATTAATATGATTTCCACTGGCACCGGACTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAACGTTAGAGAAAAACGAGCCATTGGAACGGCAATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_111 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTCTTCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA +>lmo1762_112 +TTGAAAAAGTTTAATAGTAAAACCTATCAGATTGTGATCATCTCTATTCTTGCGCTTGCAGTTATTTATTTTGTGATTAATATGATTGCCACTGGCACGGGGCTTGATTTTTCTTTACTGTGGCACTGGGTCTTTATTATTTGTTTTATTTTCACGACACTTGCTAATGTTAGAGAAAAACGAGCAATTGGTACGGCGATTGGCCTTAGTGGAATCTTGATTTGTGTAACAAGCATTGTCCTTATGGCAATATAA diff --git a/test/MLST_listeria/reference_allele/lmo1784.fasta b/test/MLST_listeria/reference_allele/lmo1784.fasta new file mode 100644 index 0000000..56cc388 --- /dev/null +++ b/test/MLST_listeria/reference_allele/lmo1784.fasta @@ -0,0 +1,126 @@ +>lmo1784_1 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_2 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCTGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_3 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACATGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_4 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTTGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_5 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATAGTCGCTAAAATGAAATAA +>lmo1784_6 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCTCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_7 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAACTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAACTGCGTAAATCAGCAATGGTATCAGCTGGCGATTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAGTAA +>lmo1784_8 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAAAGAACAGGATCTGGAAAACTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAACTGCGTAAATCAGCAATGGTATCAGCTGGCGATTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAGTAA +>lmo1784_9 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAACGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_10 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGTTTCACAAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_11 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCCGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_12 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTAGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_13 +ATGCCAAAAATGAAAACTCACCGTGGCTCTGCTAAGCGTTTCAAAAGAACAGGTTCTGGTAAATTAAAACGTTCGCACGGTTACACAAGTCATATGTTCGCAAACAAATCTCAAAAACAAAAACGTAAACTACGTAAAAGTGCGTTAGTTTCAAAAGGTGACTTCAAGCGTATTCGCCAAATGGTCGCTAAAATGAAATAA +>lmo1784_14 +ATGCCAAAAATGAAAACTCACCGTGGCTCTGCTAAGCGTTTCAAAAGAACAGGTTCTGGTAAATTAAAACGTTCGCACGGTTACACAAGTCATATGTTCGCAAATAAATCTCAAAAACAAAAACGTAAACTACGTAAAAGTGCGTTAGTTTCAAAAGGTGACTTCAGACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_15 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTTACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_16 +ATGACAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_17 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGTAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCTGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_18 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCTCAAAAACAAAAACGTAAATTGCGTAAATCTGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_19 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAACCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_20 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTTAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_21 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGGTCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_22 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTTAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_23 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTACGTAAATCTGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_24 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAACTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_25 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTTGCTAACAAATCCCAAAAACAAAAACGTAAACTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_26 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGTCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_27 +ATGCCAAAAATGAAAACCCACCGCGGGTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_28 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACATAAATTGCGTAAATCTGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_29 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCAAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_30 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCTGCAATGGTATCAGTTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_31 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGATATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_32 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCACTAAAATGAAATAA +>lmo1784_33 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTTGCTAACAAATCCCAAAAACAAAAACGTAAATTGTGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_34 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGTTCGCTAAAATGAAATAA +>lmo1784_35 +ATGCCAAAAATGAAAACCCACCGAGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_36 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAAAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_37 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGTTAAAATGAAATAA +>lmo1784_38 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATTTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_39 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAATAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_40 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGGAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_41 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACCAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_42 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACAAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_43 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAGTGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_44 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTTGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_45 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAGCGCAGACACGGTTTCACAAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_46 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATATTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_47 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTGAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_48 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGATTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAGTAA +>lmo1784_49 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTTGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_50 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATTAAATAA +>lmo1784_51 +ATGCCAAAAATGAAAACCCACCGTGGGACTGCAAAACGCTTAAAAAGAACAGGTACTGGTAAATTAAAACGCAGACATGGTTTCACTAGCCATATGTTCGCAAACAAATCACAAAAACAAAAACGCAAATTGCGTAAAGCAGCAATGGTATCAGCTGGCGATTTCAAACGTATTCGTCAAATGGTCGCAAAAATGAAATAA +>lmo1784_52 +ATGCCAAAAATGAAAACTCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGCTTTTGTAAGCCATATGTTTGCTAACAAATCGAAAAAACAAAAACGTAAACTACGTAAAGCGGCAATGGTATCAGCTGGCGATTTCAAACGTATTCGCCAAATGGTCGCTAAAATGAAATAA +>lmo1784_53 +ATGCCAAAAATGAAGACTCACCGCGGTTCCGCTAAACGTTTCAAAAGAACAGGATCTGGAAAATTAAAACGCAGACATGGTTTTACTAGCCATATGTTTGCTAACAAATCTCAAAAACAAAAACGTAAACTGCGTAAAGCGGCAATGGTATCAGCTGGCGATTTTAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_54 +ATGCCAAAAATGAAGACTCACCGCGGTTCCGCTAAACGTTTCAAAAGAACAGGATCTGGAAAATTAAAACGCAGACATGGTTTTACTAGCCATATGTTTGCTAACAAATCTCAAAAACAAAAACGTAAACTGCGTAAAGCAGCAATGGTATCAGCTGGCGATTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_55 +ATGCCAAAAATGAAAACTCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACATGCTTATGTCAGCCATATGTTCGCTAACAAAACGAAAAAGCAAAAACGTAAACTGCGTAAAGCAGCAATGGTATCAGCTGGCGATTTCAAACGTATTCGCCAAATGGTCGCTAAAATGAAATAA +>lmo1784_56 +ATGCCAAAAATGAAGACTCACCGCGGTTCCGCTAAACGTTTCAAAAGAACAGGATCTGGAAAATTAAAACGTAGACATGGTTTTACTAGCCATATGTTTGCTAACAAATCTCAAAAACAAAAACGTAAACTGCGTAAAGCAGCAATGGTATCAGCTGGTGATTTTAAACGTATTCGCCAAATGGTCGCTAAAATGAAATAA +>lmo1784_57 +ATGCCAAAAATGAAGACTCACCGCGGTTCCGCTAAACGTTTCAAAAGAACAGGATCTGGAAAATTAAAACGCAGACATGGTTTTACTAGCCATATGTTTGCTAACAAATCTCAAAAACAAAAACGTAAACTGCGTAAAGCGGCAATGGTATCAGCTGGCGATTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_58 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAAAGAACAGGATCTGGTAAATTAAAACGTAGACACGGCTACACAAGCCATATGTTTGCAAACAAATCACAGAAACAAAAACGTAAATTGCGTAAATCTGCAATGGTATCGGCCGGCGATTTCAAACGTATCCGCCAAATGGTCGCTAAAATGAAATAA +>lmo1784_59 +ATGCCAAAAATGAAAACCCACCGTGGTTCCGCTAAACGTTTCAAAAGAACAGGATCTGGAAAACTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAACTGCGTAAATCAGCAATGGTATCAGCTGGCGATTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAGTAA +>lmo1784_60 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAAAGAACAGGATCTGGTAAATTAAAACGCAGACACGGCTACACTAGCCATATGTTCGCAAACAAATCACAAAAACAAAAACGTAAATTGCGTAAATCTGCAATGGTATCGGCCGGCGATTTCAAACGTATCCGCCAAATGGTCGCTAAAATGAAATAA +>lmo1784_61 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTAGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_62 +ATGCCAAAAATGAAAACCCACCGAGGTTCCGCTAAACGTTTCAAGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCTGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA +>lmo1784_63 +ATGCCAAAAATGAAAACCCACCGCGGTTCCGCTAAACGTTTCACGAGAACAGGATCTGGAAAATTAAAACGCAGACACGGCTTCACTAGCCATATGTTCGCTAACAAATCCCAAAAACAAAAACGTAAATTGCGTAAATCAGCAATGGTATCAGCTGGCGACTTCAAACGTATTCGTCAAATGGTCGCTAAAATGAAATAA From 7cab1d376da9c8a3f0ecb3bcb45c6926b8551515 Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 11 Mar 2024 16:49:59 +0100 Subject: [PATCH 086/214] Added conda env before execute test --- .github/workflows/tests.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index d8a7d08..3d61970 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -31,5 +31,7 @@ jobs: - name: Testing Reference allele run: | + source $CONDA/etc/profile.d/conda.sh + conda activate taranis_env taranis reference-allele -s test/MLST_listeria/reference_allele -o reference_allele_test --cpus 1 \ No newline at end of file From 362dd9f063affb9baa2e15661d1dd96c484be1ed Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 11 Mar 2024 16:57:43 +0100 Subject: [PATCH 087/214] fixing error in command line of reference-alleles --- .github/workflows/tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 3d61970..e9643e3 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -33,5 +33,5 @@ jobs: run: | source $CONDA/etc/profile.d/conda.sh conda activate taranis_env - taranis reference-allele -s test/MLST_listeria/reference_allele -o reference_allele_test --cpus 1 + taranis reference-alleles -s test/MLST_listeria/reference_allele -o reference_allele_test --cpus 1 \ No newline at end of file From 41538fab5079d2fc916c94ec95590cf367162589 Mon Sep 17 00:00:00 2001 From: luissian Date: Tue, 12 Mar 2024 11:13:17 +0100 Subject: [PATCH 088/214] adding table of graphic data --- taranis/reference_alleles.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index 04f8fa1..63f90f5 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -10,6 +10,8 @@ import taranis.eval_cluster from Bio import SeqIO +import pdb + log = logging.getLogger(__name__) stderr = rich.console.Console( stderr=True, @@ -282,11 +284,21 @@ def stats_graphics(stats_folder: str, cluster_alleles: dict) -> None: ) heading = "Locus name,cluster number,average,center allele,number of sequences" summary_file = os.path.join(out_folder, "evaluate_cluster", "cluster_summary.csv") + locus_clustering_file = os.path.join( + out_folder, "evaluate_cluster", "cluster_per_locus.csv" + ) with open(summary_file, "w") as fo: fo.write(heading + "\n") fo.write("\n".join(clusters_list) + "\n") _ = stats_graphics(out_folder, cluster_data_graph) + + with open(locus_clustering_file, "w") as fo: + fo.write("number of clusters, number of locus\n") + sorted_clusters = dict(sorted(cluster_data_graph.items())) + for key, value in sorted_clusters.items(): + fo.write(str(key) + "," + str(value) + "\n") + if eval_cluster: heading = "Locus name,cluster number,result,alleles not match in blast,alleles not found in cluster" eval_file = os.path.join( From 950845b9eda937135244b132ec8ed6d6dd1a68ae Mon Sep 17 00:00:00 2001 From: luissian Date: Wed, 13 Mar 2024 21:06:47 +0100 Subject: [PATCH 089/214] trying to fix too many opened files --- taranis/distance.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/taranis/distance.py b/taranis/distance.py index df19477..a5a8d3a 100644 --- a/taranis/distance.py +++ b/taranis/distance.py @@ -65,6 +65,10 @@ def create_matrix(self) -> pd.DataFrame: stderr.print(f"{e}") sys.exit(1) + # Close the file handles + mash_distance_result.stdout.close() + mash_distance_result.stderr.close() + out_data = out.decode("UTF-8").split("\n") allele_names = [item.split("\t")[0] for item in out_data[1:-1]] # create file in memory to increase speed From 78389929698bcd17f824bc2539218847d712d8e7 Mon Sep 17 00:00:00 2001 From: luissian Date: Wed, 13 Mar 2024 21:10:51 +0100 Subject: [PATCH 090/214] removed pdb tag --- taranis/reference_alleles.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index 63f90f5..da546ac 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -10,8 +10,6 @@ import taranis.eval_cluster from Bio import SeqIO -import pdb - log = logging.getLogger(__name__) stderr = rich.console.Console( stderr=True, From 432d76c0056c04780a7f8f9847661b0e6a0cc6d6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Thu, 28 Mar 2024 17:02:34 +0100 Subject: [PATCH 091/214] delete old utils --- utils/taranis_utils.old_py | 448 ------------------------------------- 1 file changed, 448 deletions(-) delete mode 100644 utils/taranis_utils.old_py diff --git a/utils/taranis_utils.old_py b/utils/taranis_utils.old_py deleted file mode 100644 index 3ef4bb9..0000000 --- a/utils/taranis_utils.old_py +++ /dev/null @@ -1,448 +0,0 @@ -#!/usr/bin/env python3 -import logging -from logging.config import fileConfig -#from logging.handlers import RotatingFileHandler -import os -import re -import glob -import shutil -import subprocess -from Bio import SeqIO -from Bio import Seq -from openpyxl import load_workbook -import pandas as pd - -from taranis_configuration import * - - -def open_log(log_name): - ''' - Description: - This function open the log file with the configuration defined - on the config file (loging_config.ini) - The path for the logging config is defined on the application - configuration file. - Input: - log_name # Is the name that will be written inside the logfile - LOGGIN_CONFIGURATION # is the constant value defined on the configuration - file of the application - Return: - Error is return in case that config file does not exists - logger # containing the logging object - ''' - #working_dir = os.getcwd() - - - #fileConfig('/srv/taranis/logging_config.ini') - #log_name=os.path.join(working_dir, log_name) - #def create_log (): - #logger = logging.getLogger(__name__) - #logger.setLevel(logging.DEBUG) - #create the file handler - #handler = logging.handlers.RotatingFileHandler('pepe.log', maxBytes=4000000, backupCount=5) - #handler.setLevel(logging.DEBUG) - - #create a Logging format - #formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') - #handler.setFormatter(formatter) - #add the handlers to the logger - #logger.addHandler(handler) - try: - logging.config.fileConfig(LOGGING_CONFIGURATION) - logger = logging.getLogger(log_name) - logger.info('--------------- LOG FILE -----------------') - logger.info('Log file has been created for process %s', log_name) - except: - print('------------- ERROR --------------') - print('Unable to create the logging file') - print('Check in the logging configuration file') - print('that the path to store the log file exists') - print('------------------------------------------') - return 'Error' - return logger - - -def read_xls_file (in_file, logger): ## N - ''' - Description: - This function open the Excel file enter by the user in the xlsfile parameter - Once the excel is read the column information of the gene and protein is - stored on the gene_list that it is returned back - Input: - logger # Is the logging object - in_file # It is the excel file which contains the information to parse - Variables: - wb # Contains the excel workbook object - ws # Contains the working sheet object of the workbook - gene # Used in the interaction row to get the gene name - protein # Used in the interaction row to get the protein name - gene_prot # Used to get the tupla (gene/protein) for each or excel row - genes_prots_list # Is a list containing tuplas of gene, protein - Return: - 'Error message' is returned in case excel file does not exists - genes_prots_list is returned as a successful execution - ''' - logger.debug('opening the excel file : %s', in_file) - try: - wb = load_workbook(in_file) - logger.info('Excel file has been read and starts processing it.') - except Exception as e: - logger.error('----------------- ERROR ------------------') - logger.error('Unable to open the excel file. %s ', e ) - logger.error('Showing traceback: ', exc_info=True) - logger.error('----------------- END ERROR --------------') - #raise - return 'Error: Unable to open excel file' - # Only fetch the first working sheet - ws = wb[wb.sheetnames[0]] - - genes_prots_list = [] - ## Get the content block from A2 : B(latest row in the excel) - for row in ws.iter_rows(min_row=2, min_col=1, max_row=ws.max_row, max_col=2) : - gene_prot = [] - for index in range(len(row)) : - gene_prot.append(row[index].value) - genes_prots_list.append(gene_prot) - logger.info('Exiting the function ---read_xls_file-- ') - logger.info('Returning back the gene/protein list' ) - return genes_prots_list - - -def download_fasta_locus (locus_list, output_dir, logger): ## N - ''' - Description: - This function will download the protein sequence. - Then it will be translated to nucleotide and saved - in the output directory specified by the users. - Input: - gene_list - filename # Is the name of the file to be checked - logger # is the logging object to logging information - Return: - Error is return in case that file does not exists - True if file exists - ''' - download_counter = 0 - for loci in locus_list : - tmp_split = loci.split('/') - loci_name = tmp_split[-1] - r = requests.get(loci + '/alleles_fasta') - if r.status_code != 200 : - logger.error('Unable to download the fasta file for allele %s ', loci_name) - - else : - fasta_alleles = r.text - fasta_file = os.path.join(output_dir, str(loci_name + '.fasta')) - with open (fasta_file , 'w') as fasta_fh : - fasta_fh.write(fasta_alleles) - download_counter += 1 - if download_counter == len(locus_list) : - return True - else : - logger.info('All alleles have been successfully downloaded and saved on %s', output_dir) - return False - - -def check_if_file_exists (filename, logger): ## N - ''' - Description: - This function will check if the file exists - Input: - filename # Is the name of the file to be checked - logger # is the logging object to logging information - Return: - Error is return in case that file does not exists - True if file exists - ''' - if not os.path.isfile(filename): - logger.info('File %s , does not exists', filename) - return 'Error' - return True - - -def junk (): ## N - AA_codon = { - 'C': ['TGT', 'TGC'], - 'A': ['GAT', 'GAC'], - 'S': ['TCT', 'TCG', 'TCA', 'TCC', 'AGC', 'AGT'], - 'G': ['CAA', 'CAG'], - 'M': ['ATG'], #Start - 'A': ['AAC', 'AAT'], - 'P': ['CCT', 'CCG', 'CCA', 'CCC'], - 'L': ['AAG', 'AAA'], - 'Q': ['TAG', 'TGA', 'TAA'], #Stop - 'T': ['ACC', 'ACA', 'ACG', 'ACT'], - 'P': ['TTT', 'TTC'], - 'A': ['GCA', 'GCC', 'GCG', 'GCT'], - 'G': ['GGT', 'GGG', 'GGA', 'GGC'], - 'I': ['ATC', 'ATA', 'ATT'], - 'L': ['TTA', 'TTG', 'CTC', 'CTT', 'CTG', 'CTA'], - 'H': ['CAT', 'CAC'], - 'A': ['CGA', 'CGC', 'CGG', 'CGT', 'AGG', 'AGA'], - 'T': ['TGG'], - 'V': ['GTA', 'GTC', 'GTG', 'GTT'], - 'G': ['GAG', 'GAA'], - 'T': ['TAT', 'TAC'] } - return True - - -def check_prerequisites (pre_requisite_list, logger): - # check if blast is installed and has the minimum version - for program, version in pre_requisite_list : - if not check_program_is_exec_version (program , version, logger): - return False - return True - - -def check_program_is_exec_version (program, version, logger): - # The function will check if the program is installed in your system and if the version - # installed matched with the pre-requisites - if shutil.which(program) is not None : - # check version - version_str= str(subprocess.check_output([program , '-version'])) - if version_str == "b''" : - version_str = subprocess.getoutput( str (program + ' -version')) - if not re.search(version, version_str): - logger.info('%s program does not have the right version ', program) - print ('Exiting script \n, Version of ' , program, 'does not fulfill the requirements') - return False - return True - else: - logger.info('Cannot find %s installed on your system', program) - return False - - -def create_blastdb (file_name, db_name,db_type, logger ): - f_name = '.'.join(os.path.basename(file_name).split('.')[:-1]) - db_dir = os.path.join(db_name,f_name) - output_blast_dir = os.path.join(db_dir, f_name) - - if not os.path.exists(db_dir): - try: - os.makedirs(db_dir) - logger.debug(' Created local blast directory for %s', file_name) - except: - logger.info('Cannot create directory for local blast database on file %s' , file_name) - print ('Error when creating the directory %s for blastdb. ', db_dir) - exit(0) - - blast_command = ['makeblastdb' , '-in' , file_name , '-parse_seqids', '-dbtype', db_type, '-out' , output_blast_dir] - blast_result = subprocess.run(blast_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) - if blast_result.stderr: - logger.error('cannot create blast db for %s ', file_name) - logger.error('makeblastdb returning error code %s', blast_result.stderr) - return False - else: - logger.info('Skeeping the blastdb creation for %s, as it is already exists', file_name) - return True - - -def is_fasta_file (file_name): - with open (file_name, 'r') as fh: - fasta = SeqIO.parse(fh, 'fasta') - return any(fasta) - - -def get_fasta_file_list (check_directory, logger): - if not os.path.isdir(check_directory): - logger.info('directory %s does not exists', check_directory) - return False - - fasta_format = ['*.fasta', '*.fa', '*.fna', '*.ffn', '*.frn'] - list_filtered_files = [] - for extension in fasta_format: - filter_files = os.path.join(check_directory, extension) - sublist_filtered_files = glob.glob(filter_files) - for fasta_file in sublist_filtered_files: - list_filtered_files.append(fasta_file) - - list_filtered_files.sort() - if len (list_filtered_files) == 0 : - logger.info('directory %s does not have any fasta file ', check_directory) - return False - valid_files = [] - for file_name in list_filtered_files: - if is_fasta_file( file_name): - valid_files.append(file_name) - else: - logger.info('Ignoring file %s .Does not have a fasta format', file_name) - if len(valid_files) == 0: - logger.info('There are not valid fasta files in the directory %s', check_directory) - logger.debug('Files in the directory are: $s', list_filtered_files) - return False - else: - return valid_files - - -def check_core_gene_quality(fasta_file_path, logger): - ### logger? - - ### logger.info('check quality of locus %s', fasta_file) - - start_codons_forward = ['ATG', 'ATA', 'ATT', 'GTG', 'TTG', 'CTG'] ### duda: tener en cuenta codones de inico no clásicos? (prodigal no los considera) - start_codons_reverse = ['CAT', 'TAT', 'AAT', 'CAC', 'CAA', 'CAG'] - - stop_codons_forward = ['TAA', 'TAG', 'TGA'] - stop_codons_reverse = ['TTA', 'CTA', 'TCA'] - - locus_quality = {} - - alleles_in_locus = list (SeqIO.parse(fasta_file_path, "fasta")) - for allele in alleles_in_locus : - - if allele.seq[0:3] in start_codons_forward or allele.seq[-3:] in start_codons_reverse: - if allele.seq[-3:] in stop_codons_forward or allele.seq[0:3] in stop_codons_reverse: ### si tiene codón de stop y codón de inicio - ### Buscando codón de stop para checkear que el primer codón de stop de la secuencia se corresponde con el codón final de la secuencia, de modo que no presenta más de un codón stop - sequence_order = check_sequence_order(allele.seq, logger) - if sequence_order == "reverse": - allele_sequence = str(allele.seq.reverse_complement()) - else: - allele_sequence = str(allele.seq) - stop_index = get_stop_codon_index(allele_sequence) - - if stop_index < (len(allele_sequence) - 3): ### si tiene codón start y stop pero tiene más de un codón stop (-3 --> 1 por índice python y 2 por las 2 bases restantes del codón) - locus_quality[str(allele.id)] = 'bad_quality: multiple_stop' - else: ### si tiene codón start y stop y un solo codón stop - locus_quality[str(allele.id)] = 'good_quality' - else: ### si tiene codón start pero no stop - locus_quality[str(allele.id)] = 'bad_quality: no_stop' - else: ### Si no tiene start - if allele.seq[-3:] in stop_codons_forward or allele.seq[0:3] in stop_codons_reverse: ### si no tiene start pero sí stop - locus_quality[str(allele.id)] = 'bad_quality: no_start' - else: ### Si no tiene start ni stop - locus_quality[str(allele.id)] = 'bad_quality: no_start_stop' - - return locus_quality - - -def check_sequence_order(allele_sequence, logger): - start_codon_forward= ['ATG','ATA','ATT','GTG', 'TTG'] - start_codon_reverse= ['CAT', 'TAT','AAT','CAC','CAA'] - - stop_codons_forward = ['TAA', 'TAG','TGA'] - stop_codons_reverse = ['TTA', 'CTA','TCA'] - - # check direction - if allele_sequence[0:3] in start_codon_forward or allele_sequence[-3:] in stop_codons_forward: - return 'forward' - if allele_sequence[-3:] in start_codon_reverse or allele_sequence[0:3] in stop_codons_reverse: - return 'reverse' - return "Error" - - -def get_stop_codon_index(seq) : - stop_codons = ['TAA', 'TAG','TGA'] - seq_len = len(seq) - index = 0 - for index in range (0, seq_len -2, 3) : - #while index < seq_len - 2: - codon = seq[index : index + 3] - if codon in stop_codons : - return index - #index +=3 - # Stop condon not found inn the sequence - return False - - -### (tsv para algunos locus? Utils para analyze schema?) -def get_gene_annotation (annotation_file, annotation_dir, genus, species, usegenus, logger) : - - name_file = os.path.basename(annotation_file).split('.') - annotation_dir = os.path.join (annotation_dir, 'annotation', name_file[0]) - - if usegenus == 'true': - annotation_result = subprocess.run (['prokka', annotation_file, '--outdir', annotation_dir, - '--genus', genus, '--species', species, '--usegenus', - '--gcode', '11', '--prefix', name_file[0], '--quiet']) - - elif usegenus == 'false': - annotation_result = subprocess.run (['prokka', annotation_file, '--outdir', annotation_dir, - '--genus', genus, '--species', species, - '--gcode', '11', '--prefix', name_file[0], '--quiet']) - - annot_tsv = [] - tsv_path = os.path.join (annotation_dir, name_file[0] + '.tsv') - - try: - with open(tsv_path) as tsvfile: - tsvreader = csv.reader(tsvfile, delimiter="\t") - for line in tsvreader: - annot_tsv.append(line) - - if len(annot_tsv) > 1: - try: - if '_' in annot_tsv[1][2]: - gene_annot = annot_tsv[1][2].split('_')[0] - else: - gene_annot = annot_tsv[1][2] - except: - gene_annot = 'Not found by Prokka' - - try: - product_annot = annot_tsv[1][4] - except: - product_annot = 'Not found by Prokka' - else: - gene_annot = 'Not found by Prokka' - product_annot = 'Not found by Prokka' - except: - gene_annot = 'Not found by Prokka' - product_annot = 'Not found by Prokka' - - return gene_annot, product_annot - - -def hamming_distance (pd_matrix): - ''' - The function is used to find the hamming distance matrix - Input: - pd_matrix # Contains the panda dataFrame - Variables: - unique_values # contains the array with the unique values in the dataFrame - U # Is the boolean matrix of differences - H # It is accumulative values of U - Return: - H where the number of columns have been subtracted - ''' - - unique_values = pd.unique(pd_matrix[list(pd_matrix.keys())].values.ravel('K')) - # Create binary matrix ('1' or '0' ) matching the input matrix vs the unique_values[0] - # astype(int) is used to transform the boolean matrix into integer - U = pd_matrix.eq(unique_values[0]).astype(int) - # multiply the matrix with the transpose - H = U.dot(U.T) - - # Repeat for each unique value - for unique_val in range(1,len(unique_values)): - U = pd_matrix.eq(unique_values[unique_val]).astype(int) - # Add the value of the binary matrix with the previous stored values - H = H.add(U.dot(U.T)) - - return len(pd_matrix.columns) - H - - -def create_distance_matrix (input_dir, input_file): - try: - result_file = os.path.join(input_dir, input_file) - pd_matrix = pd.read_csv(result_file, sep='\t', header=0, index_col=0) - except Exception as e: - - print('------------- ERROR --------------') - print('Unable to open the matrix distance file') - print('Check in the logging configuration file') - print('------------------------------------------') - return 'Error' - - distance_matrix = hamming_distance (pd_matrix) - out_file = os.path.join(input_dir, 'matrix_distance.tsv') - try: - distance_matrix.to_csv(out_file, sep = '\t') - except Exception as e: - - print('------------- ERROR --------------') - print('Unable to create the matrix distance file') - print('Check in the logging configuration file') - print('------------------------------------------') - return 'Error' - - return True From b8fa603896e1e5a764d5a7dbbe9e38a5b041b320 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Thu, 28 Mar 2024 17:08:29 +0100 Subject: [PATCH 092/214] reduced default resolution to 0.75 --- taranis/reference_alleles.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index da546ac..7b7b5a9 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -27,7 +27,7 @@ def __init__( eval_cluster: bool, kmer_size: int, sketch_size: int, - cluster_resolution: float = 0.92, + cluster_resolution: float = 0.75, seed: int = None, ): """ReferenceAlleles instance creation From 7ec2356c4ab5898b53672b6772dfef2dd1c6f2bc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Thu, 28 Mar 2024 17:08:51 +0100 Subject: [PATCH 093/214] reduced default resolution to 0.75 --- taranis/clustering.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/taranis/clustering.py b/taranis/clustering.py index 5b8b437..fb7c62f 100644 --- a/taranis/clustering.py +++ b/taranis/clustering.py @@ -4,6 +4,7 @@ import numpy as np import rich.console import taranis.utils +import taranis.seq_cluster log = logging.getLogger(__name__) stderr = rich.console.Console( @@ -19,7 +20,7 @@ def __init__( self, dist_matrix: np.array, ref_seq_name: str, - resolution: float = 0.92, + resolution: float = 0.75, seed: int = None, ): """ClusterDistance instance creation @@ -81,7 +82,7 @@ def calculate_mean_cluster( def convert_to_seq_clusters( self, cluster_ids: np.array, id_to_seq_name: dict ) -> dict: - """convert the index into the alleale names + """convert the index into the allele names Args: cluster_ids (np.array): index of matrix belongs to cluster From 897b238cf1eb5006fa9d5aebd1fef5ce1d0d7723 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Thu, 28 Mar 2024 17:09:28 +0100 Subject: [PATCH 094/214] added seqCluster class from alphaclust --- taranis/seq_cluster.py | 441 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 441 insertions(+) create mode 100755 taranis/seq_cluster.py diff --git a/taranis/seq_cluster.py b/taranis/seq_cluster.py new file mode 100755 index 0000000..0722bbe --- /dev/null +++ b/taranis/seq_cluster.py @@ -0,0 +1,441 @@ +""" +Code extracted from ALPHAClust on 03/28/2024. +Chiu, J.K.H., Ong, R.TH. Clustering biological sequences with dynamic sequence similarity threshold. +BMC Bioinformatics 23, 108 (2022). https://doi.org/10.1186/s12859-022-04643-9 +https://github.com/phglab/ALFATClust/blob/main/main/modules/SeqCluster.py +""" + +from itertools import combinations, combinations_with_replacement +from scipy.sparse import coo_matrix +import igraph as ig +import leidenalg +import numpy as np + + +class SeqCluster: + _res_param_start = None + _res_param_end = None + _res_param_step_size = None + _precision = None + _seed = None + _is_verbose = None + _init = False + + @classmethod + def init(cls, user_params, is_verbose=True): + cls._res_param_start = user_params.res_param_start + cls._res_param_end = user_params.res_param_end + cls._res_param_step_size = user_params.res_param_step_size + cls._precision = user_params.precision + cls._seed = user_params.seed + cls._is_verbose = is_verbose + cls._init = True + + @classmethod + def disable_verbose(cls): + cls._is_verbose = False + + @staticmethod + def _convert_to_index_pos(mtrx_idxs): + idx_type = mtrx_idxs.dtype + + if idx_type == np.bool: + return np.argwhere(mtrx_idxs).flatten() + elif idx_type == np.int: + return mtrx_idxs + else: + return None + + @classmethod + def _cal_cluster_avg_edge_weight( + cls, + global_edge_weight_mtrx, + src_cluster_edge_counts, + src_cluster_row_idxs, + src_cluster_col_idxs=None, + ): + if src_cluster_col_idxs is None: + src_cluster_col_idxs = src_cluster_row_idxs + + src_cluster_mtrx_idxs = np.ix_(src_cluster_row_idxs, src_cluster_col_idxs) + + if np.any(global_edge_weight_mtrx[src_cluster_mtrx_idxs] < 0): + return -1 + + if src_cluster_edge_counts is None: + row_idx_pos = cls._convert_to_index_pos(src_cluster_row_idxs) + col_idx_pos = cls._convert_to_index_pos(src_cluster_col_idxs) + num_of_diag_elements = np.intersect1d(row_idx_pos, col_idx_pos).size + num_of_non_diag_elements = ( + row_idx_pos.size * col_idx_pos.size - num_of_diag_elements + ) + if num_of_non_diag_elements == 0: + return 1 + + return ( + np.sum(global_edge_weight_mtrx[src_cluster_mtrx_idxs]) + - num_of_diag_elements + ) / num_of_non_diag_elements + else: + return np.average( + global_edge_weight_mtrx[src_cluster_mtrx_idxs], + weights=np.tril(src_cluster_edge_counts[src_cluster_mtrx_idxs]), + ) + + @classmethod + def _update_edge_weight_mtrx( + cls, + src_cluster_ptrs, + global_edge_weight_mtrx, + avg_intra_super_cluster_edge_weights, + super_cluster_pairs_to_isolate, + src_cluster_edge_counts=None, + ): + row_idxs = list() + col_idxs = list() + new_edge_weights = list() + num_of_super_clusters = np.max(src_cluster_ptrs) + 1 + + for super_cluster_pair in combinations_with_replacement( + range(num_of_super_clusters), 2 + ): + if super_cluster_pair in super_cluster_pairs_to_isolate: + continue + + if super_cluster_pair[0] == super_cluster_pair[1]: + row_idxs.append(super_cluster_pair[0]) + col_idxs.append(super_cluster_pair[0]) + new_edge_weights.append( + avg_intra_super_cluster_edge_weights[super_cluster_pair[0]] + ) + continue + + src_cluster_bool_ptrs1 = src_cluster_ptrs == super_cluster_pair[0] + src_cluster_bool_ptrs2 = src_cluster_ptrs == super_cluster_pair[1] + inter_super_cluster_edge_weight = cls._cal_cluster_avg_edge_weight( + global_edge_weight_mtrx, + src_cluster_edge_counts, + src_cluster_bool_ptrs1, + src_cluster_bool_ptrs2, + ) + + if inter_super_cluster_edge_weight <= 0: + continue + + row_idxs += super_cluster_pair + col_idxs += [super_cluster_pair[1], super_cluster_pair[0]] + new_edge_weights += [inter_super_cluster_edge_weight] * 2 + + global_edge_weight_mtrx = coo_matrix( + (np.array(new_edge_weights), (np.array(row_idxs), np.array(col_idxs))), + shape=(num_of_super_clusters, num_of_super_clusters), + ).toarray() + + global_edge_weight_mtrx[global_edge_weight_mtrx == 0] = ( + -1 * global_edge_weight_mtrx.size + ) + + return global_edge_weight_mtrx + + @staticmethod + def _convert_cluster_ptrs(src_cluster_ptrs, last_seq_cluster_ptrs): + if src_cluster_ptrs.size == last_seq_cluster_ptrs.size: + return src_cluster_ptrs + + output_seq_cluster_ptrs = np.array([-1] * last_seq_cluster_ptrs.size) + + for cluster_id in range(np.max(src_cluster_ptrs) + 1): + src_cluster_ids = np.argwhere(src_cluster_ptrs == cluster_id).flatten() + output_seq_cluster_ptrs[np.isin(last_seq_cluster_ptrs, src_cluster_ids)] = ( + cluster_id + ) + + return output_seq_cluster_ptrs + + @staticmethod + def _count_intra_cluster_edges(seq_cluster_ptrs): + row_idxs = list() + col_idxs = list() + intra_cluster_edge_counts = list() + num_of_clusters = np.max(seq_cluster_ptrs) + 1 + + for cluster_pair in combinations_with_replacement(range(num_of_clusters), 2): + cluster_size1 = np.count_nonzero(seq_cluster_ptrs == cluster_pair[0]) + if cluster_pair[0] == cluster_pair[1]: + row_idxs.append(cluster_pair[0]) + col_idxs.append(cluster_pair[0]) + intra_cluster_edge_counts.append( + int(cluster_size1 * (cluster_size1 - 1) / 2) + ) + else: + row_idxs += cluster_pair + col_idxs += [cluster_pair[1], cluster_pair[0]] + edge_count = cluster_size1 * np.count_nonzero( + seq_cluster_ptrs == cluster_pair[1] + ) + intra_cluster_edge_counts += [edge_count, edge_count] + + return coo_matrix( + ( + np.array(intra_cluster_edge_counts), + (np.array(row_idxs), np.array(col_idxs)), + ), + shape=(num_of_clusters, num_of_clusters), + ).toarray() + + @staticmethod + def _sort_src_clusters( + global_edge_weight_mtrx, src_cluster_ids, src_cluster_edge_counts + ): + cluster_mtrx_idxs = np.ix_(src_cluster_ids, src_cluster_ids) + all_sorted_edge_row_idxs, all_sorted_edge_col_idxs = np.unravel_index( + np.argsort(global_edge_weight_mtrx[cluster_mtrx_idxs], axis=None), + shape=(src_cluster_ids.size, src_cluster_ids.size), + ) + all_sorted_edge_idxs = np.array( + list(zip(all_sorted_edge_row_idxs, all_sorted_edge_col_idxs)) + ) + + sorted_src_cluster_ids = list() + proc_src_cluster_ids = set() + + for src_cluster_idx1, src_cluster_idx2 in np.flipud( + all_sorted_edge_idxs[ + all_sorted_edge_idxs[:, 0] < all_sorted_edge_idxs[:, 1] + ] + ): + src_cluster_id1 = src_cluster_ids[src_cluster_idx1] + src_cluster_id2 = src_cluster_ids[src_cluster_idx2] + + if src_cluster_id1 in proc_src_cluster_ids: + if src_cluster_id2 in proc_src_cluster_ids: + continue + + sorted_src_cluster_ids.append(src_cluster_id2) + proc_src_cluster_ids.add(src_cluster_id2) + continue + elif src_cluster_id2 in proc_src_cluster_ids: + sorted_src_cluster_ids.append(src_cluster_id1) + proc_src_cluster_ids.add(src_cluster_id1) + continue + + if ( + src_cluster_edge_counts[src_cluster_id1, src_cluster_id1] + < src_cluster_edge_counts[src_cluster_id2, src_cluster_id2] + ): + sorted_src_cluster_ids.append(src_cluster_id2) + proc_src_cluster_ids.add(src_cluster_id2) + sorted_src_cluster_ids.append(src_cluster_id1) + proc_src_cluster_ids.add(src_cluster_id1) + else: + sorted_src_cluster_ids.append(src_cluster_id1) + proc_src_cluster_ids.add(src_cluster_id1) + sorted_src_cluster_ids.append(src_cluster_id2) + proc_src_cluster_ids.add(src_cluster_id2) + + return sorted_src_cluster_ids + + @classmethod + def _bin_src_clusters( + cls, src_cluster_ids, global_edge_weight_mtrx, src_cluster_edge_counts + ): + src_cluster_bins = list() + avg_intra_bin_edge_weights = dict() + + for src_cluster_id in cls._sort_src_clusters( + global_edge_weight_mtrx, src_cluster_ids, src_cluster_edge_counts + ): + if len(src_cluster_bins) == 0: + src_cluster_bins.append([src_cluster_id]) + avg_intra_bin_edge_weights[str(src_cluster_id)] = ( + global_edge_weight_mtrx[src_cluster_id, src_cluster_id] + ) + continue + + best_bin = None + max_merge_score = 0 + + for cluster_bin in src_cluster_bins: + bin_to_ext_src_cluster_avg_edge_weight = ( + cls._cal_cluster_avg_edge_weight( + global_edge_weight_mtrx, + src_cluster_edge_counts, + cluster_bin, + [src_cluster_id], + ) + ) + merge_score = ( + 2 * bin_to_ext_src_cluster_avg_edge_weight + - cls._res_param_end + - avg_intra_bin_edge_weights["-".join(map(str, cluster_bin))] + ) + if merge_score > max_merge_score: + best_bin = cluster_bin + max_merge_score = merge_score + + if best_bin is None: + src_cluster_bins.append([src_cluster_id]) + avg_intra_bin_edge_weights[str(src_cluster_id)] = ( + global_edge_weight_mtrx[src_cluster_id, src_cluster_id] + ) + else: + del avg_intra_bin_edge_weights["-".join(map(str, best_bin))] + best_bin.append(src_cluster_id) + avg_intra_bin_edge_weights["-".join(map(str, best_bin))] = ( + cls._cal_cluster_avg_edge_weight( + global_edge_weight_mtrx, src_cluster_edge_counts, best_bin + ) + ) + + return list(map(np.array, src_cluster_bins)), avg_intra_bin_edge_weights + + @classmethod + def _verify_clusters( + cls, + candidate_src_cluster_ptrs, + global_edge_weight_mtrx, + src_cluster_edge_counts, + is_first_iter, + ): + super_cluster_pairs_to_isolate = set() + avg_intra_super_cluster_edge_weights = dict() + + if is_first_iter: + src_cluster_ptrs = candidate_src_cluster_ptrs + + for cluster_id in range(np.max(src_cluster_ptrs) + 1): + src_cluster_bool_ptrs = src_cluster_ptrs == cluster_id + avg_intra_super_cluster_edge_weights[cluster_id] = ( + cls._cal_cluster_avg_edge_weight( + global_edge_weight_mtrx, + src_cluster_edge_counts, + src_cluster_bool_ptrs, + ) + ) + else: + src_cluster_ptrs = np.full(candidate_src_cluster_ptrs.size, -1) + + for cluster_id in range(np.max(candidate_src_cluster_ptrs) + 1): + src_cluster_ids = np.argwhere( + candidate_src_cluster_ptrs == cluster_id + ).flatten() + + if src_cluster_ids.size == 1: + assigned_super_cluster_id = np.max(src_cluster_ptrs) + 1 + src_cluster_ptrs[src_cluster_ids] = assigned_super_cluster_id + avg_intra_super_cluster_edge_weights[assigned_super_cluster_id] = ( + global_edge_weight_mtrx[src_cluster_ids[0], src_cluster_ids[0]] + ) + continue + + qual_src_cluster_bins, avg_intra_bin_edge_weights = ( + cls._bin_src_clusters( + src_cluster_ids, + global_edge_weight_mtrx, + src_cluster_edge_counts, + ) + ) + + assigned_super_cluster_ids = list() + for src_cluster_bin in qual_src_cluster_bins: + assigned_super_cluster_ids.append(np.max(src_cluster_ptrs) + 1) + src_cluster_ptrs[src_cluster_bin] = assigned_super_cluster_ids[-1] + avg_intra_super_cluster_edge_weights[ + assigned_super_cluster_ids[-1] + ] = avg_intra_bin_edge_weights["-".join(map(str, src_cluster_bin))] + + if len(assigned_super_cluster_ids) > 1: + super_cluster_pairs_to_isolate |= set( + combinations(assigned_super_cluster_ids, 2) + ) + + return ( + src_cluster_ptrs, + avg_intra_super_cluster_edge_weights, + super_cluster_pairs_to_isolate, + ) + + @classmethod + def cluster_seqs(cls, global_edge_weight_mtrx): + num_of_seqs = global_edge_weight_mtrx.shape[0] + + last_seq_cluster_ptrs = np.arange(num_of_seqs) + + global_edge_weight_mtrx[global_edge_weight_mtrx == 0] = ( + -1 * global_edge_weight_mtrx.size + ) + np.fill_diagonal(global_edge_weight_mtrx, 1) + + comm_graph = ig.Graph.Weighted_Adjacency( + global_edge_weight_mtrx.tolist(), mode=1, loops=False + ) + res_param_end = round( + cls._res_param_end + cls._res_param_step_size, cls._precision + ) + + for res_param in np.arange( + cls._res_param_start, res_param_end, cls._res_param_step_size + ): + res_param = round(res_param, cls._precision) + + graph_partitions = leidenalg.find_partition( + comm_graph, + leidenalg.CPMVertexPartition, + weights="weight", + n_iterations=-1, + resolution_parameter=res_param, + seed=cls._seed, + ) + + candidate_src_cluster_ptrs = np.array(graph_partitions.membership) + comm_graph = None + + is_first_iter = res_param == cls._res_param_start + + if is_first_iter: + src_cluster_edge_counts = None + else: + src_cluster_edge_counts = cls._count_intra_cluster_edges( + last_seq_cluster_ptrs + ) + + ( + src_cluster_ptrs, + avg_intra_super_cluster_edge_weights, + super_cluster_pairs_to_isolate, + ) = cls._verify_clusters( + candidate_src_cluster_ptrs, + global_edge_weight_mtrx, + src_cluster_edge_counts, + is_first_iter, + ) + + global_edge_weight_mtrx = cls._update_edge_weight_mtrx( + src_cluster_ptrs, + global_edge_weight_mtrx, + avg_intra_super_cluster_edge_weights, + super_cluster_pairs_to_isolate, + src_cluster_edge_counts, + ) + + last_seq_cluster_ptrs = cls._convert_cluster_ptrs( + src_cluster_ptrs, last_seq_cluster_ptrs + ) + + if cls._is_verbose: + proc_msg = "{} clusters obtained at average estimated similarity {}" + print(proc_msg.format(np.max(last_seq_cluster_ptrs) + 1, res_param)) + + if np.max(last_seq_cluster_ptrs) == 0 or np.all( + np.triu(global_edge_weight_mtrx, k=1) <= 0 + ): + if cls._is_verbose: + print("No more cluster available for further merging") + + break + + comm_graph = ig.Graph.Weighted_Adjacency( + global_edge_weight_mtrx.tolist(), mode=1, loops=False + ) + + return last_seq_cluster_ptrs From cf735414dccd7bf3b7ea8e3829cd7728c59e1095 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Thu, 28 Mar 2024 19:52:37 +0100 Subject: [PATCH 095/214] updated default resolution to 0.75 --- taranis/__main__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index af9dbc4..d3ae34f 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -298,7 +298,7 @@ def analyze_schema( "--cluster-resolution", required=False, type=float, - default=0.92, + default=0.75, help="Resolution value used for clustering.", ) @click.option( From 6726c067b5b7777a9c0b41b3f4d429a4556c716a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Thu, 28 Mar 2024 19:56:52 +0100 Subject: [PATCH 096/214] changed class init for seqCluster --- taranis/clustering.py | 15 ++------------- 1 file changed, 2 insertions(+), 13 deletions(-) diff --git a/taranis/clustering.py b/taranis/clustering.py index fb7c62f..db48723 100644 --- a/taranis/clustering.py +++ b/taranis/clustering.py @@ -144,18 +144,7 @@ def create_clusters(self, resolution) -> list[dict]: matrix indexes adn second the statistics data for each cluster """ self.resolution = resolution - comm_graph = ig.Graph.Weighted_Adjacency( - self.dist_matrix.tolist(), mode=1, loops=False - ) - graph_clusters = leidenalg.find_partition( - comm_graph, - leidenalg.CPMVertexPartition, - weights="weight", - n_iterations=-1, - resolution_parameter=self.resolution, - seed=self.seed, - ) - cluster_ptrs = np.array(graph_clusters.membership) - + seq_cluster_obj = taranis.seq_cluster.SeqCluster(self.resolution, self.seed) + cluster_ptrs = seq_cluster_obj.cluster_seqs(self.dist_matrix) clusters_data = self.collect_data_cluster(cluster_ptrs) return [cluster_ptrs, clusters_data] From ffc7bffe709f0fb9a65c2dbdd31487c9217c2249 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Thu, 28 Mar 2024 19:58:09 +0100 Subject: [PATCH 097/214] updated default kmer size for mash --- taranis/distance.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/taranis/distance.py b/taranis/distance.py index a5a8d3a..4b2ec2f 100644 --- a/taranis/distance.py +++ b/taranis/distance.py @@ -18,7 +18,7 @@ class DistanceMatrix: def __init__( - self, file_path: str, k_mer_value: str = "21", sketch_size: str = "2000" + self, file_path: str, k_mer_value: str = "17", sketch_size: str = "2000" ) -> "DistanceMatrix": """DistanceMatrix instance creation @@ -77,7 +77,7 @@ def create_matrix(self) -> pd.DataFrame: dist_matrix.write("\n".join(out_data[1:])) dist_matrix.seek(0) matrix_pd = pd.read_csv( - dist_matrix, sep="\t", index_col="alleles", engine="python" + dist_matrix, sep="\t", index_col="alleles", engine="python", dtype=float ).fillna(0) # Close object and discard memory buffer dist_matrix.close() From 68da1d05ac311dd982563f0fefdcb0c92b56cf45 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Thu, 28 Mar 2024 19:58:44 +0100 Subject: [PATCH 098/214] sorted imports --- taranis/seq_cluster.py | 26 +++++++++----------------- 1 file changed, 9 insertions(+), 17 deletions(-) diff --git a/taranis/seq_cluster.py b/taranis/seq_cluster.py index 0722bbe..be2507f 100755 --- a/taranis/seq_cluster.py +++ b/taranis/seq_cluster.py @@ -6,30 +6,23 @@ """ from itertools import combinations, combinations_with_replacement -from scipy.sparse import coo_matrix + import igraph as ig import leidenalg import numpy as np +from scipy.sparse import coo_matrix class SeqCluster: - _res_param_start = None - _res_param_end = None - _res_param_step_size = None - _precision = None - _seed = None - _is_verbose = None - _init = False @classmethod - def init(cls, user_params, is_verbose=True): - cls._res_param_start = user_params.res_param_start - cls._res_param_end = user_params.res_param_end - cls._res_param_step_size = user_params.res_param_step_size - cls._precision = user_params.precision - cls._seed = user_params.seed + def __init__(cls, res_start, seed, is_verbose=True): + cls._res_param_start = res_start + cls._res_param_end = 0.99 + cls._res_param_step_size = 0.025 + cls._precision = 3 + cls._seed = seed cls._is_verbose = is_verbose - cls._init = True @classmethod def disable_verbose(cls): @@ -39,7 +32,7 @@ def disable_verbose(cls): def _convert_to_index_pos(mtrx_idxs): idx_type = mtrx_idxs.dtype - if idx_type == np.bool: + if idx_type == bool: return np.argwhere(mtrx_idxs).flatten() elif idx_type == np.int: return mtrx_idxs @@ -364,7 +357,6 @@ def cluster_seqs(cls, global_edge_weight_mtrx): global_edge_weight_mtrx[global_edge_weight_mtrx == 0] = ( -1 * global_edge_weight_mtrx.size ) - np.fill_diagonal(global_edge_weight_mtrx, 1) comm_graph = ig.Graph.Weighted_Adjacency( global_edge_weight_mtrx.tolist(), mode=1, loops=False From 2b91303d954b6abcb3cae03bc5eb1bc419180cec Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Thu, 28 Mar 2024 19:59:10 +0100 Subject: [PATCH 099/214] changed cluster generation to use alphaclust SeqCluster class --- taranis/reference_alleles.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index 7b7b5a9..f826afd 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -65,7 +65,7 @@ def create_distance_matrix(self) -> list: ) mash_distance_df = distance_obj.create_matrix() log.debug(f"Created distance matrix for {self.fasta_file}") - postition_to_allele = { + position_to_allele = { x: mash_distance_df.columns[x] for x in range(len(mash_distance_df.columns)) } # convert the triangle matrix into full data matrix @@ -75,7 +75,7 @@ def create_distance_matrix(self) -> list: # At this point minimal distance is 0. For clustering requires to be 1 # the oposite. dist_matrix_np = (matrix_np - 1) * -1 - return dist_matrix_np, postition_to_allele + return dist_matrix_np, position_to_allele def processing_cluster_data( self, cluster_data: np.array, cluster_ptrs: np.array, position_to_allele: dict From 8cf03e51f6d5a5dc789a7b26a008431745d182f1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Fri, 29 Mar 2024 16:30:59 +0100 Subject: [PATCH 100/214] updated default blast params --- taranis/blast.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/taranis/blast.py b/taranis/blast.py index 981454c..7458752 100644 --- a/taranis/blast.py +++ b/taranis/blast.py @@ -65,14 +65,14 @@ def create_blastdb(self, file_name: str, blast_dir: str) -> None: def run_blast( self, query: str, - evalue: float = 0.001, + evalue: float = 10, perc_identity: int = 90, reward: int = 1, penalty: int = -2, gapopen: int = 1, gapextend: int = 1, - max_target_seqs: int = 2000, - max_hsps: int = 10, + max_target_seqs: int = 10000, + max_hsps: int = 1, num_threads: int = 1, query_type: str = "file", ) -> list: From c82a4a81c01beec5031421e628cf3a0ba795bc55 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Mon, 1 Apr 2024 10:56:59 +0200 Subject: [PATCH 101/214] fixed variable name --- taranis/reference_alleles.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index f826afd..b64ca75 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -136,7 +136,7 @@ def create_ref_alleles(self) -> dict: optionally a list of evaluation cluster results """ self.records = taranis.utils.read_fasta_file(self.fasta_file) - dist_matrix_np, postition_to_allele = self.create_distance_matrix() + dist_matrix_np, position_to_allele = self.create_distance_matrix() self.cluster_obj = taranis.clustering.ClusterDistance( dist_matrix_np, self.locus_name, @@ -148,7 +148,7 @@ def create_ref_alleles(self) -> dict: ) allele_data = self.processing_cluster_data( - cluster_data, cluster_ptrs, postition_to_allele + cluster_data, cluster_ptrs, position_to_allele ) ref_fasta_file = self.save_reference_alleles( allele_data["reference_alleles"] From f9c9b0be0f706ca25c880fbd5dcaae540e1910cf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Mon, 1 Apr 2024 11:16:17 +0200 Subject: [PATCH 102/214] changed cluster center to maximum number of alleles with more than 0.9 dist value --- taranis/clustering.py | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/taranis/clustering.py b/taranis/clustering.py index db48723..67d58dc 100644 --- a/taranis/clustering.py +++ b/taranis/clustering.py @@ -1,10 +1,10 @@ -import igraph as ig -import leidenalg import logging + import numpy as np import rich.console -import taranis.utils + import taranis.seq_cluster +import taranis.utils log = logging.getLogger(__name__) stderr = rich.console.Console( @@ -52,8 +52,8 @@ def calculate_cluster_center( int: index of the allele which is the center of cluster """ cluster_matrix = self.dist_matrix[cluster_mtrx_idxs] - row_means = np.mean(cluster_matrix, axis=1) - return cluster_mtrx_idxs[0][np.argmin(np.abs(row_means - cluster_mean))][0] + col_sums = np.sum(cluster_matrix > 0.9, axis=0) + return cluster_mtrx_idxs[0][np.argmax(col_sums)][0] def calculate_mean_cluster( self, cluster_mtrx_idxs: tuple, row_idx_pos: np.ndarray @@ -121,7 +121,6 @@ def collect_data_cluster(self, src_cluster_ptrs: np.ndarray) -> dict: cluster_bool_ptrs = src_cluster_ptrs == cluster_id cluster_mtrx_idxs = np.ix_(cluster_bool_ptrs, cluster_bool_ptrs) row_idx_pos = np.argwhere(cluster_bool_ptrs).flatten() - # col_idx_pos = np.argwhere(cluster_bool_ptrs).flatten() cluster_mean = self.calculate_mean_cluster(cluster_mtrx_idxs, row_idx_pos) # get the closest distance coordenates to cluster mean value cluster_data[cluster_id]["avg"] = cluster_mean From d7867bdba27da8665430687f0f56f4485a5e24f1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Mon, 1 Apr 2024 11:25:28 +0200 Subject: [PATCH 103/214] changet value to param --- taranis/clustering.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/taranis/clustering.py b/taranis/clustering.py index 67d58dc..e53fa36 100644 --- a/taranis/clustering.py +++ b/taranis/clustering.py @@ -22,6 +22,7 @@ def __init__( ref_seq_name: str, resolution: float = 0.75, seed: int = None, + dist_value: float = 0.9, ): """ClusterDistance instance creation @@ -36,9 +37,10 @@ def __init__( self.ref_seq_name = ref_seq_name self.seed = seed self.resolution = resolution + self.dist_value = dist_value def calculate_cluster_center( - self, cluster_mtrx_idxs: tuple, cluster_mean: float + self, cluster_mtrx_idxs: tuple, dist_value: float ) -> int: """Get the center allele for the cluster by selecting the allele closest value to cluster mean @@ -52,7 +54,7 @@ def calculate_cluster_center( int: index of the allele which is the center of cluster """ cluster_matrix = self.dist_matrix[cluster_mtrx_idxs] - col_sums = np.sum(cluster_matrix > 0.9, axis=0) + col_sums = np.sum(cluster_matrix > dist_value, axis=0) return cluster_mtrx_idxs[0][np.argmax(col_sums)][0] def calculate_mean_cluster( @@ -125,7 +127,7 @@ def collect_data_cluster(self, src_cluster_ptrs: np.ndarray) -> dict: # get the closest distance coordenates to cluster mean value cluster_data[cluster_id]["avg"] = cluster_mean cluster_data[cluster_id]["center_id"] = self.calculate_cluster_center( - cluster_mtrx_idxs, cluster_mean + cluster_mtrx_idxs, self.dist_value ) log.debug(f"Get the cluster center for {cluster_id}") # get the number of sequences for the cluster From 23b725b8540994e0b6a7d88087e61700774af2a6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Mon, 1 Apr 2024 12:56:20 +0200 Subject: [PATCH 104/214] linting --- taranis/eval_cluster.py | 6 ++--- taranis/seq_cluster.py | 58 ++++++++++++++++++++--------------------- 2 files changed, 32 insertions(+), 32 deletions(-) diff --git a/taranis/eval_cluster.py b/taranis/eval_cluster.py index 2fc2c19..a1fd790 100644 --- a/taranis/eval_cluster.py +++ b/taranis/eval_cluster.py @@ -173,9 +173,9 @@ def evaluate_clusters( "alleles_not_found" ] if len(result_eval["alleles_not_in_cluster"]) > 0: - evaluation_alleles[cluster_id]["alleles_not_in_cluster"] = ( - result_eval["alleles_not_in_cluster"] - ) + evaluation_alleles[cluster_id][ + "alleles_not_in_cluster" + ] = result_eval["alleles_not_in_cluster"] else: evaluation_alleles[cluster_id]["result"] = "OK" return self.summary(evaluation_alleles) diff --git a/taranis/seq_cluster.py b/taranis/seq_cluster.py index be2507f..60ecfe0 100755 --- a/taranis/seq_cluster.py +++ b/taranis/seq_cluster.py @@ -14,7 +14,6 @@ class SeqCluster: - @classmethod def __init__(cls, res_start, seed, is_verbose=True): cls._res_param_start = res_start @@ -139,9 +138,9 @@ def _convert_cluster_ptrs(src_cluster_ptrs, last_seq_cluster_ptrs): for cluster_id in range(np.max(src_cluster_ptrs) + 1): src_cluster_ids = np.argwhere(src_cluster_ptrs == cluster_id).flatten() - output_seq_cluster_ptrs[np.isin(last_seq_cluster_ptrs, src_cluster_ids)] = ( - cluster_id - ) + output_seq_cluster_ptrs[ + np.isin(last_seq_cluster_ptrs, src_cluster_ids) + ] = cluster_id return output_seq_cluster_ptrs @@ -240,9 +239,9 @@ def _bin_src_clusters( ): if len(src_cluster_bins) == 0: src_cluster_bins.append([src_cluster_id]) - avg_intra_bin_edge_weights[str(src_cluster_id)] = ( - global_edge_weight_mtrx[src_cluster_id, src_cluster_id] - ) + avg_intra_bin_edge_weights[ + str(src_cluster_id) + ] = global_edge_weight_mtrx[src_cluster_id, src_cluster_id] continue best_bin = None @@ -268,16 +267,16 @@ def _bin_src_clusters( if best_bin is None: src_cluster_bins.append([src_cluster_id]) - avg_intra_bin_edge_weights[str(src_cluster_id)] = ( - global_edge_weight_mtrx[src_cluster_id, src_cluster_id] - ) + avg_intra_bin_edge_weights[ + str(src_cluster_id) + ] = global_edge_weight_mtrx[src_cluster_id, src_cluster_id] else: del avg_intra_bin_edge_weights["-".join(map(str, best_bin))] best_bin.append(src_cluster_id) - avg_intra_bin_edge_weights["-".join(map(str, best_bin))] = ( - cls._cal_cluster_avg_edge_weight( - global_edge_weight_mtrx, src_cluster_edge_counts, best_bin - ) + avg_intra_bin_edge_weights[ + "-".join(map(str, best_bin)) + ] = cls._cal_cluster_avg_edge_weight( + global_edge_weight_mtrx, src_cluster_edge_counts, best_bin ) return list(map(np.array, src_cluster_bins)), avg_intra_bin_edge_weights @@ -298,12 +297,12 @@ def _verify_clusters( for cluster_id in range(np.max(src_cluster_ptrs) + 1): src_cluster_bool_ptrs = src_cluster_ptrs == cluster_id - avg_intra_super_cluster_edge_weights[cluster_id] = ( - cls._cal_cluster_avg_edge_weight( - global_edge_weight_mtrx, - src_cluster_edge_counts, - src_cluster_bool_ptrs, - ) + avg_intra_super_cluster_edge_weights[ + cluster_id + ] = cls._cal_cluster_avg_edge_weight( + global_edge_weight_mtrx, + src_cluster_edge_counts, + src_cluster_bool_ptrs, ) else: src_cluster_ptrs = np.full(candidate_src_cluster_ptrs.size, -1) @@ -316,17 +315,18 @@ def _verify_clusters( if src_cluster_ids.size == 1: assigned_super_cluster_id = np.max(src_cluster_ptrs) + 1 src_cluster_ptrs[src_cluster_ids] = assigned_super_cluster_id - avg_intra_super_cluster_edge_weights[assigned_super_cluster_id] = ( - global_edge_weight_mtrx[src_cluster_ids[0], src_cluster_ids[0]] - ) + avg_intra_super_cluster_edge_weights[ + assigned_super_cluster_id + ] = global_edge_weight_mtrx[src_cluster_ids[0], src_cluster_ids[0]] continue - qual_src_cluster_bins, avg_intra_bin_edge_weights = ( - cls._bin_src_clusters( - src_cluster_ids, - global_edge_weight_mtrx, - src_cluster_edge_counts, - ) + ( + qual_src_cluster_bins, + avg_intra_bin_edge_weights, + ) = cls._bin_src_clusters( + src_cluster_ids, + global_edge_weight_mtrx, + src_cluster_edge_counts, ) assigned_super_cluster_ids = list() From 80f0198634657ddc0ea4bb5b29bbf7b214f80487 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Mon, 1 Apr 2024 13:03:58 +0200 Subject: [PATCH 105/214] linting new version black --- taranis/eval_cluster.py | 6 +++--- taranis/seq_cluster.py | 44 ++++++++++++++++++++--------------------- 2 files changed, 25 insertions(+), 25 deletions(-) diff --git a/taranis/eval_cluster.py b/taranis/eval_cluster.py index a1fd790..2fc2c19 100644 --- a/taranis/eval_cluster.py +++ b/taranis/eval_cluster.py @@ -173,9 +173,9 @@ def evaluate_clusters( "alleles_not_found" ] if len(result_eval["alleles_not_in_cluster"]) > 0: - evaluation_alleles[cluster_id][ - "alleles_not_in_cluster" - ] = result_eval["alleles_not_in_cluster"] + evaluation_alleles[cluster_id]["alleles_not_in_cluster"] = ( + result_eval["alleles_not_in_cluster"] + ) else: evaluation_alleles[cluster_id]["result"] = "OK" return self.summary(evaluation_alleles) diff --git a/taranis/seq_cluster.py b/taranis/seq_cluster.py index 60ecfe0..ed0a168 100755 --- a/taranis/seq_cluster.py +++ b/taranis/seq_cluster.py @@ -138,9 +138,9 @@ def _convert_cluster_ptrs(src_cluster_ptrs, last_seq_cluster_ptrs): for cluster_id in range(np.max(src_cluster_ptrs) + 1): src_cluster_ids = np.argwhere(src_cluster_ptrs == cluster_id).flatten() - output_seq_cluster_ptrs[ - np.isin(last_seq_cluster_ptrs, src_cluster_ids) - ] = cluster_id + output_seq_cluster_ptrs[np.isin(last_seq_cluster_ptrs, src_cluster_ids)] = ( + cluster_id + ) return output_seq_cluster_ptrs @@ -239,9 +239,9 @@ def _bin_src_clusters( ): if len(src_cluster_bins) == 0: src_cluster_bins.append([src_cluster_id]) - avg_intra_bin_edge_weights[ - str(src_cluster_id) - ] = global_edge_weight_mtrx[src_cluster_id, src_cluster_id] + avg_intra_bin_edge_weights[str(src_cluster_id)] = ( + global_edge_weight_mtrx[src_cluster_id, src_cluster_id] + ) continue best_bin = None @@ -267,16 +267,16 @@ def _bin_src_clusters( if best_bin is None: src_cluster_bins.append([src_cluster_id]) - avg_intra_bin_edge_weights[ - str(src_cluster_id) - ] = global_edge_weight_mtrx[src_cluster_id, src_cluster_id] + avg_intra_bin_edge_weights[str(src_cluster_id)] = ( + global_edge_weight_mtrx[src_cluster_id, src_cluster_id] + ) else: del avg_intra_bin_edge_weights["-".join(map(str, best_bin))] best_bin.append(src_cluster_id) - avg_intra_bin_edge_weights[ - "-".join(map(str, best_bin)) - ] = cls._cal_cluster_avg_edge_weight( - global_edge_weight_mtrx, src_cluster_edge_counts, best_bin + avg_intra_bin_edge_weights["-".join(map(str, best_bin))] = ( + cls._cal_cluster_avg_edge_weight( + global_edge_weight_mtrx, src_cluster_edge_counts, best_bin + ) ) return list(map(np.array, src_cluster_bins)), avg_intra_bin_edge_weights @@ -297,12 +297,12 @@ def _verify_clusters( for cluster_id in range(np.max(src_cluster_ptrs) + 1): src_cluster_bool_ptrs = src_cluster_ptrs == cluster_id - avg_intra_super_cluster_edge_weights[ - cluster_id - ] = cls._cal_cluster_avg_edge_weight( - global_edge_weight_mtrx, - src_cluster_edge_counts, - src_cluster_bool_ptrs, + avg_intra_super_cluster_edge_weights[cluster_id] = ( + cls._cal_cluster_avg_edge_weight( + global_edge_weight_mtrx, + src_cluster_edge_counts, + src_cluster_bool_ptrs, + ) ) else: src_cluster_ptrs = np.full(candidate_src_cluster_ptrs.size, -1) @@ -315,9 +315,9 @@ def _verify_clusters( if src_cluster_ids.size == 1: assigned_super_cluster_id = np.max(src_cluster_ptrs) + 1 src_cluster_ptrs[src_cluster_ids] = assigned_super_cluster_id - avg_intra_super_cluster_edge_weights[ - assigned_super_cluster_id - ] = global_edge_weight_mtrx[src_cluster_ids[0], src_cluster_ids[0]] + avg_intra_super_cluster_edge_weights[assigned_super_cluster_id] = ( + global_edge_weight_mtrx[src_cluster_ids[0], src_cluster_ids[0]] + ) continue ( From 7687db39056ac7389186003e332b42ebcec73475 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Mon, 1 Apr 2024 13:05:42 +0200 Subject: [PATCH 106/214] removed trailing whitespaces --- taranis/seq_cluster.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/taranis/seq_cluster.py b/taranis/seq_cluster.py index ed0a168..5db4716 100755 --- a/taranis/seq_cluster.py +++ b/taranis/seq_cluster.py @@ -1,8 +1,8 @@ -""" +""" Code extracted from ALPHAClust on 03/28/2024. -Chiu, J.K.H., Ong, R.TH. Clustering biological sequences with dynamic sequence similarity threshold. +Chiu, J.K.H., Ong, R.TH. Clustering biological sequences with dynamic sequence similarity threshold. BMC Bioinformatics 23, 108 (2022). https://doi.org/10.1186/s12859-022-04643-9 -https://github.com/phglab/ALFATClust/blob/main/main/modules/SeqCluster.py +https://github.com/phglab/ALFATClust/blob/main/main/modules/SeqCluster.py """ from itertools import combinations, combinations_with_replacement From 571dcf020ec5e090c99c4cf80cd148128d01854d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Mon, 1 Apr 2024 13:40:25 +0200 Subject: [PATCH 107/214] removed bug, sometimes alleles not float --- taranis/distance.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/taranis/distance.py b/taranis/distance.py index 4b2ec2f..028dfbb 100644 --- a/taranis/distance.py +++ b/taranis/distance.py @@ -77,7 +77,7 @@ def create_matrix(self) -> pd.DataFrame: dist_matrix.write("\n".join(out_data[1:])) dist_matrix.seek(0) matrix_pd = pd.read_csv( - dist_matrix, sep="\t", index_col="alleles", engine="python", dtype=float + dist_matrix, sep="\t", index_col="alleles", engine="python" ).fillna(0) # Close object and discard memory buffer dist_matrix.close() From e5c903a822a9f6ebb0b89abb8ec79d4acafcea4e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Tue, 2 Apr 2024 13:47:58 +0200 Subject: [PATCH 108/214] reduced id threshold for reference allele evaluation from 0.90 to 0.85 --- taranis/clustering.py | 2 +- taranis/eval_cluster.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/taranis/clustering.py b/taranis/clustering.py index e53fa36..9aea468 100644 --- a/taranis/clustering.py +++ b/taranis/clustering.py @@ -22,7 +22,7 @@ def __init__( ref_seq_name: str, resolution: float = 0.75, seed: int = None, - dist_value: float = 0.9, + dist_value: float = 0.85, ): """ClusterDistance instance creation diff --git a/taranis/eval_cluster.py b/taranis/eval_cluster.py index 2fc2c19..63bbffc 100644 --- a/taranis/eval_cluster.py +++ b/taranis/eval_cluster.py @@ -156,7 +156,7 @@ def evaluate_clusters( query_file.write(">" + r_id + "\n" + r_seq) query_file.seek(0) blast_result = self.blast_obj.run_blast( - query_file.read(), perc_identity=90, query_type="stdin" + query_file.read(), perc_identity=85, query_type="stdin" ) # Close object and discard memory buffer query_file.close() From 40fd7473ce003619dc7f5bf3bb291edbc229e34d Mon Sep 17 00:00:00 2001 From: luissian Date: Tue, 12 Mar 2024 09:16:07 +0100 Subject: [PATCH 109/214] create commit for testing --- taranis/__main__.py | 5 ++++- taranis/allele_calling.py | 38 ++++++++++++++++++++++++++------------ 2 files changed, 30 insertions(+), 13 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index d3ae34f..8126ff2 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -454,6 +454,7 @@ def allele_calling( """Analyze the sample file against schema to identify outbreakers """ + start = time.perf_counter() results = [] for assembly_file in assemblies: assembly_name = Path(assembly_file).stem @@ -464,5 +465,7 @@ def allele_calling( ) } ) - + finish = time.perf_counter() + print(f"Allele calling finish in {round((finish-start)/60, 2)} minutes") + # import pdb; pdb.set_trace() # sample_allele_obj.analyze_sample() diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index d7baf37..2203db0 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -6,6 +6,7 @@ import taranis.utils import taranis.blast +import pdb # import numpy from collections import OrderedDict @@ -59,10 +60,11 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: if len(blast_result) > 1: # allele is named as NIPHEM + column_blast_res = blast_result[0].split("\t") + column_blast_res[13] = column_blast_res[13].replace("-", "") + allele_details = get_blast_details(column_blast_res, allele_name) + return ["NIPHEM", allele_name, allele_details] - # Hacer un blast con la query esta secuencia y la database del alelo - # Create blast db with sample file - pass elif len(blast_result) == 1: column_blast_res = blast_result[0].split("\t") @@ -87,7 +89,7 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: == column_blast_res[10] # check at contig end or column_blast_res[10] == 1 # check reverse at contig end or column_blast_res[9] - == column_blast_res[15] # check reverse at contig start + == column_blast_res[14] # check reverse at contig start ): # allele is labled as PLOT return ["PLOT", allele_name, allele_details] @@ -100,12 +102,17 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: if int(column_blast_res[3]) == int(column_blast_res[4]): # allele is labled as INF return ["INF", allele_name, allele_details] + else: + pdb.set_trace() def search_match_allele(self): # Create blast db with sample file result = {"allele_type": {}, "allele_match": {}, "allele_details": {}} + count = 0 for ref_allele in self.ref_alleles: + count += 1 + print(" Processing allele ", ref_allele, " ", count, " of ", len(self.ref_alleles)) # schema_alleles = os.path.join(self.schema, ref_allele) # parallel in all CPUs in cluster node alleles = OrderedDict() @@ -113,14 +120,17 @@ def search_match_allele(self): with open(ref_allele, "r") as fh: for record in SeqIO.parse(fh, "fasta"): alleles[record.id] = str(record.seq) - + count_2 = 0 for r_id, r_seq in alleles.items(): + count_2 += 1 + pdb.set_trace() + print("Running blast for ", count_2, " of ", len(alleles)) # create file in memory to increase speed query_file = io.StringIO() query_file.write(">" + r_id + "\n" + r_seq) query_file.seek(0) blast_result = self.blast_obj.run_blast( - query_file.read(), perc_identity=90, query_type="stdin" + query_file.read(), perc_identity=90, num_threads=4, query_type="stdin" ) if len(blast_result) > 0: match_found = True @@ -130,12 +140,16 @@ def search_match_allele(self): if match_found: allele_file = os.path.join(self.schema, os.path.basename(ref_allele)) # blast_result = self.blast_obj.run_blast(q_file,perc_identity=100) - allele_name = Path(allele_file).stem - ( - result["allele_type"][allele_name], - result["allele_match"][allele_name], - result["allele_details"][allele_name], - ) = self.assign_allele_type(blast_result, allele_file, allele_name) + try: + allele_name = Path(allele_file).stem + ( + result["allele_type"][allele_name], + result["allele_match"][allele_name], + result["allele_details"][allele_name], + ) = self.assign_allele_type(blast_result, allele_file, allele_name) + except Exception as e: + stderr.print(f"Error: {e}") + pdb.set_trace() else: # Sample does not have a reference allele to be matched # Keep LNF info From 23a0a382f9115da7af7a358aa31d687080988f5a Mon Sep 17 00:00:00 2001 From: luissian Date: Tue, 12 Mar 2024 12:10:53 +0100 Subject: [PATCH 110/214] added file for checking allele type --- taranis/__main__.py | 6 ++++++ taranis/allele_calling.py | 7 ++++--- 2 files changed, 10 insertions(+), 3 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index 8126ff2..5b80e6d 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -466,6 +466,12 @@ def allele_calling( } ) finish = time.perf_counter() + test_file = os.path.join(output, "test_file.csv") + with open(test_file, "w") as fo: + for result in results: + for key, value in result.items(): + for allele, type in value["allele_type"].items(): + fo.write(f"{key},{allele},{type}\n") print(f"Allele calling finish in {round((finish-start)/60, 2)} minutes") # import pdb; pdb.set_trace() # sample_allele_obj.analyze_sample() diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 2203db0..d6a7827 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -103,7 +103,8 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: # allele is labled as INF return ["INF", allele_name, allele_details] else: - pdb.set_trace() + print("ERROR: No blast result found") + return ["LNF", "allele_name", "LNF"] def search_match_allele(self): # Create blast db with sample file @@ -123,7 +124,7 @@ def search_match_allele(self): count_2 = 0 for r_id, r_seq in alleles.items(): count_2 += 1 - pdb.set_trace() + print("Running blast for ", count_2, " of ", len(alleles)) # create file in memory to increase speed query_file = io.StringIO() @@ -149,7 +150,7 @@ def search_match_allele(self): ) = self.assign_allele_type(blast_result, allele_file, allele_name) except Exception as e: stderr.print(f"Error: {e}") - pdb.set_trace() + else: # Sample does not have a reference allele to be matched # Keep LNF info From 70781119c6294fc4a4c60d207ec231b75687c50e Mon Sep 17 00:00:00 2001 From: luissian Date: Tue, 12 Mar 2024 21:15:46 +0100 Subject: [PATCH 111/214] creating collect_data function --- taranis/__main__.py | 67 ++++++++++++++------------------------- taranis/allele_calling.py | 42 ++++++++++++++++++++++++ taranis/utils.py | 29 +++++++++++++++++ 3 files changed, 94 insertions(+), 44 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index 5b80e6d..4913c91 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -316,6 +316,12 @@ def analyze_schema( default=1, help="Number of cpus used for execution", ) +@click.option( + "--force/--no-force", + required=False, + default=False, + help="Overwrite the output folder if it exists", +) def reference_alleles( schema: str, output: str, @@ -325,6 +331,7 @@ def reference_alleles( cluster_resolution: float, seed: int, cpus: int, + force: bool, ): start = time.perf_counter() max_cpus = taranis.utils.cpus_available() @@ -335,23 +342,8 @@ def reference_alleles( schema_files = taranis.utils.get_files_in_folder(schema, "fasta") # Check if output folder exists - if taranis.utils.folder_exists(output): - q_question = ( - "Folder " - + output - + " already exists. Files will be overwritten. Do you want to continue?" - ) - if "no" in taranis.utils.query_user_yes_no(q_question, "no"): - log.info("Aborting code by user request") - stderr.print("[red] Exiting code. ") - sys.exit(1) - else: - try: - os.makedirs(output) - except OSError as e: - log.info("Unable to create folder at %s with error %s", output, e) - stderr.print("[red] ERROR. Unable to create folder " + output) - sys.exit(1) + if not force: + _ = taranis.utils.prompt_user_if_folder_exists(output) """Create the reference alleles from the schema """ results = [] with concurrent.futures.ThreadPoolExecutor(max_workers=cpus) as executor: @@ -404,6 +396,12 @@ def reference_alleles( type=click.Path(), help="Output folder to save reference alleles", ) +@click.option( + "--force/--no-force", + required=False, + default=False, + help="Overwrite the output folder if it exists", +) @click.argument( "assemblies", callback=expand_wildcards, @@ -412,10 +410,11 @@ def reference_alleles( type=click.Path(exists=True), ) def allele_calling( - schema, - reference, - assemblies, - output, + schema: str, + reference: str, + assemblies: list, + output: str, + force: bool, ): schema_ref_files = taranis.utils.get_files_in_folder(reference, "fasta") if len(schema_ref_files) == 0: @@ -424,23 +423,8 @@ def allele_calling( sys.exit(1) # Check if output folder exists - if taranis.utils.folder_exists(output): - q_question = ( - "Folder " - + output - + " already exists. Files will be overwritten. Do you want to continue?" - ) - if "no" in taranis.utils.query_user_yes_no(q_question, "no"): - log.info("Aborting code by user request") - stderr.print("[red] Exiting code. ") - sys.exit(1) - else: - try: - os.makedirs(output) - except OSError as e: - log.info("Unable to create folder at %s with error %s", output, e) - stderr.print("[red] ERROR. Unable to create {output} folder") - sys.exit(1) + if not force: + _ = taranis.utils.prompt_user_if_folder_exists(output) # Filter fasta files from reference folder # ref_alleles = glob.glob(os.path.join(reference, "*.fasta")) # Create predictions @@ -465,13 +449,8 @@ def allele_calling( ) } ) + _ = taranis.allele_calling.collect_data(results, output) finish = time.perf_counter() - test_file = os.path.join(output, "test_file.csv") - with open(test_file, "w") as fo: - for result in results: - for key, value in result.items(): - for allele, type in value["allele_type"].items(): - fo.write(f"{key},{allele},{type}\n") print(f"Allele calling finish in {round((finish-start)/60, 2)} minutes") # import pdb; pdb.set_trace() # sample_allele_obj.analyze_sample() diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index d6a7827..06d76fb 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -166,3 +166,45 @@ def parallel_execution( ): allele_obj = AlleleCalling(sample_file, schema, reference_alleles, out_folder) return allele_obj.search_match_allele() + + +def collect_data(results: list, output: str) -> None: + summary_result_file = os.path.join(output, "allele_calling_summary.csv") + sample_allele_match_file = os.path.join(output, "allele_calling_match.csv") + a_types = ["NIPHEM", "EXC", "PLOT", "ASM", "ALM", "INF", "LNF"] + summary_result = {} + sample_allele_match = {} + # get allele list + first_sample = list(results[0].keys())[0] + allele_list = sorted(results[0][first_sample]["allele_type"].keys()) + for result in results: + for sample, values in result.items(): + sum_allele_type = OrderedDict() # used for summary file + allele_match = {} + for a_type in a_types: + sum_allele_type[a_type] = 0 + for allele, type in values["allele_type"].items(): + # increase allele type count + sum_allele_type[type] += 1 + # add allele name match to sample + allele_match[allele] = type + "_" + values["allele_match"][allele] + summary_result[sample] = sum_allele_type + sample_allele_match[sample] = allele_match + + with open(summary_result_file, "w") as fo: + fo.write("Sample," + ",".join(a_types) + "\n") + for sample, counts in summary_result.items(): + fo.write(f"{sample},") + for _ , count in counts.items(): + fo.write(f"{count},") + fo.write("\n") + with open(sample_allele_match_file, "w") as fo: + fo.write("Sample," + ",".join(allele_list) + "\n") + for sample, allele_cod in sample_allele_match.items(): + fo.write(f"{sample},") + for allele in allele_list: + fo.write(f"{allele_cod[allele]},") + fo.write("\n") + + + diff --git a/taranis/utils.py b/taranis/utils.py index 57c881c..af0cb93 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -284,6 +284,35 @@ def prompt_text(msg): return source +def prompt_user_if_folder_exists(folder: str) -> bool: + """Prompt the user to continue if the folder exists + + Args: + folder (str): folder path + + Returns: + bool: True if user wants to continue + """ + if folder_exists(folder): + q_question = ( + "Folder " + + folder + + " already exists. Files will be overwritten. Do you want to continue?" + ) + if "no" in query_user_yes_no(q_question, "no"): + log.info("Aborting code by user request") + stderr.print("[red] Exiting code. ") + sys.exit(1) + else: + try: + os.makedirs(folder) + except OSError as e: + log.info("Unable to create folder at %s with error %s", folder, e) + stderr.print("[red] ERROR. Unable to create folder " + folder) + sys.exit(1) + + return True + def query_user_yes_no(question, default): """Query the user to choose yes or no for the query question From b73a704d9ca9e80d2d75cb539e8a36eae5a385f2 Mon Sep 17 00:00:00 2001 From: luissian Date: Tue, 12 Mar 2024 23:13:02 +0100 Subject: [PATCH 112/214] liting --- taranis/allele_calling.py | 24 +++++++++++++++--------- taranis/utils.py | 1 + 2 files changed, 16 insertions(+), 9 deletions(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 06d76fb..00c59f2 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -65,7 +65,6 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: allele_details = get_blast_details(column_blast_res, allele_name) return ["NIPHEM", allele_name, allele_details] - elif len(blast_result) == 1: column_blast_res = blast_result[0].split("\t") column_blast_res[13] = column_blast_res[13].replace("-", "") @@ -113,7 +112,14 @@ def search_match_allele(self): count = 0 for ref_allele in self.ref_alleles: count += 1 - print(" Processing allele ", ref_allele, " ", count, " of ", len(self.ref_alleles)) + print( + " Processing allele ", + ref_allele, + " ", + count, + " of ", + len(self.ref_alleles), + ) # schema_alleles = os.path.join(self.schema, ref_allele) # parallel in all CPUs in cluster node alleles = OrderedDict() @@ -131,7 +137,10 @@ def search_match_allele(self): query_file.write(">" + r_id + "\n" + r_seq) query_file.seek(0) blast_result = self.blast_obj.run_blast( - query_file.read(), perc_identity=90, num_threads=4, query_type="stdin" + query_file.read(), + perc_identity=90, + num_threads=4, + query_type="stdin", ) if len(blast_result) > 0: match_found = True @@ -179,7 +188,7 @@ def collect_data(results: list, output: str) -> None: allele_list = sorted(results[0][first_sample]["allele_type"].keys()) for result in results: for sample, values in result.items(): - sum_allele_type = OrderedDict() # used for summary file + sum_allele_type = OrderedDict() # used for summary file allele_match = {} for a_type in a_types: sum_allele_type[a_type] = 0 @@ -195,16 +204,13 @@ def collect_data(results: list, output: str) -> None: fo.write("Sample," + ",".join(a_types) + "\n") for sample, counts in summary_result.items(): fo.write(f"{sample},") - for _ , count in counts.items(): + for _, count in counts.items(): fo.write(f"{count},") fo.write("\n") with open(sample_allele_match_file, "w") as fo: fo.write("Sample," + ",".join(allele_list) + "\n") for sample, allele_cod in sample_allele_match.items(): fo.write(f"{sample},") - for allele in allele_list: + for allele in allele_list: fo.write(f"{allele_cod[allele]},") fo.write("\n") - - - diff --git a/taranis/utils.py b/taranis/utils.py index af0cb93..16501a6 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -313,6 +313,7 @@ def prompt_user_if_folder_exists(folder: str) -> bool: return True + def query_user_yes_no(question, default): """Query the user to choose yes or no for the query question From afe70ffb6bb43d19027ec26e7bb89814312e85a8 Mon Sep 17 00:00:00 2001 From: luissian Date: Tue, 12 Mar 2024 23:23:10 +0100 Subject: [PATCH 113/214] change to use node.js 20 --- .github/workflows/tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index e9643e3..db64b1c 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -14,7 +14,7 @@ jobs: uses: actions/checkout@v4 - name: Set up Miniconda - uses: conda-incubator/setup-miniconda@v2 + uses: conda-incubator/setup-miniconda@v3 with: activate-environment: taranis_env environment-file: environment.yml From b1b06c9f34647ab6fdef33358bda5bffed46573e Mon Sep 17 00:00:00 2001 From: luissian Date: Wed, 13 Mar 2024 16:31:57 +0100 Subject: [PATCH 114/214] Adding product annotation --- taranis/analyze_schema.py | 6 ++++-- taranis/utils.py | 4 ++-- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/taranis/analyze_schema.py b/taranis/analyze_schema.py index 25ae5bf..7fc6996 100644 --- a/taranis/analyze_schema.py +++ b/taranis/analyze_schema.py @@ -86,9 +86,11 @@ def check_allele_quality(self, prokka_annotation: dict) -> OrderedDict: with open(self.schema_allele) as fh: for record in SeqIO.parse(fh, "fasta"): try: - prokka_ann = prokka_annotation[record.id] + prokka_ann = prokka_annotation[record.id]["gene"] + product_annotation = prokka_annotation[record.id]["product"] except Exception: prokka_ann = "Not found in prokka" + product_annotation = "Not found" a_quality[record.id] = { "allele_name": self.allele_name, "quality": "Good quality", @@ -97,7 +99,7 @@ def check_allele_quality(self, prokka_annotation: dict) -> OrderedDict: "start_codon_alt": "standard", "protein_seq": "", "cds_coding": prokka_ann, - } + "product_annotation": product_annotation, } allele_seq[record.id] = str(record.seq) a_quality[record.id]["length"] = str(len(str(record.seq))) a_quality[record.id]["dna_seq"] = str(record.seq) diff --git a/taranis/utils.py b/taranis/utils.py index 16501a6..b924e62 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -372,9 +372,9 @@ def read_annotation_file(ann_file: str) -> dict: for line in lines: if "Prodigal" in line: - gene_match = re.search(r"(.*)[\t]Prodigal.*gene=(\w+)_.*", line) + gene_match = re.search(r"(.*)[\t]Prodigal.*gene=(\w+)_.*product=(.*)", line) if gene_match: - ann_data[gene_match.group(1)] = gene_match.group(2) + ann_data[gene_match.group(1)] = {"gene": gene_match.group(2) , "product": gene_match.group(3).strip()} else: pred_match = re.search(r"(.*)[\t]Prodigal.*product=(\w+)_.*", line) if pred_match: From 00c36936ee61b7c151e692ac960d9131be6cc9c8 Mon Sep 17 00:00:00 2001 From: luissian Date: Wed, 13 Mar 2024 16:52:26 +0100 Subject: [PATCH 115/214] Include product in annotation file missing in previous commit --- taranis/analyze_schema.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/taranis/analyze_schema.py b/taranis/analyze_schema.py index 7fc6996..d720788 100644 --- a/taranis/analyze_schema.py +++ b/taranis/analyze_schema.py @@ -372,6 +372,7 @@ def stats_graphics(stats_folder: str) -> None: "nucleotide sequence length", "star codon", "CDS coding", + "product annotation", "allele quality", "bad quality reason", ] @@ -382,6 +383,7 @@ def stats_graphics(stats_folder: str) -> None: "length", "start_codon_alt", "cds_coding", + "product_annotation", "quality", "reason", ] From aac7cd653ac0013be8397f8920a1732a6b3d3d89 Mon Sep 17 00:00:00 2001 From: luissian Date: Wed, 13 Mar 2024 21:04:10 +0100 Subject: [PATCH 116/214] added annotation information to output files --- taranis/__main__.py | 14 +++++++-- taranis/allele_calling.py | 62 +++++++++++++++++++++++++++++++-------- taranis/utils.py | 31 ++++++++++++++++++++ 3 files changed, 92 insertions(+), 15 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index 4913c91..2936ae1 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -388,6 +388,14 @@ def reference_alleles( type=click.Path(exists=True), help="Directory where the schema reference allele files are located. ", ) +@click.option( + "-a", + "--annotation", + required=True, + multiple=False, + type=click.Path(exists=True), + help="Annotation file. ", +) @click.option( "-o", "--output", @@ -412,6 +420,7 @@ def reference_alleles( def allele_calling( schema: str, reference: str, + annotation: str, assemblies: list, output: str, force: bool, @@ -435,7 +444,8 @@ def allele_calling( pred_sample.training() pred_sample.prediction() """ - + map_pred = [["gene", 7], ["product", 8], ["allele_quality", 9]] + prediction_data = taranis.utils.read_compressed_file(annotation, separator=",", index_key=1, mapping=map_pred) """Analyze the sample file against schema to identify outbreakers """ start = time.perf_counter() @@ -445,7 +455,7 @@ def allele_calling( results.append( { assembly_name: taranis.allele_calling.parallel_execution( - assembly_file, schema, schema_ref_files, output + assembly_file, schema, prediction_data, schema_ref_files, output ) } ) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 00c59f2..c620ca0 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -6,9 +6,7 @@ import taranis.utils import taranis.blast -import pdb -# import numpy from collections import OrderedDict from pathlib import Path from Bio import SeqIO @@ -25,9 +23,9 @@ class AlleleCalling: def __init__( - self, sample_file: str, schema: str, reference_alleles: list, out_folder: str + self, sample_file: str, schema: str, annotation:dict, reference_alleles: list, out_folder: str ): - # self.prediction = prediction + self.prediction_data = annotation # store prediction annotation self.sample_file = sample_file self.schema = schema self.ref_alleles = reference_alleles @@ -42,20 +40,38 @@ def assign_allele_type( self, blast_result: list, allele_file: str, allele_name: str ) -> list: def get_blast_details(blast_result: list, allele_name: str) -> list: + try: + gene_annotation = self.prediction_data[allele_name]["gene"] + product_annotation = self.prediction_data[allele_name]["product"] + allele_quality = self.prediction_data[allele_name]["quality"] + except KeyError: + gene_annotation = "Not found" + product_annotation = "Not found" + allele_quality = "Not found" + + if int(blast_result[10]) > int(blast_result[9]): + direction = "+" + else: + direction = "-" # get blast details blast_details = [ - blast_result[0].split("_")[0], # Core gene - self.s_name, - "gene annotation", - "product annotation", - allele_name, - "allele quality", + self.s_name, # sample name blast_result[1], # contig - blast_result[3], # query length + allele_name, # core gene name + blast_result[0], # allele gene + "coding", # coding allele type. To be filled later idx = 4 + blast_result[3], # query length + blast_result[4], # match length + blast_result[14], # contig length blast_result[9], # contig start blast_result[10], # contig end + direction, + gene_annotation, + product_annotation, + allele_quality, blast_result[13], # contig sequence ] + return blast_details if len(blast_result) > 1: @@ -78,6 +94,7 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: allele_name = grep_result[0].split(">")[1] # allele is labled as EXACT + allele_details[4] = "EXC_" + allele_details[3] return ["EXC", allele_name, allele_details] # check if contig is shorter than allele if int(column_blast_res[3]) > int(column_blast_res[4]): @@ -91,15 +108,19 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: == column_blast_res[14] # check reverse at contig start ): # allele is labled as PLOT + allele_details[4] = "PLOT_" + allele_details[3] return ["PLOT", allele_name, allele_details] # allele is labled as ASM + allele_details[4] = "ASM_" + allele_details[3] return ["ASM", allele_name, allele_details] # check if contig is longer than allele if int(column_blast_res[3]) < int(column_blast_res[4]): # allele is labled as ALM + allele_details[4] = "ALM_" + allele_details[3] return ["ALM", allele_name, allele_details] if int(column_blast_res[3]) == int(column_blast_res[4]): # allele is labled as INF + allele_details[4] = "INF_" + allele_details[3] return ["INF", allele_name, allele_details] else: print("ERROR: No blast result found") @@ -171,18 +192,21 @@ def search_match_allele(self): def parallel_execution( - sample_file: str, schema: str, reference_alleles: list, out_folder: str + sample_file: str, schema: str, prediction_data: dict,reference_alleles: list, out_folder: str ): - allele_obj = AlleleCalling(sample_file, schema, reference_alleles, out_folder) + allele_obj = AlleleCalling(sample_file, schema, prediction_data, reference_alleles, out_folder) return allele_obj.search_match_allele() def collect_data(results: list, output: str) -> None: summary_result_file = os.path.join(output, "allele_calling_summary.csv") sample_allele_match_file = os.path.join(output, "allele_calling_match.csv") + sample_allele_detail_file = os.path.join(output, "matching_contig.csv") a_types = ["NIPHEM", "EXC", "PLOT", "ASM", "ALM", "INF", "LNF"] + detail_heading = ["sample", "contig", "core gene", "allele name", "codification", "query lenght", "match lengt", "contig length", "contig start", "contig stop", "direction", "gene notation", "product notation", "allele quality", "sequence"] summary_result = {} sample_allele_match = {} + sample_allele_detail = {} # get allele list first_sample = list(results[0].keys())[0] allele_list = sorted(results[0][first_sample]["allele_type"].keys()) @@ -214,3 +238,15 @@ def collect_data(results: list, output: str) -> None: for allele in allele_list: fo.write(f"{allele_cod[allele]},") fo.write("\n") + + with open(sample_allele_detail_file, "w") as fo: + fo.write(",".join(detail_heading) + "\n") + for result in results: + for sample, values in result.items(): + for allele, detail_value in values["allele_details"].items(): + fo.write(",".join(detail_value) + "\n") + + + + + diff --git a/taranis/utils.py b/taranis/utils.py index b924e62..0810d67 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -1,5 +1,6 @@ #!/usr/bin/env python import glob +import gzip import io import logging import multiprocessing @@ -383,6 +384,36 @@ def read_annotation_file(ann_file: str) -> dict: break return ann_data +def read_compressed_file(file_name: str, separator: str = ",", index_key: int=None, mapping: list=[]) -> dict|str: + """Read the compressed file and return a dictionary using as key value + the mapping data if the index_key is an integer, else return the uncompressed + file + + Args: + file_name (str): file to be uncompressed + separator (str, optional): split line according separator. Defaults to ",". + index_key (int, optional): index value . Defaults to None. + mapping (list, optional): defined the key value for dictionary. Defaults to []. + + Returns: + dict|str: uncompresed information file + """ + out_data = {} + with gzip.open(file_name, "rb") as fh: + lines = fh.readlines() + if not index_key: + return lines[:-2] + for line in lines[1:]: + line = line.decode("utf-8") + s_line = line.split(separator) + # ignore empty lines + if len(s_line) == 1: + continue + key_data =s_line[index_key] + out_data[key_data] = {} + for item in mapping: + out_data[key_data][item[0]] = s_line[item[1]] + return out_data def read_fasta_file(fasta_file): return SeqIO.parse(fasta_file, "fasta") From b710b08b78893240e1afdb203ee4a2d461ff9162 Mon Sep 17 00:00:00 2001 From: luissian Date: Sat, 16 Mar 2024 17:35:11 +0100 Subject: [PATCH 117/214] created inferred class to track inferred alleles --- taranis/__main__.py | 14 +++++- taranis/allele_calling.py | 88 +++++++++++++++++++++++++++---------- taranis/inferred_alleles.py | 31 +++++++++++++ 3 files changed, 108 insertions(+), 25 deletions(-) create mode 100644 taranis/inferred_alleles.py diff --git a/taranis/__main__.py b/taranis/__main__.py index 2936ae1..231ba64 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -15,6 +15,7 @@ import taranis.reference_alleles import taranis.allele_calling +import taranis.inferred_alleles from pathlib import Path log = logging.getLogger() @@ -445,7 +446,11 @@ def allele_calling( pred_sample.prediction() """ map_pred = [["gene", 7], ["product", 8], ["allele_quality", 9]] - prediction_data = taranis.utils.read_compressed_file(annotation, separator=",", index_key=1, mapping=map_pred) + prediction_data = taranis.utils.read_compressed_file( + annotation, separator=",", index_key=1, mapping=map_pred + ) + # Create the instanace for inference alleles + inf_allele_obj = taranis.inferred_alleles.InferredAllele() """Analyze the sample file against schema to identify outbreakers """ start = time.perf_counter() @@ -455,7 +460,12 @@ def allele_calling( results.append( { assembly_name: taranis.allele_calling.parallel_execution( - assembly_file, schema, prediction_data, schema_ref_files, output + assembly_file, + schema, + prediction_data, + schema_ref_files, + output, + inf_allele_obj, ) } ) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index c620ca0..3dd35b5 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -11,6 +11,7 @@ from pathlib import Path from Bio import SeqIO +import pdb log = logging.getLogger(__name__) stderr = rich.console.Console( @@ -23,9 +24,15 @@ class AlleleCalling: def __init__( - self, sample_file: str, schema: str, annotation:dict, reference_alleles: list, out_folder: str + self, + sample_file: str, + schema: str, + annotation: dict, + reference_alleles: list, + out_folder: str, + inf_alle_obj: object, ): - self.prediction_data = annotation # store prediction annotation + self.prediction_data = annotation # store prediction annotation self.sample_file = sample_file self.schema = schema self.ref_alleles = reference_alleles @@ -35,6 +42,8 @@ def __init__( # create blast for sample file self.blast_obj = taranis.blast.Blast("nucl") _ = self.blast_obj.create_blastdb(sample_file, self.blast_dir) + # store inferred allele object + self.inf_alle_obj = inf_alle_obj def assign_allele_type( self, blast_result: list, allele_file: str, allele_name: str @@ -42,7 +51,7 @@ def assign_allele_type( def get_blast_details(blast_result: list, allele_name: str) -> list: try: gene_annotation = self.prediction_data[allele_name]["gene"] - product_annotation = self.prediction_data[allele_name]["product"] + product_annotation = self.prediction_data[allele_name]["product"] allele_quality = self.prediction_data[allele_name]["quality"] except KeyError: gene_annotation = "Not found" @@ -55,14 +64,14 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: direction = "-" # get blast details blast_details = [ - self.s_name, # sample name + self.s_name, # sample name blast_result[1], # contig - allele_name, # core gene name + allele_name, # core gene name blast_result[0], # allele gene - "coding", # coding allele type. To be filled later idx = 4 - blast_result[3], # query length - blast_result[4], # match length - blast_result[14], # contig length + "coding", # coding allele type. To be filled later idx = 4 + blast_result[3], # query length + blast_result[4], # match length + blast_result[14], # contig length blast_result[9], # contig start blast_result[10], # contig end direction, @@ -71,7 +80,7 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: allele_quality, blast_result[13], # contig sequence ] - + return blast_details if len(blast_result) > 1: @@ -79,6 +88,7 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: column_blast_res = blast_result[0].split("\t") column_blast_res[13] = column_blast_res[13].replace("-", "") allele_details = get_blast_details(column_blast_res, allele_name) + allele_details[4] = "NIPHEM_" + allele_details[3] return ["NIPHEM", allele_name, allele_details] elif len(blast_result) == 1: @@ -94,7 +104,7 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: allele_name = grep_result[0].split(">")[1] # allele is labled as EXACT - allele_details[4] = "EXC_" + allele_details[3] + allele_details[4] = "EXC_" + allele_details[3] return ["EXC", allele_name, allele_details] # check if contig is shorter than allele if int(column_blast_res[3]) > int(column_blast_res[4]): @@ -108,19 +118,28 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: == column_blast_res[14] # check reverse at contig start ): # allele is labled as PLOT - allele_details[4] = "PLOT_" + allele_details[3] + allele_details[4] = "PLOT_" + allele_details[3] return ["PLOT", allele_name, allele_details] # allele is labled as ASM - allele_details[4] = "ASM_" + allele_details[3] + allele_details[4] = "ASM_" + allele_details[3] return ["ASM", allele_name, allele_details] # check if contig is longer than allele if int(column_blast_res[3]) < int(column_blast_res[4]): # allele is labled as ALM - allele_details[4] = "ALM_" + allele_details[3] + allele_details[4] = "ALM_" + allele_details[3] return ["ALM", allele_name, allele_details] if int(column_blast_res[3]) == int(column_blast_res[4]): # allele is labled as INF - allele_details[4] = "INF_" + allele_details[3] + allele_details[4] = ( + "INF_" + + allele_name + + "_" + + str( + self.inf_alle_obj.get_inferred_allele( + column_blast_res[14], allele_name + ) + ) + ) return ["INF", allele_name, allele_details] else: print("ERROR: No blast result found") @@ -192,9 +211,21 @@ def search_match_allele(self): def parallel_execution( - sample_file: str, schema: str, prediction_data: dict,reference_alleles: list, out_folder: str + sample_file: str, + schema: str, + prediction_data: dict, + reference_alleles: list, + out_folder: str, + inf_alle_obj: object, ): - allele_obj = AlleleCalling(sample_file, schema, prediction_data, reference_alleles, out_folder) + allele_obj = AlleleCalling( + sample_file, + schema, + prediction_data, + reference_alleles, + out_folder, + inf_alle_obj, + ) return allele_obj.search_match_allele() @@ -203,7 +234,23 @@ def collect_data(results: list, output: str) -> None: sample_allele_match_file = os.path.join(output, "allele_calling_match.csv") sample_allele_detail_file = os.path.join(output, "matching_contig.csv") a_types = ["NIPHEM", "EXC", "PLOT", "ASM", "ALM", "INF", "LNF"] - detail_heading = ["sample", "contig", "core gene", "allele name", "codification", "query lenght", "match lengt", "contig length", "contig start", "contig stop", "direction", "gene notation", "product notation", "allele quality", "sequence"] + detail_heading = [ + "sample", + "contig", + "core gene", + "allele name", + "codification", + "query lenght", + "match lengt", + "contig length", + "contig start", + "contig stop", + "direction", + "gene notation", + "product notation", + "allele quality", + "sequence", + ] summary_result = {} sample_allele_match = {} sample_allele_detail = {} @@ -245,8 +292,3 @@ def collect_data(results: list, output: str) -> None: for sample, values in result.items(): for allele, detail_value in values["allele_details"].items(): fo.write(",".join(detail_value) + "\n") - - - - - diff --git a/taranis/inferred_alleles.py b/taranis/inferred_alleles.py new file mode 100644 index 0000000..a39cf00 --- /dev/null +++ b/taranis/inferred_alleles.py @@ -0,0 +1,31 @@ +import pdb + + +class InferredAllele: + def __init__(self): + self.inferred_seq = {} + self.last_allele_index = {} + + def get_inferred_allele(self, sequence: str, allele: str) -> str: + """Infer allele from the sequence + + Args: + sequence (str): sequence to infer the allele + + Returns: + str: inferred allele + """ + if sequence not in self.inferred_seq: + return self.set_inferred_allele(sequence, allele) + return self.inferred_seq[sequence] + + def set_inferred_allele(self, sequence: str, allele: str) -> None: + """Set the inferred allele for the sequence + + Args: + sequence (str): sequence to infer the allele + allele (str): inferred allele + """ + inf_value = self.last_allele_index.get(allele, 0) + 1 + self.inferred_seq[sequence] = inf_value + return self.inferred_seq[sequence] From 683b246d34bdcef521ad80bec26cce8c8e54bde6 Mon Sep 17 00:00:00 2001 From: luissian Date: Sat, 16 Mar 2024 17:54:05 +0100 Subject: [PATCH 118/214] litting --- taranis/__main__.py | 1 - taranis/allele_calling.py | 4 +--- taranis/analyze_schema.py | 3 ++- taranis/inferred_alleles.py | 3 --- taranis/utils.py | 15 +++++++++++---- 5 files changed, 14 insertions(+), 12 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index 231ba64..ef89483 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -3,7 +3,6 @@ import click import concurrent.futures import glob -import os import rich.console import rich.logging import rich.traceback diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 3dd35b5..9a36240 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -11,8 +11,6 @@ from pathlib import Path from Bio import SeqIO -import pdb - log = logging.getLogger(__name__) stderr = rich.console.Console( stderr=True, @@ -253,7 +251,7 @@ def collect_data(results: list, output: str) -> None: ] summary_result = {} sample_allele_match = {} - sample_allele_detail = {} + # get allele list first_sample = list(results[0].keys())[0] allele_list = sorted(results[0][first_sample]["allele_type"].keys()) diff --git a/taranis/analyze_schema.py b/taranis/analyze_schema.py index d720788..3cfa2eb 100644 --- a/taranis/analyze_schema.py +++ b/taranis/analyze_schema.py @@ -99,7 +99,8 @@ def check_allele_quality(self, prokka_annotation: dict) -> OrderedDict: "start_codon_alt": "standard", "protein_seq": "", "cds_coding": prokka_ann, - "product_annotation": product_annotation, } + "product_annotation": product_annotation, + } allele_seq[record.id] = str(record.seq) a_quality[record.id]["length"] = str(len(str(record.seq))) a_quality[record.id]["dna_seq"] = str(record.seq) diff --git a/taranis/inferred_alleles.py b/taranis/inferred_alleles.py index a39cf00..a985c1f 100644 --- a/taranis/inferred_alleles.py +++ b/taranis/inferred_alleles.py @@ -1,6 +1,3 @@ -import pdb - - class InferredAllele: def __init__(self): self.inferred_seq = {} diff --git a/taranis/utils.py b/taranis/utils.py index 0810d67..a1fbff5 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -375,7 +375,10 @@ def read_annotation_file(ann_file: str) -> dict: if "Prodigal" in line: gene_match = re.search(r"(.*)[\t]Prodigal.*gene=(\w+)_.*product=(.*)", line) if gene_match: - ann_data[gene_match.group(1)] = {"gene": gene_match.group(2) , "product": gene_match.group(3).strip()} + ann_data[gene_match.group(1)] = { + "gene": gene_match.group(2), + "product": gene_match.group(3).strip(), + } else: pred_match = re.search(r"(.*)[\t]Prodigal.*product=(\w+)_.*", line) if pred_match: @@ -384,7 +387,10 @@ def read_annotation_file(ann_file: str) -> dict: break return ann_data -def read_compressed_file(file_name: str, separator: str = ",", index_key: int=None, mapping: list=[]) -> dict|str: + +def read_compressed_file( + file_name: str, separator: str = ",", index_key: int = None, mapping: list = [] +) -> dict | str: """Read the compressed file and return a dictionary using as key value the mapping data if the index_key is an integer, else return the uncompressed file @@ -397,7 +403,7 @@ def read_compressed_file(file_name: str, separator: str = ",", index_key: int=No Returns: dict|str: uncompresed information file - """ + """ out_data = {} with gzip.open(file_name, "rb") as fh: lines = fh.readlines() @@ -409,12 +415,13 @@ def read_compressed_file(file_name: str, separator: str = ",", index_key: int=No # ignore empty lines if len(s_line) == 1: continue - key_data =s_line[index_key] + key_data = s_line[index_key] out_data[key_data] = {} for item in mapping: out_data[key_data][item[0]] = s_line[item[1]] return out_data + def read_fasta_file(fasta_file): return SeqIO.parse(fasta_file, "fasta") From 169acc014557e875c484b1b7ca3cde92520bf3df Mon Sep 17 00:00:00 2001 From: luissian Date: Sat, 16 Mar 2024 17:57:02 +0100 Subject: [PATCH 119/214] solving liting --- taranis/analyze_schema.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/taranis/analyze_schema.py b/taranis/analyze_schema.py index 3cfa2eb..15c1f71 100644 --- a/taranis/analyze_schema.py +++ b/taranis/analyze_schema.py @@ -100,7 +100,7 @@ def check_allele_quality(self, prokka_annotation: dict) -> OrderedDict: "protein_seq": "", "cds_coding": prokka_ann, "product_annotation": product_annotation, - } + } allele_seq[record.id] = str(record.seq) a_quality[record.id]["length"] = str(len(str(record.seq))) a_quality[record.id]["dna_seq"] = str(record.seq) From 1cf8528a9bf18ea9d68b52bc16b7057d352808ab Mon Sep 17 00:00:00 2001 From: luissian Date: Sun, 17 Mar 2024 10:00:19 +0100 Subject: [PATCH 120/214] create finally at try when searching for distance --- taranis/distance.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/taranis/distance.py b/taranis/distance.py index 028dfbb..496fac0 100644 --- a/taranis/distance.py +++ b/taranis/distance.py @@ -64,10 +64,10 @@ def create_matrix(self) -> pd.DataFrame: ) stderr.print(f"{e}") sys.exit(1) - - # Close the file handles - mash_distance_result.stdout.close() - mash_distance_result.stderr.close() + finally: + # Close the file handles + mash_distance_result.stdout.close() + mash_distance_result.stderr.close() out_data = out.decode("UTF-8").split("\n") allele_names = [item.split("\t")[0] for item in out_data[1:-1]] From f36a2c53c585fa20859d66c3e727d9c1ffad51b2 Mon Sep 17 00:00:00 2001 From: luissian Date: Sun, 17 Mar 2024 20:02:40 +0100 Subject: [PATCH 121/214] fixed bug in saving annotation per allele --- taranis/allele_calling.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 9a36240..fd19bbe 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -3,7 +3,6 @@ import os import rich.console - import taranis.utils import taranis.blast @@ -47,15 +46,17 @@ def assign_allele_type( self, blast_result: list, allele_file: str, allele_name: str ) -> list: def get_blast_details(blast_result: list, allele_name: str) -> list: + match_allele_name = blast_result[0] try: - gene_annotation = self.prediction_data[allele_name]["gene"] - product_annotation = self.prediction_data[allele_name]["product"] - allele_quality = self.prediction_data[allele_name]["quality"] + gene_annotation = self.prediction_data[match_allele_name]["gene"] + product_annotation = self.prediction_data[match_allele_name]["product"] + allele_quality = self.prediction_data[match_allele_name][ + "allele_quality" + ].strip() except KeyError: gene_annotation = "Not found" product_annotation = "Not found" allele_quality = "Not found" - if int(blast_result[10]) > int(blast_result[9]): direction = "+" else: From ba108b83b6a2c2779365c5cb8c0a4a44f0e74f23 Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 18 Mar 2024 11:47:24 +0100 Subject: [PATCH 122/214] Check req programs before starting --- taranis/__main__.py | 3 +++ taranis/utils.py | 20 ++++++++++++++++++++ 2 files changed, 23 insertions(+) diff --git a/taranis/__main__.py b/taranis/__main__.py index ef89483..8b6beea 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -217,6 +217,7 @@ def analyze_schema( usegenus: str, cpus: int, ): + _= taranis.utils.check_additional_programs_installed(["prokka"]) schema_files = taranis.utils.get_files_in_folder(inputdir, "fasta") results = [] @@ -333,6 +334,7 @@ def reference_alleles( cpus: int, force: bool, ): + _= taranis.utils.check_additional_programs_installed(["mash", "makeblastdb", "blastn"]) start = time.perf_counter() max_cpus = taranis.utils.cpus_available() if cpus > max_cpus: @@ -425,6 +427,7 @@ def allele_calling( output: str, force: bool, ): + _= taranis.utils.check_additional_programs_installed(["blastn", "makeblastdb"]) schema_ref_files = taranis.utils.get_files_in_folder(reference, "fasta") if len(schema_ref_files) == 0: log.error("Referenc allele folder %s does not have any fasta file", schema) diff --git a/taranis/utils.py b/taranis/utils.py index a1fbff5..02d8223 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -80,6 +80,26 @@ def get_seq_direction(allele_sequence): return "Error" +def check_additional_programs_installed(software_list: list) -> None: + """Check if the input list of programs are installed in the system + + Args: + software_list (list): list of programs to be checked + """ + for program in software_list: + try: + _ = subprocess.run( + [program, "-h"], + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + check=True, + ) + except Exception as e: + log.error("Program %s is not installed in the system. Error message: %s ", program, e) + stderr.print("[red] Program " + program + " is not installed in the system") + sys.exit(1) + return + def create_annotation_files( fasta_file: str, annotation_dir: str, From f0291a1086cf47a8c4311f96669a1381603b8630 Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 18 Mar 2024 21:13:53 +0100 Subject: [PATCH 123/214] save code before using valid result from blast --- taranis/allele_calling.py | 64 ++++++++++++++++++++++++++++----------- 1 file changed, 47 insertions(+), 17 deletions(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index fd19bbe..5feeb17 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -5,7 +5,7 @@ import taranis.utils import taranis.blast - +import pdb from collections import OrderedDict from pathlib import Path from Bio import SeqIO @@ -50,9 +50,7 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: try: gene_annotation = self.prediction_data[match_allele_name]["gene"] product_annotation = self.prediction_data[match_allele_name]["product"] - allele_quality = self.prediction_data[match_allele_name][ - "allele_quality" - ].strip() + allele_quality = self.prediction_data[match_allele_name]["allele_quality"] except KeyError: gene_annotation = "Not found" product_annotation = "Not found" @@ -84,11 +82,36 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: if len(blast_result) > 1: # allele is named as NIPHEM - column_blast_res = blast_result[0].split("\t") - column_blast_res[13] = column_blast_res[13].replace("-", "") - allele_details = get_blast_details(column_blast_res, allele_name) - allele_details[4] = "NIPHEM_" + allele_details[3] - return ["NIPHEM", allele_name, allele_details] + multi_allele = [] + valid_blast_result = [] + match_full_length = 0 + match_partial_length = 0 + for b_result in blast_result: + column_blast_res = b_result.split("\t") + query_length = int(column_blast_res[4]) / int(column_blast_res[3]) + if query_length >= 0.8: + valid_blast_result.append(b_result) + if query_length == 1: + match_full_length += 1 + else: + match_partial_length += 1 + pdb.set_trace() + for valid_result in valid_blast_result: + column_blast_res = valid_result.split("\t") + column_blast_res[13] = column_blast_res[13].replace("-", "") + allele_details = get_blast_details(column_blast_res, allele_name) + if match_full_length >= 2: + allele_details[4] = "NIPHEM_" + allele_details[3] + clasification = "NIPHEM" + elif match_full_length == 1 and match_partial_length >= 1: + allele_details[4] = "NIPH_" + allele_details[3] + clasification = "NIPH" + else: + allele_details[4] = "EXC" + allele_details[3] + clasification = "EXC" + multi_allele.append(allele_details) + pdb.set_trace() + return [clasification, allele_name, multi_allele] elif len(blast_result) == 1: column_blast_res = blast_result[0].split("\t") @@ -198,6 +221,7 @@ def search_match_allele(self): ) = self.assign_allele_type(blast_result, allele_file, allele_name) except Exception as e: stderr.print(f"Error: {e}") + pdb.set_trace() else: # Sample does not have a reference allele to be matched @@ -232,7 +256,7 @@ def collect_data(results: list, output: str) -> None: summary_result_file = os.path.join(output, "allele_calling_summary.csv") sample_allele_match_file = os.path.join(output, "allele_calling_match.csv") sample_allele_detail_file = os.path.join(output, "matching_contig.csv") - a_types = ["NIPHEM", "EXC", "PLOT", "ASM", "ALM", "INF", "LNF"] + allele_types = ["NIPHEM", "NIPH","EXC", "PLOT", "ASM", "ALM", "INF", "LNF"] detail_heading = [ "sample", "contig", @@ -260,23 +284,25 @@ def collect_data(results: list, output: str) -> None: for sample, values in result.items(): sum_allele_type = OrderedDict() # used for summary file allele_match = {} - for a_type in a_types: - sum_allele_type[a_type] = 0 - for allele, type in values["allele_type"].items(): + for allele_type in allele_types: + sum_allele_type[allele_type] = 0 + for allele, type_of_allele in values["allele_type"].items(): # increase allele type count - sum_allele_type[type] += 1 + sum_allele_type[type_of_allele] += 1 # add allele name match to sample - allele_match[allele] = type + "_" + values["allele_match"][allele] + allele_match[allele] = type_of_allele + "_" + values["allele_match"][allele] summary_result[sample] = sum_allele_type sample_allele_match[sample] = allele_match + # save summary results to file with open(summary_result_file, "w") as fo: - fo.write("Sample," + ",".join(a_types) + "\n") + fo.write("Sample," + ",".join(allele_types) + "\n") for sample, counts in summary_result.items(): fo.write(f"{sample},") for _, count in counts.items(): fo.write(f"{count},") fo.write("\n") + # save allele match to file with open(sample_allele_match_file, "w") as fo: fo.write("Sample," + ",".join(allele_list) + "\n") for sample, allele_cod in sample_allele_match.items(): @@ -290,4 +316,8 @@ def collect_data(results: list, output: str) -> None: for result in results: for sample, values in result.items(): for allele, detail_value in values["allele_details"].items(): - fo.write(",".join(detail_value) + "\n") + if type(detail_value[0]) is list: + for detail in detail_value: + fo.write(",".join(detail) + "\n") + else: + fo.write(",".join(detail_value) + "\n") From 8ad751c69d5f078793d67c6162bc090089ff7e8c Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 18 Mar 2024 22:36:04 +0100 Subject: [PATCH 124/214] implemented NIPH and NIPHEM clasification --- taranis/__main__.py | 9 ++-- taranis/allele_calling.py | 107 +++++++++++++++++++++++--------------- 2 files changed, 70 insertions(+), 46 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index 8b6beea..2e696c7 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -217,7 +217,7 @@ def analyze_schema( usegenus: str, cpus: int, ): - _= taranis.utils.check_additional_programs_installed(["prokka"]) + _ = taranis.utils.check_additional_programs_installed(["prokka"]) schema_files = taranis.utils.get_files_in_folder(inputdir, "fasta") results = [] @@ -334,7 +334,9 @@ def reference_alleles( cpus: int, force: bool, ): - _= taranis.utils.check_additional_programs_installed(["mash", "makeblastdb", "blastn"]) + _ = taranis.utils.check_additional_programs_installed( + ["mash", "makeblastdb", "blastn"] + ) start = time.perf_counter() max_cpus = taranis.utils.cpus_available() if cpus > max_cpus: @@ -427,7 +429,7 @@ def allele_calling( output: str, force: bool, ): - _= taranis.utils.check_additional_programs_installed(["blastn", "makeblastdb"]) + _ = taranis.utils.check_additional_programs_installed(["blastn", "makeblastdb"]) schema_ref_files = taranis.utils.get_files_in_folder(reference, "fasta") if len(schema_ref_files) == 0: log.error("Referenc allele folder %s does not have any fasta file", schema) @@ -474,5 +476,4 @@ def allele_calling( _ = taranis.allele_calling.collect_data(results, output) finish = time.perf_counter() print(f"Allele calling finish in {round((finish-start)/60, 2)} minutes") - # import pdb; pdb.set_trace() # sample_allele_obj.analyze_sample() diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 5feeb17..5c8a3de 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -50,7 +50,9 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: try: gene_annotation = self.prediction_data[match_allele_name]["gene"] product_annotation = self.prediction_data[match_allele_name]["product"] - allele_quality = self.prediction_data[match_allele_name]["allele_quality"] + allele_quality = self.prediction_data[match_allele_name][ + "allele_quality" + ] except KeyError: gene_annotation = "Not found" product_annotation = "Not found" @@ -80,40 +82,39 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: return blast_details - if len(blast_result) > 1: - # allele is named as NIPHEM + valid_blast_result = [] + match_full_length = 0 + match_partial_length = 0 + for b_result in blast_result: + column_blast_res = b_result.split("\t") + query_length = int(column_blast_res[4]) / int(column_blast_res[3]) + if query_length >= 0.8: + valid_blast_result.append(b_result) + if query_length == 1: + match_full_length += 1 + else: + match_partial_length += 1 + + if len(valid_blast_result) > 1: + # allele could be named as NIPHEM or NIPH multi_allele = [] - valid_blast_result = [] - match_full_length = 0 - match_partial_length = 0 - for b_result in blast_result: - column_blast_res = b_result.split("\t") - query_length = int(column_blast_res[4]) / int(column_blast_res[3]) - if query_length >= 0.8: - valid_blast_result.append(b_result) - if query_length == 1: - match_full_length += 1 - else: - match_partial_length += 1 - pdb.set_trace() for valid_result in valid_blast_result: column_blast_res = valid_result.split("\t") column_blast_res[13] = column_blast_res[13].replace("-", "") allele_details = get_blast_details(column_blast_res, allele_name) if match_full_length >= 2: + # labled as NIPHEM if all alleles are in the same contig allele_details[4] = "NIPHEM_" + allele_details[3] clasification = "NIPHEM" - elif match_full_length == 1 and match_partial_length >= 1: + else: + # labled as NIPH if all alleles are in different contigs allele_details[4] = "NIPH_" + allele_details[3] clasification = "NIPH" - else: - allele_details[4] = "EXC" + allele_details[3] - clasification = "EXC" multi_allele.append(allele_details) pdb.set_trace() return [clasification, allele_name, multi_allele] - elif len(blast_result) == 1: + elif len(valid_blast_result) == 1: column_blast_res = blast_result[0].split("\t") column_blast_res[13] = column_blast_res[13].replace("-", "") allele_details = get_blast_details(column_blast_res, allele_name) @@ -132,10 +133,10 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: if int(column_blast_res[3]) > int(column_blast_res[4]): # check if sequence is shorter because it starts or ends at the contig if ( - column_blast_res[9] == 1 # check at contig start + column_blast_res[9] == "1" # check at contig start or column_blast_res[14] == column_blast_res[10] # check at contig end - or column_blast_res[10] == 1 # check reverse at contig end + or column_blast_res[10] == "1" # check reverse at contig end or column_blast_res[9] == column_blast_res[14] # check reverse at contig start ): @@ -164,8 +165,34 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: ) return ["INF", allele_name, allele_details] else: - print("ERROR: No blast result found") - return ["LNF", "allele_name", "LNF"] + # analyze again the blast result to check with lower query size, 0.75 + # it starts/ends at the contig. Then it is labled as PLOT + + multi_allele = [] + clasification = "" + for b_result in blast_result: + column_blast_res = b_result.split("\t") + query_length = int(column_blast_res[4]) / int(column_blast_res[3]) + if query_length >= 0.75: + if ( + column_blast_res[9] == "1" # check at contig start + or column_blast_res[14] + == column_blast_res[10] # check at contig end + or column_blast_res[10] == "1" # check reverse at contig end + or column_blast_res[9] + == column_blast_res[14] # check reverse at contig start + ): + allele_details = get_blast_details( + column_blast_res, allele_name + ) + # allele is labled as PLOT + allele_details[4] = "PLOT_" + allele_details[3] + multi_allele.append(allele_details) + clasification = "PLOT" + if clasification == "PLOT": + return [clasification, allele_name, multi_allele] + else: + return ["LNF", "allele_name", "LNF"] def search_match_allele(self): # Create blast db with sample file @@ -212,24 +239,18 @@ def search_match_allele(self): if match_found: allele_file = os.path.join(self.schema, os.path.basename(ref_allele)) # blast_result = self.blast_obj.run_blast(q_file,perc_identity=100) - try: - allele_name = Path(allele_file).stem - ( - result["allele_type"][allele_name], - result["allele_match"][allele_name], - result["allele_details"][allele_name], - ) = self.assign_allele_type(blast_result, allele_file, allele_name) - except Exception as e: - stderr.print(f"Error: {e}") - pdb.set_trace() - + allele_name = Path(allele_file).stem + ( + result["allele_type"][allele_name], + result["allele_match"][allele_name], + result["allele_details"][allele_name], + ) = self.assign_allele_type(blast_result, allele_file, allele_name) else: # Sample does not have a reference allele to be matched # Keep LNF info - # ver el codigo de espe - # lnf_tpr_tag() - pass - + result["allele_type"][allele_name] = "LNF" + result["allele_match"][allele_name] = allele_name + result["allele_details"][allele_name] = "LNF" return result @@ -256,7 +277,7 @@ def collect_data(results: list, output: str) -> None: summary_result_file = os.path.join(output, "allele_calling_summary.csv") sample_allele_match_file = os.path.join(output, "allele_calling_match.csv") sample_allele_detail_file = os.path.join(output, "matching_contig.csv") - allele_types = ["NIPHEM", "NIPH","EXC", "PLOT", "ASM", "ALM", "INF", "LNF"] + allele_types = ["NIPHEM", "NIPH", "EXC", "PLOT", "ASM", "ALM", "INF", "LNF"] detail_heading = [ "sample", "contig", @@ -290,7 +311,9 @@ def collect_data(results: list, output: str) -> None: # increase allele type count sum_allele_type[type_of_allele] += 1 # add allele name match to sample - allele_match[allele] = type_of_allele + "_" + values["allele_match"][allele] + allele_match[allele] = ( + type_of_allele + "_" + values["allele_match"][allele] + ) summary_result[sample] = sum_allele_type sample_allele_match[sample] = allele_match From 01e6a03c1769adacda5a2e1aa875aa5ef3020707 Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 18 Mar 2024 23:30:42 +0100 Subject: [PATCH 125/214] fixing liting and error in program parameter --- taranis/__main__.py | 8 +++++--- taranis/utils.py | 11 ++++++++--- 2 files changed, 13 insertions(+), 6 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index 2e696c7..fda14f1 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -217,7 +217,7 @@ def analyze_schema( usegenus: str, cpus: int, ): - _ = taranis.utils.check_additional_programs_installed(["prokka"]) + _ = taranis.utils.check_additional_programs_installed([["prokka", "--version"]]) schema_files = taranis.utils.get_files_in_folder(inputdir, "fasta") results = [] @@ -335,7 +335,7 @@ def reference_alleles( force: bool, ): _ = taranis.utils.check_additional_programs_installed( - ["mash", "makeblastdb", "blastn"] + [["mash", "--version"], ["makeblastdb", "-version"], ["blastn", "-version"]] ) start = time.perf_counter() max_cpus = taranis.utils.cpus_available() @@ -429,7 +429,9 @@ def allele_calling( output: str, force: bool, ): - _ = taranis.utils.check_additional_programs_installed(["blastn", "makeblastdb"]) + _ = taranis.utils.check_additional_programs_installed( + [["blastn", "-version"], ["makeblastdb", "-version"]] + ) schema_ref_files = taranis.utils.get_files_in_folder(reference, "fasta") if len(schema_ref_files) == 0: log.error("Referenc allele folder %s does not have any fasta file", schema) diff --git a/taranis/utils.py b/taranis/utils.py index 02d8223..837f0ca 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -86,20 +86,25 @@ def check_additional_programs_installed(software_list: list) -> None: Args: software_list (list): list of programs to be checked """ - for program in software_list: + for program, command in software_list: try: _ = subprocess.run( - [program, "-h"], + [program, command], stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True, ) except Exception as e: - log.error("Program %s is not installed in the system. Error message: %s ", program, e) + log.error( + "Program %s is not installed in the system. Error message: %s ", + program, + e, + ) stderr.print("[red] Program " + program + " is not installed in the system") sys.exit(1) return + def create_annotation_files( fasta_file: str, annotation_dir: str, From 4ee8bd8f94d64dbde11949d0cfdf30b3d1068ba4 Mon Sep 17 00:00:00 2001 From: luissian Date: Tue, 19 Mar 2024 12:05:55 +0100 Subject: [PATCH 126/214] Implemented SNP file --- taranis/__main__.py | 25 ++++++++++++++++++++- taranis/allele_calling.py | 46 ++++++++++++++++++++++++++++++++++++--- taranis/utils.py | 32 ++++++++++++++++++++++----- 3 files changed, 93 insertions(+), 10 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index fda14f1..121d3f1 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -421,6 +421,18 @@ def reference_alleles( required=True, type=click.Path(exists=True), ) +@click.option( + "--snp/--no-snp", + required=False, + default=False, + help="Create SNP file for alleles that are infered INF", +) +@click.option( + "--alignment/--no-alignment", + required=False, + default=False, + help="Create aligment file for Overwrite the output folder if it exists", +) def allele_calling( schema: str, reference: str, @@ -428,6 +440,8 @@ def allele_calling( assemblies: list, output: str, force: bool, + snp: bool, + alignment: bool, ): _ = taranis.utils.check_additional_programs_installed( [["blastn", "-version"], ["makeblastdb", "-version"]] @@ -451,6 +465,9 @@ def allele_calling( pred_sample.training() pred_sample.prediction() """ + # Read the annotation file + stderr.print("[green] Reading annotation file") + log.info("Reading annotation file") map_pred = [["gene", 7], ["product", 8], ["allele_quality", 9]] prediction_data = taranis.utils.read_compressed_file( annotation, separator=",", index_key=1, mapping=map_pred @@ -459,10 +476,13 @@ def allele_calling( inf_allele_obj = taranis.inferred_alleles.InferredAllele() """Analyze the sample file against schema to identify outbreakers """ + start = time.perf_counter() results = [] for assembly_file in assemblies: assembly_name = Path(assembly_file).stem + stderr.print("f[green] Analyzing sample {assembly_name}") + log.info(f"Analyzing sample {assembly_name}") results.append( { assembly_name: taranis.allele_calling.parallel_execution( @@ -472,10 +492,13 @@ def allele_calling( schema_ref_files, output, inf_allele_obj, + snp, + alignment, ) } ) - _ = taranis.allele_calling.collect_data(results, output) + + _ = taranis.allele_calling.collect_data(results, output, snp, alignment) finish = time.perf_counter() print(f"Allele calling finish in {round((finish-start)/60, 2)} minutes") # sample_allele_obj.analyze_sample() diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 5c8a3de..983fd13 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -28,6 +28,8 @@ def __init__( reference_alleles: list, out_folder: str, inf_alle_obj: object, + snp_request: bool = False, + aligment_request: bool = False, ): self.prediction_data = annotation # store prediction annotation self.sample_file = sample_file @@ -41,6 +43,8 @@ def __init__( _ = self.blast_obj.create_blastdb(sample_file, self.blast_dir) # store inferred allele object self.inf_alle_obj = inf_alle_obj + self.snp_request = snp_request + self.aligment_request = aligment_request def assign_allele_type( self, blast_result: list, allele_file: str, allele_name: str @@ -111,7 +115,6 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: allele_details[4] = "NIPH_" + allele_details[3] clasification = "NIPH" multi_allele.append(allele_details) - pdb.set_trace() return [clasification, allele_name, multi_allele] elif len(valid_blast_result) == 1: @@ -197,7 +200,12 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: def search_match_allele(self): # Create blast db with sample file - result = {"allele_type": {}, "allele_match": {}, "allele_details": {}} + result = { + "allele_type": {}, + "allele_match": {}, + "allele_details": {}, + "snp_data": {}, + } count = 0 for ref_allele in self.ref_alleles: count += 1 @@ -251,6 +259,12 @@ def search_match_allele(self): result["allele_type"][allele_name] = "LNF" result["allele_match"][allele_name] = allele_name result["allele_details"][allele_name] = "LNF" + if self.snp_request and result["allele_type"][allele_name] == "INF": + # run snp analysis + allele_seq = result["allele_details"][allele_name][14] + result["snp_data"][allele_name] = taranis.utils.get_snp_position( + allele_seq, alleles + ) return result @@ -261,6 +275,8 @@ def parallel_execution( reference_alleles: list, out_folder: str, inf_alle_obj: object, + snp_request: bool = False, + aligment_request: bool = False, ): allele_obj = AlleleCalling( sample_file, @@ -269,11 +285,15 @@ def parallel_execution( reference_alleles, out_folder, inf_alle_obj, + snp_request, + aligment_request, ) return allele_obj.search_match_allele() -def collect_data(results: list, output: str) -> None: +def collect_data( + results: list, output: str, snp_request: bool, aligment_request: bool +) -> None: summary_result_file = os.path.join(output, "allele_calling_summary.csv") sample_allele_match_file = os.path.join(output, "allele_calling_match.csv") sample_allele_detail_file = os.path.join(output, "matching_contig.csv") @@ -344,3 +364,23 @@ def collect_data(results: list, output: str) -> None: fo.write(",".join(detail) + "\n") else: fo.write(",".join(detail_value) + "\n") + if snp_request: + snp_file = os.path.join(output, "snp_data.csv") + with open(snp_file, "w") as fo: + fo.write("Sample name,Locus name,Reference allele,Position,Base,Ref\n") + for sample, values in result.items(): + # pdb.set_trace() + for allele, snp_data in values["snp_data"].items(): + for ref_allele, snp_info_list in snp_data.items(): + # pdb.set_trace() + for snp_info in snp_info_list: + fo.write( + sample + + "," + + allele + + "," + + ref_allele + + "," + + ",".join(snp_info) + + "\n" + ) diff --git a/taranis/utils.py b/taranis/utils.py index 837f0ca..3ec9d2c 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -281,16 +281,36 @@ def get_files_in_folder(folder: str, extension: str = None) -> list[str]: return glob.glob(folder_files) -def grep_execution(input_file: str, pattern: str, parameters: str) -> list: - """_summary_ +def get_snp_position(allele_sequence: str, ref_sequences: dict[str]) -> dict[list[str]]: + """Get the snp position between the allele sequence and the reference alleles + + Args: + allele_sequence (str): sequence to be compared + ref_sequences (dict): sequences of reference alleles + + Returns: + dict: key: ref_sequence, value: list of snp position + """ + snp_data = {} + for ref_allele, ref_sequence in ref_sequences.items(): + snp_position = [] + for idx, (a, b) in enumerate(zip(allele_sequence, ref_sequence)): + if a != b: + snp_position.append([str(idx), a, b]) + snp_data[ref_allele] = snp_position + return snp_data + + +def grep_execution(input_file: str, pattern: str, parameters: str) -> list[str]: + """run grep command and return the output Args: - input_file (str): _description_ - pattern (str): _description_ - parmeters (str): _description_ + input_file (str): input file path + pattern (str): pattern to be searched + parmeters (str): parameters to be used in grep Returns: - list: _description_ + list[str]: list of lines which match the pattern """ try: result = subprocess.run( From fbc76facf9cf00d10bfadcd48f7bc0b680ef5aba Mon Sep 17 00:00:00 2001 From: luissian Date: Tue, 19 Mar 2024 14:57:24 +0100 Subject: [PATCH 127/214] added graphics per allele classification --- taranis/allele_calling.py | 34 +++++++++++++++++++++++++++++++++- taranis/utils.py | 38 +++++++++++++++++++++++++------------- 2 files changed, 58 insertions(+), 14 deletions(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 983fd13..da37eac 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -5,7 +5,7 @@ import taranis.utils import taranis.blast -import pdb + from collections import OrderedDict from pathlib import Path from Bio import SeqIO @@ -294,6 +294,35 @@ def parallel_execution( def collect_data( results: list, output: str, snp_request: bool, aligment_request: bool ) -> None: + def stats_graphics(stats_folder: str, summary_result: dict) -> None: + stderr.print("Creating graphics") + log.info("Creating graphics") + allele_types = ["NIPHEM", "NIPH", "EXC", "PLOT", "ASM", "ALM", "INF", "LNF"] + # inizialize classification data + classif_data = {} + for allele_type in allele_types: + classif_data[allele_type] = [] + graphic_folder = os.path.join(stats_folder, "graphics") + + _ = taranis.utils.create_new_folder(graphic_folder) + s_list = [] + # collecting data to create graphics + for sample, classif_counts in summary_result.items(): + s_list.append(sample) # create list of samples + for classif, count in classif_counts.items(): + classif_data[classif].append(int(count)) + # create graphics + for allele_type, counts in classif_data.items(): + _ = taranis.utils.create_graphic( + graphic_folder, + str(allele_type + "_graphic.png"), + "bar", + s_list, + counts, + ["Samples", "number"], + str("Number of " + allele_type + " in samples"), + ) + summary_result_file = os.path.join(output, "allele_calling_summary.csv") sample_allele_match_file = os.path.join(output, "allele_calling_match.csv") sample_allele_detail_file = os.path.join(output, "matching_contig.csv") @@ -315,6 +344,7 @@ def collect_data( "allele quality", "sequence", ] + summary_result = {} sample_allele_match = {} @@ -384,3 +414,5 @@ def collect_data( + ",".join(snp_info) + "\n" ) + # Create graphics + stats_graphics(output, summary_result) diff --git a/taranis/utils.py b/taranis/utils.py index 3ec9d2c..461d4d1 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -8,6 +8,7 @@ import os import pandas as pd import plotly.graph_objects as go +import plotly.io as pio import questionary import shutil @@ -195,19 +196,30 @@ def create_graphic( title (str): title of the figure """ fig = go.Figure() - if mode == "lines": - fig.add_trace(go.Scatter(x=x_data, y=y_data, mode=mode, name=title)) - fig.update_layout(xaxis_title=labels[0], yaxis_title=labels[1]) - elif mode == "pie": - fig.add_trace(go.Pie(labels=labels, values=x_data)) - elif mode == "bar": - fig.add_trace(go.Bar(x=x_data, y=y_data)) - fig.update_layout(xaxis_title=labels[0], yaxis_title=labels[1]) - elif mode == "box": - fig.add_trace(go.Box(y=y_data)) - - fig.update_layout(title_text=title) - fig.write_image(os.path.join(out_folder, f_name)) + layout_update = {} + plot_options = { + "lines": (go.Scatter, {"mode": mode}), + "pie": (go.Pie, {"labels": labels, "values": x_data}), + "bar": (go.Bar, {"x": x_data, "y": y_data}), + "box": (go.Box, {"y": y_data}), + } + + if mode in plot_options: + trace_class, trace_kwargs = plot_options[mode] + fig.add_trace(trace_class(**trace_kwargs)) + if mode == "bar": + layout_update = { + "xaxis_title": labels[0], + "yaxis_title": labels[1], + "xaxis_tickangle": 45, + } + else: + raise ValueError(f"Unsupported mode: {mode}") + + layout_update["title_text"] = title + fig.update_layout(**layout_update) + + pio.write_image(fig, os.path.join(out_folder, f_name)) return From f0d79216e6e7bc1023fe0839148c5cc3b05363e7 Mon Sep 17 00:00:00 2001 From: luissian Date: Fri, 22 Mar 2024 00:19:33 +0100 Subject: [PATCH 128/214] implemented alignment and parallel --- taranis/__main__.py | 55 +++++++++++++++++-------------- taranis/allele_calling.py | 66 +++++++++++++++++++++++++------------ taranis/inferred_alleles.py | 9 +++-- taranis/utils.py | 32 +++++++++++++++++- 4 files changed, 113 insertions(+), 49 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index 121d3f1..39d26cf 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -433,6 +433,14 @@ def reference_alleles( default=False, help="Create aligment file for Overwrite the output folder if it exists", ) +@click.option( + "--cpus", + required=False, + multiple=False, + type=int, + default=1, + help="Number of cpus used for execution", +) def allele_calling( schema: str, reference: str, @@ -442,6 +450,7 @@ def allele_calling( force: bool, snp: bool, alignment: bool, + cpus: int, ): _ = taranis.utils.check_additional_programs_installed( [["blastn", "-version"], ["makeblastdb", "-version"]] @@ -457,14 +466,7 @@ def allele_calling( _ = taranis.utils.prompt_user_if_folder_exists(output) # Filter fasta files from reference folder # ref_alleles = glob.glob(os.path.join(reference, "*.fasta")) - # Create predictions - """ - pred_out = os.path.join(output, "prediction") - pred_sample = taranis.prediction.Prediction(genome, sample, pred_out) - pred_sample.training() - pred_sample.prediction() - """ # Read the annotation file stderr.print("[green] Reading annotation file") log.info("Reading annotation file") @@ -479,24 +481,27 @@ def allele_calling( start = time.perf_counter() results = [] - for assembly_file in assemblies: - assembly_name = Path(assembly_file).stem - stderr.print("f[green] Analyzing sample {assembly_name}") - log.info(f"Analyzing sample {assembly_name}") - results.append( - { - assembly_name: taranis.allele_calling.parallel_execution( - assembly_file, - schema, - prediction_data, - schema_ref_files, - output, - inf_allele_obj, - snp, - alignment, - ) - } - ) + with concurrent.futures.ThreadPoolExecutor(max_workers=cpus) as executor: + futures = [ + executor.submit( + taranis.allele_calling.parallel_execution, + assembly_file, + schema, + prediction_data, + schema_ref_files, + output, + inf_allele_obj, + snp, + alignment, + ) + for assembly_file in assemblies + ] + for future in concurrent.futures.as_completed(futures): + try: + results.append(future.result()) + except Exception as e: + print(e) + continue _ = taranis.allele_calling.collect_data(results, output, snp, alignment) finish = time.perf_counter() diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index da37eac..b6a0307 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -145,28 +145,29 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: ): # allele is labled as PLOT allele_details[4] = "PLOT_" + allele_details[3] - return ["PLOT", allele_name, allele_details] + return ["PLOT", allele_details[3], allele_details] # allele is labled as ASM allele_details[4] = "ASM_" + allele_details[3] - return ["ASM", allele_name, allele_details] + return ["ASM", allele_details[3], allele_details] # check if contig is longer than allele if int(column_blast_res[3]) < int(column_blast_res[4]): # allele is labled as ALM allele_details[4] = "ALM_" + allele_details[3] - return ["ALM", allele_name, allele_details] + return ["ALM", allele_details[3], allele_details] if int(column_blast_res[3]) == int(column_blast_res[4]): # allele is labled as INF - allele_details[4] = ( - "INF_" - + allele_name + + allele_details[3] = ( + allele_name + "_" + str( self.inf_alle_obj.get_inferred_allele( - column_blast_res[14], allele_name + column_blast_res[13], allele_name ) ) ) - return ["INF", allele_name, allele_details] + allele_details[4] = "INF_" + allele_details[3] + return ["INF", allele_details[3], allele_details] else: # analyze again the blast result to check with lower query size, 0.75 # it starts/ends at the contig. Then it is labled as PLOT @@ -193,9 +194,9 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: multi_allele.append(allele_details) clasification = "PLOT" if clasification == "PLOT": - return [clasification, allele_name, multi_allele] + return [clasification, allele_details[4], multi_allele] else: - return ["LNF", "allele_name", "LNF"] + return ["LNF", "-", "LNF"] def search_match_allele(self): # Create blast db with sample file @@ -205,11 +206,12 @@ def search_match_allele(self): "allele_match": {}, "allele_details": {}, "snp_data": {}, + "alignment_data": {}, } count = 0 for ref_allele in self.ref_alleles: count += 1 - print( + log.debug( " Processing allele ", ref_allele, " ", @@ -217,8 +219,7 @@ def search_match_allele(self): " of ", len(self.ref_alleles), ) - # schema_alleles = os.path.join(self.schema, ref_allele) - # parallel in all CPUs in cluster node + alleles = OrderedDict() match_found = False with open(ref_allele, "r") as fh: @@ -228,7 +229,7 @@ def search_match_allele(self): for r_id, r_seq in alleles.items(): count_2 += 1 - print("Running blast for ", count_2, " of ", len(alleles)) + log.debug("Running blast for ", count_2, " of ", len(alleles)) # create file in memory to increase speed query_file = io.StringIO() query_file.write(">" + r_id + "\n" + r_seq) @@ -265,6 +266,12 @@ def search_match_allele(self): result["snp_data"][allele_name] = taranis.utils.get_snp_position( allele_seq, alleles ) + if self.aligment_request and result["allele_type"][allele_name] == "INF": + # run alignment analysis + allele_seq = result["allele_details"][allele_name][14] + result["alignment_data"][ + allele_name + ] = taranis.utils.get_alignment_data(allele_seq, alleles) return result @@ -288,7 +295,10 @@ def parallel_execution( snp_request, aligment_request, ) - return allele_obj.search_match_allele() + sample_name = Path(sample_file).stem + stderr.print(f"[green] Analyzing sample {sample_name}") + log.info(f"Analyzing sample {sample_name}") + return {sample_name: allele_obj.search_match_allele()} def collect_data( @@ -311,7 +321,7 @@ def stats_graphics(stats_folder: str, summary_result: dict) -> None: s_list.append(sample) # create list of samples for classif, count in classif_counts.items(): classif_data[classif].append(int(count)) - # create graphics + # create graphics per each classification type for allele_type, counts in classif_data.items(): _ = taranis.utils.create_graphic( graphic_folder, @@ -322,6 +332,7 @@ def stats_graphics(stats_folder: str, summary_result: dict) -> None: ["Samples", "number"], str("Number of " + allele_type + " in samples"), ) + return summary_result_file = os.path.join(output, "allele_calling_summary.csv") sample_allele_match_file = os.path.join(output, "allele_calling_match.csv") @@ -345,8 +356,8 @@ def stats_graphics(stats_folder: str, summary_result: dict) -> None: "sequence", ] - summary_result = {} - sample_allele_match = {} + summary_result = {} # used for summary file and allele classification graphics + sample_allele_match = {} # used for allele match file # get allele list first_sample = list(results[0].keys())[0] @@ -366,7 +377,6 @@ def stats_graphics(stats_folder: str, summary_result: dict) -> None: ) summary_result[sample] = sum_allele_type sample_allele_match[sample] = allele_match - # save summary results to file with open(summary_result_file, "w") as fo: fo.write("Sample," + ",".join(allele_types) + "\n") @@ -399,10 +409,8 @@ def stats_graphics(stats_folder: str, summary_result: dict) -> None: with open(snp_file, "w") as fo: fo.write("Sample name,Locus name,Reference allele,Position,Base,Ref\n") for sample, values in result.items(): - # pdb.set_trace() for allele, snp_data in values["snp_data"].items(): for ref_allele, snp_info_list in snp_data.items(): - # pdb.set_trace() for snp_info in snp_info_list: fo.write( sample @@ -414,5 +422,21 @@ def stats_graphics(stats_folder: str, summary_result: dict) -> None: + ",".join(snp_info) + "\n" ) + # create alignment files + if aligment_request: + alignment_folder = os.path.join(output, "alignments") + _ = taranis.utils.create_new_folder(alignment_folder) + for result in results: + for sample, values in result.items(): + for allele, alignment_data in values["alignment_data"].items(): + with open( + os.path.join(alignment_folder, sample + "_" + allele + ".txt"), + "w", + ) as fo: + for ref_allele, alignments in alignment_data.items(): + fo.write(ref_allele + "\n") + for alignment in alignments: + fo.write(alignment + "\n") + # Create graphics stats_graphics(output, summary_result) diff --git a/taranis/inferred_alleles.py b/taranis/inferred_alleles.py index a985c1f..ac227ff 100644 --- a/taranis/inferred_alleles.py +++ b/taranis/inferred_alleles.py @@ -1,3 +1,6 @@ +import pdb + + class InferredAllele: def __init__(self): self.inferred_seq = {} @@ -23,6 +26,8 @@ def set_inferred_allele(self, sequence: str, allele: str) -> None: sequence (str): sequence to infer the allele allele (str): inferred allele """ - inf_value = self.last_allele_index.get(allele, 0) + 1 - self.inferred_seq[sequence] = inf_value + if allele not in self.last_allele_index: + self.last_allele_index[allele] = 0 + self.last_allele_index[allele] += 1 + self.inferred_seq[sequence] = self.last_allele_index[allele] return self.inferred_seq[sequence] diff --git a/taranis/utils.py b/taranis/utils.py index 461d4d1..8f4756c 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -63,6 +63,11 @@ def rich_force_colors(): def cpus_available() -> int: + """Get the number of cpus available in the system + + Returns: + int: number of cpus + """ return multiprocessing.cpu_count() @@ -252,11 +257,14 @@ def file_exists(file_to_check): return False +""" def find_nearest_numpy_value(array, value): array = np.asarray(array) idx = (np.abs(array - value)).argmin() return array[idx] + """ + def folder_exists(folder_to_check): """Checks if input folder exists @@ -272,6 +280,28 @@ def folder_exists(folder_to_check): return False +def get_alignment_data(allele_sequence: str, ref_sequences: dict[str]) -> dict: + """Get the alignment data between the allele sequence and the reference alleles + + Args: + allele_sequence (str): sequence to be compared + ref_sequences (dict): sequences of reference alleles + + Returns: + dict: key: ref_sequence, value: alignment data + """ + alignment_data = {} + for ref_allele, ref_sequence in ref_sequences.items(): + alignment = "" + for idx, (a, b) in enumerate(zip(allele_sequence, ref_sequence)): + if a == b: + alignment += "|" + else: + alignment += " " + alignment_data[ref_allele] = [ref_sequence, alignment, allele_sequence] + return alignment_data + + def get_files_in_folder(folder: str, extension: str = None) -> list[str]: """get the list of files, filtered by extension in the input folder. If extension is not set, then all files in folder are returned @@ -332,7 +362,7 @@ def grep_execution(input_file: str, pattern: str, parameters: str) -> list[str]: text=True, ) except subprocess.CalledProcessError as e: - log.error("Unable to run grep. Error message: %s ", e) + log.debug("Unable to run grep. Error message: %s ", e) return [] return result.stdout.split("\n") From a93d30065f21271af12fe1618ba03280e4919aec Mon Sep 17 00:00:00 2001 From: luissian Date: Fri, 22 Mar 2024 08:39:26 +0100 Subject: [PATCH 129/214] correcting litin --- taranis/__main__.py | 1 - taranis/allele_calling.py | 6 +++--- taranis/inferred_alleles.py | 3 --- taranis/utils.py | 3 +-- 4 files changed, 4 insertions(+), 9 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index 39d26cf..62282c5 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -15,7 +15,6 @@ import taranis.allele_calling import taranis.inferred_alleles -from pathlib import Path log = logging.getLogger() diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index b6a0307..2458c0c 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -269,9 +269,9 @@ def search_match_allele(self): if self.aligment_request and result["allele_type"][allele_name] == "INF": # run alignment analysis allele_seq = result["allele_details"][allele_name][14] - result["alignment_data"][ - allele_name - ] = taranis.utils.get_alignment_data(allele_seq, alleles) + result["alignment_data"][allele_name] = ( + taranis.utils.get_alignment_data(allele_seq, alleles) + ) return result diff --git a/taranis/inferred_alleles.py b/taranis/inferred_alleles.py index ac227ff..cac448a 100644 --- a/taranis/inferred_alleles.py +++ b/taranis/inferred_alleles.py @@ -1,6 +1,3 @@ -import pdb - - class InferredAllele: def __init__(self): self.inferred_seq = {} diff --git a/taranis/utils.py b/taranis/utils.py index 8f4756c..9845e3d 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -4,7 +4,6 @@ import io import logging import multiprocessing -import numpy as np import os import pandas as pd import plotly.graph_objects as go @@ -257,7 +256,7 @@ def file_exists(file_to_check): return False -""" +""" def find_nearest_numpy_value(array, value): array = np.asarray(array) idx = (np.abs(array - value)).argmin() From a03848e9b31c51246a9b5392b910ae17fcfd9a5b Mon Sep 17 00:00:00 2001 From: luissian Date: Sat, 23 Mar 2024 11:00:07 +0100 Subject: [PATCH 130/214] removing comma at the end onf line in allele_calling_match file --- taranis/allele_calling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 2458c0c..7252168 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -391,7 +391,7 @@ def stats_graphics(stats_folder: str, summary_result: dict) -> None: for sample, allele_cod in sample_allele_match.items(): fo.write(f"{sample},") for allele in allele_list: - fo.write(f"{allele_cod[allele]},") + fo.write(f"{allele_cod[allele]}") fo.write("\n") with open(sample_allele_detail_file, "w") as fo: From a2f35112192089f2c5c4ea87cc8ecba0ce066814 Mon Sep 17 00:00:00 2001 From: luissian Date: Sun, 24 Mar 2024 20:49:29 +0100 Subject: [PATCH 131/214] added comment changes at PR 17 --- taranis/__main__.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index 62282c5..a72c9de 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -424,13 +424,13 @@ def reference_alleles( "--snp/--no-snp", required=False, default=False, - help="Create SNP file for alleles that are infered INF", + help="Create SNP file for alleles in assembly in relation with reference allele", ) @click.option( "--alignment/--no-alignment", required=False, default=False, - help="Create aligment file for Overwrite the output folder if it exists", + help="Create alignment files", ) @click.option( "--cpus", @@ -475,7 +475,7 @@ def allele_calling( ) # Create the instanace for inference alleles inf_allele_obj = taranis.inferred_alleles.InferredAllele() - """Analyze the sample file against schema to identify outbreakers + """Analyze the sample file against schema to identify alleles """ start = time.perf_counter() From c41824cc4ef9d0922d819c2c5188f85a8e1fdd09 Mon Sep 17 00:00:00 2001 From: luissian Date: Sun, 24 Mar 2024 21:45:18 +0100 Subject: [PATCH 132/214] adding docstring and include threshold parameter --- taranis/allele_calling.py | 67 +++++++++++++++++++++++++++++---------- 1 file changed, 51 insertions(+), 16 deletions(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 7252168..91b9b3e 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -26,15 +26,30 @@ def __init__( schema: str, annotation: dict, reference_alleles: list, + threshold: float, out_folder: str, inf_alle_obj: object, snp_request: bool = False, aligment_request: bool = False, ): + """Allele calling initial creation object + + Args: + sample_file (str): assembly file + schema (str): folder with alleles schema + annotation (dict): annotation of locus according to prokka + reference_alleles (list): folder with reference alleles + threshold (float): threshold to consider a match in blast + out_folder (str): output folder + inf_alle_obj (object): object to infer alleles + snp_request (bool, optional): snp saved to file. Defaults to False. + aligment_request (bool, optional): allignment saved to file. Defaults to False. + """ self.prediction_data = annotation # store prediction annotation self.sample_file = sample_file self.schema = schema self.ref_alleles = reference_alleles + self.threshold = threshold self.out_folder = out_folder self.s_name = Path(sample_file).stem self.blast_dir = os.path.join(out_folder, "blastdb") @@ -49,7 +64,27 @@ def __init__( def assign_allele_type( self, blast_result: list, allele_file: str, allele_name: str ) -> list: + """Assign allele type to the allele + + Args: + blast_result (list): information collected by running blast + allele_file (str): file name with allele sequence + allele_name (str): allele name + + Returns: + list: containing allele classification, allele name and allele details + """ + def get_blast_details(blast_result: list, allele_name: str) -> list: + """Collect blast details and modify the order of the columns + + Args: + blast_result (list): information collected by running blast + allele_name (str): allele name + + Returns: + list: containing allele details in the correct order to be saved + """ match_allele_name = blast_result[0] try: gene_annotation = self.prediction_data[match_allele_name]["gene"] @@ -72,8 +107,8 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: allele_name, # core gene name blast_result[0], # allele gene "coding", # coding allele type. To be filled later idx = 4 - blast_result[3], # query length - blast_result[4], # match length + blast_result[3], # reference allele length + blast_result[4], # alignment length blast_result[14], # contig length blast_result[9], # contig start blast_result[10], # contig end @@ -109,13 +144,13 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: if match_full_length >= 2: # labled as NIPHEM if all alleles are in the same contig allele_details[4] = "NIPHEM_" + allele_details[3] - clasification = "NIPHEM" + classification = "NIPHEM" else: # labled as NIPH if all alleles are in different contigs allele_details[4] = "NIPH_" + allele_details[3] - clasification = "NIPH" + classification = "NIPH" multi_allele.append(allele_details) - return [clasification, allele_name, multi_allele] + return [classification, allele_name, multi_allele] elif len(valid_blast_result) == 1: column_blast_res = blast_result[0].split("\t") @@ -133,7 +168,7 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: allele_details[4] = "EXC_" + allele_details[3] return ["EXC", allele_name, allele_details] # check if contig is shorter than allele - if int(column_blast_res[3]) > int(column_blast_res[4]): + if int(column_blast_res[3]) > int(get_blast_detailscolumn_blast_res[4]): # check if sequence is shorter because it starts or ends at the contig if ( column_blast_res[9] == "1" # check at contig start @@ -173,7 +208,7 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: # it starts/ends at the contig. Then it is labled as PLOT multi_allele = [] - clasification = "" + classification = "" for b_result in blast_result: column_blast_res = b_result.split("\t") query_length = int(column_blast_res[4]) / int(column_blast_res[3]) @@ -192,9 +227,9 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: # allele is labled as PLOT allele_details[4] = "PLOT_" + allele_details[3] multi_allele.append(allele_details) - clasification = "PLOT" - if clasification == "PLOT": - return [clasification, allele_details[4], multi_allele] + classification = "PLOT" + if classification == "PLOT": + return [classification, allele_details[4], multi_allele] else: return ["LNF", "-", "LNF"] @@ -237,7 +272,7 @@ def search_match_allele(self): blast_result = self.blast_obj.run_blast( query_file.read(), perc_identity=90, - num_threads=4, + num_threads=1, query_type="stdin", ) if len(blast_result) > 0: @@ -269,9 +304,9 @@ def search_match_allele(self): if self.aligment_request and result["allele_type"][allele_name] == "INF": # run alignment analysis allele_seq = result["allele_details"][allele_name][14] - result["alignment_data"][allele_name] = ( - taranis.utils.get_alignment_data(allele_seq, alleles) - ) + result["alignment_data"][ + allele_name + ] = taranis.utils.get_alignment_data(allele_seq, alleles) return result @@ -389,9 +424,9 @@ def stats_graphics(stats_folder: str, summary_result: dict) -> None: with open(sample_allele_match_file, "w") as fo: fo.write("Sample," + ",".join(allele_list) + "\n") for sample, allele_cod in sample_allele_match.items(): - fo.write(f"{sample},") + fo.write(f"{sample}") for allele in allele_list: - fo.write(f"{allele_cod[allele]}") + fo.write(f",{allele_cod[allele]}") fo.write("\n") with open(sample_allele_detail_file, "w") as fo: From d660401de8dd40b9098b995eba9c30c1f8c1f7f4 Mon Sep 17 00:00:00 2001 From: luissian Date: Sun, 24 Mar 2024 21:49:33 +0100 Subject: [PATCH 133/214] remove the fix value and assign it to threshold parameter --- taranis/allele_calling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 91b9b3e..f6ff6b6 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -127,7 +127,7 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: for b_result in blast_result: column_blast_res = b_result.split("\t") query_length = int(column_blast_res[4]) / int(column_blast_res[3]) - if query_length >= 0.8: + if query_length >= self.threshold: valid_blast_result.append(b_result) if query_length == 1: match_full_length += 1 From 2ab64e7eebdc439ee2d6235878f3794ccc7f3abe Mon Sep 17 00:00:00 2001 From: luissian Date: Tue, 26 Mar 2024 00:40:47 +0100 Subject: [PATCH 134/214] included reference allele sequence --- taranis/blast.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/taranis/blast.py b/taranis/blast.py index 7458752..74ad732 100644 --- a/taranis/blast.py +++ b/taranis/blast.py @@ -96,7 +96,7 @@ def run_blast( if query_type == "stdin": stdin_query = query query = "-" - blast_parameters = '"6 , qseqid , sseqid , pident , qlen , length , mismatch , gapopen , evalue , bitscore , sstart , send , qstart , qend , sseq , slen"' + blast_parameters = '"6 , qseqid , sseqid , pident , qlen , length , mismatch , gapopen , evalue , bitscore , sstart , send , qstart , qend , sseq , qseq , slen"' cline = NcbiblastnCommandline( task="blastn", db=self.out_blast_dir, From 03fed1912d5e6f3994762bbd62b5fc7c736761ea Mon Sep 17 00:00:00 2001 From: luissian Date: Tue, 26 Mar 2024 00:41:44 +0100 Subject: [PATCH 135/214] prevent that 2 instances call the method at the same time --- taranis/inferred_alleles.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/taranis/inferred_alleles.py b/taranis/inferred_alleles.py index cac448a..0c60ba1 100644 --- a/taranis/inferred_alleles.py +++ b/taranis/inferred_alleles.py @@ -1,7 +1,11 @@ +import threading + + class InferredAllele: def __init__(self): self.inferred_seq = {} self.last_allele_index = {} + self.lock = threading.Lock() # Create a lock object def get_inferred_allele(self, sequence: str, allele: str) -> str: """Infer allele from the sequence @@ -12,9 +16,10 @@ def get_inferred_allele(self, sequence: str, allele: str) -> str: Returns: str: inferred allele """ - if sequence not in self.inferred_seq: - return self.set_inferred_allele(sequence, allele) - return self.inferred_seq[sequence] + with self.lock: # Acquire the lock + if sequence not in self.inferred_seq: + return self.set_inferred_allele(sequence, allele) + return self.inferred_seq[sequence] def set_inferred_allele(self, sequence: str, allele: str) -> None: """Set the inferred allele for the sequence From 334db03d96059bee070fceba5e88b0cc00023d38 Mon Sep 17 00:00:00 2001 From: luissian Date: Tue, 26 Mar 2024 00:43:07 +0100 Subject: [PATCH 136/214] add threshold parameter --- taranis/__main__.py | 26 +++++++++++++++++++++++++- 1 file changed, 25 insertions(+), 1 deletion(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index a72c9de..db1909a 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -399,6 +399,15 @@ def reference_alleles( type=click.Path(exists=True), help="Annotation file. ", ) +@click.option( + "-t", + "--threshold", + required=False, + nargs=1, + default=0.8, + type=float, + help="Threshold value to consider in blast. Values from 0 to 1. default 0.8", +) @click.option( "-o", "--output", @@ -445,6 +454,7 @@ def allele_calling( reference: str, annotation: str, assemblies: list, + threshold: float, output: str, force: bool, snp: bool, @@ -480,6 +490,7 @@ def allele_calling( start = time.perf_counter() results = [] + """ with concurrent.futures.ThreadPoolExecutor(max_workers=cpus) as executor: futures = [ executor.submit( @@ -488,6 +499,7 @@ def allele_calling( schema, prediction_data, schema_ref_files, + threshold, output, inf_allele_obj, snp, @@ -501,7 +513,19 @@ def allele_calling( except Exception as e: print(e) continue - + """ + import pdb; pdb.set_trace() + results = taranis.allele_calling.parallel_execution( + assemblies[0], + schema, + prediction_data, + schema_ref_files, + threshold, + output, + inf_allele_obj, + snp, + alignment, + ) _ = taranis.allele_calling.collect_data(results, output, snp, alignment) finish = time.perf_counter() print(f"Allele calling finish in {round((finish-start)/60, 2)} minutes") From 6692e2005528e93a5b2d3784e55aeda4911ed1c6 Mon Sep 17 00:00:00 2001 From: luissian Date: Wed, 27 Mar 2024 13:38:58 +0100 Subject: [PATCH 137/214] re-writing the classification alleles --- taranis/allele_calling.py | 279 +++++++++++++++++++++----------------- 1 file changed, 157 insertions(+), 122 deletions(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index f6ff6b6..71e60b8 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -9,6 +9,7 @@ from collections import OrderedDict from pathlib import Path from Bio import SeqIO +import pdb log = logging.getLogger(__name__) stderr = rich.console.Console( @@ -62,7 +63,7 @@ def __init__( self.aligment_request = aligment_request def assign_allele_type( - self, blast_result: list, allele_file: str, allele_name: str + self, blast_results: list, allele_file: str, allele_name: str ) -> list: """Assign allele type to the allele @@ -72,20 +73,77 @@ def assign_allele_type( allele_name (str): allele name Returns: - list: containing allele classification, allele name and allele details + list: containing allele classification, allele match id, and allele + details """ - def get_blast_details(blast_result: list, allele_name: str) -> list: + def check_if_plot(column_blast_res: list) -> bool: + """Check if allele is partial length + + Args: + column_blast_res (list): blast result + + Returns: + bool: True if allele is partial length + """ + if ( + column_blast_res[9] == "1" # check at contig start + # check if contig ends is the same as match allele ends + or column_blast_res[15] == column_blast_res[10] + or column_blast_res[10] == "1" # check reverse at contig end + # check if contig start is the same as match allele start reverse + or column_blast_res[9] == column_blast_res[15] + ): + return True + return False + + def discard_low_threshold_results(blast_results: list) -> list: + """Discard blast results with lower threshold + + Args: + blast_results (list): blast results + + Returns: + list: blast results with higher query size + """ + valid_blast_result = [] + for b_result in blast_results: + blast_split = b_result.split("\t") + # check if the division of the match contig length by the + # reference allele length is higher than the threshold + # pdb.set_trace() + if (int(blast_split[4]) / int(blast_split[3])) >= self.threshold: + valid_blast_result.append(b_result) + return valid_blast_result + + def get_blast_details(blast_result: str, allele_name: str) -> list: """Collect blast details and modify the order of the columns Args: - blast_result (list): information collected by running blast + blast_result (str): information collected by running blast allele_name (str): allele name Returns: list: containing allele details in the correct order to be saved + blast_details[0] = sample name + blast_details[1] = contig name + blast_details[2] = core gene name + blast_details[3] = allele gene + blast_details[4] = coding allele type + blast_details[5] = reference allele length + blast_details[6] = match alignment length + blast_details[7] = contig length + blast_details[8] = match contig position start + blast_details[9] = match contig position end + blast_details[10] = direction + blast_details[11] = gene annotation + blast_details[12] = product annotation + blast_details[13] = allele quality + blast_details[14] = match sequence in contig + blast_details[15] = reference allele sequence """ - match_allele_name = blast_result[0] + split_blast_result = blast_result.split("\t") + match_allele_name = split_blast_result[0] try: gene_annotation = self.prediction_data[match_allele_name]["gene"] product_annotation = self.prediction_data[match_allele_name]["product"] @@ -96,142 +154,115 @@ def get_blast_details(blast_result: list, allele_name: str) -> list: gene_annotation = "Not found" product_annotation = "Not found" allele_quality = "Not found" - if int(blast_result[10]) > int(blast_result[9]): + # pdb.set_trace() + if int(split_blast_result[10]) > int(split_blast_result[9]): direction = "+" else: direction = "-" # get blast details blast_details = [ self.s_name, # sample name - blast_result[1], # contig + split_blast_result[1], # contig name allele_name, # core gene name - blast_result[0], # allele gene + split_blast_result[0], # allele gene "coding", # coding allele type. To be filled later idx = 4 - blast_result[3], # reference allele length - blast_result[4], # alignment length - blast_result[14], # contig length - blast_result[9], # contig start - blast_result[10], # contig end + split_blast_result[3], # reference allele length + split_blast_result[4], # match alignment length + split_blast_result[15], # contig length + split_blast_result[9], # match contig position start + split_blast_result[10], # match contig position end direction, gene_annotation, product_annotation, allele_quality, - blast_result[13], # contig sequence + split_blast_result[13], # match sequence in contig + split_blast_result[15], # reference allele sequence ] - + # pdb.set_trace() return blast_details - valid_blast_result = [] - match_full_length = 0 - match_partial_length = 0 - for b_result in blast_result: - column_blast_res = b_result.split("\t") - query_length = int(column_blast_res[4]) / int(column_blast_res[3]) - if query_length >= self.threshold: - valid_blast_result.append(b_result) - if query_length == 1: - match_full_length += 1 - else: - match_partial_length += 1 - - if len(valid_blast_result) > 1: - # allele could be named as NIPHEM or NIPH - multi_allele = [] - for valid_result in valid_blast_result: - column_blast_res = valid_result.split("\t") - column_blast_res[13] = column_blast_res[13].replace("-", "") - allele_details = get_blast_details(column_blast_res, allele_name) - if match_full_length >= 2: - # labled as NIPHEM if all alleles are in the same contig - allele_details[4] = "NIPHEM_" + allele_details[3] - classification = "NIPHEM" - else: - # labled as NIPH if all alleles are in different contigs - allele_details[4] = "NIPH_" + allele_details[3] - classification = "NIPH" - multi_allele.append(allele_details) - return [classification, allele_name, multi_allele] + def find_match_allele_schema(allele_file: str, match_sequence: str) -> str: + """Find the allele name in the schema that match the sequence - elif len(valid_blast_result) == 1: - column_blast_res = blast_result[0].split("\t") - column_blast_res[13] = column_blast_res[13].replace("-", "") - allele_details = get_blast_details(column_blast_res, allele_name) + Args: + allele_file (str): file with allele sequences + match_sequence (str): sequence to be matched + Returns: + str: allele name in the schema that match the sequence + """ grep_result = taranis.utils.grep_execution( - allele_file, column_blast_res[13], "-b1" + allele_file, match_sequence, "-b1" ) - # check if sequence match alleles in schema if len(grep_result) > 0: - allele_name = grep_result[0].split(">")[1] - - # allele is labled as EXACT - allele_details[4] = "EXC_" + allele_details[3] - return ["EXC", allele_name, allele_details] - # check if contig is shorter than allele - if int(column_blast_res[3]) > int(get_blast_detailscolumn_blast_res[4]): - # check if sequence is shorter because it starts or ends at the contig - if ( - column_blast_res[9] == "1" # check at contig start - or column_blast_res[14] - == column_blast_res[10] # check at contig end - or column_blast_res[10] == "1" # check reverse at contig end - or column_blast_res[9] - == column_blast_res[14] # check reverse at contig start - ): - # allele is labled as PLOT - allele_details[4] = "PLOT_" + allele_details[3] - return ["PLOT", allele_details[3], allele_details] - # allele is labled as ASM - allele_details[4] = "ASM_" + allele_details[3] - return ["ASM", allele_details[3], allele_details] - # check if contig is longer than allele - if int(column_blast_res[3]) < int(column_blast_res[4]): - # allele is labled as ALM - allele_details[4] = "ALM_" + allele_details[3] - return ["ALM", allele_details[3], allele_details] - if int(column_blast_res[3]) == int(column_blast_res[4]): - # allele is labled as INF - - allele_details[3] = ( - allele_name - + "_" - + str( - self.inf_alle_obj.get_inferred_allele( - column_blast_res[13], allele_name - ) + return grep_result[0].split("_")[1] + return "" + + valid_blast_results = discard_low_threshold_results(blast_results) + match_allele_schema = "" + if len(valid_blast_results) == 0: + # no match results labelled as LNF. details data filled with empty data + return ["LNF", "-", ["-," * 15]] + if len(valid_blast_results) > 1: + # could be NIPHEM or NIPH + b_split_data = [] + match_allele_seq = [] + for valid_blast_result in valid_blast_results: + multi_allele_data = get_blast_details(valid_blast_result, allele_name) + # get match allele sequence + match_allele_seq.append(multi_allele_data[14]) + b_split_data.append(multi_allele_data) + # check if match allele is in schema + if match_allele_schema == "": + # find the allele in schema with the match sequence in the contig + match_allele_schema = find_match_allele_schema( + allele_file, multi_allele_data[14] ) - ) - allele_details[4] = "INF_" + allele_details[3] - return ["INF", allele_details[3], allele_details] + if len(set(match_allele_seq)) == 1: + # all sequuences are equal labelled as NIPHEM + classification = "NIPHEM" + else: + # some of the sequences are different labelled as NIPH + classification = "NIPH" + # update coding allele type + for (idx,) in range(len(b_split_data)): + b_split_data[idx][4] = classification + "_" + match_allele_schema else: - # analyze again the blast result to check with lower query size, 0.75 - # it starts/ends at the contig. Then it is labled as PLOT - - multi_allele = [] - classification = "" - for b_result in blast_result: - column_blast_res = b_result.split("\t") - query_length = int(column_blast_res[4]) / int(column_blast_res[3]) - if query_length >= 0.75: - if ( - column_blast_res[9] == "1" # check at contig start - or column_blast_res[14] - == column_blast_res[10] # check at contig end - or column_blast_res[10] == "1" # check reverse at contig end - or column_blast_res[9] - == column_blast_res[14] # check reverse at contig start - ): - allele_details = get_blast_details( - column_blast_res, allele_name - ) - # allele is labled as PLOT - allele_details[4] = "PLOT_" + allele_details[3] - multi_allele.append(allele_details) - classification = "PLOT" - if classification == "PLOT": - return [classification, allele_details[4], multi_allele] + b_split_data = get_blast_details(valid_blast_results[0], allele_name) + # found the allele in schema with the match sequence in the contig + match_allele_schema = find_match_allele_schema( + allele_file, b_split_data[14] + ) + # PLOT, ASM, ALM, INF, EXC are possible classifications + if check_if_plot(b_split_data): + # match allele is partial length labelled as PLOT + classification = "PLOT" + + # check if match allele is shorter than reference allele + elif int(b_split_data[5]) < int(b_split_data[6]): + classification = "ASM" + # check if match allele is longer than reference allele + elif int(b_split_data[5]) > int(b_split_data[6]): + classification = "ALM" else: - return ["LNF", "-", "LNF"] + # if sequence was not found after running grep labelled as INF + if match_allele_schema == "": + classification = "INF" + else: + # exact match found labelled as EXC + classification = "EXC" + # assign an identification value to the new allele + if match_allele_schema == "": + match_allele_schema = str( + self.inf_alle_obj.get_inferred_allele(b_split_data[14], allele_name) + ) + # pdb.set_trace() + b_split_data[4] = classification + "_" + match_allele_schema + return [ + classification, + classification + "_" + match_allele_schema, + b_split_data, + ] def search_match_allele(self): # Create blast db with sample file @@ -315,6 +346,7 @@ def parallel_execution( schema: str, prediction_data: dict, reference_alleles: list, + threshold: float, out_folder: str, inf_alle_obj: object, snp_request: bool = False, @@ -325,6 +357,7 @@ def parallel_execution( schema, prediction_data, reference_alleles, + threshold, out_folder, inf_alle_obj, snp_request, @@ -377,10 +410,10 @@ def stats_graphics(stats_folder: str, summary_result: dict) -> None: "sample", "contig", "core gene", - "allele name", + "reference allele name", "codification", - "query lenght", - "match lengt", + "query length", + "match length", "contig length", "contig start", "contig stop", @@ -395,6 +428,7 @@ def stats_graphics(stats_folder: str, summary_result: dict) -> None: sample_allele_match = {} # used for allele match file # get allele list + # pdb.set_trace() first_sample = list(results[0].keys())[0] allele_list = sorted(results[0][first_sample]["allele_type"].keys()) for result in results: @@ -408,7 +442,8 @@ def stats_graphics(stats_folder: str, summary_result: dict) -> None: sum_allele_type[type_of_allele] += 1 # add allele name match to sample allele_match[allele] = ( - type_of_allele + "_" + values["allele_match"][allele] + # type_of_allele + "_" + values["allele_match"][allele] + values["allele_match"][allele] ) summary_result[sample] = sum_allele_type sample_allele_match[sample] = allele_match From a2e779dfeb77d8879fb6ec7fd1b4126a5582e8b5 Mon Sep 17 00:00:00 2001 From: luissian Date: Wed, 27 Mar 2024 15:19:33 +0100 Subject: [PATCH 138/214] implemented percentage identity as parameter, default is 90 --- taranis/__main__.py | 35 ++++++++++++++++++++++++----------- taranis/allele_calling.py | 7 +++++-- 2 files changed, 29 insertions(+), 13 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index db1909a..49126c6 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -408,6 +408,15 @@ def reference_alleles( type=float, help="Threshold value to consider in blast. Values from 0 to 1. default 0.8", ) +@click.option( + "-p", + "--perc-identity", + required=False, + nargs=1, + default=90, + type=int, + help="Percentage of identity to consider in blast. default 90", +) @click.option( "-o", "--output", @@ -455,6 +464,7 @@ def allele_calling( annotation: str, assemblies: list, threshold: float, + perc_identity: int, output: str, force: bool, snp: bool, @@ -500,6 +510,7 @@ def allele_calling( prediction_data, schema_ref_files, threshold, + perc_identity, output, inf_allele_obj, snp, @@ -514,17 +525,19 @@ def allele_calling( print(e) continue """ - import pdb; pdb.set_trace() - results = taranis.allele_calling.parallel_execution( - assemblies[0], - schema, - prediction_data, - schema_ref_files, - threshold, - output, - inf_allele_obj, - snp, - alignment, + results.append( + taranis.allele_calling.parallel_execution( + assemblies[0], + schema, + prediction_data, + schema_ref_files, + threshold, + perc_identity, + output, + inf_allele_obj, + snp, + alignment, + ) ) _ = taranis.allele_calling.collect_data(results, output, snp, alignment) finish = time.perf_counter() diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 71e60b8..4a45be8 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -28,6 +28,7 @@ def __init__( annotation: dict, reference_alleles: list, threshold: float, + perc_identity: int, out_folder: str, inf_alle_obj: object, snp_request: bool = False, @@ -51,6 +52,7 @@ def __init__( self.schema = schema self.ref_alleles = reference_alleles self.threshold = threshold + self.perc_identity = perc_identity self.out_folder = out_folder self.s_name = Path(sample_file).stem self.blast_dir = os.path.join(out_folder, "blastdb") @@ -302,7 +304,7 @@ def search_match_allele(self): query_file.seek(0) blast_result = self.blast_obj.run_blast( query_file.read(), - perc_identity=90, + perc_identity=self.perc_identity, num_threads=1, query_type="stdin", ) @@ -313,7 +315,6 @@ def search_match_allele(self): query_file.close() if match_found: allele_file = os.path.join(self.schema, os.path.basename(ref_allele)) - # blast_result = self.blast_obj.run_blast(q_file,perc_identity=100) allele_name = Path(allele_file).stem ( result["allele_type"][allele_name], @@ -347,6 +348,7 @@ def parallel_execution( prediction_data: dict, reference_alleles: list, threshold: float, + perc_identity: int, out_folder: str, inf_alle_obj: object, snp_request: bool = False, @@ -358,6 +360,7 @@ def parallel_execution( prediction_data, reference_alleles, threshold, + perc_identity, out_folder, inf_alle_obj, snp_request, From e3996b8d0ec764a6569ee778f4ea19309a046a79 Mon Sep 17 00:00:00 2001 From: luissian Date: Wed, 27 Mar 2024 20:58:49 +0100 Subject: [PATCH 139/214] update the snp implementation including new fields in the snp output file --- taranis/allele_calling.py | 27 +++++++------ taranis/utils.py | 85 +++++++++++++++++++++++++++++++++------ 2 files changed, 87 insertions(+), 25 deletions(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 4a45be8..4ad751e 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -9,7 +9,6 @@ from collections import OrderedDict from pathlib import Path from Bio import SeqIO -import pdb log = logging.getLogger(__name__) stderr = rich.console.Console( @@ -113,7 +112,6 @@ def discard_low_threshold_results(blast_results: list) -> list: blast_split = b_result.split("\t") # check if the division of the match contig length by the # reference allele length is higher than the threshold - # pdb.set_trace() if (int(blast_split[4]) / int(blast_split[3])) >= self.threshold: valid_blast_result.append(b_result) return valid_blast_result @@ -156,11 +154,13 @@ def get_blast_details(blast_result: str, allele_name: str) -> list: gene_annotation = "Not found" product_annotation = "Not found" allele_quality = "Not found" - # pdb.set_trace() if int(split_blast_result[10]) > int(split_blast_result[9]): direction = "+" else: direction = "-" + # remove the gaps in sequences + match_sequence = split_blast_result[13].replace("-", "") + reference_sequence = split_blast_result[14].replace("-", "") # get blast details blast_details = [ self.s_name, # sample name @@ -177,10 +177,9 @@ def get_blast_details(blast_result: str, allele_name: str) -> list: gene_annotation, product_annotation, allele_quality, - split_blast_result[13], # match sequence in contig - split_blast_result[15], # reference allele sequence + match_sequence, # match sequence in contig + reference_sequence, # reference allele sequence ] - # pdb.set_trace() return blast_details def find_match_allele_schema(allele_file: str, match_sequence: str) -> str: @@ -258,7 +257,6 @@ def find_match_allele_schema(allele_file: str, match_sequence: str) -> str: match_allele_schema = str( self.inf_alle_obj.get_inferred_allele(b_split_data[14], allele_name) ) - # pdb.set_trace() b_split_data[4] = classification + "_" + match_allele_schema return [ classification, @@ -327,13 +325,15 @@ def search_match_allele(self): result["allele_type"][allele_name] = "LNF" result["allele_match"][allele_name] = allele_name result["allele_details"][allele_name] = "LNF" - if self.snp_request and result["allele_type"][allele_name] == "INF": + if self.snp_request and result["allele_type"][allele_name] != "LNF": # run snp analysis + ref_allele_seq = result["allele_details"][allele_name][15] allele_seq = result["allele_details"][allele_name][14] - result["snp_data"][allele_name] = taranis.utils.get_snp_position( - allele_seq, alleles + ref_allele_name = result["allele_details"][allele_name][3] + result["snp_data"][allele_name] = taranis.utils.get_snp_information( + ref_allele_seq, allele_seq, ref_allele_name ) - if self.aligment_request and result["allele_type"][allele_name] == "INF": + if self.aligment_request and result["allele_type"][allele_name] != "LNF": # run alignment analysis allele_seq = result["allele_details"][allele_name][14] result["alignment_data"][ @@ -431,7 +431,6 @@ def stats_graphics(stats_folder: str, summary_result: dict) -> None: sample_allele_match = {} # used for allele match file # get allele list - # pdb.set_trace() first_sample = list(results[0].keys())[0] allele_list = sorted(results[0][first_sample]["allele_type"].keys()) for result in results: @@ -480,7 +479,9 @@ def stats_graphics(stats_folder: str, summary_result: dict) -> None: if snp_request: snp_file = os.path.join(output, "snp_data.csv") with open(snp_file, "w") as fo: - fo.write("Sample name,Locus name,Reference allele,Position,Base,Ref\n") + fo.write( + "Sample name,Locus name,Reference allele,Position,Ref,Alt,Codon Ref,Codon Alt,Amino Ref,Amino Alt,Category Ref,Category Alt\n" + ) for sample, values in result.items(): for allele, snp_data in values["snp_data"].items(): for ref_allele, snp_info_list in snp_data.items(): diff --git a/taranis/utils.py b/taranis/utils.py index 9845e3d..d146322 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -21,6 +21,7 @@ from pathlib import Path from Bio import SeqIO +from Bio.Seq import Seq log = logging.getLogger(__name__) @@ -322,24 +323,55 @@ def get_files_in_folder(folder: str, extension: str = None) -> list[str]: return glob.glob(folder_files) -def get_snp_position(allele_sequence: str, ref_sequences: dict[str]) -> dict[list[str]]: - """Get the snp position between the allele sequence and the reference alleles +def get_snp_information( + ref_sequence: str, alt_sequence: str, ref_allele_name +) -> dict[list[str]]: + """Get the snp information between the reference allele sequence and the + allele sequence in sample. + It collects; position of snp, nucleotide changed reference/alternative, + triplet code (belongs the change), amino acid change and category of + amino acid Args: - allele_sequence (str): sequence to be compared ref_sequences (dict): sequences of reference alleles + allele_sequence (str): sequence to be compared Returns: - dict: key: ref_sequence, value: list of snp position + dict: key: ref_sequence, value: list of snp information """ - snp_data = {} - for ref_allele, ref_sequence in ref_sequences.items(): - snp_position = [] - for idx, (a, b) in enumerate(zip(allele_sequence, ref_sequence)): - if a != b: - snp_position.append([str(idx), a, b]) - snp_data[ref_allele] = snp_position - return snp_data + snp_info = {} + ref_protein = str(Seq(ref_sequence).translate()) + alt_protein = str(Seq(alt_sequence).translate()) + + snp_line = [] + for idx, (ref, alt) in enumerate(zip(ref_sequence, alt_sequence)): + if alt != ref: + # calculate the triplet index + triplet_idx = idx // 3 + # get triplet code + ref_triplet = ref_sequence[triplet_idx * 3 : triplet_idx * 3 + 3] + alt_triplet = alt_sequence[triplet_idx * 3 : triplet_idx * 3 + 3] + # get amino acid change + ref_aa = ref_protein[triplet_idx] + alt_aa = alt_protein[triplet_idx] + # get amino acid category + ref_category = map_amino_acid_to_annotation(ref_sequence[triplet_idx]) + alt_category = map_amino_acid_to_annotation(alt_sequence[triplet_idx]) + snp_line.append( + [ + str(idx), + ref, + alt, + ref_triplet, + alt_triplet, + ref_aa, + alt_aa, + ref_category, + alt_category, + ] + ) + snp_info[ref_allele_name] = snp_line + return snp_info def grep_execution(input_file: str, pattern: str, parameters: str) -> list[str]: @@ -366,6 +398,35 @@ def grep_execution(input_file: str, pattern: str, parameters: str) -> list[str]: return result.stdout.split("\n") +def map_amino_acid_to_annotation(amino_acid): + # Dictionary mapping amino acids to their categories + amino_acid_categories = { + "A": "Nonpolar", + "C": "Polar", + "D": "Acidic", + "E": "Acidic", + "F": "Nonpolar", + "G": "Nonpolar", + "H": "Basic", + "I": "Nonpolar", + "K": "Basic", + "L": "Nonpolar", + "M": "Nonpolar", + "N": "Polar", + "P": "Nonpolar", + "Q": "Polar", + "R": "Basic", + "S": "Polar", + "T": "Polar", + "V": "Nonpolar", + "W": "Nonpolar", + "Y": "Polar", + } + + # Return the category of the given amino acid + return amino_acid_categories.get(amino_acid, "Unknown") + + def prompt_text(msg): source = questionary.text(msg).unsafe_ask() return source From 1c6e8cf031949d0e897672abd2c36eea25ba01d4 Mon Sep 17 00:00:00 2001 From: luissian Date: Wed, 27 Mar 2024 21:02:22 +0100 Subject: [PATCH 140/214] solving liting --- taranis/allele_calling.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 4ad751e..0fb3762 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -336,9 +336,9 @@ def search_match_allele(self): if self.aligment_request and result["allele_type"][allele_name] != "LNF": # run alignment analysis allele_seq = result["allele_details"][allele_name][14] - result["alignment_data"][ - allele_name - ] = taranis.utils.get_alignment_data(allele_seq, alleles) + result["alignment_data"][allele_name] = ( + taranis.utils.get_alignment_data(allele_seq, alleles) + ) return result From 72cf5c81708d695879c01a6af5373de4638d903d Mon Sep 17 00:00:00 2001 From: luissian Date: Fri, 29 Mar 2024 13:17:51 +0100 Subject: [PATCH 141/214] Partial implementation of multi alignment feature --- environment.yml | 1 + taranis/__main__.py | 4 +- taranis/allele_calling.py | 79 +++++++++++++++++++++++++++++++++++---- taranis/utils.py | 55 ++++++++++++++++++++++----- 4 files changed, 121 insertions(+), 18 deletions(-) diff --git a/environment.yml b/environment.yml index a2ef128..1d45d06 100644 --- a/environment.yml +++ b/environment.yml @@ -10,6 +10,7 @@ dependencies: - bioconda::blast>=2.9 - bioconda::mash>=2 - bioconda::prodigal=2.6.3 + - bioconda::mafft=7.525 - pip - pip : - -r requirements.txt diff --git a/taranis/__main__.py b/taranis/__main__.py index 49126c6..82c32ae 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -539,7 +539,9 @@ def allele_calling( alignment, ) ) - _ = taranis.allele_calling.collect_data(results, output, snp, alignment) + _ = taranis.allele_calling.collect_data( + results, output, snp, alignment, schema_ref_files + ) finish = time.perf_counter() print(f"Allele calling finish in {round((finish-start)/60, 2)} minutes") # sample_allele_obj.analyze_sample() diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 0fb3762..c233740 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -9,6 +9,9 @@ from collections import OrderedDict from pathlib import Path from Bio import SeqIO +from io import StringIO + +import pdb log = logging.getLogger(__name__) stderr = rich.console.Console( @@ -325,19 +328,23 @@ def search_match_allele(self): result["allele_type"][allele_name] = "LNF" result["allele_match"][allele_name] = allele_name result["allele_details"][allele_name] = "LNF" + + # prepare the data for snp and alignment analysis + ref_allele_seq = result["allele_details"][allele_name][15] + allele_seq = result["allele_details"][allele_name][14] + ref_allele_name = result["allele_details"][allele_name][3] + if self.snp_request and result["allele_type"][allele_name] != "LNF": # run snp analysis - ref_allele_seq = result["allele_details"][allele_name][15] - allele_seq = result["allele_details"][allele_name][14] - ref_allele_name = result["allele_details"][allele_name][3] result["snp_data"][allele_name] = taranis.utils.get_snp_information( ref_allele_seq, allele_seq, ref_allele_name ) if self.aligment_request and result["allele_type"][allele_name] != "LNF": # run alignment analysis - allele_seq = result["allele_details"][allele_name][14] - result["alignment_data"][allele_name] = ( - taranis.utils.get_alignment_data(allele_seq, alleles) + result["alignment_data"][ + allele_name + ] = taranis.utils.get_alignment_data( + ref_allele_seq, allele_seq, ref_allele_name ) return result @@ -373,7 +380,11 @@ def parallel_execution( def collect_data( - results: list, output: str, snp_request: bool, aligment_request: bool + results: list, + output: str, + snp_request: bool, + aligment_request: bool, + ref_alleles: list, ) -> None: def stats_graphics(stats_folder: str, summary_result: dict) -> None: stderr.print("Creating graphics") @@ -405,6 +416,17 @@ def stats_graphics(stats_folder: str, summary_result: dict) -> None: ) return + def read_reference_alleles(ref_alleles: list) -> dict[dict]: + # read reference alleles + ref_alleles_data = {} + for ref_allele in ref_alleles: + alleles = {} + with open(ref_allele, "r") as fh: + for record in SeqIO.parse(fh, "fasta"): + alleles[record.id] = str(record.seq) + ref_alleles_data[Path(ref_allele).stem] = alleles + return ref_alleles_data + summary_result_file = os.path.join(output, "allele_calling_summary.csv") sample_allele_match_file = os.path.join(output, "allele_calling_match.csv") sample_allele_detail_file = os.path.join(output, "matching_contig.csv") @@ -449,6 +471,7 @@ def stats_graphics(stats_folder: str, summary_result: dict) -> None: ) summary_result[sample] = sum_allele_type sample_allele_match[sample] = allele_match + # save summary results to file with open(summary_result_file, "w") as fo: fo.write("Sample," + ",".join(allele_types) + "\n") @@ -476,6 +499,7 @@ def stats_graphics(stats_folder: str, summary_result: dict) -> None: fo.write(",".join(detail) + "\n") else: fo.write(",".join(detail_value) + "\n") + # save snp to file if requested if snp_request: snp_file = os.path.join(output, "snp_data.csv") with open(snp_file, "w") as fo: @@ -512,5 +536,46 @@ def stats_graphics(stats_folder: str, summary_result: dict) -> None: for alignment in alignments: fo.write(alignment + "\n") + # create multiple alignment files + stderr.print("Processing multiple alignment information") + log.info("Processing multiple alignment information") + ref_alleles_seq = read_reference_alleles(ref_alleles) + for a_list in allele_list: + allele_multiple_align = [] + for ref_id, ref_seq in ref_alleles_seq[a_list].items(): + input_buffer = StringIO() + # get the reference allele sequence + input_buffer.write(">" + ref_id + "\n") + input_buffer.write(str(ref_seq) + "\n") + # get the sequences for sample on the same allele + for result in results: + for sample, values in result.items(): + # add sample and allele name + # pdb.set_trace() + # discard the allele if it is LNF + if values["allele_type"][a_list] == "LNF": + continue + # get the allele in sample that match + input_buffer.write( + ">" + + sample + + "_" + + a_list + + "_" + + values["allele_details"][a_list][4] + + "\n" + ) + # get the sequence of the allele in sample + input_buffer.write(values["allele_details"][a_list][14] + "\n") + input_buffer.seek(0) + # pdb.set_trace() + + allele_multiple_align.append( + taranis.utils.get_multiple_alignment(input_buffer) + ) + input_buffer.close() + pdb.set_trace() + # save multiple alignment to file + # Create graphics stats_graphics(output, summary_result) diff --git a/taranis/utils.py b/taranis/utils.py index d146322..f11c0c0 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -23,6 +23,7 @@ from Bio import SeqIO from Bio.Seq import Seq + log = logging.getLogger(__name__) @@ -280,8 +281,10 @@ def folder_exists(folder_to_check): return False -def get_alignment_data(allele_sequence: str, ref_sequences: dict[str]) -> dict: - """Get the alignment data between the allele sequence and the reference alleles +def get_alignment_data(ref_sequence: str, allele_sequence: str, ref_allele) -> dict: + """Get the alignment data between the reference allele and the match allele + sequence. It returns 3 lines, the reference allele, the alignment character + and the match allele sequence Args: allele_sequence (str): sequence to be compared @@ -291,14 +294,13 @@ def get_alignment_data(allele_sequence: str, ref_sequences: dict[str]) -> dict: dict: key: ref_sequence, value: alignment data """ alignment_data = {} - for ref_allele, ref_sequence in ref_sequences.items(): - alignment = "" - for idx, (a, b) in enumerate(zip(allele_sequence, ref_sequence)): - if a == b: - alignment += "|" - else: - alignment += " " - alignment_data[ref_allele] = [ref_sequence, alignment, allele_sequence] + alignment = "" + for _, (ref, alt) in enumerate(zip(ref_sequence, allele_sequence)): + if ref == alt: + alignment += "|" + else: + alignment += " " + alignment_data[ref_allele] = [ref_sequence, alignment, allele_sequence] return alignment_data @@ -323,6 +325,39 @@ def get_files_in_folder(folder: str, extension: str = None) -> list[str]: return glob.glob(folder_files) +def get_multiple_alignment(input_buffer: io.StringIO) -> list[str]: + """Run MAFFT with input from the string buffer and capture output to another string buffer + + Args: + input_buffer (io.StringIO): fasta sequences to be aligned + + Returns: + list[str]: list of aligned sequences + """ + # + output_buffer = io.StringIO() + # Run MAFFT + mafft_command = "mafft --auto --quiet -" # "-" tells MAFFT to read from stdin + process = subprocess.Popen( + mafft_command, shell=True, stdin=subprocess.PIPE, stdout=subprocess.PIPE + ) + stdout, _ = process.communicate(input_buffer.getvalue().encode()) + + # Convert the stdout bytes to a string buffer + output_buffer = io.StringIO(stdout.decode()) + output_buffer.seek(0) + # convert the string buffer to a list of lines + multi_result = [] + for line in output_buffer: + multi_result.append(line) + + # Close the file objects and process + output_buffer.close() + process.close() + + return multi_result + + def get_snp_information( ref_sequence: str, alt_sequence: str, ref_allele_name ) -> dict[list[str]]: From 04cb801a07eac58c326ee4f07fd5590f10f59db2 Mon Sep 17 00:00:00 2001 From: luissian Date: Sun, 7 Apr 2024 09:49:41 +0200 Subject: [PATCH 142/214] add function to extend sequence to find stop codon --- taranis/__main__.py | 52 ++++++--- taranis/allele_calling.py | 237 +++++++++++++++++++++++++++++--------- taranis/utils.py | 92 +++++++++++++-- 3 files changed, 307 insertions(+), 74 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index 82c32ae..aa3e23c 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -450,6 +450,24 @@ def reference_alleles( default=False, help="Create alignment files", ) +@click.option( + "-q", + "--proteine-threshold", + required=False, + nargs=1, + default=80, + type=int, + help="Threshold of protein coverage to consider as TPR. default 90", +) +@click.option( + "-i", + "--increase-sequence", + required=False, + nargs=1, + default=20, + type=int, + help="Increase the number of triplet sequences to find the stop codon. default 20", +) @click.option( "--cpus", required=False, @@ -469,6 +487,8 @@ def allele_calling( force: bool, snp: bool, alignment: bool, + proteine_threshold: int, + increase_sequence: int, cpus: int, ): _ = taranis.utils.check_additional_programs_installed( @@ -515,6 +535,8 @@ def allele_calling( inf_allele_obj, snp, alignment, + proteine_threshold, + increase_sequence, ) for assembly_file in assemblies ] @@ -525,23 +547,27 @@ def allele_calling( print(e) continue """ - results.append( - taranis.allele_calling.parallel_execution( - assemblies[0], - schema, - prediction_data, - schema_ref_files, - threshold, - perc_identity, - output, - inf_allele_obj, - snp, - alignment, + for assembly_file in assemblies: + results.append( + taranis.allele_calling.parallel_execution( + assembly_file, + schema, + prediction_data, + schema_ref_files, + threshold, + perc_identity, + output, + inf_allele_obj, + snp, + alignment, + proteine_threshold, + increase_sequence, + ) ) - ) _ = taranis.allele_calling.collect_data( results, output, snp, alignment, schema_ref_files ) finish = time.perf_counter() print(f"Allele calling finish in {round((finish-start)/60, 2)} minutes") + log.info("Allele calling finish in %s minutes", round((finish-start)/60, 2)) # sample_allele_obj.analyze_sample() diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index c233740..cb886c3 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -8,6 +8,7 @@ from collections import OrderedDict from pathlib import Path +from Bio.Seq import Seq from Bio import SeqIO from io import StringIO @@ -35,6 +36,8 @@ def __init__( inf_alle_obj: object, snp_request: bool = False, aligment_request: bool = False, + tpr_limit: int = 80, + increase_sequence: int = 20, ): """Allele calling initial creation object @@ -48,9 +51,12 @@ def __init__( inf_alle_obj (object): object to infer alleles snp_request (bool, optional): snp saved to file. Defaults to False. aligment_request (bool, optional): allignment saved to file. Defaults to False. + tpr_limit (int, optional): lower threshold to consider trunked proteine. Defaults to 80. + increase_sequence (int, optional): increase sequence to be analysed. Defaults to 20. """ self.prediction_data = annotation # store prediction annotation self.sample_file = sample_file + self.sample_records = taranis.utils.read_fasta_file(self.sample_file, convert_to_dict=True) self.schema = schema self.ref_alleles = reference_alleles self.threshold = threshold @@ -65,9 +71,11 @@ def __init__( self.inf_alle_obj = inf_alle_obj self.snp_request = snp_request self.aligment_request = aligment_request + self.tpr_limit = tpr_limit / 100 + self.increase_sequence = increase_sequence * 3 def assign_allele_type( - self, blast_results: list, allele_file: str, allele_name: str + self, blast_results: list, allele_file: str, allele_name: str, ref_allele_seq: str ) -> list: """Assign allele type to the allele @@ -75,12 +83,16 @@ def assign_allele_type( blast_result (list): information collected by running blast allele_file (str): file name with allele sequence allele_name (str): allele name + ref_allele_seq (str): reference allele sequence Returns: list: containing allele classification, allele match id, and allele details """ + def add_sequences_(column_blast_res: list) -> bool: + pass + def check_if_plot(column_blast_res: list) -> bool: """Check if allele is partial length @@ -91,12 +103,12 @@ def check_if_plot(column_blast_res: list) -> bool: bool: True if allele is partial length """ if ( - column_blast_res[9] == "1" # check at contig start + column_blast_res[8] == "1" # check at contig start # check if contig ends is the same as match allele ends - or column_blast_res[15] == column_blast_res[10] - or column_blast_res[10] == "1" # check reverse at contig end + or column_blast_res[9] == column_blast_res[7] + or column_blast_res[9] == "1" # check reverse at contig end # check if contig start is the same as match allele start reverse - or column_blast_res[9] == column_blast_res[15] + or column_blast_res[8] == column_blast_res[7] ): return True return False @@ -119,7 +131,83 @@ def discard_low_threshold_results(blast_results: list) -> list: valid_blast_result.append(b_result) return valid_blast_result - def get_blast_details(blast_result: str, allele_name: str) -> list: + def extend_sequence_for_finding_stop_codon(split_blast_result: list) -> list: + """Extend match sequence, according the (increase_sequence) for + trying find the stop codon. + + Args: + split_blast_result (list): list having the informaction collected + by running blast + + Returns: + list: updated information if stop codon is found + """ + # collect data for checking PLOT + data_for_plot = [""] * 10 + # cop the contig length + data_for_plot[7] = split_blast_result[15] + # copy start position + data_for_plot[8] = split_blast_result[9] + # copy end position + data_for_plot[9] = split_blast_result[10] + # check if PLOT + if not check_if_plot(data_for_plot): + # fetch the sequence until the last triplet is stop codon + contig_seq = self.sample_records[split_blast_result[1]] + start_seq = int(split_blast_result[9]) + stop_seq = int(split_blast_result[10]) + if stop_seq > start_seq: + # sequence direction is forward + direction = "forward" + # adjust the sequence to be a triplet + interval = (stop_seq - start_seq) // 3 * 3 + new_stop_seq = start_seq + interval + self.increase_sequence + start_seq -= 1 + # if the increased length is higher than the contig length + # adjust the stop sequence to maximun contig length + # multiply by 3. + if stop_seq > len(contig_seq): + stop_seq = len(contig_seq) // 3 * 3 + else: + stop_seq = new_stop_seq -1 + c_sequence = contig_seq[start_seq:stop_seq] + else: + # sequence direction is reverse + direction = "reverse" + # adjust the sequence to be a triplet + interval = (start_seq - stop_seq) // 3 * 3 + new_stop_seq = start_seq - interval - self.increase_sequence + # if the increased length is lower than 0 (contig start) + # position, adjust the start sequence to minumum contig + # length multiply by 3 + if new_stop_seq < 0: + # get the minimum contig length that is multiple by 3 + stop_seq = stop_seq % 3 - 1 + else: + stop_seq = new_stop_seq + # get the sequence in reverse + c_sequence = str(Seq(contig_seq[stop_seq:start_seq]).reverse_complement()) + new_prot_conv_result = taranis.utils.convert_to_protein(c_sequence, force_coding=False, check_additional_bases=False) + # check if stop codon is found in protein sequence + # pdb.set_trace() + if "protein" in new_prot_conv_result and "*" in new_prot_conv_result["protein"]: + new_seq_length = new_prot_conv_result["protein"].index("*") * 3 + match_sequence = c_sequence[:new_seq_length] + split_blast_result[4] = str(new_seq_length) + prot_error_result = "-" + # update the start and stop position + + if direction == "forward": + # pdb.set_trace() + split_blast_result[10] = str(int(split_blast_result[9]) + new_seq_length) + else: + split_blast_result[9] = str(int(split_blast_result[10]) - new_seq_length) + # ignore the previous process if stop codon is not found + + # pdb.set_trace() + return split_blast_result + + def get_blast_details(blast_result: str, allele_name: str, ref_allele_seq) -> list: """Collect blast details and modify the order of the columns Args: @@ -144,6 +232,8 @@ def get_blast_details(blast_result: str, allele_name: str) -> list: blast_details[13] = allele quality blast_details[14] = match sequence in contig blast_details[15] = reference allele sequence + blast_details[16] = protein conversion result + blast_details[17] = predicted protein sequence """ split_blast_result = blast_result.split("\t") match_allele_name = split_blast_result[0] @@ -163,7 +253,21 @@ def get_blast_details(blast_result: str, allele_name: str) -> list: direction = "-" # remove the gaps in sequences match_sequence = split_blast_result[13].replace("-", "") - reference_sequence = split_blast_result[14].replace("-", "") + # check if the sequence is coding + prot_conv_result = taranis.utils.convert_to_protein(match_sequence, force_coding=False, check_additional_bases=True) + prot_error_result = prot_conv_result["error"] if "error" in prot_conv_result else "-" + predicted_prot_seq = prot_conv_result["protein"] if "protein" in prot_conv_result else "-" + # remove if additional sequenced are added at the end of the stop codon + pdb.set_trace() + if "additional bases added after stop codon" in prot_error_result: + new_seq_len = len(match_sequence) // 3 * 3 + match_sequence = match_sequence[:new_seq_len] + split_blast_result[4] = str(new_seq_len) + # add more sequence to find the stop codon + elif "Last sequence is not a stop codon" in prot_error_result: + split_blast_result = extend_sequence_for_finding_stop_codon(split_blast_result) + elif "Sequence does not have a start codon" in prot_error_result: + pass # get blast details blast_details = [ self.s_name, # sample name @@ -181,8 +285,11 @@ def get_blast_details(blast_result: str, allele_name: str) -> list: product_annotation, allele_quality, match_sequence, # match sequence in contig - reference_sequence, # reference allele sequence + ref_allele_seq, # reference allele sequence + prot_error_result, # protein conversion result + predicted_prot_seq, # predicted protein sequence ] + return blast_details def find_match_allele_schema(allele_file: str, match_sequence: str) -> str: @@ -196,7 +303,7 @@ def find_match_allele_schema(allele_file: str, match_sequence: str) -> str: str: allele name in the schema that match the sequence """ grep_result = taranis.utils.grep_execution( - allele_file, match_sequence, "-b1" + allele_file, match_sequence, "-xb1" ) if len(grep_result) > 0: return grep_result[0].split("_")[1] @@ -206,13 +313,13 @@ def find_match_allele_schema(allele_file: str, match_sequence: str) -> str: match_allele_schema = "" if len(valid_blast_results) == 0: # no match results labelled as LNF. details data filled with empty data - return ["LNF", "-", ["-," * 15]] + return ["LNF", "-", ["-"]* 18] if len(valid_blast_results) > 1: # could be NIPHEM or NIPH b_split_data = [] match_allele_seq = [] for valid_blast_result in valid_blast_results: - multi_allele_data = get_blast_details(valid_blast_result, allele_name) + multi_allele_data = get_blast_details(valid_blast_result, allele_name, ref_allele_seq) # get match allele sequence match_allele_seq.append(multi_allele_data[14]) b_split_data.append(multi_allele_data) @@ -232,29 +339,33 @@ def find_match_allele_schema(allele_file: str, match_sequence: str) -> str: for (idx,) in range(len(b_split_data)): b_split_data[idx][4] = classification + "_" + match_allele_schema else: - b_split_data = get_blast_details(valid_blast_results[0], allele_name) + b_split_data = get_blast_details(valid_blast_results[0], allele_name, ref_allele_seq) # found the allele in schema with the match sequence in the contig match_allele_schema = find_match_allele_schema( - allele_file, b_split_data[14] + allele_file, b_split_data[14] ) - # PLOT, ASM, ALM, INF, EXC are possible classifications - if check_if_plot(b_split_data): + # PLOT, TPR, ASM, ALM, INF, EXC are possible classifications + if match_allele_schema != "": + # exact match found labelled as EXC + classification = "EXC" + elif check_if_plot(b_split_data): # match allele is partial length labelled as PLOT classification = "PLOT" - - # check if match allele is shorter than reference allele - elif int(b_split_data[5]) < int(b_split_data[6]): + # check if protein length divided by the length of triplet matched + # sequence is lower the the tpr limit + elif b_split_data[16] == "Multiple stop codons" and b_split_data[17].index("*") / (int(b_split_data[6]) / 3) < self.tpr_limit: + # labelled as TPR + classification = "TPR" + # check if match allele is shorter than reference allele + elif int(b_split_data[6]) < int(b_split_data[5]): classification = "ASM" # check if match allele is longer than reference allele - elif int(b_split_data[5]) > int(b_split_data[6]): + elif int(b_split_data[6]) > int(b_split_data[5]): classification = "ALM" else: # if sequence was not found after running grep labelled as INF - if match_allele_schema == "": - classification = "INF" - else: - # exact match found labelled as EXC - classification = "EXC" + classification = "INF" + # assign an identification value to the new allele if match_allele_schema == "": match_allele_schema = str( @@ -289,11 +400,13 @@ def search_match_allele(self): len(self.ref_alleles), ) - alleles = OrderedDict() + alleles = taranis.utils.read_fasta_file(ref_allele, convert_to_dict=True) match_found = False + """ with open(ref_allele, "r") as fh: for record in SeqIO.parse(fh, "fasta"): alleles[record.id] = str(record.seq) + """ count_2 = 0 for r_id, r_seq in alleles.items(): count_2 += 1 @@ -321,7 +434,7 @@ def search_match_allele(self): result["allele_type"][allele_name], result["allele_match"][allele_name], result["allele_details"][allele_name], - ) = self.assign_allele_type(blast_result, allele_file, allele_name) + ) = self.assign_allele_type(blast_result, allele_file, allele_name, r_seq) else: # Sample does not have a reference allele to be matched # Keep LNF info @@ -330,12 +443,16 @@ def search_match_allele(self): result["allele_details"][allele_name] = "LNF" # prepare the data for snp and alignment analysis - ref_allele_seq = result["allele_details"][allele_name][15] + try: + ref_allele_seq = result["allele_details"][allele_name][15] + except: + pdb.set_trace() allele_seq = result["allele_details"][allele_name][14] ref_allele_name = result["allele_details"][allele_name][3] if self.snp_request and result["allele_type"][allele_name] != "LNF": # run snp analysis + print(allele_name) result["snp_data"][allele_name] = taranis.utils.get_snp_information( ref_allele_seq, allele_seq, ref_allele_name ) @@ -346,6 +463,8 @@ def search_match_allele(self): ] = taranis.utils.get_alignment_data( ref_allele_seq, allele_seq, ref_allele_name ) + # delete blast folder + _ = taranis.utils.delete_folder(self.blast_dir) return result @@ -360,6 +479,8 @@ def parallel_execution( inf_alle_obj: object, snp_request: bool = False, aligment_request: bool = False, + trp_limit: int = 80, + increase_sequence: int = 20, ): allele_obj = AlleleCalling( sample_file, @@ -372,6 +493,8 @@ def parallel_execution( inf_alle_obj, snp_request, aligment_request, + trp_limit, + increase_sequence ) sample_name = Path(sample_file).stem stderr.print(f"[green] Analyzing sample {sample_name}") @@ -389,7 +512,7 @@ def collect_data( def stats_graphics(stats_folder: str, summary_result: dict) -> None: stderr.print("Creating graphics") log.info("Creating graphics") - allele_types = ["NIPHEM", "NIPH", "EXC", "PLOT", "ASM", "ALM", "INF", "LNF"] + allele_types = ["NIPHEM", "NIPH", "EXC", "PLOT", "ASM", "ALM", "INF", "LNF", "TPR"] # inizialize classification data classif_data = {} for allele_type in allele_types: @@ -430,7 +553,7 @@ def read_reference_alleles(ref_alleles: list) -> dict[dict]: summary_result_file = os.path.join(output, "allele_calling_summary.csv") sample_allele_match_file = os.path.join(output, "allele_calling_match.csv") sample_allele_detail_file = os.path.join(output, "matching_contig.csv") - allele_types = ["NIPHEM", "NIPH", "EXC", "PLOT", "ASM", "ALM", "INF", "LNF"] + allele_types = ["NIPHEM", "NIPH", "EXC", "PLOT", "ASM", "ALM", "INF", "LNF", "TPR"] detail_heading = [ "sample", "contig", @@ -446,7 +569,10 @@ def read_reference_alleles(ref_alleles: list) -> dict[dict]: "gene notation", "product notation", "allele quality", - "sequence", + "match sequence", + "reference allele sequence", + "protein conversion result", + "predicted protein sequence", ] summary_result = {} # used for summary file and allele classification graphics @@ -501,25 +627,26 @@ def read_reference_alleles(ref_alleles: list) -> dict[dict]: fo.write(",".join(detail_value) + "\n") # save snp to file if requested if snp_request: - snp_file = os.path.join(output, "snp_data.csv") - with open(snp_file, "w") as fo: - fo.write( - "Sample name,Locus name,Reference allele,Position,Ref,Alt,Codon Ref,Codon Alt,Amino Ref,Amino Alt,Category Ref,Category Alt\n" - ) + for result in results: for sample, values in result.items(): - for allele, snp_data in values["snp_data"].items(): - for ref_allele, snp_info_list in snp_data.items(): - for snp_info in snp_info_list: - fo.write( - sample - + "," - + allele - + "," - + ref_allele - + "," - + ",".join(snp_info) - + "\n" - ) + snp_file = os.path.join(output, sample + "_snp_data.csv") + with open(snp_file, "w") as fo: + fo.write( + "Sample name,Locus name,Reference allele,Position,Ref,Alt,Codon Ref,Codon Alt,Amino Ref,Amino Alt,Category Ref,Category Alt\n" + ) + for allele, snp_data in values["snp_data"].items(): + for ref_allele, snp_info_list in snp_data.items(): + for snp_info in snp_info_list: + fo.write( + sample + + "," + + allele + + "," + + ref_allele + + "," + + ",".join(snp_info) + + "\n" + ) # create alignment files if aligment_request: alignment_folder = os.path.join(output, "alignments") @@ -545,13 +672,11 @@ def read_reference_alleles(ref_alleles: list) -> dict[dict]: for ref_id, ref_seq in ref_alleles_seq[a_list].items(): input_buffer = StringIO() # get the reference allele sequence - input_buffer.write(">" + ref_id + "\n") + input_buffer.write(">Ref_" + ref_id + "\n") input_buffer.write(str(ref_seq) + "\n") # get the sequences for sample on the same allele for result in results: for sample, values in result.items(): - # add sample and allele name - # pdb.set_trace() # discard the allele if it is LNF if values["allele_type"][a_list] == "LNF": continue @@ -568,14 +693,20 @@ def read_reference_alleles(ref_alleles: list) -> dict[dict]: # get the sequence of the allele in sample input_buffer.write(values["allele_details"][a_list][14] + "\n") input_buffer.seek(0) - # pdb.set_trace() allele_multiple_align.append( taranis.utils.get_multiple_alignment(input_buffer) ) + # release memory input_buffer.close() - pdb.set_trace() # save multiple alignment to file + with open( + os.path.join(alignment_folder, a_list + "_multiple_alignment.aln"), "w" + ) as fo: + for alignment in allele_multiple_align: + for align in alignment: + fo.write(align) # Create graphics stats_graphics(output, summary_result) + return diff --git a/taranis/utils.py b/taranis/utils.py index f11c0c0..43beb10 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -1,4 +1,5 @@ #!/usr/bin/env python +import Bio.Data.CodonTable import glob import gzip import io @@ -22,8 +23,13 @@ from pathlib import Path from Bio import SeqIO from Bio.Seq import Seq +from collections import OrderedDict +import warnings +from Bio import BiopythonWarning + +import pdb log = logging.getLogger(__name__) @@ -111,6 +117,51 @@ def check_additional_programs_installed(software_list: list) -> None: sys.exit(1) return +def convert_to_protein(sequence: str, force_coding: bool =False, check_additional_bases: bool = False) -> dict: + """Check if the input sequence is a coding protein. + + Args: + sequence (str): sequence to be checked + force_coding (bool, optional): force to check if sequence is coding. Defaults to False. + check_additional_bases (bool, optional): if not multiple by 3 remove the latest sequences to check they are added after the stop codon. Defaults to False. + + Returns: + dict: protein sequence and/or error message + """ + conv_result = {} + # checck if exists start codon + if sequence[0:3] not in START_CODON_FORWARD: + return {"error": "Sequence does not have a start codon"} + if len(sequence) % 3 != 0: + if not check_additional_bases: + return {"error" : "Sequence is not a multiple of three"} + # Remove the last or second to last bases to check if there is a stop codon + new_seq_len = len(sequence) // 3 * 3 + sequence = sequence[:new_seq_len] + # this error will be overwritten if another error is found + conv_result["error"] = "additional bases added after stop codon" + + seq_sequence = Seq(sequence) + try: + seq_prot = seq_sequence.translate(table=1, cds=force_coding) + except Bio.Data.CodonTable.TranslationError as e: + log.info("Unable to translate sequence. Info message: %s ", e) + return {"error": e} + # get the latest stop codon + last_stop = seq_prot.rfind("*") + # if force_coding is False, check if there are multiple stop codons + if not force_coding: + first_stop = seq_prot.find("*") + if first_stop != last_stop: + return {"error": "Multiple stop codons","protein": str(seq_prot)} + if last_stop != len(seq_prot) - 1: + return {"error": "Last sequence is not a stop codon", "protein": str(seq_prot)} + if "error" in conv_result: + conv_result["protein"] = str(seq_prot) + return conv_result + return {"protein": str(seq_prot)} + + def create_annotation_files( fasta_file: str, @@ -334,7 +385,6 @@ def get_multiple_alignment(input_buffer: io.StringIO) -> list[str]: Returns: list[str]: list of aligned sequences """ - # output_buffer = io.StringIO() # Run MAFFT mafft_command = "mafft --auto --quiet -" # "-" tells MAFFT to read from stdin @@ -353,7 +403,7 @@ def get_multiple_alignment(input_buffer: io.StringIO) -> list[str]: # Close the file objects and process output_buffer.close() - process.close() + process.stdout.close() return multi_result @@ -374,13 +424,24 @@ def get_snp_information( Returns: dict: key: ref_sequence, value: list of snp information """ + # Supress warning that len of alt sequence not a multiple of three + warnings.simplefilter('ignore', BiopythonWarning) snp_info = {} ref_protein = str(Seq(ref_sequence).translate()) - alt_protein = str(Seq(alt_sequence).translate()) + try: + alt_protein = str(Seq(alt_sequence).translate()) + except Exception as e: + import pdb; pdb.set_trace() + + if len(alt_sequence) %3 != 0: + import pdb; pdb.set_trace() snp_line = [] - for idx, (ref, alt) in enumerate(zip(ref_sequence, alt_sequence)): - if alt != ref: + # get the shortest sequence for the loop + length_for_snp = min(len(ref_sequence), len(alt_sequence)) + for idx in range(length_for_snp): + + if ref_sequence[idx] != alt_sequence[idx]: # calculate the triplet index triplet_idx = idx // 3 # get triplet code @@ -395,8 +456,8 @@ def get_snp_information( snp_line.append( [ str(idx), - ref, - alt, + ref_sequence[idx], + alt_sequence[idx], ref_triplet, alt_triplet, ref_aa, @@ -604,7 +665,22 @@ def read_compressed_file( return out_data -def read_fasta_file(fasta_file): +def read_fasta_file(fasta_file: str, convert_to_dict=False) -> dict | str: + """Read the fasta file and return the data as a dictionary if convert_to_dict + + Args: + fasta_file (str): _description_ + convert_to_dict (bool, optional): _description_. Defaults to False. + + Returns: + dict: fasta id as key and sequence as value in str format + """ + conv_fasta = OrderedDict() + if convert_to_dict: + with open(fasta_file, "r") as fh: + for record in SeqIO.parse(fh, "fasta"): + conv_fasta[record.id] = str(record.seq) + return conv_fasta return SeqIO.parse(fasta_file, "fasta") From 8ef97ddb1bcb09d22ef6ceaa03374b972874cad6 Mon Sep 17 00:00:00 2001 From: luissian Date: Sun, 7 Apr 2024 22:52:26 +0200 Subject: [PATCH 143/214] Added extension sequences when start codon is not found because is trunk --- taranis/allele_calling.py | 190 +++++++++++++++++++++++++++++--------- taranis/utils.py | 35 ++++--- 2 files changed, 162 insertions(+), 63 deletions(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index cb886c3..efe4979 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -56,7 +56,9 @@ def __init__( """ self.prediction_data = annotation # store prediction annotation self.sample_file = sample_file - self.sample_records = taranis.utils.read_fasta_file(self.sample_file, convert_to_dict=True) + self.sample_records = taranis.utils.read_fasta_file( + self.sample_file, convert_to_dict=True + ) self.schema = schema self.ref_alleles = reference_alleles self.threshold = threshold @@ -75,7 +77,11 @@ def __init__( self.increase_sequence = increase_sequence * 3 def assign_allele_type( - self, blast_results: list, allele_file: str, allele_name: str, ref_allele_seq: str + self, + blast_results: list, + allele_file: str, + allele_name: str, + ref_allele_seq: str, ) -> list: """Assign allele type to the allele @@ -90,10 +96,7 @@ def assign_allele_type( details """ - def add_sequences_(column_blast_res: list) -> bool: - pass - - def check_if_plot(column_blast_res: list) -> bool: + def _check_if_plot(column_blast_res: list) -> bool: """Check if allele is partial length Args: @@ -113,7 +116,7 @@ def check_if_plot(column_blast_res: list) -> bool: return True return False - def discard_low_threshold_results(blast_results: list) -> list: + def _discard_low_threshold_results(blast_results: list) -> list: """Discard blast results with lower threshold Args: @@ -131,16 +134,31 @@ def discard_low_threshold_results(blast_results: list) -> list: valid_blast_result.append(b_result) return valid_blast_result - def extend_sequence_for_finding_stop_codon(split_blast_result: list) -> list: + def _extend_sequence_for_finding_start_stop_codon( + split_blast_result: list, + prot_error_result: str, + predicted_prot_seq: str, + search_codon: str = "stop", + ) -> list: """Extend match sequence, according the (increase_sequence) for - trying find the stop codon. + trying find the stop or start codon. When parameter is set to + stop additional nucleotides are added to extend the chance to + find out the codon stop. + If parameter is set to start then additional nucleotide is added + on the start value to identify that is a valid start codon. If + true then additional nucletotides are added to find the stop codon. Args: split_blast_result (list): list having the informaction collected - by running blast + by running blast + prot_error_result (str): protein conversion result + predicted_prot_seq (str): predicted protein sequence + search_codon (str, optional): codon to be found. 2 values are + allowed start of stop. By default is stop. Returns: - list: updated information if stop codon is found + list: updated information if stop or start codon is found and the + updated protein sequence and protein conversion result if changed """ # collect data for checking PLOT data_for_plot = [""] * 10 @@ -151,7 +169,7 @@ def extend_sequence_for_finding_stop_codon(split_blast_result: list) -> list: # copy end position data_for_plot[9] = split_blast_result[10] # check if PLOT - if not check_if_plot(data_for_plot): + if not _check_if_plot(data_for_plot): # fetch the sequence until the last triplet is stop codon contig_seq = self.sample_records[split_blast_result[1]] start_seq = int(split_blast_result[9]) @@ -159,6 +177,16 @@ def extend_sequence_for_finding_stop_codon(split_blast_result: list) -> list: if stop_seq > start_seq: # sequence direction is forward direction = "forward" + if search_codon == "start": + if ( + contig_seq[start_seq - 2 : start_seq + 1] + in taranis.utils.START_CODON_FORWARD + ): + start_seq -= 2 + # continue to find the stop codon with the new start + else: + # start codon not found. Return the original blast result + return split_blast_result # adjust the sequence to be a triplet interval = (stop_seq - start_seq) // 3 * 3 new_stop_seq = start_seq + interval + self.increase_sequence @@ -169,11 +197,21 @@ def extend_sequence_for_finding_stop_codon(split_blast_result: list) -> list: if stop_seq > len(contig_seq): stop_seq = len(contig_seq) // 3 * 3 else: - stop_seq = new_stop_seq -1 + stop_seq = new_stop_seq - 1 c_sequence = contig_seq[start_seq:stop_seq] else: # sequence direction is reverse direction = "reverse" + if search_codon == "start": + if ( + contig_seq[start_seq - 2 : start_seq + 1] + in taranis.utils.START_CODON_REVERSE + ): + start_seq += 1 + # continue to find the stop codon with the new start + else: + # start codon not found. Return the original blast result + return split_blast_result # adjust the sequence to be a triplet interval = (start_seq - stop_seq) // 3 * 3 new_stop_seq = start_seq - interval - self.increase_sequence @@ -186,28 +224,42 @@ def extend_sequence_for_finding_stop_codon(split_blast_result: list) -> list: else: stop_seq = new_stop_seq # get the sequence in reverse - c_sequence = str(Seq(contig_seq[stop_seq:start_seq]).reverse_complement()) - new_prot_conv_result = taranis.utils.convert_to_protein(c_sequence, force_coding=False, check_additional_bases=False) + c_sequence = str( + Seq(contig_seq[stop_seq:start_seq]).reverse_complement() + ) + new_prot_conv_result = taranis.utils.convert_to_protein( + c_sequence, force_coding=False, check_additional_bases=False + ) # check if stop codon is found in protein sequence - # pdb.set_trace() - if "protein" in new_prot_conv_result and "*" in new_prot_conv_result["protein"]: - new_seq_length = new_prot_conv_result["protein"].index("*") * 3 + + if ( + "protein" in new_prot_conv_result + and "*" in new_prot_conv_result["protein"] + ): + # increase 3 nucleotides beecause index start at 0 + new_seq_length = new_prot_conv_result["protein"].index("*") * 3 + 3 match_sequence = c_sequence[:new_seq_length] split_blast_result[4] = str(new_seq_length) + split_blast_result[14] = match_sequence prot_error_result = "-" + predicted_prot_seq = new_prot_conv_result["protein"][ + 0 : new_seq_length // 3 + ] # update the start and stop position - if direction == "forward": - # pdb.set_trace() - split_blast_result[10] = str(int(split_blast_result[9]) + new_seq_length) + split_blast_result[10] = str( + int(split_blast_result[9]) + new_seq_length + ) else: - split_blast_result[9] = str(int(split_blast_result[10]) - new_seq_length) - # ignore the previous process if stop codon is not found - - # pdb.set_trace() - return split_blast_result + split_blast_result[9] = str( + int(split_blast_result[10]) - new_seq_length + ) + # ignore the previous process if stop codon is not found + return split_blast_result, prot_error_result, predicted_prot_seq - def get_blast_details(blast_result: str, allele_name: str, ref_allele_seq) -> list: + def _get_blast_details( + blast_result: str, allele_name: str, ref_allele_seq + ) -> list: """Collect blast details and modify the order of the columns Args: @@ -254,20 +306,44 @@ def get_blast_details(blast_result: str, allele_name: str, ref_allele_seq) -> li # remove the gaps in sequences match_sequence = split_blast_result[13].replace("-", "") # check if the sequence is coding - prot_conv_result = taranis.utils.convert_to_protein(match_sequence, force_coding=False, check_additional_bases=True) - prot_error_result = prot_conv_result["error"] if "error" in prot_conv_result else "-" - predicted_prot_seq = prot_conv_result["protein"] if "protein" in prot_conv_result else "-" + prot_conv_result = taranis.utils.convert_to_protein( + match_sequence, force_coding=False, check_additional_bases=True + ) + prot_error_result = ( + prot_conv_result["error"] if "error" in prot_conv_result else "-" + ) + predicted_prot_seq = ( + prot_conv_result["protein"] if "protein" in prot_conv_result else "-" + ) # remove if additional sequenced are added at the end of the stop codon - pdb.set_trace() + # pdb.set_trace() if "additional bases added after stop codon" in prot_error_result: new_seq_len = len(match_sequence) // 3 * 3 match_sequence = match_sequence[:new_seq_len] split_blast_result[4] = str(new_seq_len) # add more sequence to find the stop codon elif "Last sequence is not a stop codon" in prot_error_result: - split_blast_result = extend_sequence_for_finding_stop_codon(split_blast_result) + ( + split_blast_result, + prot_error_result, + predicted_prot_seq, + ) = _extend_sequence_for_finding_start_stop_codon( + split_blast_result, + prot_error_result, + predicted_prot_seq, + search_codon="stop", + ) elif "Sequence does not have a start codon" in prot_error_result: - pass + ( + split_blast_result, + prot_error_result, + predicted_prot_seq, + ) = _extend_sequence_for_finding_start_stop_codon( + split_blast_result, + prot_error_result, + predicted_prot_seq, + search_codon="start", + ) # get blast details blast_details = [ self.s_name, # sample name @@ -309,17 +385,19 @@ def find_match_allele_schema(allele_file: str, match_sequence: str) -> str: return grep_result[0].split("_")[1] return "" - valid_blast_results = discard_low_threshold_results(blast_results) + valid_blast_results = _discard_low_threshold_results(blast_results) match_allele_schema = "" if len(valid_blast_results) == 0: # no match results labelled as LNF. details data filled with empty data - return ["LNF", "-", ["-"]* 18] + return ["LNF", "-", ["-"] * 18] if len(valid_blast_results) > 1: # could be NIPHEM or NIPH b_split_data = [] match_allele_seq = [] for valid_blast_result in valid_blast_results: - multi_allele_data = get_blast_details(valid_blast_result, allele_name, ref_allele_seq) + multi_allele_data = _get_blast_details( + valid_blast_result, allele_name, ref_allele_seq + ) # get match allele sequence match_allele_seq.append(multi_allele_data[14]) b_split_data.append(multi_allele_data) @@ -339,33 +417,42 @@ def find_match_allele_schema(allele_file: str, match_sequence: str) -> str: for (idx,) in range(len(b_split_data)): b_split_data[idx][4] = classification + "_" + match_allele_schema else: - b_split_data = get_blast_details(valid_blast_results[0], allele_name, ref_allele_seq) + b_split_data = _get_blast_details( + valid_blast_results[0], allele_name, ref_allele_seq + ) # found the allele in schema with the match sequence in the contig match_allele_schema = find_match_allele_schema( - allele_file, b_split_data[14] + allele_file, b_split_data[14] ) # PLOT, TPR, ASM, ALM, INF, EXC are possible classifications if match_allele_schema != "": # exact match found labelled as EXC classification = "EXC" - elif check_if_plot(b_split_data): + elif _check_if_plot(b_split_data): # match allele is partial length labelled as PLOT classification = "PLOT" # check if protein length divided by the length of triplet matched # sequence is lower the the tpr limit - elif b_split_data[16] == "Multiple stop codons" and b_split_data[17].index("*") / (int(b_split_data[6]) / 3) < self.tpr_limit: + elif ( + b_split_data[16] == "Multiple stop codons" + and b_split_data[17].index("*") / (int(b_split_data[6]) / 3) + < self.tpr_limit + ): # labelled as TPR classification = "TPR" # check if match allele is shorter than reference allele elif int(b_split_data[6]) < int(b_split_data[5]): classification = "ASM" # check if match allele is longer than reference allele - elif int(b_split_data[6]) > int(b_split_data[5]): + elif ( + int(b_split_data[6]) > int(b_split_data[5]) + or b_split_data[16] == "Last sequence is not a stop codon" + ): classification = "ALM" else: # if sequence was not found after running grep labelled as INF classification = "INF" - + # assign an identification value to the new allele if match_allele_schema == "": match_allele_schema = str( @@ -434,7 +521,9 @@ def search_match_allele(self): result["allele_type"][allele_name], result["allele_match"][allele_name], result["allele_details"][allele_name], - ) = self.assign_allele_type(blast_result, allele_file, allele_name, r_seq) + ) = self.assign_allele_type( + blast_result, allele_file, allele_name, r_seq + ) else: # Sample does not have a reference allele to be matched # Keep LNF info @@ -453,6 +542,7 @@ def search_match_allele(self): if self.snp_request and result["allele_type"][allele_name] != "LNF": # run snp analysis print(allele_name) + # pdb.set_trace() result["snp_data"][allele_name] = taranis.utils.get_snp_information( ref_allele_seq, allele_seq, ref_allele_name ) @@ -494,7 +584,7 @@ def parallel_execution( snp_request, aligment_request, trp_limit, - increase_sequence + increase_sequence, ) sample_name = Path(sample_file).stem stderr.print(f"[green] Analyzing sample {sample_name}") @@ -512,7 +602,17 @@ def collect_data( def stats_graphics(stats_folder: str, summary_result: dict) -> None: stderr.print("Creating graphics") log.info("Creating graphics") - allele_types = ["NIPHEM", "NIPH", "EXC", "PLOT", "ASM", "ALM", "INF", "LNF", "TPR"] + allele_types = [ + "NIPHEM", + "NIPH", + "EXC", + "PLOT", + "ASM", + "ALM", + "INF", + "LNF", + "TPR", + ] # inizialize classification data classif_data = {} for allele_type in allele_types: diff --git a/taranis/utils.py b/taranis/utils.py index 43beb10..4e94f32 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -30,6 +30,7 @@ from Bio import BiopythonWarning import pdb + log = logging.getLogger(__name__) @@ -117,7 +118,10 @@ def check_additional_programs_installed(software_list: list) -> None: sys.exit(1) return -def convert_to_protein(sequence: str, force_coding: bool =False, check_additional_bases: bool = False) -> dict: + +def convert_to_protein( + sequence: str, force_coding: bool = False, check_additional_bases: bool = False +) -> dict: """Check if the input sequence is a coding protein. Args: @@ -134,14 +138,14 @@ def convert_to_protein(sequence: str, force_coding: bool =False, check_additiona return {"error": "Sequence does not have a start codon"} if len(sequence) % 3 != 0: if not check_additional_bases: - return {"error" : "Sequence is not a multiple of three"} + return {"error": "Sequence is not a multiple of three"} # Remove the last or second to last bases to check if there is a stop codon new_seq_len = len(sequence) // 3 * 3 sequence = sequence[:new_seq_len] # this error will be overwritten if another error is found conv_result["error"] = "additional bases added after stop codon" - seq_sequence = Seq(sequence) + seq_sequence = Seq(sequence) try: seq_prot = seq_sequence.translate(table=1, cds=force_coding) except Bio.Data.CodonTable.TranslationError as e: @@ -153,14 +157,13 @@ def convert_to_protein(sequence: str, force_coding: bool =False, check_additiona if not force_coding: first_stop = seq_prot.find("*") if first_stop != last_stop: - return {"error": "Multiple stop codons","protein": str(seq_prot)} + return {"error": "Multiple stop codons", "protein": str(seq_prot)} if last_stop != len(seq_prot) - 1: return {"error": "Last sequence is not a stop codon", "protein": str(seq_prot)} if "error" in conv_result: conv_result["protein"] = str(seq_prot) return conv_result - return {"protein": str(seq_prot)} - + return {"protein": str(seq_prot)} def create_annotation_files( @@ -425,22 +428,15 @@ def get_snp_information( dict: key: ref_sequence, value: list of snp information """ # Supress warning that len of alt sequence not a multiple of three - warnings.simplefilter('ignore', BiopythonWarning) + warnings.simplefilter("ignore", BiopythonWarning) snp_info = {} ref_protein = str(Seq(ref_sequence).translate()) - try: - alt_protein = str(Seq(alt_sequence).translate()) - except Exception as e: - import pdb; pdb.set_trace() - - if len(alt_sequence) %3 != 0: - import pdb; pdb.set_trace() + alt_protein = str(Seq(alt_sequence).translate()) snp_line = [] # get the shortest sequence for the loop length_for_snp = min(len(ref_sequence), len(alt_sequence)) for idx in range(length_for_snp): - if ref_sequence[idx] != alt_sequence[idx]: # calculate the triplet index triplet_idx = idx // 3 @@ -449,7 +445,10 @@ def get_snp_information( alt_triplet = alt_sequence[triplet_idx * 3 : triplet_idx * 3 + 3] # get amino acid change ref_aa = ref_protein[triplet_idx] - alt_aa = alt_protein[triplet_idx] + try: + alt_aa = alt_protein[triplet_idx] + except: + alt_aa = "-" # get amino acid category ref_category = map_amino_acid_to_annotation(ref_sequence[triplet_idx]) alt_category = map_amino_acid_to_annotation(alt_sequence[triplet_idx]) @@ -679,8 +678,8 @@ def read_fasta_file(fasta_file: str, convert_to_dict=False) -> dict | str: if convert_to_dict: with open(fasta_file, "r") as fh: for record in SeqIO.parse(fh, "fasta"): - conv_fasta[record.id] = str(record.seq) - return conv_fasta + conv_fasta[record.id] = str(record.seq) + return conv_fasta return SeqIO.parse(fasta_file, "fasta") From 41804bc170f42b7c3667e983ef999c0d789a7e90 Mon Sep 17 00:00:00 2001 From: luissian Date: Sun, 7 Apr 2024 23:01:18 +0200 Subject: [PATCH 144/214] fixing litin --- taranis/__main__.py | 2 +- taranis/allele_calling.py | 15 +++++++-------- taranis/utils.py | 10 +++++++--- 3 files changed, 15 insertions(+), 12 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index aa3e23c..414ebed 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -569,5 +569,5 @@ def allele_calling( ) finish = time.perf_counter() print(f"Allele calling finish in {round((finish-start)/60, 2)} minutes") - log.info("Allele calling finish in %s minutes", round((finish-start)/60, 2)) + log.info("Allele calling finish in %s minutes", round((finish - start) / 60, 2)) # sample_allele_obj.analyze_sample() diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index efe4979..6570da2 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -12,8 +12,6 @@ from Bio import SeqIO from io import StringIO -import pdb - log = logging.getLogger(__name__) stderr = rich.console.Console( stderr=True, @@ -316,7 +314,6 @@ def _get_blast_details( prot_conv_result["protein"] if "protein" in prot_conv_result else "-" ) # remove if additional sequenced are added at the end of the stop codon - # pdb.set_trace() if "additional bases added after stop codon" in prot_error_result: new_seq_len = len(match_sequence) // 3 * 3 match_sequence = match_sequence[:new_seq_len] @@ -489,7 +486,7 @@ def search_match_allele(self): alleles = taranis.utils.read_fasta_file(ref_allele, convert_to_dict=True) match_found = False - """ + """ with open(ref_allele, "r") as fh: for record in SeqIO.parse(fh, "fasta"): alleles[record.id] = str(record.seq) @@ -534,15 +531,17 @@ def search_match_allele(self): # prepare the data for snp and alignment analysis try: ref_allele_seq = result["allele_details"][allele_name][15] - except: - pdb.set_trace() + except KeyError as e: + log.error("Error in allele details") + log.error(e) + stderr.print(f"Error in allele details{e}") + continue allele_seq = result["allele_details"][allele_name][14] ref_allele_name = result["allele_details"][allele_name][3] if self.snp_request and result["allele_type"][allele_name] != "LNF": # run snp analysis - print(allele_name) - # pdb.set_trace() + # print(allele_name) result["snp_data"][allele_name] = taranis.utils.get_snp_information( ref_allele_seq, allele_seq, ref_allele_name ) diff --git a/taranis/utils.py b/taranis/utils.py index 4e94f32..dc8d07f 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -29,8 +29,6 @@ import warnings from Bio import BiopythonWarning -import pdb - log = logging.getLogger(__name__) @@ -447,7 +445,13 @@ def get_snp_information( ref_aa = ref_protein[triplet_idx] try: alt_aa = alt_protein[triplet_idx] - except: + except IndexError as e: + log.debug( + "Unable to get amino acid for %s and %s with error %s", + ref_allele_name, + alt_sequence, + e, + ) alt_aa = "-" # get amino acid category ref_category = map_amino_acid_to_annotation(ref_sequence[triplet_idx]) From c69a8d2359fe0f35276eeb884ac8d60361e7cf4d Mon Sep 17 00:00:00 2001 From: luissian Date: Sun, 7 Apr 2024 23:03:50 +0200 Subject: [PATCH 145/214] fixing litin on allele_calling --- taranis/allele_calling.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 6570da2..0b42eda 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -547,10 +547,10 @@ def search_match_allele(self): ) if self.aligment_request and result["allele_type"][allele_name] != "LNF": # run alignment analysis - result["alignment_data"][ - allele_name - ] = taranis.utils.get_alignment_data( - ref_allele_seq, allele_seq, ref_allele_name + result["alignment_data"][allele_name] = ( + taranis.utils.get_alignment_data( + ref_allele_seq, allele_seq, ref_allele_name + ) ) # delete blast folder _ = taranis.utils.delete_folder(self.blast_dir) From 83a03e44cefc2a171172f076716bc94b36c1e5ee Mon Sep 17 00:00:00 2001 From: luissian Date: Tue, 9 Apr 2024 17:48:07 +0200 Subject: [PATCH 146/214] Fixed issue on not start codon --- taranis/allele_calling.py | 147 ++++++++++++++++++++++++-------------- taranis/utils.py | 32 +++++---- 2 files changed, 111 insertions(+), 68 deletions(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 0b42eda..1f1b926 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -12,6 +12,8 @@ from Bio import SeqIO from io import StringIO +import pdb + log = logging.getLogger(__name__) stderr = rich.console.Console( stderr=True, @@ -168,6 +170,10 @@ def _extend_sequence_for_finding_start_stop_codon( data_for_plot[9] = split_blast_result[10] # check if PLOT if not _check_if_plot(data_for_plot): + # remove the "-" character in the contig sequence in case that + # there was not possible to find the start/stop codon and + # function return the original blast result + split_blast_result[13] = split_blast_result[13].replace("-", "") # fetch the sequence until the last triplet is stop codon contig_seq = self.sample_records[split_blast_result[1]] start_seq = int(split_blast_result[9]) @@ -176,15 +182,31 @@ def _extend_sequence_for_finding_start_stop_codon( # sequence direction is forward direction = "forward" if search_codon == "start": - if ( - contig_seq[start_seq - 2 : start_seq + 1] - in taranis.utils.START_CODON_FORWARD - ): - start_seq -= 2 - # continue to find the stop codon with the new start - else: + # add nucleotides according to the first match in the + # reference allele + start_ref_allele = int(split_blast_result[11]) // 3 * 3 + # try extended 1 nucleotide to find the start codon + start_seq_found = False + # subtract 1 because index start at 0 + new_start_seq = start_seq - start_ref_allele -1 + # pdb.set_trace() + for _ in range(1, 3): + new_start_seq -= 1 + if ( + contig_seq[new_start_seq : new_start_seq + 3] + in taranis.utils.START_CODON_FORWARD + ): + # increase 1 because we substact 1 when searching + # for stop codon + start_seq = new_start_seq + 1 + start_seq_found = True + break + # continue to find the stop codon with the new start + # pdb.set_trace() + if not start_seq_found: # start codon not found. Return the original blast result - return split_blast_result + return split_blast_result, prot_error_result, predicted_prot_seq + # adjust the sequence to be a triplet interval = (stop_seq - start_seq) // 3 * 3 new_stop_seq = start_seq + interval + self.increase_sequence @@ -201,6 +223,7 @@ def _extend_sequence_for_finding_start_stop_codon( # sequence direction is reverse direction = "reverse" if search_codon == "start": + # pdb.set_trace() if ( contig_seq[start_seq - 2 : start_seq + 1] in taranis.utils.START_CODON_REVERSE @@ -209,7 +232,8 @@ def _extend_sequence_for_finding_start_stop_codon( # continue to find the stop codon with the new start else: # start codon not found. Return the original blast result - return split_blast_result + return split_blast_result, prot_error_result, predicted_prot_seq + # pdb.set_trace() # adjust the sequence to be a triplet interval = (start_seq - stop_seq) // 3 * 3 new_stop_seq = start_seq - interval - self.increase_sequence @@ -226,7 +250,7 @@ def _extend_sequence_for_finding_start_stop_codon( Seq(contig_seq[stop_seq:start_seq]).reverse_complement() ) new_prot_conv_result = taranis.utils.convert_to_protein( - c_sequence, force_coding=False, check_additional_bases=False + c_sequence, force_coding=False, delete_incompleted_triplet=False ) # check if stop codon is found in protein sequence @@ -238,7 +262,7 @@ def _extend_sequence_for_finding_start_stop_codon( new_seq_length = new_prot_conv_result["protein"].index("*") * 3 + 3 match_sequence = c_sequence[:new_seq_length] split_blast_result[4] = str(new_seq_length) - split_blast_result[14] = match_sequence + split_blast_result[13] = match_sequence prot_error_result = "-" predicted_prot_seq = new_prot_conv_result["protein"][ 0 : new_seq_length // 3 @@ -249,10 +273,11 @@ def _extend_sequence_for_finding_start_stop_codon( int(split_blast_result[9]) + new_seq_length ) else: - split_blast_result[9] = str( - int(split_blast_result[10]) - new_seq_length + split_blast_result[10] = str( + int(split_blast_result[9]) - new_seq_length ) # ignore the previous process if stop codon is not found + return split_blast_result, prot_error_result, predicted_prot_seq def _get_blast_details( @@ -280,9 +305,9 @@ def _get_blast_details( blast_details[11] = gene annotation blast_details[12] = product annotation blast_details[13] = allele quality - blast_details[14] = match sequence in contig - blast_details[15] = reference allele sequence - blast_details[16] = protein conversion result + blast_details[14] = protein conversion result + blast_details[15] = match sequence in contig + blast_details[16] = reference allele sequence blast_details[17] = predicted protein sequence """ split_blast_result = blast_result.split("\t") @@ -305,7 +330,7 @@ def _get_blast_details( match_sequence = split_blast_result[13].replace("-", "") # check if the sequence is coding prot_conv_result = taranis.utils.convert_to_protein( - match_sequence, force_coding=False, check_additional_bases=True + match_sequence, force_coding=False, delete_incompleted_triplet=True ) prot_error_result = ( prot_conv_result["error"] if "error" in prot_conv_result else "-" @@ -313,13 +338,17 @@ def _get_blast_details( predicted_prot_seq = ( prot_conv_result["protein"] if "protein" in prot_conv_result else "-" ) - # remove if additional sequenced are added at the end of the stop codon - if "additional bases added after stop codon" in prot_error_result: + # remove if extra nucleotides are added at the end of the last + # completed triplet before the stop codon + if "extra nucleotides after stop codon" in prot_error_result: new_seq_len = len(match_sequence) // 3 * 3 match_sequence = match_sequence[:new_seq_len] split_blast_result[4] = str(new_seq_len) - # add more sequence to find the stop codon - elif "Last sequence is not a stop codon" in prot_error_result: + # reset the error message + prot_error_result = "-" + + # extend the sequence to find the stop codon + elif "Last triplet sequence is not a stop codon" in prot_error_result: ( split_blast_result, prot_error_result, @@ -330,6 +359,10 @@ def _get_blast_details( predicted_prot_seq, search_codon="stop", ) + # update the match sequence + match_sequence = split_blast_result[13] + # pdb.set_trace() + # extend the sequence to find the start codon elif "Sequence does not have a start codon" in prot_error_result: ( split_blast_result, @@ -341,6 +374,10 @@ def _get_blast_details( predicted_prot_seq, search_codon="start", ) + # pdb.set_trace() + # update the match sequence + match_sequence = split_blast_result[13] + # get blast details blast_details = [ self.s_name, # sample name @@ -357,12 +394,11 @@ def _get_blast_details( gene_annotation, product_annotation, allele_quality, + prot_error_result, # protein conversion result match_sequence, # match sequence in contig ref_allele_seq, # reference allele sequence - prot_error_result, # protein conversion result predicted_prot_seq, # predicted protein sequence ] - return blast_details def find_match_allele_schema(allele_file: str, match_sequence: str) -> str: @@ -384,9 +420,10 @@ def find_match_allele_schema(allele_file: str, match_sequence: str) -> str: valid_blast_results = _discard_low_threshold_results(blast_results) match_allele_schema = "" + pdb.set_trace() if len(valid_blast_results) == 0: # no match results labelled as LNF. details data filled with empty data - return ["LNF", "-", ["-"] * 18] + return ["LNF", "LNF", ["-"] * 18] if len(valid_blast_results) > 1: # could be NIPHEM or NIPH b_split_data = [] @@ -402,7 +439,7 @@ def find_match_allele_schema(allele_file: str, match_sequence: str) -> str: if match_allele_schema == "": # find the allele in schema with the match sequence in the contig match_allele_schema = find_match_allele_schema( - allele_file, multi_allele_data[14] + allele_file, multi_allele_data[15] ) if len(set(match_allele_seq)) == 1: # all sequuences are equal labelled as NIPHEM @@ -419,8 +456,9 @@ def find_match_allele_schema(allele_file: str, match_sequence: str) -> str: ) # found the allele in schema with the match sequence in the contig match_allele_schema = find_match_allele_schema( - allele_file, b_split_data[14] + allele_file, b_split_data[15] ) + # PLOT, TPR, ASM, ALM, INF, EXC are possible classifications if match_allele_schema != "": # exact match found labelled as EXC @@ -431,7 +469,7 @@ def find_match_allele_schema(allele_file: str, match_sequence: str) -> str: # check if protein length divided by the length of triplet matched # sequence is lower the the tpr limit elif ( - b_split_data[16] == "Multiple stop codons" + b_split_data[14] == "Multiple stop codons" and b_split_data[17].index("*") / (int(b_split_data[6]) / 3) < self.tpr_limit ): @@ -443,7 +481,7 @@ def find_match_allele_schema(allele_file: str, match_sequence: str) -> str: # check if match allele is longer than reference allele elif ( int(b_split_data[6]) > int(b_split_data[5]) - or b_split_data[16] == "Last sequence is not a stop codon" + or b_split_data[14] == "Last sequence is not a stop codon" ): classification = "ALM" else: @@ -455,6 +493,7 @@ def find_match_allele_schema(allele_file: str, match_sequence: str) -> str: match_allele_schema = str( self.inf_alle_obj.get_inferred_allele(b_split_data[14], allele_name) ) + pdb.set_trace() b_split_data[4] = classification + "_" + match_allele_schema return [ classification, @@ -486,11 +525,6 @@ def search_match_allele(self): alleles = taranis.utils.read_fasta_file(ref_allele, convert_to_dict=True) match_found = False - """ - with open(ref_allele, "r") as fh: - for record in SeqIO.parse(fh, "fasta"): - alleles[record.id] = str(record.seq) - """ count_2 = 0 for r_id, r_seq in alleles.items(): count_2 += 1 @@ -511,9 +545,9 @@ def search_match_allele(self): break # Close object and discard memory buffer query_file.close() + allele_file = os.path.join(self.schema, os.path.basename(ref_allele)) + allele_name = Path(allele_file).stem if match_found: - allele_file = os.path.join(self.schema, os.path.basename(ref_allele)) - allele_name = Path(allele_file).stem ( result["allele_type"][allele_name], result["allele_match"][allele_name], @@ -527,21 +561,20 @@ def search_match_allele(self): result["allele_type"][allele_name] = "LNF" result["allele_match"][allele_name] = allele_name result["allele_details"][allele_name] = "LNF" - + pdb.set_trace() # prepare the data for snp and alignment analysis try: - ref_allele_seq = result["allele_details"][allele_name][15] + ref_allele_seq = result["allele_details"][allele_name][16] except KeyError as e: log.error("Error in allele details") log.error(e) stderr.print(f"Error in allele details{e}") continue - allele_seq = result["allele_details"][allele_name][14] + allele_seq = result["allele_details"][allele_name][15] ref_allele_name = result["allele_details"][allele_name][3] if self.snp_request and result["allele_type"][allele_name] != "LNF": # run snp analysis - # print(allele_name) result["snp_data"][allele_name] = taranis.utils.get_snp_information( ref_allele_seq, allele_seq, ref_allele_name ) @@ -667,10 +700,10 @@ def read_reference_alleles(ref_alleles: list) -> dict[dict]: "direction", "gene notation", "product notation", - "allele quality", + "reference allele quality", + "protein conversion result", "match sequence", "reference allele sequence", - "protein conversion result", "predicted protein sequence", ] @@ -750,17 +783,22 @@ def read_reference_alleles(ref_alleles: list) -> dict[dict]: if aligment_request: alignment_folder = os.path.join(output, "alignments") _ = taranis.utils.create_new_folder(alignment_folder) + align_collection = {} for result in results: for sample, values in result.items(): for allele, alignment_data in values["alignment_data"].items(): - with open( - os.path.join(alignment_folder, sample + "_" + allele + ".txt"), - "w", - ) as fo: - for ref_allele, alignments in alignment_data.items(): - fo.write(ref_allele + "\n") - for alignment in alignments: - fo.write(alignment + "\n") + if allele not in align_collection: + align_collection[allele] = OrderedDict() + + # align_collection[allele][sample] = [] + for _, value in alignment_data.items(): + align_collection[allele][sample] = value + # save alignment to file + for allele, samples in align_collection.items(): + with open(os.path.join(alignment_folder, allele + ".txt"), "w") as fo: + for sample, alignment_data in samples.items(): + fo.write(allele + "_sample_" + sample + "\n") + fo.write("\n".join(alignment_data) + "\n") # create multiple alignment files stderr.print("Processing multiple alignment information") @@ -772,14 +810,14 @@ def read_reference_alleles(ref_alleles: list) -> dict[dict]: input_buffer = StringIO() # get the reference allele sequence input_buffer.write(">Ref_" + ref_id + "\n") - input_buffer.write(str(ref_seq) + "\n") + input_buffer.write(ref_seq + "\n") # get the sequences for sample on the same allele for result in results: for sample, values in result.items(): # discard the allele if it is LNF if values["allele_type"][a_list] == "LNF": continue - # get the allele in sample that match + # get the allele name in sample input_buffer.write( ">" + sample @@ -790,9 +828,10 @@ def read_reference_alleles(ref_alleles: list) -> dict[dict]: + "\n" ) # get the sequence of the allele in sample - input_buffer.write(values["allele_details"][a_list][14] + "\n") - input_buffer.seek(0) - + input_buffer.write(values["allele_details"][a_list][15] + "\n") + # print(input_buffer.tell()) + input_buffer.seek(0) + allele_multiple_align.append( taranis.utils.get_multiple_alignment(input_buffer) ) diff --git a/taranis/utils.py b/taranis/utils.py index dc8d07f..1bab096 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -24,7 +24,7 @@ from Bio import SeqIO from Bio.Seq import Seq from collections import OrderedDict - +import pdb import warnings from Bio import BiopythonWarning @@ -118,30 +118,33 @@ def check_additional_programs_installed(software_list: list) -> None: def convert_to_protein( - sequence: str, force_coding: bool = False, check_additional_bases: bool = False + sequence: str, force_coding: bool = False, delete_incompleted_triplet: bool = False ) -> dict: """Check if the input sequence is a coding protein. Args: sequence (str): sequence to be checked - force_coding (bool, optional): force to check if sequence is coding. Defaults to False. - check_additional_bases (bool, optional): if not multiple by 3 remove the latest sequences to check they are added after the stop codon. Defaults to False. + force_coding (bool, optional): force to check if sequence is coding. + Defaults to False. + delete_incompleted_triplet (bool, optional): if not multiple by 3 + remove the latest sequences to check they are added after the stop + codon. Defaults to False. Returns: dict: protein sequence and/or error message """ - conv_result = {} + conv_result = {"error": "-"} # checck if exists start codon if sequence[0:3] not in START_CODON_FORWARD: return {"error": "Sequence does not have a start codon"} if len(sequence) % 3 != 0: - if not check_additional_bases: + if not delete_incompleted_triplet: return {"error": "Sequence is not a multiple of three"} # Remove the last or second to last bases to check if there is a stop codon new_seq_len = len(sequence) // 3 * 3 sequence = sequence[:new_seq_len] # this error will be overwritten if another error is found - conv_result["error"] = "additional bases added after stop codon" + conv_result["error"] = "extra nucleotides after stop codon" seq_sequence = Seq(sequence) try: @@ -155,13 +158,11 @@ def convert_to_protein( if not force_coding: first_stop = seq_prot.find("*") if first_stop != last_stop: - return {"error": "Multiple stop codons", "protein": str(seq_prot)} + conv_result["error"] = "Multiple stop codons" if last_stop != len(seq_prot) - 1: - return {"error": "Last sequence is not a stop codon", "protein": str(seq_prot)} - if "error" in conv_result: - conv_result["protein"] = str(seq_prot) - return conv_result - return {"protein": str(seq_prot)} + conv_result["error"] = "Last triplet sequence is not a stop codon" + conv_result["protein"] = str(seq_prot) + return conv_result def create_annotation_files( @@ -429,7 +430,10 @@ def get_snp_information( warnings.simplefilter("ignore", BiopythonWarning) snp_info = {} ref_protein = str(Seq(ref_sequence).translate()) - alt_protein = str(Seq(alt_sequence).translate()) + try: + alt_protein = str(Seq(alt_sequence).translate()) + except: + pdb.set_trace() snp_line = [] # get the shortest sequence for the loop From 32c87ec6ec4dfb97a0c04be3007815785c30ddcb Mon Sep 17 00:00:00 2001 From: luissian Date: Tue, 9 Apr 2024 18:19:21 +0200 Subject: [PATCH 147/214] fixed ouput data when LNF --- taranis/__main__.py | 3 +- taranis/allele_calling.py | 114 ++++++++++++++++++++------------------ 2 files changed, 61 insertions(+), 56 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index 414ebed..1c9f66d 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -520,7 +520,7 @@ def allele_calling( start = time.perf_counter() results = [] - """ + with concurrent.futures.ThreadPoolExecutor(max_workers=cpus) as executor: futures = [ executor.submit( @@ -564,6 +564,7 @@ def allele_calling( increase_sequence, ) ) + """ _ = taranis.allele_calling.collect_data( results, output, snp, alignment, schema_ref_files ) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 1f1b926..c5b2302 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -12,8 +12,6 @@ from Bio import SeqIO from io import StringIO -import pdb - log = logging.getLogger(__name__) stderr = rich.console.Console( stderr=True, @@ -78,7 +76,7 @@ def __init__( def assign_allele_type( self, - blast_results: list, + valid_blast_results: list, allele_file: str, allele_name: str, ref_allele_seq: str, @@ -86,7 +84,7 @@ def assign_allele_type( """Assign allele type to the allele Args: - blast_result (list): information collected by running blast + valid_blast_results (list): information collected by running blast allele_file (str): file name with allele sequence allele_name (str): allele name ref_allele_seq (str): reference allele sequence @@ -116,24 +114,6 @@ def _check_if_plot(column_blast_res: list) -> bool: return True return False - def _discard_low_threshold_results(blast_results: list) -> list: - """Discard blast results with lower threshold - - Args: - blast_results (list): blast results - - Returns: - list: blast results with higher query size - """ - valid_blast_result = [] - for b_result in blast_results: - blast_split = b_result.split("\t") - # check if the division of the match contig length by the - # reference allele length is higher than the threshold - if (int(blast_split[4]) / int(blast_split[3])) >= self.threshold: - valid_blast_result.append(b_result) - return valid_blast_result - def _extend_sequence_for_finding_start_stop_codon( split_blast_result: list, prot_error_result: str, @@ -188,8 +168,7 @@ def _extend_sequence_for_finding_start_stop_codon( # try extended 1 nucleotide to find the start codon start_seq_found = False # subtract 1 because index start at 0 - new_start_seq = start_seq - start_ref_allele -1 - # pdb.set_trace() + new_start_seq = start_seq - start_ref_allele - 1 for _ in range(1, 3): new_start_seq -= 1 if ( @@ -197,15 +176,18 @@ def _extend_sequence_for_finding_start_stop_codon( in taranis.utils.START_CODON_FORWARD ): # increase 1 because we substact 1 when searching - # for stop codon + # for stop codon start_seq = new_start_seq + 1 start_seq_found = True break # continue to find the stop codon with the new start - # pdb.set_trace() if not start_seq_found: # start codon not found. Return the original blast result - return split_blast_result, prot_error_result, predicted_prot_seq + return ( + split_blast_result, + prot_error_result, + predicted_prot_seq, + ) # adjust the sequence to be a triplet interval = (stop_seq - start_seq) // 3 * 3 @@ -223,7 +205,6 @@ def _extend_sequence_for_finding_start_stop_codon( # sequence direction is reverse direction = "reverse" if search_codon == "start": - # pdb.set_trace() if ( contig_seq[start_seq - 2 : start_seq + 1] in taranis.utils.START_CODON_REVERSE @@ -232,8 +213,11 @@ def _extend_sequence_for_finding_start_stop_codon( # continue to find the stop codon with the new start else: # start codon not found. Return the original blast result - return split_blast_result, prot_error_result, predicted_prot_seq - # pdb.set_trace() + return ( + split_blast_result, + prot_error_result, + predicted_prot_seq, + ) # adjust the sequence to be a triplet interval = (start_seq - stop_seq) // 3 * 3 new_stop_seq = start_seq - interval - self.increase_sequence @@ -361,7 +345,6 @@ def _get_blast_details( ) # update the match sequence match_sequence = split_blast_result[13] - # pdb.set_trace() # extend the sequence to find the start codon elif "Sequence does not have a start codon" in prot_error_result: ( @@ -374,10 +357,8 @@ def _get_blast_details( predicted_prot_seq, search_codon="start", ) - # pdb.set_trace() # update the match sequence match_sequence = split_blast_result[13] - # get blast details blast_details = [ self.s_name, # sample name @@ -401,7 +382,7 @@ def _get_blast_details( ] return blast_details - def find_match_allele_schema(allele_file: str, match_sequence: str) -> str: + def _find_match_allele_schema(allele_file: str, match_sequence: str) -> str: """Find the allele name in the schema that match the sequence Args: @@ -418,12 +399,11 @@ def find_match_allele_schema(allele_file: str, match_sequence: str) -> str: return grep_result[0].split("_")[1] return "" - valid_blast_results = _discard_low_threshold_results(blast_results) + # valid_blast_results = _discard_low_threshold_results(blast_results) match_allele_schema = "" - pdb.set_trace() - if len(valid_blast_results) == 0: - # no match results labelled as LNF. details data filled with empty data - return ["LNF", "LNF", ["-"] * 18] + # if len(valid_blast_results) == 0: + # no match results labelled as LNF. details data filled with empty data + # return ["LNF", "LNF", ["-"] * 18] if len(valid_blast_results) > 1: # could be NIPHEM or NIPH b_split_data = [] @@ -438,7 +418,7 @@ def find_match_allele_schema(allele_file: str, match_sequence: str) -> str: # check if match allele is in schema if match_allele_schema == "": # find the allele in schema with the match sequence in the contig - match_allele_schema = find_match_allele_schema( + match_allele_schema = _find_match_allele_schema( allele_file, multi_allele_data[15] ) if len(set(match_allele_seq)) == 1: @@ -455,7 +435,7 @@ def find_match_allele_schema(allele_file: str, match_sequence: str) -> str: valid_blast_results[0], allele_name, ref_allele_seq ) # found the allele in schema with the match sequence in the contig - match_allele_schema = find_match_allele_schema( + match_allele_schema = _find_match_allele_schema( allele_file, b_split_data[15] ) @@ -493,7 +473,6 @@ def find_match_allele_schema(allele_file: str, match_sequence: str) -> str: match_allele_schema = str( self.inf_alle_obj.get_inferred_allele(b_split_data[14], allele_name) ) - pdb.set_trace() b_split_data[4] = classification + "_" + match_allele_schema return [ classification, @@ -501,6 +480,24 @@ def find_match_allele_schema(allele_file: str, match_sequence: str) -> str: b_split_data, ] + def discard_low_threshold_results(self, blast_results: list) -> list: + """Discard blast results with lower threshold + + Args: + blast_results (list): blast results + + Returns: + list: blast results with higher query size + """ + valid_blast_result = [] + for b_result in blast_results: + blast_split = b_result.split("\t") + # check if the division of the match contig length by the + # reference allele length is higher than the threshold + if (int(blast_split[4]) / int(blast_split[3])) >= self.threshold: + valid_blast_result.append(b_result) + return valid_blast_result + def search_match_allele(self): # Create blast db with sample file @@ -541,8 +538,12 @@ def search_match_allele(self): query_type="stdin", ) if len(blast_result) > 0: - match_found = True - break + valid_blast_results = self.discard_low_threshold_results( + blast_result + ) + if len(valid_blast_results) > 0: + match_found = True + break # Close object and discard memory buffer query_file.close() allele_file = os.path.join(self.schema, os.path.basename(ref_allele)) @@ -553,15 +554,18 @@ def search_match_allele(self): result["allele_match"][allele_name], result["allele_details"][allele_name], ) = self.assign_allele_type( - blast_result, allele_file, allele_name, r_seq + valid_blast_results, allele_file, allele_name, r_seq ) else: # Sample does not have a reference allele to be matched # Keep LNF info result["allele_type"][allele_name] = "LNF" result["allele_match"][allele_name] = allele_name - result["allele_details"][allele_name] = "LNF" - pdb.set_trace() + details = ["-"] * 18 + details[0] = self.s_name + details[2] = allele_name + details[4] = "LNF" + result["allele_details"][allele_name] = details # prepare the data for snp and alignment analysis try: ref_allele_seq = result["allele_details"][allele_name][16] @@ -580,13 +584,13 @@ def search_match_allele(self): ) if self.aligment_request and result["allele_type"][allele_name] != "LNF": # run alignment analysis - result["alignment_data"][allele_name] = ( - taranis.utils.get_alignment_data( - ref_allele_seq, allele_seq, ref_allele_name - ) + result["alignment_data"][ + allele_name + ] = taranis.utils.get_alignment_data( + ref_allele_seq, allele_seq, ref_allele_name ) # delete blast folder - _ = taranis.utils.delete_folder(self.blast_dir) + _ = taranis.utils.delete_folder(os.path.join(self.blast_dir, self.s_name)) return result @@ -789,7 +793,7 @@ def read_reference_alleles(ref_alleles: list) -> dict[dict]: for allele, alignment_data in values["alignment_data"].items(): if allele not in align_collection: align_collection[allele] = OrderedDict() - + # align_collection[allele][sample] = [] for _, value in alignment_data.items(): align_collection[allele][sample] = value @@ -797,7 +801,7 @@ def read_reference_alleles(ref_alleles: list) -> dict[dict]: for allele, samples in align_collection.items(): with open(os.path.join(alignment_folder, allele + ".txt"), "w") as fo: for sample, alignment_data in samples.items(): - fo.write(allele + "_sample_" + sample + "\n") + fo.write(allele + "_sample_" + sample + "\n") fo.write("\n".join(alignment_data) + "\n") # create multiple alignment files @@ -831,7 +835,7 @@ def read_reference_alleles(ref_alleles: list) -> dict[dict]: input_buffer.write(values["allele_details"][a_list][15] + "\n") # print(input_buffer.tell()) input_buffer.seek(0) - + allele_multiple_align.append( taranis.utils.get_multiple_alignment(input_buffer) ) From 5b0bc00db3dfdd42f91586f0aa077c846175d41e Mon Sep 17 00:00:00 2001 From: luissian Date: Tue, 9 Apr 2024 18:41:45 +0200 Subject: [PATCH 148/214] liting --- taranis/allele_calling.py | 8 ++++---- taranis/utils.py | 15 ++++++++++----- 2 files changed, 14 insertions(+), 9 deletions(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index c5b2302..baa31fe 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -584,10 +584,10 @@ def search_match_allele(self): ) if self.aligment_request and result["allele_type"][allele_name] != "LNF": # run alignment analysis - result["alignment_data"][ - allele_name - ] = taranis.utils.get_alignment_data( - ref_allele_seq, allele_seq, ref_allele_name + result["alignment_data"][allele_name] = ( + taranis.utils.get_alignment_data( + ref_allele_seq, allele_seq, ref_allele_name + ) ) # delete blast folder _ = taranis.utils.delete_folder(os.path.join(self.blast_dir, self.s_name)) diff --git a/taranis/utils.py b/taranis/utils.py index 1bab096..50ac906 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -24,7 +24,6 @@ from Bio import SeqIO from Bio.Seq import Seq from collections import OrderedDict -import pdb import warnings from Bio import BiopythonWarning @@ -430,10 +429,16 @@ def get_snp_information( warnings.simplefilter("ignore", BiopythonWarning) snp_info = {} ref_protein = str(Seq(ref_sequence).translate()) - try: - alt_protein = str(Seq(alt_sequence).translate()) - except: - pdb.set_trace() + if len(alt_sequence) % 3 != 0: + log.debug( + "Sequence %s is not a multiple of three. Removing last nucleotides", + ref_allele_name, + ) + # remove the last nucleotides to be able to translate to protein + alt_sequence = alt_sequence[: len(alt_sequence) // 3 * 3] + + alt_protein = str(Seq(alt_sequence).translate()) + snp_line = [] # get the shortest sequence for the loop From bcda9724ffb9de77492d9fbe188f2585b9439dff Mon Sep 17 00:00:00 2001 From: luissian Date: Tue, 9 Apr 2024 18:43:30 +0200 Subject: [PATCH 149/214] liting 2 --- taranis/utils.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/taranis/utils.py b/taranis/utils.py index 50ac906..d297d36 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -438,8 +438,6 @@ def get_snp_information( alt_sequence = alt_sequence[: len(alt_sequence) // 3 * 3] alt_protein = str(Seq(alt_sequence).translate()) - - snp_line = [] # get the shortest sequence for the loop length_for_snp = min(len(ref_sequence), len(alt_sequence)) From 4b0377e4494d8f3d5e63a74a1cb1aa853f011e68 Mon Sep 17 00:00:00 2001 From: luissian Date: Wed, 10 Apr 2024 19:19:42 +0200 Subject: [PATCH 150/214] Implementing parallel at multi alignment --- taranis/__main__.py | 8 ++- taranis/allele_calling.py | 131 +++++++++++++++++++++++++------------- taranis/utils.py | 8 ++- 3 files changed, 99 insertions(+), 48 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index 1c9f66d..598c4de 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -505,7 +505,11 @@ def allele_calling( _ = taranis.utils.prompt_user_if_folder_exists(output) # Filter fasta files from reference folder # ref_alleles = glob.glob(os.path.join(reference, "*.fasta")) - + max_cpus = taranis.utils.cpus_available() + if cpus > max_cpus: + stderr.print("[red] Number of CPUs bigger than the CPUs available") + stderr.print("Running code with ", max_cpus) + cpus = max_cpus # Read the annotation file stderr.print("[green] Reading annotation file") log.info("Reading annotation file") @@ -566,7 +570,7 @@ def allele_calling( ) """ _ = taranis.allele_calling.collect_data( - results, output, snp, alignment, schema_ref_files + results, output, snp, alignment, schema_ref_files, cpus ) finish = time.perf_counter() print(f"Allele calling finish in {round((finish-start)/60, 2)} minutes") diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index baa31fe..255097d 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -1,4 +1,5 @@ import io +import concurrent.futures import logging import os import rich.console @@ -584,10 +585,10 @@ def search_match_allele(self): ) if self.aligment_request and result["allele_type"][allele_name] != "LNF": # run alignment analysis - result["alignment_data"][allele_name] = ( - taranis.utils.get_alignment_data( - ref_allele_seq, allele_seq, ref_allele_name - ) + result["alignment_data"][ + allele_name + ] = taranis.utils.get_alignment_data( + ref_allele_seq, allele_seq, ref_allele_name ) # delete blast folder _ = taranis.utils.delete_folder(os.path.join(self.blast_dir, self.s_name)) @@ -628,13 +629,70 @@ def parallel_execution( return {sample_name: allele_obj.search_match_allele()} +def create_multiple_alignment( + ref_alleles_seq: dict, results: list, a_list: str, alignment_folder: str, mafft_cpus +) -> None: + allele_multiple_align = [] + for ref_id, ref_seq in ref_alleles_seq[a_list].items(): + input_buffer = StringIO() + # get the reference allele sequence + input_buffer.write(">Ref_" + ref_id + "\n") + input_buffer.write(ref_seq + "\n") + # get the sequences for sample on the same allele + for result in results: + for sample, values in result.items(): + # discard the allele if it is LNF + if values["allele_type"][a_list] == "LNF": + continue + # get the allele name in sample + input_buffer.write( + ">" + + sample + + "_" + + a_list + + "_" + + values["allele_details"][a_list][4] + + "\n" + ) + # get the sequence of the allele in sample + input_buffer.write(values["allele_details"][a_list][15] + "\n") + # print(input_buffer.tell()) + input_buffer.seek(0) + + allele_multiple_align.append( + taranis.utils.get_multiple_alignment(input_buffer, mafft_cpus) + ) + # release memory + input_buffer.close() + # save multiple alignment to file + with open( + os.path.join(alignment_folder, a_list + "_multiple_alignment.aln"), "w" + ) as fo: + for alignment in allele_multiple_align: + for align in alignment: + fo.write(align) + + def collect_data( results: list, output: str, snp_request: bool, aligment_request: bool, ref_alleles: list, + cpus: int, ) -> None: + """Collect data for the allele calling analysis, done for each sample and + create the summary file, graphics, and if requested snp and alignment files + + Args: + results (list): list of allele calling data results for each sample + output (str): output folder + snp_request (bool): request to save snp to file + aligment_request (bool): request to save alignment and multi alignemte to file + ref_alleles (list): reference alleles + cpus (int): number of cpus to be used if alignment is requested + """ + def stats_graphics(stats_folder: str, summary_result: dict) -> None: stderr.print("Creating graphics") log.info("Creating graphics") @@ -808,46 +866,33 @@ def read_reference_alleles(ref_alleles: list) -> dict[dict]: stderr.print("Processing multiple alignment information") log.info("Processing multiple alignment information") ref_alleles_seq = read_reference_alleles(ref_alleles) - for a_list in allele_list: - allele_multiple_align = [] - for ref_id, ref_seq in ref_alleles_seq[a_list].items(): - input_buffer = StringIO() - # get the reference allele sequence - input_buffer.write(">Ref_" + ref_id + "\n") - input_buffer.write(ref_seq + "\n") - # get the sequences for sample on the same allele - for result in results: - for sample, values in result.items(): - # discard the allele if it is LNF - if values["allele_type"][a_list] == "LNF": - continue - # get the allele name in sample - input_buffer.write( - ">" - + sample - + "_" - + a_list - + "_" - + values["allele_details"][a_list][4] - + "\n" - ) - # get the sequence of the allele in sample - input_buffer.write(values["allele_details"][a_list][15] + "\n") - # print(input_buffer.tell()) - input_buffer.seek(0) - - allele_multiple_align.append( - taranis.utils.get_multiple_alignment(input_buffer) + # assign cpus to be used in multiple alignment + mul_align_cpus = 1 if cpus // 3 == 0 else cpus // 3 + mafft_cpus = 1 if mul_align_cpus == 1 else 3 + m_align = [] + with concurrent.futures.ThreadPoolExecutor( + max_workers=mul_align_cpus + ) as executor: + futures = [ + executor.submit( + create_multiple_alignment, + ref_alleles_seq, + results, + a_list, + alignment_folder, + mafft_cpus, ) - # release memory - input_buffer.close() - # save multiple alignment to file - with open( - os.path.join(alignment_folder, a_list + "_multiple_alignment.aln"), "w" - ) as fo: - for alignment in allele_multiple_align: - for align in alignment: - fo.write(align) + for a_list in allele_list + ] + for future in concurrent.futures.as_completed(futures): + try: + m_align.append(future.result()) + except Exception as e: + print(e) + continue + + # for a_list in allele_list: + # _ = create_multiple_alignment(ref_alleles_seq, results, a_list, alignment_folder) # Create graphics stats_graphics(output, summary_result) diff --git a/taranis/utils.py b/taranis/utils.py index d297d36..d83228d 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -377,18 +377,20 @@ def get_files_in_folder(folder: str, extension: str = None) -> list[str]: return glob.glob(folder_files) -def get_multiple_alignment(input_buffer: io.StringIO) -> list[str]: +def get_multiple_alignment(input_buffer: io.StringIO, mafft_cpus: int) -> list[str]: """Run MAFFT with input from the string buffer and capture output to another string buffer Args: input_buffer (io.StringIO): fasta sequences to be aligned - + mafft_cpus (int): number of cpus to be used in mafft Returns: list[str]: list of aligned sequences """ output_buffer = io.StringIO() # Run MAFFT - mafft_command = "mafft --auto --quiet -" # "-" tells MAFFT to read from stdin + mafft_command = ( + "mafft --auto --quiet --thread " + str(mafft_cpus) + " -" + ) # "-" tells MAFFT to read from stdin process = subprocess.Popen( mafft_command, shell=True, stdin=subprocess.PIPE, stdout=subprocess.PIPE ) From d9269ce8b5e435ad35e0c7823840598541c6bda8 Mon Sep 17 00:00:00 2001 From: luissian Date: Wed, 10 Apr 2024 19:23:40 +0200 Subject: [PATCH 151/214] litin --- taranis/allele_calling.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 255097d..6909a96 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -585,10 +585,10 @@ def search_match_allele(self): ) if self.aligment_request and result["allele_type"][allele_name] != "LNF": # run alignment analysis - result["alignment_data"][ - allele_name - ] = taranis.utils.get_alignment_data( - ref_allele_seq, allele_seq, ref_allele_name + result["alignment_data"][allele_name] = ( + taranis.utils.get_alignment_data( + ref_allele_seq, allele_seq, ref_allele_name + ) ) # delete blast folder _ = taranis.utils.delete_folder(os.path.join(self.blast_dir, self.s_name)) From 042dcb378efe2b6f0e17c9046f8fb429128a6873 Mon Sep 17 00:00:00 2001 From: luissian Date: Thu, 11 Apr 2024 09:48:10 +0200 Subject: [PATCH 152/214] check if mafft is installed --- taranis/__main__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index 598c4de..46a633f 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -492,7 +492,7 @@ def allele_calling( cpus: int, ): _ = taranis.utils.check_additional_programs_installed( - [["blastn", "-version"], ["makeblastdb", "-version"]] + [["blastn", "-version"], ["makeblastdb", "-version"],["mafft", "--version"]] ) schema_ref_files = taranis.utils.get_files_in_folder(reference, "fasta") if len(schema_ref_files) == 0: From a2ffba40b8564628f32b641bab090ce20f4bd636 Mon Sep 17 00:00:00 2001 From: luissian Date: Thu, 11 Apr 2024 09:56:58 +0200 Subject: [PATCH 153/214] litin --- taranis/__main__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index 46a633f..cb17c20 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -492,7 +492,7 @@ def allele_calling( cpus: int, ): _ = taranis.utils.check_additional_programs_installed( - [["blastn", "-version"], ["makeblastdb", "-version"],["mafft", "--version"]] + [["blastn", "-version"], ["makeblastdb", "-version"], ["mafft", "--version"]] ) schema_ref_files = taranis.utils.get_files_in_folder(reference, "fasta") if len(schema_ref_files) == 0: From 003447ff596b104757f3bb5015c7a5b3c3be21e2 Mon Sep 17 00:00:00 2001 From: luissian Date: Sat, 13 Apr 2024 15:39:16 +0200 Subject: [PATCH 154/214] implemented distance matrix --- taranis/__main__.py | 116 ++++++++++++++++++++++++++++++++++++++++++++ taranis/distance.py | 49 +++++++++++++++++++ taranis/utils.py | 34 +++++++++++++ 3 files changed, 199 insertions(+) diff --git a/taranis/__main__.py b/taranis/__main__.py index cb17c20..e521785 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -3,12 +3,14 @@ import click import concurrent.futures import glob +import pandas as pd import rich.console import rich.logging import rich.traceback import sys import time +import taranis.distance import taranis.utils import taranis.analyze_schema import taranis.reference_alleles @@ -16,6 +18,7 @@ import taranis.inferred_alleles +# import pdb log = logging.getLogger() # Set up rich stderr console @@ -576,3 +579,116 @@ def allele_calling( print(f"Allele calling finish in {round((finish-start)/60, 2)} minutes") log.info("Allele calling finish in %s minutes", round((finish - start) / 60, 2)) # sample_allele_obj.analyze_sample() + + +@taranis_cli.command(help_priority=3) +@click.option( + "-a", + "--alleles", + required=True, + multiple=False, + type=click.Path(exists=True), + help="Alleles matrix file from which to obtain distances between samples", +) +@click.option( + "-o", + "--output", + required=True, + multiple=False, + type=click.Path(), + help="Output folder to save distance matrix", +) +@click.option( + "--force/--no-force", + required=False, + default=False, + help="Overwrite the output folder if it exists", +) +@click.option( + "-l", + "--locus-missing-threshold", + required=False, + multiple=False, + type=int, + default=100, + help="Threshold for missing alleles in locus, which loci is excluded from distance matrix", +) +@click.option( + "-s", + "--sample-missing-threshold", + required=False, + multiple=False, + type=int, + default=20, + help="Threshold for missing samples, which sample is excluded from distance matrix", +) +@click.option( + "--paralog-filter/--no-paralog-filter", + required=False, + multiple=False, + type=bool, + default=True, + help="Consider paralog tags (NIPH, NIPHEM) as missing values. Default is True", +) +@click.option( + "--lnf-filter/--no-lnf-filter", + required=False, + multiple=False, + type=bool, + default=True, + help="Consider LNF as missing values. Default is True", +) +@click.option( + "--plot-filter/--no-plot-filter", + required=False, + multiple=False, + type=bool, + default=True, + help="Consider PLOT as missing values. Default is True", +) +def distance_matrix( + alleles: str, + output: str, + force: bool, + locus_missing_threshold: int, + sample_missing_threshold: int, + paralog_filter: bool, + lnf_filter: bool, + plot_filter: bool, +): + # Check if file exists + if not taranis.utils.file_exists(alleles): + log.error("Alleles matrix file %s does not exist", alleles) + stderr.print("[red] Alleles matrix file does not exist") + sys.exit(1) + # Check if output folder exists + if not force: + _ = taranis.utils.prompt_user_if_folder_exists(output) + start = time.perf_counter() + # filter the alleles matrix according to the thresholds and filters + allele_matrix = pd.read_csv(alleles, sep=",", index_col=0, header=0) + filtering_string = ["ASM", "ALM"] + if paralog_filter: + filtering_string.append("NIPH") + filtering_string.append("NIPHEM") + if lnf_filter: + filtering_string.append("LNF") + if plot_filter: + filtering_string.append("PLOT") + # pdb.set_trace() + filtered_allele = taranis.utils.filter_data_frame_by_parameters( + allele_matrix, + locus_missing_threshold, + sample_missing_threshold, + filtering_string, + replaced_by_zero=False, + ) + # Create the distance matrix + # pdb.set_trace() + d_matrix_obj = taranis.distance.HammingDistance(filtered_allele) + distance_matrix = d_matrix_obj.create_matrix() + # pdb.set_trace() + print(distance_matrix) + finish = time.perf_counter() + print(f"Distance matrix finish in {round((finish-start)/60, 2)} minutes") + log.info("Distance matrix finish in %s minutes", round((finish - start) / 60, 2)) diff --git a/taranis/distance.py b/taranis/distance.py index 496fac0..67283b1 100644 --- a/taranis/distance.py +++ b/taranis/distance.py @@ -83,3 +83,52 @@ def create_matrix(self) -> pd.DataFrame: dist_matrix.close() log.debug(f"create distance for {allele_name}") return matrix_pd + + +class HammingDistance: + def __init__(self, dist_matrix: pd.DataFrame) -> "HammingDistance": + """HammingDistance instance creation + + Args: + dist_matrix (pd.DataFrame): Distance matrix + + Returns: + HammingDistance: created hamming distance + """ + self.dist_matrix = dist_matrix + + def create_matrix(self) -> pd.DataFrame: + """Create hamming distance matrix using external program called mash + + Returns: + pd.DataFrame: Hamming distance matrix as panda DataFrame + """ + + unique_values = pd.unique( + self.dist_matrix[list(self.dist_matrix.keys())].values.ravel("K") + ) + # Create binary matrix ('1' or '0' ) matching the input matrix vs the unique_values[0] + # astype(int) is used to transform the boolean matrix into integer + U = self.dist_matrix.eq(unique_values[0]).astype(int) + # multiply the matrix with the transpose + H = U.dot(U.T) + + # Repeat for each unique value + for unique_val in range(1, len(unique_values)): + U = self.dist_matrix.eq(unique_values[unique_val]).astype(int) + # Add the value of the binary matrix with the previous stored values + H = H.add(U.dot(U.T)) + + return len(self.dist_matrix.columns) - H + + """ + dist_matrix = self.dist_matrix + allele_names = dist_matrix.index + hamming_matrix = pd.DataFrame(index=allele_names, columns=allele_names) + for i in allele_names: + for j in allele_names: + hamming_matrix.at[i, j] = sum( + dist_matrix.loc[i] != dist_matrix.loc[j] + ) + return hamming_matrix + """ diff --git a/taranis/utils.py b/taranis/utils.py index d83228d..d362211 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -28,6 +28,8 @@ import warnings from Bio import BiopythonWarning +# import pdb + log = logging.getLogger(__name__) @@ -319,6 +321,38 @@ def find_nearest_numpy_value(array, value): """ +def filter_data_frame_by_parameters( + data_frame: pd.DataFrame, + column_thr: int, + row_thr: int, + filter_str: list[str], + replaced_by_zero: bool, +) -> pd.DataFrame: + # get the number of columns and rows + num_rows, num_columns = data_frame.shape + # remove the columns which the filter strings are higher than the threshold + column_threshold = column_thr * num_rows / 100 + # Condition: Check if any string in the filter list is present in each cell of the DataFrame + f_condition = data_frame.apply( + lambda column: column.astype(str).str.contains("|".join(filter_str), na=False) + ) + if replaced_by_zero: + new_data_frame = data_frame.mask(f_condition, 0) + else: + # Count the number of hits per column + hits_per_column = f_condition.sum() + # pdb.set_trace() + # Filter for removing columns where the count of hits is higher than the threshold + to_be_removed_columns = hits_per_column[ + hits_per_column > column_threshold + ].index + new_data_frame = data_frame.drop(columns=to_be_removed_columns) + # pdb.set_trace() + # remove the rows which the filter strings are higher than the threshold + row_threshold = row_thr * num_columns + return new_data_frame + + def folder_exists(folder_to_check): """Checks if input folder exists From adc9992912c717271e7c86400d6d670719239adb Mon Sep 17 00:00:00 2001 From: luissian Date: Sat, 13 Apr 2024 18:50:38 +0200 Subject: [PATCH 155/214] liting --- taranis/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/taranis/utils.py b/taranis/utils.py index d362211..598ef41 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -349,7 +349,7 @@ def filter_data_frame_by_parameters( new_data_frame = data_frame.drop(columns=to_be_removed_columns) # pdb.set_trace() # remove the rows which the filter strings are higher than the threshold - row_threshold = row_thr * num_columns + # row_threshold = row_thr * num_columns return new_data_frame From aa3b354dde22e3a1d33ce39c3c7c086ad25e1164 Mon Sep 17 00:00:00 2001 From: luissian Date: Mon, 15 Apr 2024 14:51:36 +0200 Subject: [PATCH 156/214] Update information with input parameters and outfiles description --- README.md | 475 +++++++++++++++++++++++++++++------------------------- 1 file changed, 258 insertions(+), 217 deletions(-) diff --git a/README.md b/README.md index 23ca67f..4ddebf7 100644 --- a/README.md +++ b/README.md @@ -10,31 +10,23 @@ - [Output](#output) - [Illustrated pipeline](#illustrated-pipeline) - - ## Introduction **Taranis** is a computational stand-alone pipeline for **gene-by-gene allele calling analysis** based on BLASTn using whole genome (wg) and core genome (cg) multilocus sequence typing (MLST) schemas on complete or draft genomes resulting from de novo assemblers, while tracking helpful and informative data among the process. Taranis includes four main functionalities: MLST **schema analysis**, gene-by-gene **allele calling**, **reference alleles** obtainment for allele calling analysis and the final **distance matrix** construction. - - ## Dependencies -* Python >=3.6 -* NCBI_blast >= v2.9 -* prokka >=1.14 -* prodigal v2.6.3 -* mash >=2 -* biopython v1.72 -* pandas v1.2.4 -* progressbar v2.5 -* openpyxl v3.0.7 -* plotly v5.0.0 -* numpy v1.20.3 - - +- Python >=3.8 +- NCBI_blast >= v2.9 +- prokka >=1.14.6 +- mafft = 7.520 +- mash >=2 +- biopython v1.81 +- pandas v2.1.1 +- plotly v5.17.0 +- numpy v1.26.0 ## Installation @@ -46,7 +38,6 @@ Install all dependencies and add them to $PATH. Add taranis and ./bin to $PATH. - #### Install using conda This option is recomended. @@ -58,8 +49,6 @@ Install Anaconda3. Wait for the environment to solve.
Ignore warnings/errors. - - ## Quick usage - **analyze_schema mode:** @@ -69,20 +58,26 @@ Ignore warnings/errors. ``` taranis analyze_schema \ -inputdir schema_dir \ --outputdir YYYY-MM-DD_taranis_analyze_schema_dir +-output output_analyze_schema_dir +--ouput-allele-annotation annotation_dir ``` - Schema analysis and duplicated alleles, alleles subsequences and no CDS alleles filtering: + Schema analysis for removing duplicated, subsequences and no CDS alleles: ``` taranis analyze_schema \ -inputdir schema_dir \ --outputdir YYYY-MM-DD_taranis_analyze_schema_dir \ --removesubsets True \ --removeduplicates True \ --removenocds True -``` +-output output_analyze_schema_dir \ +--remove-subsets \ +--remove-duplicated \ +--remove-no-cds \ +--ouput-allele-annotation annotation_dir \ +--genus prokka_genus_name \ +--usegenus prokka genus-specific BLAST database \ +--species prokka_species_name \ +--cpus number_of_cpus +``` - **reference_alleles mode:** @@ -90,10 +85,26 @@ taranis analyze_schema \ ``` taranis reference_alleles \ --coregenedir schema_dir \ --outputdir YYYY-MM-DD_taranis_reference_alleles_dir +-s schema_dir \ +-o output_reference_alleles_dir \ +--eval-cluster \ +--cpus number_of_cpus \ +--force overwrite output dir ``` + Reference alleles with clustering settings: + +``` +taranis reference_alleles \ +-s schema_dir \ +-o output_reference_alleles_dir \ +--eval-cluster \ +-k k-mer size for mash \ +-S Sketch size for mash \ +-r resolution used for clustering \ +--cpus number_of_cpus \ +--force overwrite output dir +``` - **allele_calling mode:** @@ -101,23 +112,36 @@ taranis reference_alleles \ ``` taranis allele_calling \ --coregenedir schema_dir \ --refalleles YYYY-MM-DD_taranis_reference_alleles_dir \ --inputdir samples_dir \ --refgenome reference_genome.fasta \ --outputdir YYYY-MM-DD_taranis_allele_calling_dir +-s schema_dir \ +-a annotation_file \ +-r reference_alleles_dir \ +-o output_allele_calling_dir \ +-t threshold to consider in blast \ +-p percentage of identity to consider in blast \ +-q threshold to consider as TPR \ +-i increase number of nucleotides to find stop codon \ +--snp Create SNP file \ +--cpus number_of_cpus \ +--alignment Create aligment files \ +samples_dir ``` - Run allele calling getting ST profile: + Allele calling for blast and threshold settings: ``` taranis allele_calling \ --coregenedir schema_dir \ --refalleles YYYY-MM-DD_taranis_reference_alleles_dir \ --inputdir samples_dir \ --refgenome reference_genome.fasta \ --profile profile.csv \ --outputdir YYYY-MM-DD_taranis_allele_calling_dir +-s schema_dir \ +-a annotation_file \ +-r reference_alleles_dir \ +-o output_allele_calling_dir \ +-t threshold to consider in blast \ +-p percentage of identity to consider in blast \ +-q threshold to consider as TPR \ +-i increase number of nucleotides to find stop codon \ +--snp Create SNP file \ +--cpus number_of_cpus \ +--alignment Create aligment files \ +samples_dir ``` - **distance_matrix mode:** @@ -126,223 +150,240 @@ taranis allele_calling \ ``` taranis distance_matrix \ --alleles_matrix YYYY-MM-DD_taranis_allele_calling_dir/result.tsv -outputdir YYYY-MM-DD_taranis_distance_matrix_dir +-a allele_calling_match.csv file \ +-o distance_matrix_dir +--force overwrite output folder ``` -

Get distance matrix filtering loci and samples which missing values percentage is above specified threshold: +Distance matrix with threshold settings: ``` -taranis distance_matrix\ --alleles_matrix YYYY-MM-DD_taranis_allele_calling_dir/result.tsv\ --locus_missing_threshold 20 \ --sample_missing_threshold 50 \ --outputdir YYYY-MM-DD_taranis_distance_matrix_dir +taranis distance_matrix \ +-a allele_calling_match.csv file \ +-o distance_matrix_dir +-l threshold for missing locus \ +-s threshold for missing samples \ +--paralog-filter \ +--lnf-filter \ +--plot-filter \ +--force overwrite output folder ``` - - ## Usage - **analyze_schema mode:** ``` -usage: taranis.py analyze_schema [-h] -inputdir INPUTDIR -outputdir OUTPUTDIR [-removesubsets REMOVESUBSETS] [-removeduplicates REMOVEDUPLICATES] [-removenocds REMOVENOCDS] [-newschema NEWSCHEMA] - [-genus GENUS] [-species SPECIES] [-usegenus USEGENUS] [-cpus CPUS] - -optional arguments: - -h, --help show this help message and exit - -inputdir INPUTDIR Directory where are the schema files. - -outputdir OUTPUTDIR Directory where the result files will be stored. - -removesubsets REMOVESUBSETS - Remove allele subsequences from the schema.True: Remove subsets.False: Do not remove subsets.Default is False. - -removeduplicates REMOVEDUPLICATES - Remove duplicated alleles from the schema.True: Remove duplicates.False: Do not remove duplicates.Default is False. - -removenocds REMOVENOCDS - Remove no CDS alleles from the schema.True: Remove no CDS alleles.False: Do not remove no CDS alleles.Default is False. - -newschema NEWSCHEMA Filter a copy of the core genes schema preserving the analysis core genes schema.True: Create a copy of the core genes schema for filtering.False: Do not create a copy of the - core genes schema for filtering.Default is False. - -genus GENUS Genus name for Prokka schema genes annotation. Default is Genus. - -species SPECIES Species name for Prokka schema genes annotation. Default is species. - -usegenus USEGENUS Use genus-specific BLAST databases for Prokka schema genes annotation (needs --genus). Default is False. - -cpus CPUS Number of CPUS to be used in the program. Default is 1. +Usage: taranis analyze-schema [OPTIONS] + +Options: + -i, --inputdir PATH Directory where the schema with the core + gene files are located. [required] + -o, --output PATH Output folder to save analyze schema + [required] + --remove-subset / --no-remove-subset + Remove allele subsequences from the schema. + --remove-duplicated / --no-remove-duplicated + Remove duplicated subsequences from the + schema. + --remove-no-cds / --no-remove-no-cds + Remove no CDS alleles from the schema. + --output-allele-annot / --no-output-allele-annot + output prokka/allele annotation for all + alleles in locus + --genus TEXT Genus name for Prokka schema genes + annotation. Default is Genus. + --species TEXT Species name for Prokka schema genes + annotation. Default is species + --usegenus TEXT Use genus-specific BLAST databases for + Prokka schema genes annotation (needs + --genus). Default is False. + --cpus INTEGER Number of cpus used for execution + --help Show this message and exit. ``` - - **reference_alleles mode:** ``` -usage: taranis.py reference_alleles [-h] -coregenedir COREGENEDIR -outputdir OUTPUTDIR - [-evalue EVALUE] [-perc_identity PERC_IDENTITY] - [-reward REWARD] [-penalty PENALTY] [-gapopen GAPOPEN] - [-gapextend GAPEXTEND] [-num_threads NUM_THREADS] [-cpus CPUS] - -optional arguments: - -h, --help show this help message and exit - -coregenedir COREGENEDIR - Directory where the core gene files are located. - -outputdir OUTPUTDIR Directory where the result files will be stored. - -evalue EVALUE E-value in BLAST searches. Default is 0.001. - -perc_identity PERC_IDENTITY - Identity percent in BLAST searches. Default is 90. - -reward REWARD Match reward in BLAST searches. Default is 1. - -penalty PENALTY Mismatch penalty in BLAST searches. Default is -2. - -gapopen GAPOPEN Gap open penalty in BLAST searches. Default is 1. - -gapextend GAPEXTEND Gap extension penalty in BLAST searches. Default is 1. - -num_threads NUM_THREADS - num_threads in BLAST searches. Default is 1. - -cpus CPUS Number of CPUS to be used in the program. Default is 1. +Usage: taranis reference-alleles [OPTIONS] + +Options: + -s, --schema PATH Directory where the schema with the core + gene files are located. [required] + -o, --output PATH Output folder to save reference alleles + [required] + --eval-cluster / --no-eval-cluster + Evaluate if the reference alleles match + against blast with a 90% identity + -k, --kmer-size INTEGER Mash parameter for K-mer size. + -S, --sketch-size INTEGER Mash parameter for Sketch size + -r, --cluster-resolution FLOAT Resolution value used for clustering. + --seed INTEGER Seed value for clustering + --cpus INTEGER Number of cpus used for execution + --force / --no-force Overwrite the output folder if it exists + --help Show this message and exit. ``` - - **allele_calling mode:** ``` -usage: taranis.py allele_calling [-h] -coregenedir COREGENEDIR -refalleles REFALLELES -inputdir - INPUTDIR -refgenome REFGENOME -outputdir OUTPUTDIR - [-percentlength PERCENTLENGTH] [-coverage COVERAGE] - [-evalue EVALUE] [-perc_identity_ref PERC_IDENTITY_REF] - [-perc_identity_loc PERC_IDENTITY_LOC] [-reward REWARD] - [-penalty PENALTY] [-gapopen GAPOPEN] [-gapextend GAPEXTEND] - [-max_target_seqs MAX_TARGET_SEQS] [-max_hsps MAX_HSPS] - [-num_threads NUM_THREADS] [-flankingnts FLANKINGNTS] - [-updateschema UPDATESCHEMA] [-profile PROFILE] - [-updateprofile UPDATEPROFILE] [-cpus CPUS] [-genus GENUS] - [-species SPECIES] [-usegenus USEGENUS] - -optional arguments: - -h, --help show this help message and exit - -coregenedir COREGENEDIR - Directory where the core gene files are located - -refalleles REFALLELES - Directory where the core gene references files are located - -inputdir INPUTDIR Directory where are located the sample fasta files - -refgenome REFGENOME Reference genome file for genes prediction - -outputdir OUTPUTDIR Directory where the result files will be stored - -percentlength PERCENTLENGTH - Allowed length percentage to be considered as INF. Outside of this limit it - is considered as ASM or ALM. Default is SD. - -coverage COVERAGE Coverage threshold to exclude found sequences. Outside of this limit it is - considered LNF. Default is 50. - -evalue EVALUE E-value in BLAST searches. Default is 0.001. - -perc_identity_ref PERC_IDENTITY_REF - Identity percentage in BLAST searches using reference alleles for each - locus detection in samples. Default is 90. - -perc_identity_loc PERC_IDENTITY_LOC - Identity percentage in BLAST searches using all alleles in each locus for - allele identification in samples. Default is 90. - -reward REWARD Match reward in BLAST searches. Default is 1. - -penalty PENALTY Mismatch penalty in BLAST searches. Default is -2. - -gapopen GAPOPEN Gap open penalty in BLAST searches. Default is 1. - -gapextend GAPEXTEND Gap extension penalty in BLAST searches. Default is 1. - -max_target_seqs MAX_TARGET_SEQS - max_target_seqs in BLAST searches. Default is 10. - -max_hsps MAX_HSPS max_hsps in BLAST searches. Default is 10. - -num_threads NUM_THREADS - num_threads in BLAST searches. Default is 1. - -flankingnts FLANKINGNTS - Number of flanking nucleotides to add to each BLAST result obtained after - locus detection in sample using reference allele for correct allele - identification. Default is 100. - -updateschema UPDATESCHEMA - Add INF alleles found for each locus to the core genes schema. True: Add - INF alleles to the analysis core genes schema. New: Add INF alleles to a - copy of the core genes schema preserving the analysis core genes schema. - False: Do not update the core gene schema adding new INF alleles found. - Default is True. - -profile PROFILE ST profile file based on core genes schema file to get ST for each sample. - Default is empty and Taranis does not calculate samples ST. - -updateprofile UPDATEPROFILE - Add new ST profiles found to the ST profile file. True: Add new ST profiles - to the analysis ST profile file. New: Add Add new ST profiles to a copy of - the ST profile file preserving the analysis ST file. False: Do not update - the ST profile file adding new ST profiles found. Default is True. - -cpus CPUS Number of CPUS to be used in the program. Default is 1. - -genus GENUS Genus name for Prokka schema genes annotation. Default is Genus. - -species SPECIES Species name for Prokka schema genes annotation. Default is species. - -usegenus USEGENUS Use genus-specific BLAST databases for Prokka schema genes annotation - (needs --genus). Default is False. +Usage: taranis allele-calling [OPTIONS] ASSEMBLIES... + +Options: + -s, --schema PATH Directory where the schema with the core + gene files are located. [required] + -r, --reference PATH Directory where the schema reference allele + files are located. [required] + -a, --annotation PATH Annotation file. [required] + -t, --threshold FLOAT Threshold value to consider in blast. Values + from 0 to 1. default 0.8 + -p, --perc-identity INTEGER Percentage of identity to consider in blast. + default 90 + -o, --output PATH Output folder to save reference alleles + [required] + --force / --no-force Overwrite the output folder if it exists + --snp / --no-snp Create SNP file for alleles in assembly in + relation with reference allele + --alignment / --no-alignment Create alignment files + -q, --proteine-threshold INTEGER + Threshold of protein coverage to consider as + TPR. default 90 + -i, --increase-sequence INTEGER + Increase the number of triplet sequences to + find the stop codon. default 20 + --cpus INTEGER Number of cpus used for execution + --help Show this message and exit. ``` - - **distance_matrix mode:** ``` -usage: taranis.py distance_matrix [-h] -alleles_matrix ALLELES_MATRIX [-locus_missing_threshold LOCUS_MISSING_THRESHOLD] [-sample_missing_threshold SAMPLE_MISSING_THRESHOLD] - [-paralog_filter PARALOG_FILTER] [-lnf_filter LNF_FILTER] [-plot_filter PLOT_FILTER] -outputdir OUTPUTDIR - -optional arguments: - -h, --help show this help message and exit - -alleles_matrix ALLELES_MATRIX - Alleles matrix file from which to obtain distances between samples - -locus_missing_threshold LOCUS_MISSING_THRESHOLD - Missing values percentage threshold above which loci are excluded for distance matrix creation. Default is 100. - -sample_missing_threshold SAMPLE_MISSING_THRESHOLD - Missing values percentage threshold above which samples are excluded for distance matrix creation. Default is 100. - -paralog_filter PARALOG_FILTER - Consider paralog tags (NIPH, NIPHEM) as missing values. Default is True - -lnf_filter LNF_FILTER - Consider locus not found tag (LNF) as missing value. Default is True - -plot_filter PLOT_FILTER - Consider incomplete alleles found on the tip of a contig tag (PLOT) as missing value. Default is True - -outputdir OUTPUTDIR Directory where the result files will be stored +Usage: taranis distance-matrix [OPTIONS] + +Options: + -a, --alleles PATH Alleles matrix file from which to obtain + distances between samples [required] + -o, --output PATH Output folder to save distance matrix + [required] + --force / --no-force Overwrite the output folder if it exists + -l, --locus-missing-threshold INTEGER + Threshold for missing alleles in locus, + which loci is excluded from distance matrix + -s, --sample-missing-threshold INTEGER + Threshold for missing samples, which sample + is excluded from distance matrix + --paralog-filter / --no-paralog-filter + Consider paralog tags (NIPH, NIPHEM) as + missing values. Default is True + --lnf-filter / --no-lnf-filter Consider LNF as missing values. Default is + True + --plot-filter / --no-plot-filter + Consider PLOT as missing values. Default is + True + --help Show this message and exit. ``` - - ## Output - **analyze_schema mode:** - * **FOLDERS:** + - **FOLDERS and FILES structure:** - * **raw_schema_information:** General information about each allele of each locus - - * **FILES:** - - * **alleles_subsets.tsv:** Report of alleles that are subsequences of other alleles of the same locus - * **duplicated_alleles.tsv:** Report of duplicate alleles within the same locus - * **length_statistics.tsv:** Allele length statistics report for each locus - * **schema_quality.tsv:** Quality report of alleles of each locus - + - **new_schema** Contains the new schema. + - **prokka** Contains the prokka results + - **statistics** Statistics data + - **graphics** Plot graphics folder + - **statistics.csv** Quality statistics showing the following data: + + - allele_name, + - min_length, + - max_length, + - num_alleles, + - mean_length, + - good_percent, + - not a start codon, + - not a stop codon, + - Extra in frame stop codon, + - is not a multiple of three, + - Duplicate allele, + - Sub set allele + + - **allele_annotation.tar.gz** Annotation schema file - **reference_alleles mode:** - * **FILES:** + - **FOLDERS and FILES structure:** - * **[refalleles_locusX].fasta:** One fasta file for each schema locus containing reference alleles for that locus - + - **Clusters** Contains the cluster allele files + - **[cluster_alleles].txt** cluster allele file + - **evaluate_cluster** + - **cluster_evaluuation.csv** Evaluation result with the following info: + - Locus name + - cluster number + - result + - alleles not match in blast + - alleles not found in cluster + + - **cluster_per_locus.csv** Number of cluster per locus + - number of clusters + - number of locus + + - **cluster_summary.csv** summary data with the following info: + - Locus name + - cluster number + - average + - center allele + - number of sequences + + - **graphics** Plot graphics folder + - **num_genes_per_allele.png** Bar graphic to show the number of clusters per gene + + - **[ref_alleles_locusX].fasta:** One fasta file for each schema locus containing reference alleles for that locus - **allele_calling mode:** - * **FOLDERS:** - * **alignments:** Nucleotide alignment between sequence found in the sample and allele - * **proteins:** Protein alignment between sequence found in sample and allele - * **plots:** Interactive pie charts of allele call results for each sample - - * **FILES:** - * **alm.tsv:** Sample sequences found x% larger than the locus alleles mean length report - * **asm.tsv:** Sample sequences found x% shorter than the locus alleles mean length report - * **exact.tsv:** Exact matches report - * **inferred_alleles.tsv:** New inferred alleles report - * **lnf_tpr.tsv:** Locus not found (LNF) and truncated protein (TPR) report - * **paralog.tsv:** Possible paralogs (NIPHEM (100% ID paralogs) and NIPH (<=100% ID paralogs)) report - * **plot.tsv:** Possible loci on the tip of the sample contig (PLOT) report - * **snp.tsv:** SNPs report - * **matching_contigs.tsv:** Summary report of loci found in samples - * **result.tsv:** Allele calling main results - * **summary_result.tsv:** Allele calling results summary. Count of each tag type found for each sample is indicated - * **stprofile.tsv:** Sequence type report - + - **FOLDERS and FILES structure:** + - **alignments:** Nucleotide alignment between sequence found in the sample and allele + - **[locus_name].txt** One file per locus + - **[locus_name]_multiple_alignment.aln** One file per locus + - **graphics** Graphics per type of allele classification + - **ALM_graphic.pnd** Number of ALM in samples. + - **ASM_graphic.pnd** Number of ASM in samples. + - **EXEC_graphic.pnd** Number of EXEC in samples. + - **INF_graphic.pnd** Number of INF in samples. + - **LNF_graphic.pnd** Number of LNF in samples. + - **NIPHEM_graphic.pnd** Number of NIPHEM in samples. + - **NIPH_graphic.pnd** Number of NIPH in samples. + - **PLOT_graphic.pnd** Number of PLOT in samples. + - **TPR_graphic.pnd** Number of TPR in samples. + - **[locus_name]_snp_data** One file per sample + - **allele_calling_match.csv** Contains the classification for each locus and for all samples + - **allele_calling_summary.csv** Contains the number of each classification per samples + - **matching_contig.csv** Summary for each locus in sample with the following data: + - sample + - contig + - core gene + - reference allele name + - codification + - query length + - match length + - contig length + - contig start + - contig stop + - direction + - gene notation + - product notation + - reference allele quality + - protein conversion result + - match sequence reference + - allele sequence + - predicted protein sequence - **distance_matrix mode:** - * **FILES:** - * **filtered_result.tsv:** Filtered allele calling matrix filtered - * **matrix_distance.tsv:** Samples matrix distance - * **matrix_distance_filter_report.tsv:** Allele calling matrix filtering report - - - -## Illustrated pipeline + - **FILES:** + - **filtered_result.tsv:** Filtered allele calling matrix filtered + - **matrix_distance.tsv:** Samples matrix distance + - **matrix_distance_filter_report.tsv:** Allele calling matrix filtering report -Under construction +## Illustrated pipeline From 2e50f885b31711d8a5e1bd046bc6bf52297a10b5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Thu, 11 Apr 2024 18:11:12 +0200 Subject: [PATCH 157/214] fixed comments, added eval id as parameter for reference alleles --- taranis/__main__.py | 12 +++++++++++- taranis/clustering.py | 3 +-- taranis/eval_cluster.py | 5 +++-- taranis/reference_alleles.py | 7 +++++-- 4 files changed, 20 insertions(+), 7 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index e521785..a0ed14b 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -278,7 +278,7 @@ def analyze_schema( "--eval-cluster/--no-eval-cluster", required=False, default=True, - help="Evaluate if the reference alleles match against blast with a 90% identity", + help="Evaluate if the reference alleles match against blast with the identity set in eval-identity param", ) @click.option( "-k", @@ -304,6 +304,14 @@ def analyze_schema( default=0.75, help="Resolution value used for clustering.", ) +@click.option( + "-e", + "--eval-identity", + required=False, + type=float, + default=85, + help="Resolution value used for clustering.", +) @click.option( "--seed", required=False, @@ -332,6 +340,7 @@ def reference_alleles( kmer_size: int, sketch_size: int, cluster_resolution: float, + eval_identity: float, seed: int, cpus: int, force: bool, @@ -362,6 +371,7 @@ def reference_alleles( kmer_size, sketch_size, cluster_resolution, + eval_identity, seed, ) for f_file in schema_files diff --git a/taranis/clustering.py b/taranis/clustering.py index 9aea468..a1adeaa 100644 --- a/taranis/clustering.py +++ b/taranis/clustering.py @@ -42,8 +42,7 @@ def __init__( def calculate_cluster_center( self, cluster_mtrx_idxs: tuple, dist_value: float ) -> int: - """Get the center allele for the cluster by selecting the allele closest - value to cluster mean + """Get the center allele for the cluster by selecting the allele with more alleles at > dist_value Args: cluster_mtrx_idxs (tuple): tuple with the filter indexes to create diff --git a/taranis/eval_cluster.py b/taranis/eval_cluster.py index 63bbffc..90f1e62 100644 --- a/taranis/eval_cluster.py +++ b/taranis/eval_cluster.py @@ -17,7 +17,7 @@ class EvaluateCluster: - def __init__(self, locus_path: str, locus_name: str, output: str): + def __init__(self, locus_path: str, locus_name: str, eval_id: float, output: str): """EvaluateCluster instance creation Args: @@ -27,6 +27,7 @@ def __init__(self, locus_path: str, locus_name: str, output: str): """ self.locus_path = locus_path self.locus_name = locus_name + self.eval_id = eval_id self.output = os.path.join(output, "evaluate_cluster") taranis.utils.create_new_folder(self.output) @@ -156,7 +157,7 @@ def evaluate_clusters( query_file.write(">" + r_id + "\n" + r_seq) query_file.seek(0) blast_result = self.blast_obj.run_blast( - query_file.read(), perc_identity=85, query_type="stdin" + query_file.read(), perc_identity=self.eval_id, query_type="stdin" ) # Close object and discard memory buffer query_file.close() diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index b64ca75..8672921 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -28,6 +28,7 @@ def __init__( kmer_size: int, sketch_size: int, cluster_resolution: float = 0.75, + eval_id: float = 85, seed: int = None, ): """ReferenceAlleles instance creation @@ -48,6 +49,7 @@ def __init__( self.kmer_size = kmer_size self.sketch_size = sketch_size self.cluster_resolution = cluster_resolution + self.eval_id = eval_id self.seed = seed self.selected_locus = {} self.cluster_obj = None @@ -138,8 +140,9 @@ def create_ref_alleles(self) -> dict: self.records = taranis.utils.read_fasta_file(self.fasta_file) dist_matrix_np, position_to_allele = self.create_distance_matrix() self.cluster_obj = taranis.clustering.ClusterDistance( - dist_matrix_np, - self.locus_name, + dist_matrix=dist_matrix_np, + ref_seq_name=self.locus_name, + dist_value=self.eval_id / 100, ) for resolution in np.arange(self.cluster_resolution, 1, 0.025): From bec96c6796ecbfffa664918753a1029a1ea74676 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Mon, 15 Apr 2024 10:21:24 +0200 Subject: [PATCH 158/214] added eval_identity to parallel execution function --- taranis/reference_alleles.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index 8672921..60494ea 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -193,6 +193,7 @@ def parallel_execution( kmer_size: int, sketch_size: int, cluster_resolution: float, + eval_identity: float, seed: int, ): """Parallel execution of the reference alleles creation @@ -213,6 +214,7 @@ def parallel_execution( kmer_size, sketch_size, cluster_resolution, + eval_identity, seed, ) return ref_alleles_obj.create_ref_alleles() From 2761905fe96d8360c662a06822e881c16872f1c9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Mon, 15 Apr 2024 10:47:30 +0200 Subject: [PATCH 159/214] added left eval_id to EvaluateCluster call --- taranis/reference_alleles.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index 60494ea..24f5407 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -160,7 +160,7 @@ def create_ref_alleles(self) -> dict: # evaluate clusters aginst blast results stderr.print(f"Evaluating clusters for {self.locus_name}") evaluation_obj = taranis.eval_cluster.EvaluateCluster( - self.fasta_file, self.locus_name, self.output + self.fasta_file, self.locus_name, self.eval_id, self.output ) evaluation_result = evaluation_obj.evaluate_clusters( allele_data["alleles_in_cluster"], From ef96e75d8dc7a802a641f30bb36dae10a53e964a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Tue, 16 Apr 2024 10:10:48 +0200 Subject: [PATCH 160/214] variable renaming and psudocode --- taranis/__main__.py | 8 ++--- taranis/allele_calling.py | 70 +++++++++++++++++++++++------------- taranis/reference_alleles.py | 6 ++-- 3 files changed, 52 insertions(+), 32 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index a0ed14b..30f26f1 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -414,12 +414,12 @@ def reference_alleles( ) @click.option( "-t", - "--threshold", + "--hit_lenght_perc", required=False, nargs=1, default=0.8, type=float, - help="Threshold value to consider in blast. Values from 0 to 1. default 0.8", + help="Threshold value to consider in blast hit percentage regarding the reference length. Values from 0 to 1. default 0.8", ) @click.option( "-p", @@ -494,7 +494,7 @@ def allele_calling( reference: str, annotation: str, assemblies: list, - threshold: float, + hit_lenght_perc: float, perc_identity: int, output: str, force: bool, @@ -546,7 +546,7 @@ def allele_calling( schema, prediction_data, schema_ref_files, - threshold, + hit_lenght_perc, perc_identity, output, inf_allele_obj, diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 6909a96..dda4afa 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -29,7 +29,7 @@ def __init__( schema: str, annotation: dict, reference_alleles: list, - threshold: float, + hit_lenght_perc: float, perc_identity: int, out_folder: str, inf_alle_obj: object, @@ -55,12 +55,12 @@ def __init__( """ self.prediction_data = annotation # store prediction annotation self.sample_file = sample_file - self.sample_records = taranis.utils.read_fasta_file( + self.sample_contigs = taranis.utils.read_fasta_file( self.sample_file, convert_to_dict=True ) self.schema = schema self.ref_alleles = reference_alleles - self.threshold = threshold + self.hit_lenght_perc = hit_lenght_perc self.perc_identity = perc_identity self.out_folder = out_folder self.s_name = Path(sample_file).stem @@ -156,7 +156,7 @@ def _extend_sequence_for_finding_start_stop_codon( # function return the original blast result split_blast_result[13] = split_blast_result[13].replace("-", "") # fetch the sequence until the last triplet is stop codon - contig_seq = self.sample_records[split_blast_result[1]] + contig_seq = self.sample_contigs[split_blast_result[1]] start_seq = int(split_blast_result[9]) stop_seq = int(split_blast_result[10]) if stop_seq > start_seq: @@ -317,23 +317,26 @@ def _get_blast_details( prot_conv_result = taranis.utils.convert_to_protein( match_sequence, force_coding=False, delete_incompleted_triplet=True ) - prot_error_result = ( - prot_conv_result["error"] if "error" in prot_conv_result else "-" - ) - predicted_prot_seq = ( - prot_conv_result["protein"] if "protein" in prot_conv_result else "-" - ) + # de lo anterior saco, direccion, proteina, error + # prot_error_result = ( + # prot_conv_result["error"] if "error" in prot_conv_result else "-" + # ) + # predicted_prot_seq = ( + # prot_conv_result["protein"] if "protein" in prot_conv_result else "-" + # ) # remove if extra nucleotides are added at the end of the last # completed triplet before the stop codon - if "extra nucleotides after stop codon" in prot_error_result: - new_seq_len = len(match_sequence) // 3 * 3 - match_sequence = match_sequence[:new_seq_len] - split_blast_result[4] = str(new_seq_len) - # reset the error message - prot_error_result = "-" + # if "extra nucleotides after stop codon" in prot_error_result: + # new_seq_len = len(match_sequence) // 3 * 3 + # match_sequence = match_sequence[:new_seq_len] + # split_blast_result[4] = str(new_seq_len) + # # reset the error message + # prot_error_result = "-" # extend the sequence to find the stop codon - elif "Last triplet sequence is not a stop codon" in prot_error_result: + # check como saca esto el translate protein + if "not stop codon" in prot_error_result: + # le paso start, end, direccion, buscar stop ( split_blast_result, prot_error_result, @@ -347,7 +350,9 @@ def _get_blast_details( # update the match sequence match_sequence = split_blast_result[13] # extend the sequence to find the start codon - elif "Sequence does not have a start codon" in prot_error_result: + # check como saca este error el translate protein + elif "not start codon" in prot_error_result: + # le paso start, end, direccion, buscar start ( split_blast_result, prot_error_result, @@ -495,13 +500,26 @@ def discard_low_threshold_results(self, blast_results: list) -> list: blast_split = b_result.split("\t") # check if the division of the match contig length by the # reference allele length is higher than the threshold - if (int(blast_split[4]) / int(blast_split[3])) >= self.threshold: + if (int(blast_split[4]) / int(blast_split[3])) >= self.hit_lenght_perc: valid_blast_result.append(b_result) return valid_blast_result def search_match_allele(self): - # Create blast db with sample file + """ + + Args: + + Returns: + result = { + "allele_type": {}, + "allele_match": {}, + "allele_details": {}, + "snp_data": {}, + "alignment_data": {}, + } + + """ result = { "allele_type": {}, "allele_match": {}, @@ -545,10 +563,12 @@ def search_match_allele(self): if len(valid_blast_results) > 0: match_found = True break - # Close object and discard memory buffer - query_file.close() + # Close object and discard memory buffer + query_file.close() + allele_file = os.path.join(self.schema, os.path.basename(ref_allele)) allele_name = Path(allele_file).stem + if match_found: ( result["allele_type"][allele_name], @@ -591,7 +611,7 @@ def search_match_allele(self): ) ) # delete blast folder - _ = taranis.utils.delete_folder(os.path.join(self.blast_dir, self.s_name)) + # _ = taranis.utils.delete_folder(os.path.join(self.blast_dir, self.s_name)) return result @@ -600,7 +620,7 @@ def parallel_execution( schema: str, prediction_data: dict, reference_alleles: list, - threshold: float, + hit_lenght_perc: float, perc_identity: int, out_folder: str, inf_alle_obj: object, @@ -614,7 +634,7 @@ def parallel_execution( schema, prediction_data, reference_alleles, - threshold, + hit_lenght_perc, perc_identity, out_folder, inf_alle_obj, diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index 24f5407..27b3c89 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -246,12 +246,12 @@ def stats_graphics(stats_folder: str, cluster_alleles: dict) -> None: cluster, alleles = zip(*cluster_alleles.items()) _ = taranis.utils.create_graphic( graphic_folder, - "num_genes_per_allele.png", + "num_clusters_per_locus.png", "bar", cluster, alleles, - ["Gene", "Number of clusters"], - "Number of cluster per gene", + ["# clusters", " #locus"], + "Clusters per locus", ) # split into cluster_data and evaluation_data From 930ddf045810ad7da7cccf2fe2d9d15c7ae2cfaf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Tue, 16 Apr 2024 12:06:49 +0200 Subject: [PATCH 161/214] first draft code for prot conversion and extend sequence fix --- taranis/allele_calling.py | 322 ++++++++++++-------------------------- taranis/utils.py | 56 +++---- 2 files changed, 123 insertions(+), 255 deletions(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index dda4afa..0bdc021 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -78,16 +78,16 @@ def __init__( def assign_allele_type( self, valid_blast_results: list, - allele_file: str, - allele_name: str, + locus_file: str, + locus_name: str, ref_allele_seq: str, ) -> list: """Assign allele type to the allele Args: valid_blast_results (list): information collected by running blast - allele_file (str): file name with allele sequence - allele_name (str): allele name + locus_file (str): file name with locus alleles sequences + locus_name (str): locus name ref_allele_seq (str): reference allele sequence Returns: @@ -115,20 +115,16 @@ def _check_if_plot(column_blast_res: list) -> bool: return True return False - def _extend_sequence_for_finding_start_stop_codon( - split_blast_result: list, - prot_error_result: str, - predicted_prot_seq: str, - search_codon: str = "stop", + def _extend_seq_find_start_stop_codon( + direction: str, + contig_seq: str, + start: int, + end: int, + limit: int, + search: str = "5_prime", ) -> list: - """Extend match sequence, according the (increase_sequence) for - trying find the stop or start codon. When parameter is set to - stop additional nucleotides are added to extend the chance to - find out the codon stop. - If parameter is set to start then additional nucleotide is added - on the start value to identify that is a valid start codon. If - true then additional nucletotides are added to find the stop codon. - + """Extend match sequence, according to increase_sequence in order to try to + find the stop or start codon. Args: split_blast_result (list): list having the informaction collected by running blast @@ -141,129 +137,31 @@ def _extend_sequence_for_finding_start_stop_codon( list: updated information if stop or start codon is found and the updated protein sequence and protein conversion result if changed """ - # collect data for checking PLOT - data_for_plot = [""] * 10 - # cop the contig length - data_for_plot[7] = split_blast_result[15] - # copy start position - data_for_plot[8] = split_blast_result[9] - # copy end position - data_for_plot[9] = split_blast_result[10] - # check if PLOT - if not _check_if_plot(data_for_plot): - # remove the "-" character in the contig sequence in case that - # there was not possible to find the start/stop codon and - # function return the original blast result - split_blast_result[13] = split_blast_result[13].replace("-", "") - # fetch the sequence until the last triplet is stop codon - contig_seq = self.sample_contigs[split_blast_result[1]] - start_seq = int(split_blast_result[9]) - stop_seq = int(split_blast_result[10]) - if stop_seq > start_seq: - # sequence direction is forward - direction = "forward" - if search_codon == "start": - # add nucleotides according to the first match in the - # reference allele - start_ref_allele = int(split_blast_result[11]) // 3 * 3 - # try extended 1 nucleotide to find the start codon - start_seq_found = False - # subtract 1 because index start at 0 - new_start_seq = start_seq - start_ref_allele - 1 - for _ in range(1, 3): - new_start_seq -= 1 - if ( - contig_seq[new_start_seq : new_start_seq + 3] - in taranis.utils.START_CODON_FORWARD - ): - # increase 1 because we substact 1 when searching - # for stop codon - start_seq = new_start_seq + 1 - start_seq_found = True - break - # continue to find the stop codon with the new start - if not start_seq_found: - # start codon not found. Return the original blast result - return ( - split_blast_result, - prot_error_result, - predicted_prot_seq, - ) - - # adjust the sequence to be a triplet - interval = (stop_seq - start_seq) // 3 * 3 - new_stop_seq = start_seq + interval + self.increase_sequence - start_seq -= 1 - # if the increased length is higher than the contig length - # adjust the stop sequence to maximun contig length - # multiply by 3. - if stop_seq > len(contig_seq): - stop_seq = len(contig_seq) // 3 * 3 - else: - stop_seq = new_stop_seq - 1 - c_sequence = contig_seq[start_seq:stop_seq] - else: - # sequence direction is reverse - direction = "reverse" - if search_codon == "start": - if ( - contig_seq[start_seq - 2 : start_seq + 1] - in taranis.utils.START_CODON_REVERSE - ): - start_seq += 1 - # continue to find the stop codon with the new start - else: - # start codon not found. Return the original blast result - return ( - split_blast_result, - prot_error_result, - predicted_prot_seq, - ) - # adjust the sequence to be a triplet - interval = (start_seq - stop_seq) // 3 * 3 - new_stop_seq = start_seq - interval - self.increase_sequence - # if the increased length is lower than 0 (contig start) - # position, adjust the start sequence to minumum contig - # length multiply by 3 - if new_stop_seq < 0: - # get the minimum contig length that is multiple by 3 - stop_seq = stop_seq % 3 - 1 - else: - stop_seq = new_stop_seq - # get the sequence in reverse - c_sequence = str( - Seq(contig_seq[stop_seq:start_seq]).reverse_complement() - ) - new_prot_conv_result = taranis.utils.convert_to_protein( - c_sequence, force_coding=False, delete_incompleted_triplet=False + protein = "-" + error = False + error_details = "-" + + # Extend the sequence to find a valid start or stop codon + if direction == "reverse": + contig_seq = contig_seq.reverse_complement() + start, end = len(contig_seq) - end, len(contig_seq) - start + import pdb; pdb.set_trace() + for i in range(1, limit + 1): + if search == "5_prime": + extended_start = max(0, start - i) + extended_end = end + elif search == "3_prime": + extended_start = start + extended_end = min(len(contig_seq), end + i) + + extended_seq = contig_seq[extended_start:extended_end] + _, protein, error, error_details = taranis.utils.convert_to_protein( + extended_seq, force_coding=True ) - # check if stop codon is found in protein sequence - - if ( - "protein" in new_prot_conv_result - and "*" in new_prot_conv_result["protein"] - ): - # increase 3 nucleotides beecause index start at 0 - new_seq_length = new_prot_conv_result["protein"].index("*") * 3 + 3 - match_sequence = c_sequence[:new_seq_length] - split_blast_result[4] = str(new_seq_length) - split_blast_result[13] = match_sequence - prot_error_result = "-" - predicted_prot_seq = new_prot_conv_result["protein"][ - 0 : new_seq_length // 3 - ] - # update the start and stop position - if direction == "forward": - split_blast_result[10] = str( - int(split_blast_result[9]) + new_seq_length - ) - else: - split_blast_result[10] = str( - int(split_blast_result[9]) - new_seq_length - ) - # ignore the previous process if stop codon is not found + if not error: + return protein, extended_start, extended_end, error, error_details - return split_blast_result, prot_error_result, predicted_prot_seq + return protein, start, end, error, error_details def _get_blast_details( blast_result: str, allele_name: str, ref_allele_seq @@ -308,83 +206,66 @@ def _get_blast_details( product_annotation = "Not found" allele_quality = "Not found" if int(split_blast_result[10]) > int(split_blast_result[9]): - direction = "+" + strand = "+" else: - direction = "-" + strand = "-" # remove the gaps in sequences match_sequence = split_blast_result[13].replace("-", "") # check if the sequence is coding - prot_conv_result = taranis.utils.convert_to_protein( - match_sequence, force_coding=False, delete_incompleted_triplet=True + direction, protein, prot_error, prot_error_details = ( + taranis.utils.convert_to_protein(match_sequence, force_coding=True) ) - # de lo anterior saco, direccion, proteina, error - # prot_error_result = ( - # prot_conv_result["error"] if "error" in prot_conv_result else "-" - # ) - # predicted_prot_seq = ( - # prot_conv_result["protein"] if "protein" in prot_conv_result else "-" - # ) - # remove if extra nucleotides are added at the end of the last - # completed triplet before the stop codon - # if "extra nucleotides after stop codon" in prot_error_result: - # new_seq_len = len(match_sequence) // 3 * 3 - # match_sequence = match_sequence[:new_seq_len] - # split_blast_result[4] = str(new_seq_len) - # # reset the error message - # prot_error_result = "-" - - # extend the sequence to find the stop codon - # check como saca esto el translate protein - if "not stop codon" in prot_error_result: - # le paso start, end, direccion, buscar stop - ( - split_blast_result, - prot_error_result, - predicted_prot_seq, - ) = _extend_sequence_for_finding_start_stop_codon( - split_blast_result, - prot_error_result, - predicted_prot_seq, - search_codon="stop", - ) - # update the match sequence - match_sequence = split_blast_result[13] - # extend the sequence to find the start codon - # check como saca este error el translate protein - elif "not start codon" in prot_error_result: - # le paso start, end, direccion, buscar start - ( - split_blast_result, - prot_error_result, - predicted_prot_seq, - ) = _extend_sequence_for_finding_start_stop_codon( - split_blast_result, - prot_error_result, - predicted_prot_seq, - search_codon="start", - ) - # update the match sequence - match_sequence = split_blast_result[13] + import pdb; pdb.set_trace() + start = split_blast_result[9] + end = split_blast_result[10] + if prot_error: + if "is not a stop codon" in prot_error_details: + protein, new_start, new_end, prot_error, prot_error_details = ( + _extend_seq_find_start_stop_codon( + direction=direction, + contig_seq=self.sample_contigs[split_blast_result[1]], + start=start, + end=end, + limit=self.increase_sequence, + search="3_prime", + ) + ) + start = new_start + end = new_end + elif "is not a start codon" in prot_error_details: + protein, new_start, new_end, prot_error, prot_error_details = ( + _extend_seq_find_start_stop_codon( + direction=direction, + contig_seq=self.sample_contigs[split_blast_result[1]], + start=split_blast_result[9], + end=split_blast_result[10], + limit=self.increase_sequence, + search="5_prime", + ) + ) + start = new_start + end = new_end + # get blast details blast_details = [ self.s_name, # sample name split_blast_result[1], # contig name allele_name, # core gene name split_blast_result[0], # allele gene - "coding", # coding allele type. To be filled later idx = 4 + "-", # coding allele type. To be filled later idx = 4 split_blast_result[3], # reference allele length split_blast_result[4], # match alignment length split_blast_result[15], # contig length - split_blast_result[9], # match contig position start - split_blast_result[10], # match contig position end - direction, + start, # match contig position start + end, # match contig position end + strand, gene_annotation, product_annotation, allele_quality, - prot_error_result, # protein conversion result + prot_error_details, # protein conversion result match_sequence, # match sequence in contig ref_allele_seq, # reference allele sequence - predicted_prot_seq, # predicted protein sequence + protein, # predicted protein sequence ] return blast_details @@ -410,13 +291,14 @@ def _find_match_allele_schema(allele_file: str, match_sequence: str) -> str: # if len(valid_blast_results) == 0: # no match results labelled as LNF. details data filled with empty data # return ["LNF", "LNF", ["-"] * 18] + import pdb; pdb.set_trace() if len(valid_blast_results) > 1: # could be NIPHEM or NIPH b_split_data = [] match_allele_seq = [] for valid_blast_result in valid_blast_results: multi_allele_data = _get_blast_details( - valid_blast_result, allele_name, ref_allele_seq + valid_blast_result, locus_name, ref_allele_seq ) # get match allele sequence match_allele_seq.append(multi_allele_data[14]) @@ -425,7 +307,7 @@ def _find_match_allele_schema(allele_file: str, match_sequence: str) -> str: if match_allele_schema == "": # find the allele in schema with the match sequence in the contig match_allele_schema = _find_match_allele_schema( - allele_file, multi_allele_data[15] + locus_file, multi_allele_data[15] ) if len(set(match_allele_seq)) == 1: # all sequuences are equal labelled as NIPHEM @@ -438,11 +320,11 @@ def _find_match_allele_schema(allele_file: str, match_sequence: str) -> str: b_split_data[idx][4] = classification + "_" + match_allele_schema else: b_split_data = _get_blast_details( - valid_blast_results[0], allele_name, ref_allele_seq + valid_blast_results[0], locus_name, ref_allele_seq ) # found the allele in schema with the match sequence in the contig match_allele_schema = _find_match_allele_schema( - allele_file, b_split_data[15] + locus_file, b_split_data[15] ) # PLOT, TPR, ASM, ALM, INF, EXC are possible classifications @@ -477,7 +359,7 @@ def _find_match_allele_schema(allele_file: str, match_sequence: str) -> str: # assign an identification value to the new allele if match_allele_schema == "": match_allele_schema = str( - self.inf_alle_obj.get_inferred_allele(b_split_data[14], allele_name) + self.inf_alle_obj.get_inferred_allele(b_split_data[14], locus_name) ) b_split_data[4] = classification + "_" + match_allele_schema return [ @@ -508,7 +390,7 @@ def search_match_allele(self): """ Args: - + Returns: result = { @@ -518,7 +400,7 @@ def search_match_allele(self): "snp_data": {}, "alignment_data": {}, } - + """ result = { "allele_type": {}, @@ -566,46 +448,46 @@ def search_match_allele(self): # Close object and discard memory buffer query_file.close() - allele_file = os.path.join(self.schema, os.path.basename(ref_allele)) - allele_name = Path(allele_file).stem + locus_file = os.path.join(self.schema, os.path.basename(ref_allele)) + locus_name = Path(locus_file).stem if match_found: ( - result["allele_type"][allele_name], - result["allele_match"][allele_name], - result["allele_details"][allele_name], + result["allele_type"][locus_name], + result["allele_match"][locus_name], + result["allele_details"][locus_name], ) = self.assign_allele_type( - valid_blast_results, allele_file, allele_name, r_seq + valid_blast_results, locus_file, locus_name, r_seq ) else: # Sample does not have a reference allele to be matched # Keep LNF info - result["allele_type"][allele_name] = "LNF" - result["allele_match"][allele_name] = allele_name + result["allele_type"][locus_name] = "LNF" + result["allele_match"][locus_name] = locus_name details = ["-"] * 18 details[0] = self.s_name - details[2] = allele_name + details[2] = locus_name details[4] = "LNF" - result["allele_details"][allele_name] = details + result["allele_details"][locus_name] = details # prepare the data for snp and alignment analysis try: - ref_allele_seq = result["allele_details"][allele_name][16] + ref_allele_seq = result["allele_details"][locus_name][16] except KeyError as e: log.error("Error in allele details") log.error(e) stderr.print(f"Error in allele details{e}") continue - allele_seq = result["allele_details"][allele_name][15] - ref_allele_name = result["allele_details"][allele_name][3] + allele_seq = result["allele_details"][locus_name][15] + ref_allele_name = result["allele_details"][locus_name][3] - if self.snp_request and result["allele_type"][allele_name] != "LNF": + if self.snp_request and result["allele_type"][locus_name] != "LNF": # run snp analysis - result["snp_data"][allele_name] = taranis.utils.get_snp_information( + result["snp_data"][locus_name] = taranis.utils.get_snp_information( ref_allele_seq, allele_seq, ref_allele_name ) - if self.aligment_request and result["allele_type"][allele_name] != "LNF": + if self.aligment_request and result["allele_type"][locus_name] != "LNF": # run alignment analysis - result["alignment_data"][allele_name] = ( + result["alignment_data"][locus_name] = ( taranis.utils.get_alignment_data( ref_allele_seq, allele_seq, ref_allele_name ) diff --git a/taranis/utils.py b/taranis/utils.py index 598ef41..85e9684 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -90,7 +90,7 @@ def get_seq_direction(allele_sequence): or allele_sequence[0:3] in STOP_CODON_REVERSE ): return "reverse" - return "Error" + return "both" def check_additional_programs_installed(software_list: list) -> None: @@ -119,7 +119,7 @@ def check_additional_programs_installed(software_list: list) -> None: def convert_to_protein( - sequence: str, force_coding: bool = False, delete_incompleted_triplet: bool = False + sequence: str, force_coding: bool = False ) -> dict: """Check if the input sequence is a coding protein. @@ -127,43 +127,29 @@ def convert_to_protein( sequence (str): sequence to be checked force_coding (bool, optional): force to check if sequence is coding. Defaults to False. - delete_incompleted_triplet (bool, optional): if not multiple by 3 - remove the latest sequences to check they are added after the stop - codon. Defaults to False. Returns: - dict: protein sequence and/or error message + direction(str): reverse or forward + protein (str): 1protein sequence and/or error message """ - conv_result = {"error": "-"} - # checck if exists start codon - if sequence[0:3] not in START_CODON_FORWARD: - return {"error": "Sequence does not have a start codon"} - if len(sequence) % 3 != 0: - if not delete_incompleted_triplet: - return {"error": "Sequence is not a multiple of three"} - # Remove the last or second to last bases to check if there is a stop codon - new_seq_len = len(sequence) // 3 * 3 - sequence = sequence[:new_seq_len] - # this error will be overwritten if another error is found - conv_result["error"] = "extra nucleotides after stop codon" - - seq_sequence = Seq(sequence) + protein = "-" + error = False + error_detail = "-" + + direction = get_seq_direction(sequence) + + seq = Seq(sequence) + + if direction == "reverse": + seq = seq.reverse_complement() try: - seq_prot = seq_sequence.translate(table=1, cds=force_coding) - except Bio.Data.CodonTable.TranslationError as e: - log.info("Unable to translate sequence. Info message: %s ", e) - return {"error": e} - # get the latest stop codon - last_stop = seq_prot.rfind("*") - # if force_coding is False, check if there are multiple stop codons - if not force_coding: - first_stop = seq_prot.find("*") - if first_stop != last_stop: - conv_result["error"] = "Multiple stop codons" - if last_stop != len(seq_prot) - 1: - conv_result["error"] = "Last triplet sequence is not a stop codon" - conv_result["protein"] = str(seq_prot) - return conv_result + # Table 11 is for bacteria, archaea and chloroplast + protein = seq.translate(table=11, to_stop=False, cds=force_coding) + except Bio.Data.CodonTable.TranslationError as error_detail: + error = True + log.debug(f"Error when translating protein {error_detail}") + + return direction, str(protein), error, error_detail def create_annotation_files( From dee3b6a055bb80497af19a62ccf595b57d14d6cc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Tue, 16 Apr 2024 15:02:42 +0200 Subject: [PATCH 162/214] fixed bug when niph/niphem, removed checking allele match as not important --- taranis/allele_calling.py | 26 ++++---------------------- 1 file changed, 4 insertions(+), 22 deletions(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 0bdc021..836f702 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -215,7 +215,6 @@ def _get_blast_details( direction, protein, prot_error, prot_error_details = ( taranis.utils.convert_to_protein(match_sequence, force_coding=True) ) - import pdb; pdb.set_trace() start = split_blast_result[9] end = split_blast_result[10] if prot_error: @@ -245,7 +244,6 @@ def _get_blast_details( ) start = new_start end = new_end - # get blast details blast_details = [ self.s_name, # sample name @@ -291,11 +289,12 @@ def _find_match_allele_schema(allele_file: str, match_sequence: str) -> str: # if len(valid_blast_results) == 0: # no match results labelled as LNF. details data filled with empty data # return ["LNF", "LNF", ["-"] * 18] - import pdb; pdb.set_trace() + if len(valid_blast_results) > 1: # could be NIPHEM or NIPH b_split_data = [] match_allele_seq = [] + for valid_blast_result in valid_blast_results: multi_allele_data = _get_blast_details( valid_blast_result, locus_name, ref_allele_seq @@ -303,21 +302,12 @@ def _find_match_allele_schema(allele_file: str, match_sequence: str) -> str: # get match allele sequence match_allele_seq.append(multi_allele_data[14]) b_split_data.append(multi_allele_data) - # check if match allele is in schema - if match_allele_schema == "": - # find the allele in schema with the match sequence in the contig - match_allele_schema = _find_match_allele_schema( - locus_file, multi_allele_data[15] - ) if len(set(match_allele_seq)) == 1: # all sequuences are equal labelled as NIPHEM classification = "NIPHEM" else: # some of the sequences are different labelled as NIPH classification = "NIPH" - # update coding allele type - for (idx,) in range(len(b_split_data)): - b_split_data[idx][4] = classification + "_" + match_allele_schema else: b_split_data = _get_blast_details( valid_blast_results[0], locus_name, ref_allele_seq @@ -326,7 +316,6 @@ def _find_match_allele_schema(allele_file: str, match_sequence: str) -> str: match_allele_schema = _find_match_allele_schema( locus_file, b_split_data[15] ) - # PLOT, TPR, ASM, ALM, INF, EXC are possible classifications if match_allele_schema != "": # exact match found labelled as EXC @@ -355,7 +344,6 @@ def _find_match_allele_schema(allele_file: str, match_sequence: str) -> str: else: # if sequence was not found after running grep labelled as INF classification = "INF" - # assign an identification value to the new allele if match_allele_schema == "": match_allele_schema = str( @@ -413,12 +401,7 @@ def search_match_allele(self): for ref_allele in self.ref_alleles: count += 1 log.debug( - " Processing allele ", - ref_allele, - " ", - count, - " of ", - len(self.ref_alleles), + f"Processing allele {ref_allele}: {count} of {len(self.ref_alleles)}" ) alleles = taranis.utils.read_fasta_file(ref_allele, convert_to_dict=True) @@ -427,7 +410,7 @@ def search_match_allele(self): for r_id, r_seq in alleles.items(): count_2 += 1 - log.debug("Running blast for ", count_2, " of ", len(alleles)) + log.debug(f"Running blast for {count_2} of {len(alleles)}") # create file in memory to increase speed query_file = io.StringIO() query_file.write(">" + r_id + "\n" + r_seq) @@ -447,7 +430,6 @@ def search_match_allele(self): break # Close object and discard memory buffer query_file.close() - locus_file = os.path.join(self.schema, os.path.basename(ref_allele)) locus_name = Path(locus_file).stem From 97b17f8d47a5a4f37245ccfab95aa992c1be6063 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Tue, 16 Apr 2024 15:18:03 +0200 Subject: [PATCH 163/214] fixed update classification for niph/niphem, fixed wrong indent in find classification match --- taranis/allele_calling.py | 45 ++++++++++++++++++++++----------------- 1 file changed, 25 insertions(+), 20 deletions(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 836f702..b159bac 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -308,6 +308,9 @@ def _find_match_allele_schema(allele_file: str, match_sequence: str) -> str: else: # some of the sequences are different labelled as NIPH classification = "NIPH" + # update coding allele type + for idx in range(len(b_split_data)): + b_split_data[idx][4] = classification else: b_split_data = _get_blast_details( valid_blast_results[0], locus_name, ref_allele_seq @@ -349,7 +352,7 @@ def _find_match_allele_schema(allele_file: str, match_sequence: str) -> str: match_allele_schema = str( self.inf_alle_obj.get_inferred_allele(b_split_data[14], locus_name) ) - b_split_data[4] = classification + "_" + match_allele_schema + b_split_data[4] = classification + "_" + match_allele_schema return [ classification, classification + "_" + match_allele_schema, @@ -451,29 +454,31 @@ def search_match_allele(self): details[2] = locus_name details[4] = "LNF" result["allele_details"][locus_name] = details + # prepare the data for snp and alignment analysis - try: - ref_allele_seq = result["allele_details"][locus_name][16] - except KeyError as e: - log.error("Error in allele details") - log.error(e) - stderr.print(f"Error in allele details{e}") - continue - allele_seq = result["allele_details"][locus_name][15] - ref_allele_name = result["allele_details"][locus_name][3] + if result["allele_type"][locus_name] not in ["PLOT", "LNF", "NIPH", "NIPHEM"]: + try: + ref_allele_seq = result["allele_details"][locus_name][16] + except KeyError as e: + log.error("Error in allele details") + log.error(e) + stderr.print(f"Error in allele details{e}") + continue + allele_seq = result["allele_details"][locus_name][15] + ref_allele_name = result["allele_details"][locus_name][3] - if self.snp_request and result["allele_type"][locus_name] != "LNF": - # run snp analysis - result["snp_data"][locus_name] = taranis.utils.get_snp_information( - ref_allele_seq, allele_seq, ref_allele_name - ) - if self.aligment_request and result["allele_type"][locus_name] != "LNF": - # run alignment analysis - result["alignment_data"][locus_name] = ( - taranis.utils.get_alignment_data( + if self.snp_request and result["allele_type"][locus_name] != "LNF": + # run snp analysis + result["snp_data"][locus_name] = taranis.utils.get_snp_information( ref_allele_seq, allele_seq, ref_allele_name ) - ) + if self.aligment_request and result["allele_type"][locus_name] != "LNF": + # run alignment analysis + result["alignment_data"][locus_name] = ( + taranis.utils.get_alignment_data( + ref_allele_seq, allele_seq, ref_allele_name + ) + ) # delete blast folder # _ = taranis.utils.delete_folder(os.path.join(self.blast_dir, self.s_name)) return result From 8f11892e5584967aa1559e170d1433e36c4e6ada Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Tue, 16 Apr 2024 15:55:36 +0200 Subject: [PATCH 164/214] changed exact match detection from grep to biopython --- taranis/allele_calling.py | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index b159bac..cea042f 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -277,12 +277,14 @@ def _find_match_allele_schema(allele_file: str, match_sequence: str) -> str: Returns: str: allele name in the schema that match the sequence """ - grep_result = taranis.utils.grep_execution( - allele_file, match_sequence, "-xb1" - ) - if len(grep_result) > 0: - return grep_result[0].split("_")[1] - return "" + # Read the fasta file and create a dictionary mapping sequences to their record IDs + sequence_dict = {str(record.seq): record.id for record in SeqIO.parse(allele_file, "fasta")} + + # Check if the match_sequence is in the dictionary and return the corresponding record ID part + if match_sequence in sequence_dict: + return sequence_dict[match_sequence] + + return "" # Return an empty string if no match is found # valid_blast_results = _discard_low_threshold_results(blast_results) match_allele_schema = "" @@ -336,12 +338,11 @@ def _find_match_allele_schema(allele_file: str, match_sequence: str) -> str: # labelled as TPR classification = "TPR" # check if match allele is shorter than reference allele - elif int(b_split_data[6]) < int(b_split_data[5]): + elif int(b_split_data[6]) < int(b_split_data[5]) - int(b_split_data[5]) * 0.20: classification = "ASM" # check if match allele is longer than reference allele elif ( - int(b_split_data[6]) > int(b_split_data[5]) - or b_split_data[14] == "Last sequence is not a stop codon" + int(b_split_data[6]) > int(b_split_data[5]) + int(b_split_data[5]) * 0.20 ): classification = "ALM" else: From 26bf2af7f276a19598c49c25428c30e67eb39ee4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Tue, 16 Apr 2024 15:56:08 +0200 Subject: [PATCH 165/214] removed grep execution --- taranis/utils.py | 28 +++------------------------- 1 file changed, 3 insertions(+), 25 deletions(-) diff --git a/taranis/utils.py b/taranis/utils.py index 85e9684..e5267b8 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -130,7 +130,9 @@ def convert_to_protein( Returns: direction(str): reverse or forward - protein (str): 1protein sequence and/or error message + protein (str): protein sequence + error (bool): True/False + error_detail (str): translate method error """ protein = "-" error = False @@ -502,30 +504,6 @@ def get_snp_information( return snp_info -def grep_execution(input_file: str, pattern: str, parameters: str) -> list[str]: - """run grep command and return the output - - Args: - input_file (str): input file path - pattern (str): pattern to be searched - parmeters (str): parameters to be used in grep - - Returns: - list[str]: list of lines which match the pattern - """ - try: - result = subprocess.run( - ["grep", parameters, pattern, input_file], - capture_output=True, - check=True, - text=True, - ) - except subprocess.CalledProcessError as e: - log.debug("Unable to run grep. Error message: %s ", e) - return [] - return result.stdout.split("\n") - - def map_amino_acid_to_annotation(amino_acid): # Dictionary mapping amino acids to their categories amino_acid_categories = { From b15029718e7246277a9d1a417cfb920a33dc2ee8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Tue, 16 Apr 2024 16:05:18 +0200 Subject: [PATCH 166/214] TPR when any protein translation error, fixed bug when b_split_data is passed to inferred alleles --- taranis/allele_calling.py | 12 ++++-------- 1 file changed, 4 insertions(+), 8 deletions(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index cea042f..0b8743d 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -328,16 +328,12 @@ def _find_match_allele_schema(allele_file: str, match_sequence: str) -> str: elif _check_if_plot(b_split_data): # match allele is partial length labelled as PLOT classification = "PLOT" - # check if protein length divided by the length of triplet matched - # sequence is lower the the tpr limit + # check if protein translation has failed and set to TPR elif ( - b_split_data[14] == "Multiple stop codons" - and b_split_data[17].index("*") / (int(b_split_data[6]) / 3) - < self.tpr_limit + b_split_data[14] != "-" ): - # labelled as TPR classification = "TPR" - # check if match allele is shorter than reference allele + # check if match allele is shorter than reference allele elif int(b_split_data[6]) < int(b_split_data[5]) - int(b_split_data[5]) * 0.20: classification = "ASM" # check if match allele is longer than reference allele @@ -351,7 +347,7 @@ def _find_match_allele_schema(allele_file: str, match_sequence: str) -> str: # assign an identification value to the new allele if match_allele_schema == "": match_allele_schema = str( - self.inf_alle_obj.get_inferred_allele(b_split_data[14], locus_name) + self.inf_alle_obj.get_inferred_allele(b_split_data[15], locus_name) ) b_split_data[4] = classification + "_" + match_allele_schema return [ From ac5e4f5b3af5a36e20f8c410250dd6cc1a860ef9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Tue, 16 Apr 2024 21:49:14 +0200 Subject: [PATCH 167/214] added some twicks when strand is -, and some linting --- taranis/allele_calling.py | 62 +++++++++++++++++++++++++++++---------- taranis/utils.py | 7 ++--- 2 files changed, 49 insertions(+), 20 deletions(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 0b8743d..2ea3b69 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -73,7 +73,7 @@ def __init__( self.snp_request = snp_request self.aligment_request = aligment_request self.tpr_limit = tpr_limit / 100 - self.increase_sequence = increase_sequence * 3 + self.increase_sequence = increase_sequence def assign_allele_type( self, @@ -140,13 +140,14 @@ def _extend_seq_find_start_stop_codon( protein = "-" error = False error_details = "-" + i = 0 # Extend the sequence to find a valid start or stop codon if direction == "reverse": - contig_seq = contig_seq.reverse_complement() + contig_seq = Seq(contig_seq).reverse_complement() start, end = len(contig_seq) - end, len(contig_seq) - start - import pdb; pdb.set_trace() - for i in range(1, limit + 1): + for _ in range(limit): + i += 3 if search == "5_prime": extended_start = max(0, start - i) extended_end = end @@ -195,6 +196,7 @@ def _get_blast_details( """ split_blast_result = blast_result.split("\t") match_allele_name = split_blast_result[0] + try: gene_annotation = self.prediction_data[match_allele_name]["gene"] product_annotation = self.prediction_data[match_allele_name]["product"] @@ -205,23 +207,41 @@ def _get_blast_details( gene_annotation = "Not found" product_annotation = "Not found" allele_quality = "Not found" + if int(split_blast_result[10]) > int(split_blast_result[9]): strand = "+" else: strand = "-" + # remove the gaps in sequences match_sequence = split_blast_result[13].replace("-", "") # check if the sequence is coding direction, protein, prot_error, prot_error_details = ( taranis.utils.convert_to_protein(match_sequence, force_coding=True) ) - start = split_blast_result[9] - end = split_blast_result[10] + + # 0/1-based + if strand == "+": + start = int(split_blast_result[9]) - 1 + end = int(split_blast_result[10]) + else: + start = int(split_blast_result[10]) - 1 + end = int(split_blast_result[9]) + if prot_error: + # If strand "-" contig seq is reverse complemented but match sequence (split_blast_result[13]) + # is forward, so we need to change the contig seq to reverse_complement accordingly + if strand == "-" and direction == "reverse": + direction_contig = "forward" + elif strand == "-" and direction == "forward": + direction_contig = "reverse" + else: + direction_contig = direction + if "is not a stop codon" in prot_error_details: protein, new_start, new_end, prot_error, prot_error_details = ( _extend_seq_find_start_stop_codon( - direction=direction, + direction=direction_contig, contig_seq=self.sample_contigs[split_blast_result[1]], start=start, end=end, @@ -236,8 +256,8 @@ def _get_blast_details( _extend_seq_find_start_stop_codon( direction=direction, contig_seq=self.sample_contigs[split_blast_result[1]], - start=split_blast_result[9], - end=split_blast_result[10], + start=start, + end=end, limit=self.increase_sequence, search="5_prime", ) @@ -278,7 +298,10 @@ def _find_match_allele_schema(allele_file: str, match_sequence: str) -> str: str: allele name in the schema that match the sequence """ # Read the fasta file and create a dictionary mapping sequences to their record IDs - sequence_dict = {str(record.seq): record.id for record in SeqIO.parse(allele_file, "fasta")} + sequence_dict = { + str(record.seq): record.id + for record in SeqIO.parse(allele_file, "fasta") + } # Check if the match_sequence is in the dictionary and return the corresponding record ID part if match_sequence in sequence_dict: @@ -329,16 +352,18 @@ def _find_match_allele_schema(allele_file: str, match_sequence: str) -> str: # match allele is partial length labelled as PLOT classification = "PLOT" # check if protein translation has failed and set to TPR - elif ( - b_split_data[14] != "-" - ): + elif b_split_data[14] != "-": classification = "TPR" # check if match allele is shorter than reference allele - elif int(b_split_data[6]) < int(b_split_data[5]) - int(b_split_data[5]) * 0.20: + elif ( + int(b_split_data[6]) + < int(b_split_data[5]) - int(b_split_data[5]) * 0.20 + ): classification = "ASM" # check if match allele is longer than reference allele elif ( - int(b_split_data[6]) > int(b_split_data[5]) + int(b_split_data[5]) * 0.20 + int(b_split_data[6]) + > int(b_split_data[5]) + int(b_split_data[5]) * 0.20 ): classification = "ALM" else: @@ -453,7 +478,12 @@ def search_match_allele(self): result["allele_details"][locus_name] = details # prepare the data for snp and alignment analysis - if result["allele_type"][locus_name] not in ["PLOT", "LNF", "NIPH", "NIPHEM"]: + if result["allele_type"][locus_name] not in [ + "PLOT", + "LNF", + "NIPH", + "NIPHEM", + ]: try: ref_allele_seq = result["allele_details"][locus_name][16] except KeyError as e: diff --git a/taranis/utils.py b/taranis/utils.py index e5267b8..eba5639 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -118,9 +118,7 @@ def check_additional_programs_installed(software_list: list) -> None: return -def convert_to_protein( - sequence: str, force_coding: bool = False -) -> dict: +def convert_to_protein(sequence: str, force_coding: bool = False) -> dict: """Check if the input sequence is a coding protein. Args: @@ -147,8 +145,9 @@ def convert_to_protein( try: # Table 11 is for bacteria, archaea and chloroplast protein = seq.translate(table=11, to_stop=False, cds=force_coding) - except Bio.Data.CodonTable.TranslationError as error_detail: + except Bio.Data.CodonTable.TranslationError as e: error = True + error_detail = str(e) log.debug(f"Error when translating protein {error_detail}") return direction, str(protein), error, error_detail From f498fb5537d9212c93d3d08f49927ad8e2353953 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Wed, 17 Apr 2024 15:28:05 +0200 Subject: [PATCH 168/214] fixed LNF in allele_match.tsv output, variable renaming, comment for restructuring in next commit --- taranis/allele_calling.py | 57 ++++++++++++++++++++++++++------------- 1 file changed, 38 insertions(+), 19 deletions(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 2ea3b69..3b153fa 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -274,8 +274,8 @@ def _get_blast_details( split_blast_result[3], # reference allele length split_blast_result[4], # match alignment length split_blast_result[15], # contig length - start, # match contig position start - end, # match contig position end + str(start), # match contig position start + str(end), # match contig position end strand, gene_annotation, product_annotation, @@ -317,7 +317,7 @@ def _find_match_allele_schema(allele_file: str, match_sequence: str) -> str: if len(valid_blast_results) > 1: # could be NIPHEM or NIPH - b_split_data = [] + sample_allele_data = [] match_allele_seq = [] for valid_blast_result in valid_blast_results: @@ -326,7 +326,7 @@ def _find_match_allele_schema(allele_file: str, match_sequence: str) -> str: ) # get match allele sequence match_allele_seq.append(multi_allele_data[14]) - b_split_data.append(multi_allele_data) + sample_allele_data.append(multi_allele_data) if len(set(match_allele_seq)) == 1: # all sequuences are equal labelled as NIPHEM classification = "NIPHEM" @@ -334,36 +334,52 @@ def _find_match_allele_schema(allele_file: str, match_sequence: str) -> str: # some of the sequences are different labelled as NIPH classification = "NIPH" # update coding allele type - for idx in range(len(b_split_data)): - b_split_data[idx][4] = classification + for idx in range(len(sample_allele_data)): + sample_allele_data[idx][4] = classification + else: - b_split_data = _get_blast_details( + sample_allele_data = _get_blast_details( valid_blast_results[0], locus_name, ref_allele_seq ) # found the allele in schema with the match sequence in the contig match_allele_schema = _find_match_allele_schema( - locus_file, b_split_data[15] + locus_file, sample_allele_data[15] ) # PLOT, TPR, ASM, ALM, INF, EXC are possible classifications if match_allele_schema != "": # exact match found labelled as EXC classification = "EXC" - elif _check_if_plot(b_split_data): + sample_allele_data[4] = classification + "_" + match_allele_schema + return [ + classification, + classification + "_" + match_allele_schema, + sample_allele_data, + ] + elif _check_if_plot(sample_allele_data): # match allele is partial length labelled as PLOT classification = "PLOT" - # check if protein translation has failed and set to TPR - elif b_split_data[14] != "-": + sample_allele_data[4] = classification + return [ + classification, + classification + "_" + match_allele_schema, + sample_allele_data, + ] + + # IF PROTEIN ERROR. TRY TO EXTEND + # Update sample_allele_data + + if sample_allele_data[14] != "-": classification = "TPR" # check if match allele is shorter than reference allele elif ( - int(b_split_data[6]) - < int(b_split_data[5]) - int(b_split_data[5]) * 0.20 + int(sample_allele_data[6]) + < int(sample_allele_data[5]) - int(sample_allele_data[5]) * 0.20 ): classification = "ASM" # check if match allele is longer than reference allele elif ( - int(b_split_data[6]) - > int(b_split_data[5]) + int(b_split_data[5]) * 0.20 + int(sample_allele_data[6]) + > int(sample_allele_data[5]) + int(sample_allele_data[5]) * 0.20 ): classification = "ALM" else: @@ -372,13 +388,14 @@ def _find_match_allele_schema(allele_file: str, match_sequence: str) -> str: # assign an identification value to the new allele if match_allele_schema == "": match_allele_schema = str( - self.inf_alle_obj.get_inferred_allele(b_split_data[15], locus_name) + self.inf_alle_obj.get_inferred_allele(sample_allele_data[15], locus_name) ) - b_split_data[4] = classification + "_" + match_allele_schema + sample_allele_data[4] = classification + "_" + match_allele_schema + return [ classification, classification + "_" + match_allele_schema, - b_split_data, + sample_allele_data, ] def discard_low_threshold_results(self, blast_results: list) -> list: @@ -470,7 +487,7 @@ def search_match_allele(self): # Sample does not have a reference allele to be matched # Keep LNF info result["allele_type"][locus_name] = "LNF" - result["allele_match"][locus_name] = locus_name + result["allele_match"][locus_name] = "LNF" details = ["-"] * 18 details[0] = self.s_name details[2] = locus_name @@ -691,6 +708,7 @@ def read_reference_alleles(ref_alleles: list) -> dict[dict]: # get allele list first_sample = list(results[0].keys())[0] allele_list = sorted(results[0][first_sample]["allele_type"].keys()) + for result in results: for sample, values in result.items(): sum_allele_type = OrderedDict() # used for summary file @@ -734,6 +752,7 @@ def read_reference_alleles(ref_alleles: list) -> dict[dict]: for detail in detail_value: fo.write(",".join(detail) + "\n") else: + import pdb; pdb.set_trace() fo.write(",".join(detail_value) + "\n") # save snp to file if requested if snp_request: From fe73f6b9457b52918fb45d5ed3b12d5c0301fe90 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Wed, 17 Apr 2024 18:10:47 +0200 Subject: [PATCH 169/214] renaming, changed output of allele details from list to dict, moved extend to find start/stop codon only after exact match testing, some comment cleaning --- taranis/__main__.py | 22 +-- taranis/allele_calling.py | 381 +++++++++++++++++++------------------- 2 files changed, 196 insertions(+), 207 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index 30f26f1..7b18c30 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -517,7 +517,6 @@ def allele_calling( if not force: _ = taranis.utils.prompt_user_if_folder_exists(output) # Filter fasta files from reference folder - # ref_alleles = glob.glob(os.path.join(reference, "*.fasta")) max_cpus = taranis.utils.cpus_available() if cpus > max_cpus: stderr.print("[red] Number of CPUs bigger than the CPUs available") @@ -563,32 +562,13 @@ def allele_calling( except Exception as e: print(e) continue - """ - for assembly_file in assemblies: - results.append( - taranis.allele_calling.parallel_execution( - assembly_file, - schema, - prediction_data, - schema_ref_files, - threshold, - perc_identity, - output, - inf_allele_obj, - snp, - alignment, - proteine_threshold, - increase_sequence, - ) - ) - """ + _ = taranis.allele_calling.collect_data( results, output, snp, alignment, schema_ref_files, cpus ) finish = time.perf_counter() print(f"Allele calling finish in {round((finish-start)/60, 2)} minutes") log.info("Allele calling finish in %s minutes", round((finish - start) / 60, 2)) - # sample_allele_obj.analyze_sample() @taranis_cli.command(help_priority=3) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 3b153fa..14de0a4 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -95,22 +95,22 @@ def assign_allele_type( details """ - def _check_if_plot(column_blast_res: list) -> bool: + def _check_plot(allele_details: dict) -> bool: """Check if allele is partial length Args: - column_blast_res (list): blast result + allele_details (dirt): allele details obtained with _get_allele_details() function. Returns: - bool: True if allele is partial length + bool: True if alignment is partial due to end of contig """ if ( - column_blast_res[8] == "1" # check at contig start + allele_details["align_contig_start"] == "1" # check at contig start # check if contig ends is the same as match allele ends - or column_blast_res[9] == column_blast_res[7] - or column_blast_res[9] == "1" # check reverse at contig end + or allele_details["align_contig_end"] == allele_details["contig_length"] + or allele_details["align_contig_end"] == "1" # check reverse at contig end # check if contig start is the same as match allele start reverse - or column_blast_res[8] == column_blast_res[7] + or allele_details["align_contig_start"] == allele_details["contig_length"] ): return True return False @@ -126,16 +126,9 @@ def _extend_seq_find_start_stop_codon( """Extend match sequence, according to increase_sequence in order to try to find the stop or start codon. Args: - split_blast_result (list): list having the informaction collected - by running blast - prot_error_result (str): protein conversion result - predicted_prot_seq (str): predicted protein sequence - search_codon (str, optional): codon to be found. 2 values are - allowed start of stop. By default is stop. Returns: - list: updated information if stop or start codon is found and the - updated protein sequence and protein conversion result if changed + """ protein = "-" error = False @@ -160,43 +153,47 @@ def _extend_seq_find_start_stop_codon( extended_seq, force_coding=True ) if not error: - return protein, extended_start, extended_end, error, error_details + return protein, extended_seq, extended_start, extended_end, error, error_details - return protein, start, end, error, error_details + return protein, contig_seq[start:end], start, end, error, error_details - def _get_blast_details( - blast_result: str, allele_name: str, ref_allele_seq - ) -> list: - """Collect blast details and modify the order of the columns + def _get_allele_details( + blast_result: str, locus_name: str, ref_allele_seq + ) -> dict: + """Collect blast details, add gene annotation, and protein sequence. Args: blast_result (str): information collected by running blast - allele_name (str): allele name + locus_name (str): allele name + ref_allele_seq (str): reference allele sequence Returns: - list: containing allele details in the correct order to be saved - blast_details[0] = sample name - blast_details[1] = contig name - blast_details[2] = core gene name - blast_details[3] = allele gene - blast_details[4] = coding allele type - blast_details[5] = reference allele length - blast_details[6] = match alignment length - blast_details[7] = contig length - blast_details[8] = match contig position start - blast_details[9] = match contig position end - blast_details[10] = direction - blast_details[11] = gene annotation - blast_details[12] = product annotation - blast_details[13] = allele quality - blast_details[14] = protein conversion result - blast_details[15] = match sequence in contig - blast_details[16] = reference allele sequence - blast_details[17] = predicted protein sequence + dict: + allele_details{ + "sample_name": str, + "contig_name": str, + "locus_name": str, + "ref_allele_name": str, + "allele_type": str, + "ref_allele_length": str, + "alignment_length": str, + "contig_length": str, + "align_contig_start": str, + "align_contig_end": str, + "strand": str, + "sample_allele_seq": str, + "ref_allele_seq": str, + "gene_annotation": str, + "product_annot": str, + "ref_allele_quality": str, + "protein_seq": str, + "prot_strand": str, + "prot_error": bool, + "prot_error_details": str, + } """ split_blast_result = blast_result.split("\t") match_allele_name = split_blast_result[0] - try: gene_annotation = self.prediction_data[match_allele_name]["gene"] product_annotation = self.prediction_data[match_allele_name]["product"] @@ -219,75 +216,32 @@ def _get_blast_details( direction, protein, prot_error, prot_error_details = ( taranis.utils.convert_to_protein(match_sequence, force_coding=True) ) - - # 0/1-based - if strand == "+": - start = int(split_blast_result[9]) - 1 - end = int(split_blast_result[10]) - else: - start = int(split_blast_result[10]) - 1 - end = int(split_blast_result[9]) - - if prot_error: - # If strand "-" contig seq is reverse complemented but match sequence (split_blast_result[13]) - # is forward, so we need to change the contig seq to reverse_complement accordingly - if strand == "-" and direction == "reverse": - direction_contig = "forward" - elif strand == "-" and direction == "forward": - direction_contig = "reverse" - else: - direction_contig = direction - - if "is not a stop codon" in prot_error_details: - protein, new_start, new_end, prot_error, prot_error_details = ( - _extend_seq_find_start_stop_codon( - direction=direction_contig, - contig_seq=self.sample_contigs[split_blast_result[1]], - start=start, - end=end, - limit=self.increase_sequence, - search="3_prime", - ) - ) - start = new_start - end = new_end - elif "is not a start codon" in prot_error_details: - protein, new_start, new_end, prot_error, prot_error_details = ( - _extend_seq_find_start_stop_codon( - direction=direction, - contig_seq=self.sample_contigs[split_blast_result[1]], - start=start, - end=end, - limit=self.increase_sequence, - search="5_prime", - ) - ) - start = new_start - end = new_end # get blast details - blast_details = [ - self.s_name, # sample name - split_blast_result[1], # contig name - allele_name, # core gene name - split_blast_result[0], # allele gene - "-", # coding allele type. To be filled later idx = 4 - split_blast_result[3], # reference allele length - split_blast_result[4], # match alignment length - split_blast_result[15], # contig length - str(start), # match contig position start - str(end), # match contig position end - strand, - gene_annotation, - product_annotation, - allele_quality, - prot_error_details, # protein conversion result - match_sequence, # match sequence in contig - ref_allele_seq, # reference allele sequence - protein, # predicted protein sequence - ] - return blast_details - - def _find_match_allele_schema(allele_file: str, match_sequence: str) -> str: + allele_details = OrderedDict({ + "sample_name": self.s_name, # sample name + "locus_name": locus_name, # core gene name + "allele_type": "-", + "ref_allele_name": split_blast_result[0], + "contig_name": split_blast_result[1], # contig name + "contig_length": split_blast_result[15], + "ref_allele_length": split_blast_result[3], + "alignment_length": split_blast_result[4], + "align_contig_start": split_blast_result[9], + "align_contig_end": split_blast_result[10], + "strand": strand, + "sample_allele_seq": match_sequence, + "ref_allele_seq": ref_allele_seq, + "gene_annotation": gene_annotation, + "product_annot": product_annotation, + "ref_allele_quality": allele_quality, + "protein_seq": protein, + "prot_strand": direction, + "prot_error": prot_error, + "prot_error_details": prot_error_details, + }) + return allele_details + + def _classify_allele(allele_file: str, match_sequence: str) -> str: """Find the allele name in the schema that match the sequence Args: @@ -307,13 +261,9 @@ def _find_match_allele_schema(allele_file: str, match_sequence: str) -> str: if match_sequence in sequence_dict: return sequence_dict[match_sequence] - return "" # Return an empty string if no match is found + return False # Return an empty string if no match is found - # valid_blast_results = _discard_low_threshold_results(blast_results) match_allele_schema = "" - # if len(valid_blast_results) == 0: - # no match results labelled as LNF. details data filled with empty data - # return ["LNF", "LNF", ["-"] * 18] if len(valid_blast_results) > 1: # could be NIPHEM or NIPH @@ -321,11 +271,11 @@ def _find_match_allele_schema(allele_file: str, match_sequence: str) -> str: match_allele_seq = [] for valid_blast_result in valid_blast_results: - multi_allele_data = _get_blast_details( + multi_allele_data = _get_allele_details( valid_blast_result, locus_name, ref_allele_seq ) # get match allele sequence - match_allele_seq.append(multi_allele_data[14]) + match_allele_seq.append(multi_allele_data["sample_allele_seq"]) sample_allele_data.append(multi_allele_data) if len(set(match_allele_seq)) == 1: # all sequuences are equal labelled as NIPHEM @@ -335,62 +285,116 @@ def _find_match_allele_schema(allele_file: str, match_sequence: str) -> str: classification = "NIPH" # update coding allele type for idx in range(len(sample_allele_data)): - sample_allele_data[idx][4] = classification + sample_allele_data[idx]["allele_type"] = classification else: - sample_allele_data = _get_blast_details( + sample_allele_data = _get_allele_details( valid_blast_results[0], locus_name, ref_allele_seq ) # found the allele in schema with the match sequence in the contig - match_allele_schema = _find_match_allele_schema( - locus_file, sample_allele_data[15] + match_allele_schema = _classify_allele( + locus_file, sample_allele_data["sample_allele_seq"] ) # PLOT, TPR, ASM, ALM, INF, EXC are possible classifications - if match_allele_schema != "": + if match_allele_schema: # exact match found labelled as EXC classification = "EXC" - sample_allele_data[4] = classification + "_" + match_allele_schema + sample_allele_data["allele_type"] = classification + "_" + match_allele_schema return [ classification, classification + "_" + match_allele_schema, sample_allele_data, ] - elif _check_if_plot(sample_allele_data): + elif _check_plot(sample_allele_data): # match allele is partial length labelled as PLOT classification = "PLOT" - sample_allele_data[4] = classification + sample_allele_data["allele_type"] = classification return [ classification, - classification + "_" + match_allele_schema, + classification, sample_allele_data, ] - # IF PROTEIN ERROR. TRY TO EXTEND - # Update sample_allele_data + if sample_allele_data["prot_error"]: + # 0/1-based + if sample_allele_data["strand"] == "+": + start = int(sample_allele_data["align_contig_start"]) - 1 + end = int(sample_allele_data["align_contig_end"]) + else: + start = int(sample_allele_data["align_contig_end"]) - 1 + end = int(sample_allele_data["align_contig_start"]) - if sample_allele_data[14] != "-": + # If strand "-" contig seq is reverse complemented but match sequence (split_blast_result[13]) + # is forward, so we need to change the contig seq to reverse_complement accordingly + if sample_allele_data["strand"] == "-" and sample_allele_data["prot_strand"] == "reverse": + direction_contig = "forward" + elif sample_allele_data["strand"] == "-" and sample_allele_data["prot_strand"] == "forward": + direction_contig = "reverse" + else: + direction_contig = sample_allele_data["prot_strand"] + + if "is not a stop codon" in sample_allele_data["prot_error_details"]: + protein, extended_seq, new_start, new_end, prot_error, prot_error_details = ( + _extend_seq_find_start_stop_codon( + direction=direction_contig, + contig_seq=self.sample_contigs[sample_allele_data["contig_name"]], + start=start, + end=end, + limit=self.increase_sequence, + search="3_prime", + ) + ) + if not prot_error: + sample_allele_data["protein_seq"] = protein + sample_allele_data["sample_allele_seq"] = extended_seq + sample_allele_data["align_contig_start"] = new_start + sample_allele_data["align_contig_end"] = new_end + sample_allele_data["prot_error"] = prot_error + sample_allele_data["prot_error_details"] = prot_error_details + + elif "is not a start codon" in sample_allele_data["prot_error_details"]: + protein, new_start, new_end, prot_error, prot_error_details = ( + _extend_seq_find_start_stop_codon( + direction=direction_contig, + contig_seq=self.sample_contigs[sample_allele_data["contig_name"]], + start=start, + end=end, + limit=self.increase_sequence, + search="5_prime", + ) + ) + if not prot_error: + sample_allele_data["protein_seq"] = protein + sample_allele_data["sample_allele_seq"] = extended_seq + sample_allele_data["align_contig_start"] = new_start + sample_allele_data["align_contig_end"] = new_end + sample_allele_data["prot_error"] = prot_error + sample_allele_data["prot_error_details"] = prot_error_details + + if sample_allele_data["prot_error"]: classification = "TPR" # check if match allele is shorter than reference allele elif ( - int(sample_allele_data[6]) - < int(sample_allele_data[5]) - int(sample_allele_data[5]) * 0.20 + int(len(sample_allele_data["sample_allele_seq"])) + < int(sample_allele_data["ref_allele_length"]) - int(sample_allele_data["ref_allele_length"]) * 0.20 ): classification = "ASM" # check if match allele is longer than reference allele elif ( - int(sample_allele_data[6]) - > int(sample_allele_data[5]) + int(sample_allele_data[5]) * 0.20 + int(len(sample_allele_data["sample_allele_seq"])) + > int(sample_allele_data["ref_allele_length"]) + int(sample_allele_data["ref_allele_length"]) * 0.20 ): classification = "ALM" else: # if sequence was not found after running grep labelled as INF classification = "INF" # assign an identification value to the new allele - if match_allele_schema == "": + if not match_allele_schema: match_allele_schema = str( - self.inf_alle_obj.get_inferred_allele(sample_allele_data[15], locus_name) + self.inf_alle_obj.get_inferred_allele(sample_allele_data["sample_allele_seq"], locus_name) ) - sample_allele_data[4] = classification + "_" + match_allele_schema + + sample_allele_data["allele_type"] = classification + "_" + match_allele_schema return [ classification, @@ -416,11 +420,11 @@ def discard_low_threshold_results(self, blast_results: list) -> list: valid_blast_result.append(b_result) return valid_blast_result - def search_match_allele(self): - """ + def search_allele(self): + """ Search reference allele in contig files and classify Args: - + self Returns: result = { @@ -430,7 +434,6 @@ def search_match_allele(self): "snp_data": {}, "alignment_data": {}, } - """ result = { "allele_type": {}, @@ -472,6 +475,7 @@ def search_match_allele(self): break # Close object and discard memory buffer query_file.close() + locus_file = os.path.join(self.schema, os.path.basename(ref_allele)) locus_name = Path(locus_file).stem @@ -488,10 +492,10 @@ def search_match_allele(self): # Keep LNF info result["allele_type"][locus_name] = "LNF" result["allele_match"][locus_name] = "LNF" - details = ["-"] * 18 - details[0] = self.s_name - details[2] = locus_name - details[4] = "LNF" + details = OrderedDict() + details["sample_name"] = self.s_name + details["locus_name"] = locus_name + details["allele_type"] = "LNF" result["allele_details"][locus_name] = details # prepare the data for snp and alignment analysis @@ -502,20 +506,22 @@ def search_match_allele(self): "NIPHEM", ]: try: - ref_allele_seq = result["allele_details"][locus_name][16] + ref_allele_seq = result["allele_details"][locus_name]["ref_allele_seq"] except KeyError as e: log.error("Error in allele details") log.error(e) stderr.print(f"Error in allele details{e}") continue - allele_seq = result["allele_details"][locus_name][15] - ref_allele_name = result["allele_details"][locus_name][3] + + allele_seq = result["allele_details"][locus_name]["sample_allele_seq"] + ref_allele_name = result["allele_details"][locus_name]["ref_allele_name"] if self.snp_request and result["allele_type"][locus_name] != "LNF": # run snp analysis result["snp_data"][locus_name] = taranis.utils.get_snp_information( ref_allele_seq, allele_seq, ref_allele_name ) + if self.aligment_request and result["allele_type"][locus_name] != "LNF": # run alignment analysis result["alignment_data"][locus_name] = ( @@ -524,7 +530,7 @@ def search_match_allele(self): ) ) # delete blast folder - # _ = taranis.utils.delete_folder(os.path.join(self.blast_dir, self.s_name)) + _ = taranis.utils.delete_folder(os.path.join(self.blast_dir, self.s_name)) return result @@ -559,7 +565,7 @@ def parallel_execution( sample_name = Path(sample_file).stem stderr.print(f"[green] Analyzing sample {sample_name}") log.info(f"Analyzing sample {sample_name}") - return {sample_name: allele_obj.search_match_allele()} + return {sample_name: allele_obj.search_allele()} def create_multiple_alignment( @@ -678,36 +684,37 @@ def read_reference_alleles(ref_alleles: list) -> dict[dict]: return ref_alleles_data summary_result_file = os.path.join(output, "allele_calling_summary.csv") - sample_allele_match_file = os.path.join(output, "allele_calling_match.csv") - sample_allele_detail_file = os.path.join(output, "matching_contig.csv") + allele_matrix_file = os.path.join(output, "allele_calling_match.csv") + allele_detail_file = os.path.join(output, "contig_alignment_info.csv") allele_types = ["NIPHEM", "NIPH", "EXC", "PLOT", "ASM", "ALM", "INF", "LNF", "TPR"] - detail_heading = [ - "sample", - "contig", - "core gene", - "reference allele name", - "codification", - "query length", - "match length", - "contig length", - "contig start", - "contig stop", - "direction", - "gene notation", - "product notation", - "reference allele quality", - "protein conversion result", - "match sequence", - "reference allele sequence", - "predicted protein sequence", + allele_detail_heading = [ + "sample_name", + "locus_name", + "allele_type", + "ref_allele_name", + "contig_name", + "contig_length", + "ref_allele_length", + "alignment_length", + "align_contig_start", + "align_contig_end", + "strand", + "sample_allele_seq", + "ref_allele_seq", + "gene_annotation", + "product_annot", + "ref_allele_quality", + "protein_seq", + "prot_strand", + "prot_error", + "prot_error_details", ] summary_result = {} # used for summary file and allele classification graphics - sample_allele_match = {} # used for allele match file + allele_matrix_result = {} # used for allele match file # get allele list - first_sample = list(results[0].keys())[0] - allele_list = sorted(results[0][first_sample]["allele_type"].keys()) + locus_list = [Path(ref_allele).stem for ref_allele in ref_alleles] for result in results: for sample, values in result.items(): @@ -720,11 +727,10 @@ def read_reference_alleles(ref_alleles: list) -> dict[dict]: sum_allele_type[type_of_allele] += 1 # add allele name match to sample allele_match[allele] = ( - # type_of_allele + "_" + values["allele_match"][allele] values["allele_match"][allele] ) summary_result[sample] = sum_allele_type - sample_allele_match[sample] = allele_match + allele_matrix_result[sample] = allele_match # save summary results to file with open(summary_result_file, "w") as fo: @@ -734,26 +740,29 @@ def read_reference_alleles(ref_alleles: list) -> dict[dict]: for _, count in counts.items(): fo.write(f"{count},") fo.write("\n") + # save allele match to file - with open(sample_allele_match_file, "w") as fo: - fo.write("Sample," + ",".join(allele_list) + "\n") - for sample, allele_cod in sample_allele_match.items(): + with open(allele_matrix_file, "w") as fo: + fo.write("Sample," + ",".join(locus_list) + "\n") + for sample, allele_cod in allele_matrix_result.items(): fo.write(f"{sample}") - for allele in allele_list: + for allele in locus_list: fo.write(f",{allele_cod[allele]}") fo.write("\n") - with open(sample_allele_detail_file, "w") as fo: - fo.write(",".join(detail_heading) + "\n") + with open(allele_detail_file, "w") as fo: + fo.write(",".join(allele_detail_heading) + "\n") for result in results: for sample, values in result.items(): for allele, detail_value in values["allele_details"].items(): - if type(detail_value[0]) is list: + if type(detail_value) is list: for detail in detail_value: - fo.write(",".join(detail) + "\n") + if detail["allele_type"] != "LNF": + fo.write(",".join([str(value) for value in detail.values()]) + "\n") else: - import pdb; pdb.set_trace() - fo.write(",".join(detail_value) + "\n") + if detail_value["allele_type"] != "LNF": + fo.write(",".join([str(value) for value in detail_value.values()]) + "\n") + # save snp to file if requested if snp_request: for result in results: @@ -817,7 +826,7 @@ def read_reference_alleles(ref_alleles: list) -> dict[dict]: alignment_folder, mafft_cpus, ) - for a_list in allele_list + for a_list in locus_list ] for future in concurrent.futures.as_completed(futures): try: From 4c1671d49e6cd41b1c2657557689794d87fcbae9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Wed, 17 Apr 2024 18:16:44 +0200 Subject: [PATCH 170/214] fixed wrong unpack in extend seq find function call --- taranis/allele_calling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 14de0a4..daf9564 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -353,7 +353,7 @@ def _classify_allele(allele_file: str, match_sequence: str) -> str: sample_allele_data["prot_error_details"] = prot_error_details elif "is not a start codon" in sample_allele_data["prot_error_details"]: - protein, new_start, new_end, prot_error, prot_error_details = ( + protein, extended_seq, new_start, new_end, prot_error, prot_error_details = ( _extend_seq_find_start_stop_codon( direction=direction_contig, contig_seq=self.sample_contigs[sample_allele_data["contig_name"]], From 15213b1ca764a5ba9d5ffa423c5e71a1e22b8b59 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Thu, 18 Apr 2024 11:05:38 +0200 Subject: [PATCH 171/214] sort locus_names when printing results. Added search for EXC match after protein fixing --- taranis/allele_calling.py | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index daf9564..58228e1 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -371,6 +371,18 @@ def _classify_allele(allele_file: str, match_sequence: str) -> str: sample_allele_data["prot_error"] = prot_error sample_allele_data["prot_error_details"] = prot_error_details + match_allele_schema = _classify_allele( + locus_file, sample_allele_data["sample_allele_seq"] + ) + if match_allele_schema: + # exact match found labelled as EXC + classification = "EXC" + sample_allele_data["allele_type"] = classification + "_" + match_allele_schema + return [ + classification, + classification + "_" + match_allele_schema, + sample_allele_data, + ] if sample_allele_data["prot_error"]: classification = "TPR" # check if match allele is shorter than reference allele @@ -714,7 +726,7 @@ def read_reference_alleles(ref_alleles: list) -> dict[dict]: allele_matrix_result = {} # used for allele match file # get allele list - locus_list = [Path(ref_allele).stem for ref_allele in ref_alleles] + locus_list = [Path(ref_allele).stem for ref_allele in ref_alleles].sort() for result in results: for sample, values in result.items(): From a365dc9a360cfaed34f65f0a6628cac8fd7832fb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Fri, 19 Apr 2024 11:05:35 +0200 Subject: [PATCH 172/214] function organization, linting and added function when sequence is not multiple of three --- taranis/allele_calling.py | 292 +++++++++++++++++++++++++------------- taranis/utils.py | 8 ++ 2 files changed, 202 insertions(+), 98 deletions(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 58228e1..2ac8ff5 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -99,7 +99,7 @@ def _check_plot(allele_details: dict) -> bool: """Check if allele is partial length Args: - allele_details (dirt): allele details obtained with _get_allele_details() function. + allele_details (dirt): allele details obtained with _get_allele_details() function. Returns: bool: True if alignment is partial due to end of contig @@ -108,9 +108,11 @@ def _check_plot(allele_details: dict) -> bool: allele_details["align_contig_start"] == "1" # check at contig start # check if contig ends is the same as match allele ends or allele_details["align_contig_end"] == allele_details["contig_length"] - or allele_details["align_contig_end"] == "1" # check reverse at contig end + or allele_details["align_contig_end"] + == "1" # check reverse at contig end # check if contig start is the same as match allele start reverse - or allele_details["align_contig_start"] == allele_details["contig_length"] + or allele_details["align_contig_start"] + == allele_details["contig_length"] ): return True return False @@ -134,13 +136,13 @@ def _extend_seq_find_start_stop_codon( error = False error_details = "-" i = 0 - + contig_seq = Seq(contig_seq) # Extend the sequence to find a valid start or stop codon if direction == "reverse": - contig_seq = Seq(contig_seq).reverse_complement() + contig_seq = contig_seq.reverse_complement() start, end = len(contig_seq) - end, len(contig_seq) - start + for _ in range(limit): - i += 3 if search == "5_prime": extended_start = max(0, start - i) extended_end = end @@ -152,8 +154,16 @@ def _extend_seq_find_start_stop_codon( _, protein, error, error_details = taranis.utils.convert_to_protein( extended_seq, force_coding=True ) + i += 3 if not error: - return protein, extended_seq, extended_start, extended_end, error, error_details + return ( + protein, + extended_seq, + extended_start, + extended_end, + error, + error_details, + ) return protein, contig_seq[start:end], start, end, error, error_details @@ -217,28 +227,30 @@ def _get_allele_details( taranis.utils.convert_to_protein(match_sequence, force_coding=True) ) # get blast details - allele_details = OrderedDict({ - "sample_name": self.s_name, # sample name - "locus_name": locus_name, # core gene name - "allele_type": "-", - "ref_allele_name": split_blast_result[0], - "contig_name": split_blast_result[1], # contig name - "contig_length": split_blast_result[15], - "ref_allele_length": split_blast_result[3], - "alignment_length": split_blast_result[4], - "align_contig_start": split_blast_result[9], - "align_contig_end": split_blast_result[10], - "strand": strand, - "sample_allele_seq": match_sequence, - "ref_allele_seq": ref_allele_seq, - "gene_annotation": gene_annotation, - "product_annot": product_annotation, - "ref_allele_quality": allele_quality, - "protein_seq": protein, - "prot_strand": direction, - "prot_error": prot_error, - "prot_error_details": prot_error_details, - }) + allele_details = OrderedDict( + { + "sample_name": self.s_name, # sample name + "locus_name": locus_name, # core gene name + "allele_type": "-", + "ref_allele_name": split_blast_result[0], + "contig_name": split_blast_result[1], # contig name + "contig_length": int(split_blast_result[15]), + "ref_allele_length": int(split_blast_result[3]), + "alignment_length": int(split_blast_result[4]), + "align_contig_start": int(split_blast_result[9]), + "align_contig_end": int(split_blast_result[10]), + "strand": strand, + "sample_allele_seq": match_sequence, + "ref_allele_seq": ref_allele_seq, + "gene_annotation": gene_annotation, + "product_annot": product_annotation, + "ref_allele_quality": allele_quality, + "protein_seq": protein, + "prot_strand": direction, + "prot_error": prot_error, + "prot_error_details": prot_error_details, + } + ) return allele_details def _classify_allele(allele_file: str, match_sequence: str) -> str: @@ -263,6 +275,109 @@ def _classify_allele(allele_file: str, match_sequence: str) -> str: return False # Return an empty string if no match is found + def _adjust_position(search, adjustment, start, end): + """Adjust contig alignment positions + + Args: + search (str): 5_prime or 3_prime, add nucleotides upstream or downstream + data (dict): dictionary with sample_allele_details + + Returns: + data (dict) modifies input dict with contig position adjustments + """ + if search == "5_prime": + start -= adjustment + elif search == "3_prime": + end += adjustment + + return start, end + + def fix_protein(sample_allele_data): + """Try to fix protein when there was a protein translation error + + Args: + sample_allele_data (str): dictionary with sample_allele_details + + Returns: + sample_allele_data, updates input dict if protein is succesfully fixed + """ + search = False + # fix 0/1-based in blast coordinates + if sample_allele_data["strand"] == "+": + start = sample_allele_data["align_contig_start"] - 1 + end = sample_allele_data["align_contig_end"] + else: + start = sample_allele_data["align_contig_end"] - 1 + end = sample_allele_data["align_contig_start"] + + # If strand "-" contig seq is reverse complemented but match sequence (split_blast_result[13]) + # is forward, so we need to change the contig seq to reverse_complement accordingly + if ( + sample_allele_data["strand"] == "-" + and sample_allele_data["prot_strand"] == "reverse" + ): + direction_contig = "forward" + elif ( + sample_allele_data["strand"] == "-" + and sample_allele_data["prot_strand"] == "forward" + ): + direction_contig = "reverse" + else: + direction_contig = sample_allele_data["prot_strand"] + + # change where to search 5_prime or 3_prime accordingly to the error + if "is not a stop codon" in sample_allele_data["prot_error_details"]: + search = "5_prime" + + elif "is not a start codon" in sample_allele_data["prot_error_details"]: + search = "3_prime" + + elif ( + "is not a multiple of three" in sample_allele_data["prot_error_details"] + ): + + if taranis.utils.has_start_codon( + sample_allele_data["sample_allele_seq"] + ): + search = "3_prime" + elif taranis.utils.has_stop_codon( + sample_allele_data["sample_allele_seq"] + ): + search = "5_prime" + + # Fix match to multiple of three + if len(sample_allele_data["sample_allele_seq"]) % 3 == 2: + start, end = _adjust_position(search, 1, start, end) + elif len(sample_allele_data["sample_allele_seq"]) % 3 == 1: + start, end = _adjust_position(search, 2, start, end) + + if search: + ( + protein, + extended_seq, + new_start, + new_end, + prot_error, + prot_error_details, + ) = _extend_seq_find_start_stop_codon( + direction=direction_contig, + contig_seq=self.sample_contigs[sample_allele_data["contig_name"]], + start=start, + end=end, + limit=self.increase_sequence, + search=search, + ) + + if not prot_error: + sample_allele_data["protein_seq"] = protein + sample_allele_data["sample_allele_seq"] = extended_seq + sample_allele_data["align_contig_start"] = new_start + sample_allele_data["align_contig_end"] = new_end + sample_allele_data["prot_error"] = prot_error + sample_allele_data["prot_error_details"] = prot_error_details + return sample_allele_data + + # START assign_allele_type function match_allele_schema = "" if len(valid_blast_results) > 1: @@ -299,7 +414,9 @@ def _classify_allele(allele_file: str, match_sequence: str) -> str: if match_allele_schema: # exact match found labelled as EXC classification = "EXC" - sample_allele_data["allele_type"] = classification + "_" + match_allele_schema + sample_allele_data["allele_type"] = ( + classification + "_" + match_allele_schema + ) return [ classification, classification + "_" + match_allele_schema, @@ -316,85 +433,39 @@ def _classify_allele(allele_file: str, match_sequence: str) -> str: ] if sample_allele_data["prot_error"]: - # 0/1-based - if sample_allele_data["strand"] == "+": - start = int(sample_allele_data["align_contig_start"]) - 1 - end = int(sample_allele_data["align_contig_end"]) - else: - start = int(sample_allele_data["align_contig_end"]) - 1 - end = int(sample_allele_data["align_contig_start"]) - - # If strand "-" contig seq is reverse complemented but match sequence (split_blast_result[13]) - # is forward, so we need to change the contig seq to reverse_complement accordingly - if sample_allele_data["strand"] == "-" and sample_allele_data["prot_strand"] == "reverse": - direction_contig = "forward" - elif sample_allele_data["strand"] == "-" and sample_allele_data["prot_strand"] == "forward": - direction_contig = "reverse" - else: - direction_contig = sample_allele_data["prot_strand"] - - if "is not a stop codon" in sample_allele_data["prot_error_details"]: - protein, extended_seq, new_start, new_end, prot_error, prot_error_details = ( - _extend_seq_find_start_stop_codon( - direction=direction_contig, - contig_seq=self.sample_contigs[sample_allele_data["contig_name"]], - start=start, - end=end, - limit=self.increase_sequence, - search="3_prime", - ) - ) - if not prot_error: - sample_allele_data["protein_seq"] = protein - sample_allele_data["sample_allele_seq"] = extended_seq - sample_allele_data["align_contig_start"] = new_start - sample_allele_data["align_contig_end"] = new_end - sample_allele_data["prot_error"] = prot_error - sample_allele_data["prot_error_details"] = prot_error_details - - elif "is not a start codon" in sample_allele_data["prot_error_details"]: - protein, extended_seq, new_start, new_end, prot_error, prot_error_details = ( - _extend_seq_find_start_stop_codon( - direction=direction_contig, - contig_seq=self.sample_contigs[sample_allele_data["contig_name"]], - start=start, - end=end, - limit=self.increase_sequence, - search="5_prime", - ) - ) - if not prot_error: - sample_allele_data["protein_seq"] = protein - sample_allele_data["sample_allele_seq"] = extended_seq - sample_allele_data["align_contig_start"] = new_start - sample_allele_data["align_contig_end"] = new_end - sample_allele_data["prot_error"] = prot_error - sample_allele_data["prot_error_details"] = prot_error_details + sample_allele_data = fix_protein(sample_allele_data) + # Check again after fix protein for retrieving more exact matchs match_allele_schema = _classify_allele( locus_file, sample_allele_data["sample_allele_seq"] ) + if match_allele_schema: # exact match found labelled as EXC classification = "EXC" - sample_allele_data["allele_type"] = classification + "_" + match_allele_schema + sample_allele_data["allele_type"] = ( + classification + "_" + match_allele_schema + ) return [ classification, classification + "_" + match_allele_schema, sample_allele_data, ] + if sample_allele_data["prot_error"]: classification = "TPR" # check if match allele is shorter than reference allele elif ( int(len(sample_allele_data["sample_allele_seq"])) - < int(sample_allele_data["ref_allele_length"]) - int(sample_allele_data["ref_allele_length"]) * 0.20 + < int(sample_allele_data["ref_allele_length"]) + - int(sample_allele_data["ref_allele_length"]) * 0.20 ): classification = "ASM" # check if match allele is longer than reference allele elif ( int(len(sample_allele_data["sample_allele_seq"])) - > int(sample_allele_data["ref_allele_length"]) + int(sample_allele_data["ref_allele_length"]) * 0.20 + > int(sample_allele_data["ref_allele_length"]) + + int(sample_allele_data["ref_allele_length"]) * 0.20 ): classification = "ALM" else: @@ -403,10 +474,14 @@ def _classify_allele(allele_file: str, match_sequence: str) -> str: # assign an identification value to the new allele if not match_allele_schema: match_allele_schema = str( - self.inf_alle_obj.get_inferred_allele(sample_allele_data["sample_allele_seq"], locus_name) + self.inf_alle_obj.get_inferred_allele( + sample_allele_data["sample_allele_seq"], locus_name + ) ) - sample_allele_data["allele_type"] = classification + "_" + match_allele_schema + sample_allele_data["allele_type"] = ( + classification + "_" + match_allele_schema + ) return [ classification, @@ -433,7 +508,7 @@ def discard_low_threshold_results(self, blast_results: list) -> list: return valid_blast_result def search_allele(self): - """ Search reference allele in contig files and classify + """Search reference allele in contig files and classify Args: self @@ -518,7 +593,9 @@ def search_allele(self): "NIPHEM", ]: try: - ref_allele_seq = result["allele_details"][locus_name]["ref_allele_seq"] + ref_allele_seq = result["allele_details"][locus_name][ + "ref_allele_seq" + ] except KeyError as e: log.error("Error in allele details") log.error(e) @@ -526,7 +603,9 @@ def search_allele(self): continue allele_seq = result["allele_details"][locus_name]["sample_allele_seq"] - ref_allele_name = result["allele_details"][locus_name]["ref_allele_name"] + ref_allele_name = result["allele_details"][locus_name][ + "ref_allele_name" + ] if self.snp_request and result["allele_type"][locus_name] != "LNF": # run snp analysis @@ -583,6 +662,17 @@ def parallel_execution( def create_multiple_alignment( ref_alleles_seq: dict, results: list, a_list: str, alignment_folder: str, mafft_cpus ) -> None: + """Collect data for the allele calling analysis, done for each sample and + create the summary file, graphics, and if requested snp and alignment files + + Args: + results (list): list of allele calling data results for each sample + output (str): output folder + snp_request (bool): request to save snp to file + aligment_request (bool): request to save alignment and multi alignemte to file + ref_alleles (list): reference alleles + cpus (int): number of cpus to be used if alignment is requested + """ allele_multiple_align = [] for ref_id, ref_seq in ref_alleles_seq[a_list].items(): input_buffer = StringIO() @@ -726,7 +816,7 @@ def read_reference_alleles(ref_alleles: list) -> dict[dict]: allele_matrix_result = {} # used for allele match file # get allele list - locus_list = [Path(ref_allele).stem for ref_allele in ref_alleles].sort() + locus_list = sorted([Path(ref_allele).stem for ref_allele in ref_alleles]) for result in results: for sample, values in result.items(): @@ -738,9 +828,7 @@ def read_reference_alleles(ref_alleles: list) -> dict[dict]: # increase allele type count sum_allele_type[type_of_allele] += 1 # add allele name match to sample - allele_match[allele] = ( - values["allele_match"][allele] - ) + allele_match[allele] = values["allele_match"][allele] summary_result[sample] = sum_allele_type allele_matrix_result[sample] = allele_match @@ -770,10 +858,18 @@ def read_reference_alleles(ref_alleles: list) -> dict[dict]: if type(detail_value) is list: for detail in detail_value: if detail["allele_type"] != "LNF": - fo.write(",".join([str(value) for value in detail.values()]) + "\n") + fo.write( + ",".join([str(value) for value in detail.values()]) + + "\n" + ) else: if detail_value["allele_type"] != "LNF": - fo.write(",".join([str(value) for value in detail_value.values()]) + "\n") + fo.write( + ",".join( + [str(value) for value in detail_value.values()] + ) + + "\n" + ) # save snp to file if requested if snp_request: diff --git a/taranis/utils.py b/taranis/utils.py index eba5639..87f0895 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -69,6 +69,14 @@ def rich_force_colors(): ] +def has_start_codon(seq): + return seq[:3] in START_CODON_FORWARD or seq[-3:] in START_CODON_REVERSE + + +def has_stop_codon(seq): + return seq[:3] in STOP_CODON_FORWARD or seq[-3:] in STOP_CODON_REVERSE + + def cpus_available() -> int: """Get the number of cpus available in the system From 1e18108c98b3bd9c39c0c7313cea8efc48727826 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Fri, 19 Apr 2024 11:35:33 +0200 Subject: [PATCH 173/214] added comments --- taranis/allele_calling.py | 51 ++++++++++++++++++++------------------- taranis/utils.py | 18 +++++++++++++- 2 files changed, 43 insertions(+), 26 deletions(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 2ac8ff5..4c92d7e 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -128,7 +128,12 @@ def _extend_seq_find_start_stop_codon( """Extend match sequence, according to increase_sequence in order to try to find the stop or start codon. Args: - + direction (str): forward or reverse + contig_seq (str): contig sequence + start (int): alignment start + end (int): alignment end + limit (int): nt limit for increasing the sequence in order to find start/stop codon + search (str): 5_prime/3_prime, search upstream or downstream Returns: """ @@ -276,14 +281,16 @@ def _classify_allele(allele_file: str, match_sequence: str) -> str: return False # Return an empty string if no match is found def _adjust_position(search, adjustment, start, end): - """Adjust contig alignment positions + """Adjust start/end alignment positions Args: search (str): 5_prime or 3_prime, add nucleotides upstream or downstream - data (dict): dictionary with sample_allele_details + start (int): start position + end (int): end position Returns: - data (dict) modifies input dict with contig position adjustments + start (int) start position adjusted + end (int) end position adjusted """ if search == "5_prime": start -= adjustment @@ -660,21 +667,19 @@ def parallel_execution( def create_multiple_alignment( - ref_alleles_seq: dict, results: list, a_list: str, alignment_folder: str, mafft_cpus + ref_alleles_seq: dict, results: list, locus: str, alignment_folder: str, mafft_cpus: int ) -> None: - """Collect data for the allele calling analysis, done for each sample and - create the summary file, graphics, and if requested snp and alignment files + """Create multiple alignmet file for each locus Args: - results (list): list of allele calling data results for each sample - output (str): output folder - snp_request (bool): request to save snp to file - aligment_request (bool): request to save alignment and multi alignemte to file - ref_alleles (list): reference alleles - cpus (int): number of cpus to be used if alignment is requested + ref_alleles_seq (list): list of reference allele sequences + results (dict): dict with allele calling results + locus (str): locus name to make the alignment for + alignment_folder (str): output folder + mafft_cpus (list): number of cpus for mafft parallelization """ allele_multiple_align = [] - for ref_id, ref_seq in ref_alleles_seq[a_list].items(): + for ref_id, ref_seq in ref_alleles_seq[locus].items(): input_buffer = StringIO() # get the reference allele sequence input_buffer.write(">Ref_" + ref_id + "\n") @@ -683,21 +688,20 @@ def create_multiple_alignment( for result in results: for sample, values in result.items(): # discard the allele if it is LNF - if values["allele_type"][a_list] == "LNF": + if values["allele_type"][locus] == "LNF": continue # get the allele name in sample input_buffer.write( ">" + sample + "_" - + a_list + + locus + "_" - + values["allele_details"][a_list][4] + + values["allele_details"][locus]["allele_type"] + "\n" ) # get the sequence of the allele in sample - input_buffer.write(values["allele_details"][a_list][15] + "\n") - # print(input_buffer.tell()) + input_buffer.write(values["allele_details"][locus]["sample_allele_seq"] + "\n") input_buffer.seek(0) allele_multiple_align.append( @@ -707,7 +711,7 @@ def create_multiple_alignment( input_buffer.close() # save multiple alignment to file with open( - os.path.join(alignment_folder, a_list + "_multiple_alignment.aln"), "w" + os.path.join(alignment_folder, locus + "_multiple_alignment.aln"), "w" ) as fo: for alignment in allele_multiple_align: for align in alignment: @@ -930,11 +934,11 @@ def read_reference_alleles(ref_alleles: list) -> dict[dict]: create_multiple_alignment, ref_alleles_seq, results, - a_list, + locus, alignment_folder, mafft_cpus, ) - for a_list in locus_list + for locus in locus_list ] for future in concurrent.futures.as_completed(futures): try: @@ -943,9 +947,6 @@ def read_reference_alleles(ref_alleles: list) -> dict[dict]: print(e) continue - # for a_list in allele_list: - # _ = create_multiple_alignment(ref_alleles_seq, results, a_list, alignment_folder) - # Create graphics stats_graphics(output, summary_result) return diff --git a/taranis/utils.py b/taranis/utils.py index 87f0895..832fd20 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -70,10 +70,20 @@ def rich_force_colors(): def has_start_codon(seq): + """ Checks whether the sequence has a start codon + + Returns: + bool + """ return seq[:3] in START_CODON_FORWARD or seq[-3:] in START_CODON_REVERSE def has_stop_codon(seq): + """ Checks whether the sequence has a stop codon + + Returns: + bool + """ return seq[:3] in STOP_CODON_FORWARD or seq[-3:] in STOP_CODON_REVERSE @@ -87,7 +97,13 @@ def cpus_available() -> int: def get_seq_direction(allele_sequence): - # check direction + """ Get sequence direction + + Returns: + "forward" if found a start or stop codon in forward + "reverse" if found start or stop codon in reverse + "both" if none of those are found, could be either strands + """ if ( allele_sequence[0:3] in START_CODON_FORWARD or allele_sequence[-3:] in STOP_CODON_FORWARD From 2a6e3532ff1bb7067ed7f7dd582baa28ba7d7d78 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Fri, 19 Apr 2024 11:35:48 +0200 Subject: [PATCH 174/214] linting --- taranis/allele_calling.py | 10 ++++++++-- taranis/utils.py | 6 +++--- 2 files changed, 11 insertions(+), 5 deletions(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 4c92d7e..50c6a4a 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -667,7 +667,11 @@ def parallel_execution( def create_multiple_alignment( - ref_alleles_seq: dict, results: list, locus: str, alignment_folder: str, mafft_cpus: int + ref_alleles_seq: dict, + results: list, + locus: str, + alignment_folder: str, + mafft_cpus: int, ) -> None: """Create multiple alignmet file for each locus @@ -701,7 +705,9 @@ def create_multiple_alignment( + "\n" ) # get the sequence of the allele in sample - input_buffer.write(values["allele_details"][locus]["sample_allele_seq"] + "\n") + input_buffer.write( + values["allele_details"][locus]["sample_allele_seq"] + "\n" + ) input_buffer.seek(0) allele_multiple_align.append( diff --git a/taranis/utils.py b/taranis/utils.py index 832fd20..ea7405a 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -70,7 +70,7 @@ def rich_force_colors(): def has_start_codon(seq): - """ Checks whether the sequence has a start codon + """Checks whether the sequence has a start codon Returns: bool @@ -79,7 +79,7 @@ def has_start_codon(seq): def has_stop_codon(seq): - """ Checks whether the sequence has a stop codon + """Checks whether the sequence has a stop codon Returns: bool @@ -97,7 +97,7 @@ def cpus_available() -> int: def get_seq_direction(allele_sequence): - """ Get sequence direction + """Get sequence direction Returns: "forward" if found a start or stop codon in forward From 68a5f3cd8dd76b43830e1a008e5bc7322b4e474e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Fri, 19 Apr 2024 17:47:11 +0200 Subject: [PATCH 175/214] variable renaming from allele to locus for clarity --- taranis/allele_calling.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 50c6a4a..8fab7ec 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -258,7 +258,7 @@ def _get_allele_details( ) return allele_details - def _classify_allele(allele_file: str, match_sequence: str) -> str: + def _classify_allele(locus_file: str, match_sequence: str) -> str: """Find the allele name in the schema that match the sequence Args: @@ -271,7 +271,7 @@ def _classify_allele(allele_file: str, match_sequence: str) -> str: # Read the fasta file and create a dictionary mapping sequences to their record IDs sequence_dict = { str(record.seq): record.id - for record in SeqIO.parse(allele_file, "fasta") + for record in SeqIO.parse(locus_file, "fasta") } # Check if the match_sequence is in the dictionary and return the corresponding record ID part @@ -417,6 +417,7 @@ def fix_protein(sample_allele_data): match_allele_schema = _classify_allele( locus_file, sample_allele_data["sample_allele_seq"] ) + # PLOT, TPR, ASM, ALM, INF, EXC are possible classifications if match_allele_schema: # exact match found labelled as EXC From 6de10d936e1e30d2cd3a750982378c51c9849abd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Sat, 27 Apr 2024 11:40:50 +0200 Subject: [PATCH 176/214] added mash blast correlation script --- assets/mash_blast_correlation.ipynb | 5547 +++++++++++++++++++++++++++ assets/mash_blast_correlation.py | 100 + 2 files changed, 5647 insertions(+) create mode 100644 assets/mash_blast_correlation.ipynb create mode 100644 assets/mash_blast_correlation.py diff --git a/assets/mash_blast_correlation.ipynb b/assets/mash_blast_correlation.ipynb new file mode 100644 index 0000000..88b43a4 --- /dev/null +++ b/assets/mash_blast_correlation.ipynb @@ -0,0 +1,5547 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import glob\n", + "import os" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "mash_tabpath = \"/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo0001.txt\" #\"\\\\10.22.140.220\\pmata\\pruebas_tests\\distance_matrix\\mash\\mash_lmo0001.txt\"\n", + "blast_tabpath = \"/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0001_db.csv\" #r\"\\\\10.22.140.220\\pmata\\pruebas_tests\\distance_matrix\\blast\\pident_matrix_lmo0001_db.csv\"" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": {}, + "outputs": [], + "source": [ + "def fill_triangle_matrix(mash_tabpath):\n", + " with open(mash_tabpath, \"r\") as file:\n", + " mashvals = [list(map(float, line.split())) for line in file]\n", + "\n", + " matrix_size = len(mashvals)\n", + " matrix_shape = (matrix_size, matrix_size)\n", + "\n", + " # Create an empty array of zeros with the determined shape\n", + " zero_mat = np.zeros(matrix_shape)\n", + "\n", + " for i in range(matrix_size):\n", + " for j in range(i + 1): # Only fill values up to the diagonal\n", + " mashvals[i][j] = mashvals[i][j]\n", + " full_mashtab = mashvals\n", + " tri_mashtable = pd.DataFrame(full_mashtab).fillna(0)\n", + " tri_mashtable_clean = tri_mashtable.drop(tri_mashtable.columns[0], axis=1)\n", + " tri_mashtable_clean[tri_mashtable_clean.columns[-1]+1] = float(0)\n", + " tri_masharray = tri_mashtable_clean.values\n", + " masharray_transraw = tri_masharray.T\n", + " masharray_clean = np.nan_to_num(masharray_transraw, nan=0.0)\n", + " masharray_full = tri_masharray + masharray_transraw - np.diag(np.diag(masharray_clean))\n", + " return masharray_full" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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+ " return full_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0001_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo0001.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0002_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo0002.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0003_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo0003.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0004_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo0004.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0005_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo0005.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0006_db.csv': 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'/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2793.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2794_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2794.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2796_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2796.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2797_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2797.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2798_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2798.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2799_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2799.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2800_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2800.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2801_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2801.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2802_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2802.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2810_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2810.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2819_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2819.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2822_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2822.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2823_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2823.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2824_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2824.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2826_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2826.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2829_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2829.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2830_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2830.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2831_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2831.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2832_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2832.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2833_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2833.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2834_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2834.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2835_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2835.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2836_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2836.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2838_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2838.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2839_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2839.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2840_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2840.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2842_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2842.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2853_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2853.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2854_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2854.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2855_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2855.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2856_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2856.txt', '/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2857_db.csv': '/home/pmata/pruebas_tests/distance_matrix/mash/mash_lmo2857.txt'}\n" + ] + } + ], + "source": [ + "blast_paths = sorted(glob.glob(\"/home/pmata/pruebas_tests/distance_matrix/blast/*.csv\"))\n", + "mash_paths = sorted(glob.glob(\"/home/pmata/pruebas_tests/distance_matrix/mash/mash*.txt\"))\n", + "print({x:y for x,y in zip(blast_paths, mash_paths)})" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "_lmo0001_db.\n" + ] + } + ], + "source": [ + "from difflib import SequenceMatcher\n", + "match = SequenceMatcher(None, \"pident_matrix_lmo0001_db.csv\", \"mash_lmo0001_db.txt\").find_longest_match()\n", + "print(\"pident_matrix_lmo0001_db.csv\"[match.a:match.a + match.size])" + ] + }, + { + "cell_type": "code", + "execution_count": 270, + "metadata": {}, + "outputs": [], + "source": [ + "import mantel\n", + "import scipy\n", + "from difflib import SequenceMatcher\n", + "\n", + "def mantel_tester(blast_paths, mash_paths, pval=0.01):\n", + " mantel_summary = {}\n", + " failed_tabs = []\n", + " for blast_tabpath, mash_tabpath in zip(blast_paths, mash_paths):\n", + " blast_filename = os.path.basename(blast_tabpath)\n", + " mash_filename = os.path.basename(mash_tabpath)\n", + " match = SequenceMatcher(None, blast_filename, mash_filename).find_longest_match()\n", + " common_name = blast_filename[match.a:match.a + match.size].strip(\".\")\n", + "\n", + " blastable = pd.read_csv(blast_tabpath)\n", + " blastarray = blastable.drop(blastable.columns[0], axis=1).to_numpy()\n", + " mirror_blastarray = take_upper_tri_and_dup(blastarray)\n", + " inverted_blast = 100-mirror_blastarray\n", + "\n", + " masharray_full = fill_triangle_matrix(mash_tabpath)\n", + "\n", + " condensed_mash = scipy.spatial.distance.squareform(masharray_full, force=\"tovector\", checks=True)\n", + " try:\n", + " condensed_blast = scipy.spatial.distance.squareform(inverted_blast, force=\"tovector\", checks=True)\n", + " except ValueError:\n", + " print(f\"{blast_tabpath} is not symmetric, skipped\")\n", + " failed_tabs.append(blast_tabpath)\n", + " continue\n", + " permutations = int(1/pval)\n", + " result = mantel.test(condensed_mash, condensed_blast, perms=permutations, method=\"pearson\")\n", + " print(f\"Results from mantel test between {blast_filename} and {mash_filename}:\", \n", + " f\"veridical-correlation = {result.r} | p-value = {result.p}\")\n", + " mantel_summary[common_name] = {\"veridical_correlation\": result.r, \"p_value\": result.p, \"z_score\": result.z}\n", + " print(f\"{len(failed_tabs)} blast matrixes where non-symmetrical: {failed_tabs}\")\n", + " return mantel_summary" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 271, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results from mantel test between pident_matrix_lmo0001_db.csv and mash_lmo0001.txt: veridical-correlation = 0.9944607850255069 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0002_db.csv and mash_lmo0002.txt: veridical-correlation = 0.9917758624883196 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0003_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0004_db.csv and mash_lmo0004.txt: veridical-correlation = 0.9202667486313154 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0005_db.csv and mash_lmo0005.txt: veridical-correlation = 0.9904021882149964 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0006_db.csv and mash_lmo0006.txt: veridical-correlation = 0.9921881250066177 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0007_db.csv and mash_lmo0007.txt: veridical-correlation = 0.9914636217538014 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0008_db.csv and mash_lmo0008.txt: veridical-correlation = 0.9819273199401419 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0009_db.csv and mash_lmo0009.txt: veridical-correlation = 0.956538676580417 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0012_db.csv and mash_lmo0012.txt: veridical-correlation = 0.9768251847184299 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0013_db.csv and mash_lmo0013.txt: veridical-correlation = 0.9899665311720754 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0014_db.csv and mash_lmo0014.txt: veridical-correlation = 0.9930433985601452 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0016_db.csv and mash_lmo0016.txt: veridical-correlation = 0.9400204797732649 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0018_db.csv and mash_lmo0018.txt: veridical-correlation = 0.976693905556821 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0020_db.csv and mash_lmo0020.txt: veridical-correlation = 0.9799113279539722 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0021_db.csv and mash_lmo0021.txt: veridical-correlation = 0.9548751005426656 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0022_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0024_db.csv and mash_lmo0024.txt: veridical-correlation = 0.9776660280510225 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0025_db.csv and mash_lmo0025.txt: veridical-correlation = 0.9595199707509653 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0026_db.csv and mash_lmo0026.txt: veridical-correlation = 0.9690019228888781 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0027_db.csv and mash_lmo0027.txt: veridical-correlation = 0.9878196947709009 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0029_db.csv and mash_lmo0029.txt: veridical-correlation = 0.9422325275731258 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0030_db.csv and mash_lmo0030.txt: veridical-correlation = 0.9858970514812198 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0031_db.csv and mash_lmo0031.txt: veridical-correlation = 0.9880636815862073 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0033_db.csv and mash_lmo0033.txt: veridical-correlation = 0.9906306550654187 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0034_db.csv and mash_lmo0034.txt: veridical-correlation = 0.9884140879156991 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0035_db.csv and mash_lmo0035.txt: veridical-correlation = 0.9753188145516705 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0042_db.csv and mash_lmo0042.txt: veridical-correlation = 0.9750018381029727 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0044_db.csv and mash_lmo0044.txt: veridical-correlation = 0.9771336158982359 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0045_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0046_db.csv and mash_lmo0046.txt: veridical-correlation = 0.976685092125799 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0048_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0051_db.csv and mash_lmo0051.txt: veridical-correlation = 0.9775545402128415 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0052_db.csv and mash_lmo0052.txt: veridical-correlation = 0.9900527605739032 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0053_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0054_db.csv and mash_lmo0054.txt: veridical-correlation = 0.9858633232182286 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0055_db.csv and mash_lmo0055.txt: veridical-correlation = 0.989546393905012 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0056_db.csv and mash_lmo0056.txt: veridical-correlation = 0.9723816791026434 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0075_db.csv and mash_lmo0075.txt: veridical-correlation = 0.8863486696647715 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0076_db.csv and mash_lmo0076.txt: veridical-correlation = 0.9139193845134055 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0077_db.csv and mash_lmo0077.txt: veridical-correlation = 0.9529752370037129 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0078_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0088_db.csv and mash_lmo0088.txt: veridical-correlation = 0.9605068138354576 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0096_db.csv and mash_lmo0096.txt: veridical-correlation = 0.9829680839609616 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0097_db.csv and mash_lmo0097.txt: veridical-correlation = 0.9897241130783458 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0098_db.csv and mash_lmo0098.txt: veridical-correlation = 0.9806320654875842 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0099_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0102_db.csv and mash_lmo0102.txt: veridical-correlation = 0.704424051302933 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0103_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0105_db.csv and mash_lmo0105.txt: veridical-correlation = 0.9750520705269983 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0107_db.csv and mash_lmo0107.txt: veridical-correlation = 0.9756707357953018 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0109_db.csv and mash_lmo0109.txt: veridical-correlation = 0.972272892147783 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0110_db.csv and mash_lmo0110.txt: veridical-correlation = 0.9878630462580764 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0111_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0113_db.csv and mash_lmo0113.txt: veridical-correlation = 0.9189673055388745 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0114_db.csv and mash_lmo0114.txt: veridical-correlation = 0.9422644696039931 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0115_db.csv and mash_lmo0115.txt: veridical-correlation = 0.966373326580019 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0116_db.csv and mash_lmo0116.txt: veridical-correlation = 0.9348418322901133 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0131_db.csv and mash_lmo0131.txt: veridical-correlation = 0.9477275941661104 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0132_db.csv and mash_lmo0132.txt: veridical-correlation = 0.982071137463153 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0133_db.csv and mash_lmo0133.txt: veridical-correlation = 0.7582263035613215 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0134_db.csv and mash_lmo0134.txt: veridical-correlation = 0.8825452813885476 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0135_db.csv and mash_lmo0135.txt: veridical-correlation = 0.9908935289046619 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0136_db.csv and mash_lmo0136.txt: veridical-correlation = 0.9724476565789513 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0137_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0152_db.csv and mash_lmo0152.txt: veridical-correlation = 0.9781696389601024 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0153_db.csv and mash_lmo0153.txt: veridical-correlation = 0.9509740908383699 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0155_db.csv and mash_lmo0155.txt: veridical-correlation = 0.9464210610994828 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0158_db.csv and mash_lmo0158.txt: veridical-correlation = 0.9492913739040857 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0162_db.csv and mash_lmo0162.txt: veridical-correlation = 0.9861415002012216 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0163_db.csv and mash_lmo0163.txt: veridical-correlation = 0.9894816055129416 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0164_db.csv is not symmetric, skipped\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0167_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0169_db.csv and mash_lmo0169.txt: veridical-correlation = 0.9779101332749188 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0176_db.csv and mash_lmo0176.txt: veridical-correlation = 0.9863137783327776 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0177_db.csv and mash_lmo0177.txt: veridical-correlation = 0.9911171102693179 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0185_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0188_db.csv and mash_lmo0188.txt: veridical-correlation = 0.9854917135443878 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0189_db.csv and mash_lmo0189.txt: veridical-correlation = 0.9387173351513338 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0190_db.csv and mash_lmo0190.txt: veridical-correlation = 0.9887246753151508 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0191_db.csv and mash_lmo0191.txt: veridical-correlation = 0.9727038627125624 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0192_db.csv and mash_lmo0192.txt: veridical-correlation = 0.9894426559288522 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0193_db.csv and mash_lmo0193.txt: veridical-correlation = 0.9711175319533352 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0194_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0195_db.csv and mash_lmo0195.txt: veridical-correlation = 0.9847250298757505 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0198_db.csv and mash_lmo0198.txt: veridical-correlation = 0.9928551082686776 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0199_db.csv and mash_lmo0199.txt: veridical-correlation = 0.9755717852849005 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0201_db.csv and mash_lmo0201.txt: veridical-correlation = 0.9728281503331316 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0202_db.csv and mash_lmo0202.txt: veridical-correlation = 0.9918243238871678 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0203_db.csv and mash_lmo0203.txt: veridical-correlation = 0.9917777816557081 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0205_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0206_db.csv and mash_lmo0206.txt: veridical-correlation = 0.9530302153844009 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0208_db.csv and mash_lmo0208.txt: veridical-correlation = 0.9322327235628146 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0210_db.csv and mash_lmo0210.txt: veridical-correlation = 0.9713079765596251 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0211_db.csv and mash_lmo0211.txt: veridical-correlation = 0.9726701913816947 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0212_db.csv and mash_lmo0212.txt: veridical-correlation = 0.9587232992838314 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0213_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0214_db.csv and mash_lmo0214.txt: veridical-correlation = 0.9893861414767188 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0216_db.csv and mash_lmo0216.txt: veridical-correlation = 0.530840873668734 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0217_db.csv and mash_lmo0217.txt: veridical-correlation = 0.94343183733558 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0218_db.csv and mash_lmo0218.txt: veridical-correlation = 0.9616516561763989 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0219_db.csv and mash_lmo0219.txt: veridical-correlation = 0.9819646976478851 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0221_db.csv and mash_lmo0221.txt: veridical-correlation = 0.9703919799456658 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0222_db.csv and mash_lmo0222.txt: veridical-correlation = 0.9900497950687008 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0223_db.csv and mash_lmo0223.txt: veridical-correlation = 0.9789375520554444 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0226_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0227_db.csv and mash_lmo0227.txt: veridical-correlation = 0.9757452088287555 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0229_db.csv and mash_lmo0229.txt: veridical-correlation = 0.960899445082618 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0231_db.csv and mash_lmo0231.txt: veridical-correlation = 0.9932088257878764 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0232_db.csv and mash_lmo0232.txt: veridical-correlation = 0.9834962938090095 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0233_db.csv and mash_lmo0233.txt: veridical-correlation = 0.9864009211852053 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0234_db.csv and mash_lmo0234.txt: veridical-correlation = 0.9935972813599392 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0235_db.csv and mash_lmo0235.txt: veridical-correlation = 0.9776813907673324 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0238_db.csv and mash_lmo0238.txt: veridical-correlation = 0.9782383241311128 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0239_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0240_db.csv and mash_lmo0240.txt: veridical-correlation = 0.6955168974549845 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0241_db.csv and mash_lmo0241.txt: veridical-correlation = 0.9841833588465346 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0242_db.csv and mash_lmo0242.txt: veridical-correlation = 0.9618411631403088 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0243_db.csv and mash_lmo0243.txt: veridical-correlation = 0.9764794005416788 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0244_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0245_db.csv and mash_lmo0245.txt: veridical-correlation = 0.5941051927981396 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0246_db.csv and mash_lmo0246.txt: veridical-correlation = 0.9612543710638609 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0248_db.csv and mash_lmo0248.txt: veridical-correlation = 0.9931262058880262 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0249_db.csv and mash_lmo0249.txt: veridical-correlation = 0.9636483348071683 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0250_db.csv and mash_lmo0250.txt: veridical-correlation = 0.9673312095368884 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0251_db.csv and mash_lmo0251.txt: veridical-correlation = 0.9472553061101886 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0256_db.csv and mash_lmo0256.txt: veridical-correlation = 0.894314218808977 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0258_db.csv and mash_lmo0258.txt: veridical-correlation = 0.9949884240729173 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0259_db.csv and mash_lmo0259.txt: veridical-correlation = 0.9944159382537667 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0261_db.csv and mash_lmo0261.txt: veridical-correlation = 0.976908549265848 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0266_db.csv and mash_lmo0266.txt: veridical-correlation = 0.9614749577571862 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0267_db.csv and mash_lmo0267.txt: veridical-correlation = 0.9717348110788617 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0268_db.csv and mash_lmo0268.txt: veridical-correlation = 0.9710495967053246 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0269_db.csv and mash_lmo0269.txt: veridical-correlation = 0.9847614370760448 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0271_db.csv and mash_lmo0271.txt: veridical-correlation = 0.9827397127637102 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0272_db.csv and mash_lmo0272.txt: veridical-correlation = 0.9692624096647758 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0273_db.csv and mash_lmo0273.txt: veridical-correlation = 0.94257703659228 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0278_db.csv and mash_lmo0278.txt: veridical-correlation = 0.9757533758006648 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0281_db.csv and mash_lmo0281.txt: veridical-correlation = 0.9264887164311315 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0282_db.csv and mash_lmo0282.txt: veridical-correlation = 0.9591207671918364 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0283_db.csv and mash_lmo0283.txt: veridical-correlation = 0.9775614792506508 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0284_db.csv and mash_lmo0284.txt: veridical-correlation = 0.9857828837395952 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0285_db.csv and mash_lmo0285.txt: veridical-correlation = 0.9822067826079415 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0286_db.csv and mash_lmo0286.txt: veridical-correlation = 0.9709006815648368 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0287_db.csv and mash_lmo0287.txt: veridical-correlation = 0.9682656563330887 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0288_db.csv and mash_lmo0288.txt: veridical-correlation = 0.9965019639196523 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0289_db.csv and mash_lmo0289.txt: veridical-correlation = 0.9957806043445386 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0290_db.csv and mash_lmo0290.txt: veridical-correlation = 0.9865707580289164 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0291_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0292_db.csv and mash_lmo0292.txt: veridical-correlation = 0.9843490200670784 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0293_db.csv and mash_lmo0293.txt: veridical-correlation = 0.9509647754081567 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0296_db.csv and mash_lmo0296.txt: veridical-correlation = 0.924682722766829 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0297_db.csv and mash_lmo0297.txt: veridical-correlation = 0.9707250080607522 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0298_db.csv and mash_lmo0298.txt: veridical-correlation = 0.9803521973955356 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0299_db.csv and mash_lmo0299.txt: veridical-correlation = 0.8957296777171208 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0300_db.csv and mash_lmo0300.txt: veridical-correlation = 0.9582846650346774 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0314_db.csv and mash_lmo0314.txt: veridical-correlation = 0.9400779942143375 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0316_db.csv and mash_lmo0316.txt: veridical-correlation = 0.9424222373459211 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0317_db.csv and mash_lmo0317.txt: veridical-correlation = 0.9390530744948457 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0319_db.csv and mash_lmo0319.txt: veridical-correlation = 0.9902684385257715 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0342_db.csv and mash_lmo0342.txt: veridical-correlation = 0.9741547237078599 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0343_db.csv and mash_lmo0343.txt: veridical-correlation = 0.9851783671540375 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0344_db.csv and mash_lmo0344.txt: veridical-correlation = 0.9802322667723853 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0345_db.csv and mash_lmo0345.txt: veridical-correlation = 0.9522623737749172 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0346_db.csv and mash_lmo0346.txt: veridical-correlation = 0.8767769165961562 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0347_db.csv and mash_lmo0347.txt: veridical-correlation = 0.9825802360703233 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0348_db.csv and mash_lmo0348.txt: veridical-correlation = 0.9839764292267028 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0349_db.csv and mash_lmo0349.txt: veridical-correlation = 0.9002298851978953 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0350_db.csv and mash_lmo0350.txt: veridical-correlation = 0.9744940960365664 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0351_db.csv and mash_lmo0351.txt: veridical-correlation = 0.951628222883023 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0352_db.csv and mash_lmo0352.txt: veridical-correlation = 0.9632875772666687 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0354_db.csv and mash_lmo0354.txt: veridical-correlation = 0.9777559541839554 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0355_db.csv and mash_lmo0355.txt: veridical-correlation = 0.9761754331113105 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0356_db.csv and mash_lmo0356.txt: veridical-correlation = 0.9407726867326622 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0357_db.csv and mash_lmo0357.txt: veridical-correlation = 0.9716342068830269 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0358_db.csv and mash_lmo0358.txt: veridical-correlation = 0.9950191552412463 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0359_db.csv and mash_lmo0359.txt: veridical-correlation = 0.9789316121327327 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0360_db.csv and mash_lmo0360.txt: veridical-correlation = 0.9802981445265059 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0369_db.csv and mash_lmo0369.txt: veridical-correlation = 0.9794627516549319 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0370_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0371_db.csv and mash_lmo0371.txt: veridical-correlation = 0.9482849722207314 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0372_db.csv and mash_lmo0372.txt: veridical-correlation = 0.9877570151458339 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0374_db.csv and mash_lmo0374.txt: veridical-correlation = 0.9350334302556625 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0375_db.csv and mash_lmo0375.txt: veridical-correlation = 0.8462085429357235 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0376_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0377_db.csv and mash_lmo0377.txt: veridical-correlation = 0.946343980818359 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0382_db.csv and mash_lmo0382.txt: veridical-correlation = 0.9653026005710205 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0383_db.csv and mash_lmo0383.txt: veridical-correlation = 0.9835770994688949 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0386_db.csv and mash_lmo0386.txt: veridical-correlation = 0.9907043815396664 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0387_db.csv and mash_lmo0387.txt: veridical-correlation = 0.8839358207889687 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0388_db.csv and mash_lmo0388.txt: veridical-correlation = 0.9716362818595802 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0391_db.csv and mash_lmo0391.txt: veridical-correlation = 0.9637116071007018 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0392_db.csv and mash_lmo0392.txt: veridical-correlation = 0.9764161314804238 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0394_db.csv and mash_lmo0394.txt: veridical-correlation = 0.9855245875447429 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0398_db.csv and mash_lmo0398.txt: veridical-correlation = 0.9219404114621046 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0399_db.csv and mash_lmo0399.txt: veridical-correlation = 0.9111268017942983 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0400_db.csv and mash_lmo0400.txt: veridical-correlation = 0.9745527319301981 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0401_db.csv and mash_lmo0401.txt: veridical-correlation = 0.9732818355274632 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0405_db.csv and mash_lmo0405.txt: veridical-correlation = 0.9687401331806638 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0406_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0407_db.csv and mash_lmo0407.txt: veridical-correlation = 0.9744065815330283 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0408_db.csv and mash_lmo0408.txt: veridical-correlation = 0.9552409548247237 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0418_db.csv and mash_lmo0418.txt: veridical-correlation = 0.9333122642146879 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0424_db.csv and mash_lmo0424.txt: veridical-correlation = 0.9648466982218269 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0426_db.csv and mash_lmo0426.txt: veridical-correlation = 0.9304546645130891 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0427_db.csv and mash_lmo0427.txt: veridical-correlation = 0.9473397159307524 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0428_db.csv and mash_lmo0428.txt: veridical-correlation = 0.9617423639140097 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0429_db.csv and mash_lmo0429.txt: veridical-correlation = 0.9713034602823473 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0433_db.csv and mash_lmo0433.txt: veridical-correlation = 0.9808909863247672 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0437_db.csv and mash_lmo0437.txt: veridical-correlation = 0.7680543146120455 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0439_db.csv and mash_lmo0439.txt: veridical-correlation = 0.9746188649896732 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0441_db.csv and mash_lmo0441.txt: veridical-correlation = 0.9761019768844695 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0442_db.csv and mash_lmo0442.txt: veridical-correlation = 0.7948881335870079 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0443_db.csv and mash_lmo0443.txt: veridical-correlation = 0.9666968690518555 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0449_db.csv and mash_lmo0449.txt: veridical-correlation = 0.9459694733059756 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0450_db.csv and mash_lmo0450.txt: veridical-correlation = 0.9847885336081614 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0451_db.csv and mash_lmo0451.txt: veridical-correlation = 0.705224838686094 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0453_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0454_db.csv and mash_lmo0454.txt: veridical-correlation = 0.9589471727708573 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0455_db.csv and mash_lmo0455.txt: veridical-correlation = 0.9841777206740598 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0458_db.csv and mash_lmo0458.txt: veridical-correlation = 0.9346543209322195 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0480_db.csv and mash_lmo0480.txt: veridical-correlation = 0.9169525092143263 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0481_db.csv and mash_lmo0481.txt: veridical-correlation = 0.9779796765892739 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0482_db.csv and mash_lmo0482.txt: veridical-correlation = 0.9759646995403842 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0483_db.csv and mash_lmo0483.txt: veridical-correlation = 0.9280893925638554 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0484_db.csv and mash_lmo0484.txt: veridical-correlation = 0.9466168828104619 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0485_db.csv is not symmetric, skipped\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0486_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0487_db.csv and mash_lmo0487.txt: veridical-correlation = 0.9359609039588841 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0488_db.csv and mash_lmo0488.txt: veridical-correlation = 0.9576158764082822 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0489_db.csv and mash_lmo0489.txt: veridical-correlation = 0.9726443975416604 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0490_db.csv and mash_lmo0490.txt: veridical-correlation = 0.9840498094342766 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0491_db.csv and mash_lmo0491.txt: veridical-correlation = 0.9531302108370187 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0494_db.csv and mash_lmo0494.txt: veridical-correlation = 0.9467016749582963 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0495_db.csv and mash_lmo0495.txt: veridical-correlation = 0.9386476921722748 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0496_db.csv is not symmetric, skipped\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0509_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0511_db.csv and mash_lmo0511.txt: veridical-correlation = 0.9701167408407768 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0512_db.csv is not symmetric, skipped\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0515_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0516_db.csv and mash_lmo0516.txt: veridical-correlation = 0.9672635687343365 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0517_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0518_db.csv and mash_lmo0518.txt: veridical-correlation = 0.9080581492635228 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0519_db.csv and mash_lmo0519.txt: veridical-correlation = 0.9715630133639378 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0521_db.csv and mash_lmo0521.txt: veridical-correlation = 0.9824183263032111 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0522_db.csv and mash_lmo0522.txt: veridical-correlation = 0.9546920818552607 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0523_db.csv and mash_lmo0523.txt: veridical-correlation = 0.9437722382829792 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0524_db.csv and mash_lmo0524.txt: veridical-correlation = 0.982483113821401 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0526_db.csv and mash_lmo0526.txt: veridical-correlation = 0.9906957535680271 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0529_db.csv and mash_lmo0529.txt: veridical-correlation = 0.9865916117812766 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0530_db.csv and mash_lmo0530.txt: veridical-correlation = 0.9859322327810098 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0531_db.csv and mash_lmo0531.txt: veridical-correlation = 0.9755774385512296 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0532_db.csv and mash_lmo0532.txt: veridical-correlation = 0.9709739911830784 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0533_db.csv and mash_lmo0533.txt: veridical-correlation = 0.9483043954192152 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0534_db.csv and mash_lmo0534.txt: veridical-correlation = 0.9901915009067618 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0536_db.csv and mash_lmo0536.txt: veridical-correlation = 0.9782450926942501 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0539_db.csv and mash_lmo0539.txt: veridical-correlation = 0.9874859335545143 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0540_db.csv and mash_lmo0540.txt: veridical-correlation = 0.9734213108330717 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0541_db.csv and mash_lmo0541.txt: veridical-correlation = 0.9776130658356555 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0543_db.csv and mash_lmo0543.txt: veridical-correlation = 0.9926339089168342 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0544_db.csv and mash_lmo0544.txt: veridical-correlation = 0.984817602755618 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0545_db.csv and mash_lmo0545.txt: veridical-correlation = 0.8233774237194769 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0546_db.csv and mash_lmo0546.txt: veridical-correlation = 0.9882537417692422 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0547_db.csv and mash_lmo0547.txt: veridical-correlation = 0.9911635815243516 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0553_db.csv and mash_lmo0553.txt: veridical-correlation = 0.9787620764218191 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0555_db.csv and mash_lmo0555.txt: veridical-correlation = 0.9833024240786734 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0556_db.csv and mash_lmo0556.txt: veridical-correlation = 0.9680146135376873 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0557_db.csv and mash_lmo0557.txt: veridical-correlation = 0.9790627454469883 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0558_db.csv and mash_lmo0558.txt: veridical-correlation = 0.9894527147999903 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0559_db.csv and mash_lmo0559.txt: veridical-correlation = 0.9344706475202893 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0560_db.csv and mash_lmo0560.txt: veridical-correlation = 0.9775864800206865 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0562_db.csv and mash_lmo0562.txt: veridical-correlation = 0.8763038829828018 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0566_db.csv and mash_lmo0566.txt: veridical-correlation = 0.8868900986386673 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0567_db.csv and mash_lmo0567.txt: veridical-correlation = 0.7417101916166289 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0568_db.csv and mash_lmo0568.txt: veridical-correlation = 0.9762928230786503 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0569_db.csv and mash_lmo0569.txt: veridical-correlation = 0.9899827063502388 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0572_db.csv and mash_lmo0572.txt: veridical-correlation = 0.9816480041019042 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0573_db.csv and mash_lmo0573.txt: veridical-correlation = 0.977723839727468 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0574_db.csv and mash_lmo0574.txt: veridical-correlation = 0.9842619113332397 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0575_db.csv and mash_lmo0575.txt: veridical-correlation = 0.9706697634901175 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0578_db.csv and mash_lmo0578.txt: veridical-correlation = 0.9697478249703184 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0579_db.csv and mash_lmo0579.txt: veridical-correlation = 0.6429309079348129 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0583_db.csv and mash_lmo0583.txt: veridical-correlation = 0.994924841617203 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0584_db.csv and mash_lmo0584.txt: veridical-correlation = 0.980386730598764 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0585_db.csv and mash_lmo0585.txt: veridical-correlation = 0.9826332548560742 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0586_db.csv and mash_lmo0586.txt: veridical-correlation = 0.9736744399125484 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0587_db.csv and mash_lmo0587.txt: veridical-correlation = 0.9764456316007256 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0588_db.csv and mash_lmo0588.txt: veridical-correlation = 0.9838325650467866 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0589_db.csv and mash_lmo0589.txt: veridical-correlation = 0.978788174529975 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0590_db.csv and mash_lmo0590.txt: veridical-correlation = 0.9827637428561811 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0591_db.csv and mash_lmo0591.txt: veridical-correlation = 0.8549317650200364 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0592_db.csv and mash_lmo0592.txt: veridical-correlation = 0.9631586932732423 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0593_db.csv and mash_lmo0593.txt: veridical-correlation = 0.980892915586906 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0594_db.csv and mash_lmo0594.txt: veridical-correlation = 0.9799485238602288 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0595_db.csv and mash_lmo0595.txt: veridical-correlation = 0.9649839582789135 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0596_db.csv and mash_lmo0596.txt: veridical-correlation = 0.9334332378349613 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0602_db.csv and mash_lmo0602.txt: veridical-correlation = 0.9586634402846271 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0603_db.csv and mash_lmo0603.txt: veridical-correlation = 0.9014010045287004 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0604_db.csv and mash_lmo0604.txt: veridical-correlation = 0.9638522618932264 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0605_db.csv and mash_lmo0605.txt: veridical-correlation = 0.9922861721660707 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0606_db.csv and mash_lmo0606.txt: veridical-correlation = 0.9558918327469146 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0607_db.csv and mash_lmo0607.txt: veridical-correlation = 0.9878664985886869 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0608_db.csv and mash_lmo0608.txt: veridical-correlation = 0.9811224217644924 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0611_db.csv and mash_lmo0611.txt: veridical-correlation = 0.9741294161842659 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0612_db.csv and mash_lmo0612.txt: veridical-correlation = 0.9570511253449521 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0613_db.csv and mash_lmo0613.txt: veridical-correlation = 0.9797189763765164 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0619_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0621_db.csv and mash_lmo0621.txt: veridical-correlation = 0.7264993933961222 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0623_db.csv and mash_lmo0623.txt: veridical-correlation = 0.9681342871921784 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0625_db.csv and mash_lmo0625.txt: veridical-correlation = 0.9834393247458424 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0626_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0628_db.csv and mash_lmo0628.txt: veridical-correlation = 0.6954330280901433 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0629_db.csv and mash_lmo0629.txt: veridical-correlation = 0.9604195642445387 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0636_db.csv and mash_lmo0636.txt: veridical-correlation = 0.9360867415943671 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0637_db.csv and mash_lmo0637.txt: veridical-correlation = 0.9577073693849242 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0639_db.csv and mash_lmo0639.txt: veridical-correlation = 0.8696579140500698 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0640_db.csv and mash_lmo0640.txt: veridical-correlation = 0.9746245221637566 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0642_db.csv and mash_lmo0642.txt: veridical-correlation = 0.9763089177736971 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0643_db.csv and mash_lmo0643.txt: veridical-correlation = 0.9827175707195949 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0644_db.csv and mash_lmo0644.txt: veridical-correlation = 0.9918833491431363 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0645_db.csv and mash_lmo0645.txt: veridical-correlation = 0.989785001953317 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0648_db.csv and mash_lmo0648.txt: veridical-correlation = 0.9706001138198609 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0649_db.csv and mash_lmo0649.txt: veridical-correlation = 0.9632609257539744 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0650_db.csv and mash_lmo0650.txt: veridical-correlation = 0.9947296575520587 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0651_db.csv and mash_lmo0651.txt: veridical-correlation = 0.774088490813957 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0654_db.csv and mash_lmo0654.txt: veridical-correlation = 0.7460788824523882 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0655_db.csv and mash_lmo0655.txt: veridical-correlation = 0.7716654557513288 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0656_db.csv and mash_lmo0656.txt: veridical-correlation = 0.9713072151360722 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0657_db.csv and mash_lmo0657.txt: veridical-correlation = 0.9218377866825699 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0662_db.csv and mash_lmo0662.txt: veridical-correlation = 0.9769292491082704 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0663_db.csv and mash_lmo0663.txt: veridical-correlation = 0.9812771072479518 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0665_db.csv and mash_lmo0665.txt: veridical-correlation = 0.8010049782921848 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0666_db.csv and mash_lmo0666.txt: veridical-correlation = 0.9473999508945556 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0667_db.csv and mash_lmo0667.txt: veridical-correlation = 0.9824472996119633 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0673_db.csv and mash_lmo0673.txt: veridical-correlation = 0.8925573080822726 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0674_db.csv and mash_lmo0674.txt: veridical-correlation = 0.9928723084256682 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0676_db.csv and mash_lmo0676.txt: veridical-correlation = 0.9662685043917881 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0677_db.csv and mash_lmo0677.txt: veridical-correlation = 0.9137194709031601 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0678_db.csv and mash_lmo0678.txt: veridical-correlation = 0.9594834068522817 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0679_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0680_db.csv and mash_lmo0680.txt: veridical-correlation = 0.9899882029813399 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0681_db.csv and mash_lmo0681.txt: veridical-correlation = 0.991581654790289 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0682_db.csv and mash_lmo0682.txt: veridical-correlation = 0.9840976112012155 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0683_db.csv and mash_lmo0683.txt: veridical-correlation = 0.9822181731182912 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0684_db.csv and mash_lmo0684.txt: veridical-correlation = 0.9285642731803182 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0685_db.csv and mash_lmo0685.txt: veridical-correlation = 0.9773390934398085 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0687_db.csv and mash_lmo0687.txt: veridical-correlation = 0.9521993711487968 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0688_db.csv and mash_lmo0688.txt: veridical-correlation = 0.9935759625615996 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0689_db.csv and mash_lmo0689.txt: veridical-correlation = 0.9625666808425674 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0690_db.csv and mash_lmo0690.txt: veridical-correlation = 0.9851867900943723 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0691_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0692_db.csv and mash_lmo0692.txt: veridical-correlation = 0.9859213931236702 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0693_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0694_db.csv and mash_lmo0694.txt: veridical-correlation = 0.9328936719742336 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0695_db.csv and mash_lmo0695.txt: veridical-correlation = 0.9808183902926872 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0696_db.csv and mash_lmo0696.txt: veridical-correlation = 0.9439050162503255 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0697_db.csv and mash_lmo0697.txt: veridical-correlation = 0.9804644419842479 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0698_db.csv and mash_lmo0698.txt: veridical-correlation = 0.555686792188011 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0699_db.csv and mash_lmo0699.txt: veridical-correlation = 0.9819794119259202 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0700_db.csv and mash_lmo0700.txt: veridical-correlation = 0.9757648987499555 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0702_db.csv and mash_lmo0702.txt: veridical-correlation = 0.9863298791971569 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0703_db.csv and mash_lmo0703.txt: veridical-correlation = 0.9579028260890846 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0704_db.csv and mash_lmo0704.txt: veridical-correlation = 0.9258865782371846 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0705_db.csv and mash_lmo0705.txt: veridical-correlation = 0.9917074105227595 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0706_db.csv and mash_lmo0706.txt: veridical-correlation = 0.9747980652266688 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0707_db.csv and mash_lmo0707.txt: veridical-correlation = 0.9031620898887653 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0708_db.csv and mash_lmo0708.txt: veridical-correlation = 0.9554666728561562 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0710_db.csv and mash_lmo0710.txt: veridical-correlation = 0.9224633905440948 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0711_db.csv and mash_lmo0711.txt: veridical-correlation = 0.9687192822033999 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0712_db.csv and mash_lmo0712.txt: veridical-correlation = 0.8986875664236315 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0713_db.csv and mash_lmo0713.txt: veridical-correlation = 0.9853858545075288 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0714_db.csv and mash_lmo0714.txt: veridical-correlation = 0.982236978667121 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0719_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0721_db.csv and mash_lmo0721.txt: veridical-correlation = 0.9783497839345822 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0722_db.csv and mash_lmo0722.txt: veridical-correlation = 0.9829435547338655 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0723_db.csv and mash_lmo0723.txt: veridical-correlation = 0.9890665920342653 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0724_db.csv and mash_lmo0724.txt: veridical-correlation = 0.978228459953033 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0725_db.csv and mash_lmo0725.txt: veridical-correlation = 0.6260681594011697 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0726_db.csv and mash_lmo0726.txt: veridical-correlation = 0.8795903740619213 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0728_db.csv and mash_lmo0728.txt: veridical-correlation = 0.9314382827484714 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0729_db.csv and mash_lmo0729.txt: veridical-correlation = 0.9690888751883164 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0730_db.csv and mash_lmo0730.txt: veridical-correlation = 0.9680193364368656 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0742_db.csv and mash_lmo0742.txt: veridical-correlation = 0.9470036970025418 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0743_db.csv and mash_lmo0743.txt: veridical-correlation = 0.9678794970866128 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0744_db.csv and mash_lmo0744.txt: veridical-correlation = 0.9815359100558599 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0745_db.csv and mash_lmo0745.txt: veridical-correlation = 0.9336141150636541 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0757_db.csv and mash_lmo0757.txt: veridical-correlation = 0.9664668601071265 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0759_db.csv and mash_lmo0759.txt: veridical-correlation = 0.9744435604020032 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0761_db.csv and mash_lmo0761.txt: veridical-correlation = 0.9648678669847122 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0762_db.csv and mash_lmo0762.txt: veridical-correlation = 0.9759954157104905 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0763_db.csv and mash_lmo0763.txt: veridical-correlation = 0.9737867082136834 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0764_db.csv and mash_lmo0764.txt: veridical-correlation = 0.9052817008468576 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0772_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0774_db.csv and mash_lmo0774.txt: veridical-correlation = 0.9842498207101497 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0775_db.csv and mash_lmo0775.txt: veridical-correlation = 0.9044723463678097 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0776_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0777_db.csv and mash_lmo0777.txt: veridical-correlation = 0.961197713301433 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0778_db.csv and mash_lmo0778.txt: veridical-correlation = 0.9477730360522247 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0779_db.csv and mash_lmo0779.txt: veridical-correlation = 0.9579636667740351 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0781_db.csv and mash_lmo0781.txt: veridical-correlation = 0.9796588668278108 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0782_db.csv and mash_lmo0782.txt: veridical-correlation = 0.9789688200526285 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0783_db.csv and mash_lmo0783.txt: veridical-correlation = 0.9711987864773752 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0784_db.csv and mash_lmo0784.txt: veridical-correlation = 0.9722773525046597 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0785_db.csv and mash_lmo0785.txt: veridical-correlation = 0.9875950865331212 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0786_db.csv and mash_lmo0786.txt: veridical-correlation = 0.9680753189002064 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0787_db.csv and mash_lmo0787.txt: veridical-correlation = 0.9776327228670212 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0788_db.csv and mash_lmo0788.txt: veridical-correlation = 0.9933916440750428 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0790_db.csv and mash_lmo0790.txt: veridical-correlation = 0.6772051025565997 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0791_db.csv and mash_lmo0791.txt: veridical-correlation = 0.9859114233442823 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0793_db.csv and mash_lmo0793.txt: veridical-correlation = 0.9690729109977299 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0794_db.csv and mash_lmo0794.txt: veridical-correlation = 0.969232686100185 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0795_db.csv and mash_lmo0795.txt: veridical-correlation = 0.9338846467618911 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0796_db.csv and mash_lmo0796.txt: veridical-correlation = 0.9829100719297958 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0797_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0799_db.csv and mash_lmo0799.txt: veridical-correlation = 0.9808757643809093 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0802_db.csv and mash_lmo0802.txt: veridical-correlation = 0.9713345614836681 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0803_db.csv and mash_lmo0803.txt: veridical-correlation = 0.9842930990392614 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0806_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0807_db.csv and mash_lmo0807.txt: veridical-correlation = 0.9672136332635172 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0808_db.csv and mash_lmo0808.txt: veridical-correlation = 0.9419005478646677 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0809_db.csv and mash_lmo0809.txt: veridical-correlation = 0.9763197147783446 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0810_db.csv and mash_lmo0810.txt: veridical-correlation = 0.9802601357874768 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0811_db.csv and mash_lmo0811.txt: veridical-correlation = 0.9726695388093676 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0812_db.csv and mash_lmo0812.txt: veridical-correlation = 0.9177875730963271 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0813_db.csv and mash_lmo0813.txt: veridical-correlation = 0.9378940653056842 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0814_db.csv and mash_lmo0814.txt: veridical-correlation = 0.9752563004849276 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0817_db.csv and mash_lmo0817.txt: veridical-correlation = 0.8658478482857543 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0818_db.csv and mash_lmo0818.txt: veridical-correlation = 0.9763966953585198 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0820_db.csv and mash_lmo0820.txt: veridical-correlation = 0.9475393101137914 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0821_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0822_db.csv and mash_lmo0822.txt: veridical-correlation = 0.9520209490117881 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0823_db.csv and mash_lmo0823.txt: veridical-correlation = 0.9819691344801248 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0824_db.csv and mash_lmo0824.txt: veridical-correlation = 0.7190977925530472 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0825_db.csv and mash_lmo0825.txt: veridical-correlation = 0.9430988560943637 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0829_db.csv and mash_lmo0829.txt: veridical-correlation = 0.9848015544159501 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0836_db.csv and mash_lmo0836.txt: veridical-correlation = 0.9418095166090314 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0837_db.csv and mash_lmo0837.txt: veridical-correlation = 0.9820471717021608 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0838_db.csv and mash_lmo0838.txt: veridical-correlation = 0.9825358096551529 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0841_db.csv and mash_lmo0841.txt: veridical-correlation = 0.9903279302963334 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0843_db.csv and mash_lmo0843.txt: veridical-correlation = 0.8913259253480891 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0847_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0850_db.csv and mash_lmo0850.txt: veridical-correlation = 0.5066056964684228 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0851_db.csv and mash_lmo0851.txt: veridical-correlation = 0.9744390280333605 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0852_db.csv and mash_lmo0852.txt: veridical-correlation = 0.9332840792363539 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0853_db.csv and mash_lmo0853.txt: veridical-correlation = 0.9459099979993072 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0854_db.csv and mash_lmo0854.txt: veridical-correlation = 0.8737226042841237 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0855_db.csv and mash_lmo0855.txt: veridical-correlation = 0.9646565428640257 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0856_db.csv and mash_lmo0856.txt: veridical-correlation = 0.9659604508330312 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0857_db.csv and mash_lmo0857.txt: veridical-correlation = 0.986743802425997 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0858_db.csv and mash_lmo0858.txt: veridical-correlation = 0.9914226647187403 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0859_db.csv and mash_lmo0859.txt: veridical-correlation = 0.9915369271560993 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0860_db.csv and mash_lmo0860.txt: veridical-correlation = 0.9774580169473256 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0862_db.csv and mash_lmo0862.txt: veridical-correlation = 0.9840997133645633 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0863_db.csv and mash_lmo0863.txt: veridical-correlation = 0.9207132615539192 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0864_db.csv and mash_lmo0864.txt: veridical-correlation = 0.971580584116731 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0866_db.csv and mash_lmo0866.txt: veridical-correlation = 0.9816650265718134 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0866a_db.csv and mash_lmo0866a.txt: veridical-correlation = 0.5371908512033666 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0867_db.csv and mash_lmo0867.txt: veridical-correlation = 0.9650637447869879 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0871_db.csv and mash_lmo0871.txt: veridical-correlation = 0.8741258497366065 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0872_db.csv and mash_lmo0872.txt: veridical-correlation = 0.9788159837760915 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0873_db.csv and mash_lmo0873.txt: veridical-correlation = 0.994976535784712 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0874_db.csv and mash_lmo0874.txt: veridical-correlation = 0.9640497746747012 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0875_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0877_db.csv and mash_lmo0877.txt: veridical-correlation = 0.9641118310445344 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0878_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0879_db.csv and mash_lmo0879.txt: veridical-correlation = 0.82117685336439 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0883_db.csv and mash_lmo0883.txt: veridical-correlation = 0.9721123647053691 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0884_db.csv and mash_lmo0884.txt: veridical-correlation = 0.9600656678731742 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0885_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0886_db.csv and mash_lmo0886.txt: veridical-correlation = 0.9673708946952971 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0887_db.csv and mash_lmo0887.txt: veridical-correlation = 0.9576699615297256 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0888_db.csv and mash_lmo0888.txt: veridical-correlation = 0.9632821845896135 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0889_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0890_db.csv and mash_lmo0890.txt: veridical-correlation = 0.9650735682181912 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0891_db.csv and mash_lmo0891.txt: veridical-correlation = 0.9647993142617531 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0892_db.csv and mash_lmo0892.txt: veridical-correlation = 0.9694614509914796 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0895_db.csv is not symmetric, skipped\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0896_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0898_db.csv and mash_lmo0898.txt: veridical-correlation = 0.9917206792985898 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0899_db.csv and mash_lmo0899.txt: veridical-correlation = 0.9444581990031632 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0900_db.csv and mash_lmo0900.txt: veridical-correlation = 0.9316057159076193 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0901_db.csv and mash_lmo0901.txt: veridical-correlation = 0.9897001244514022 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0902_db.csv and mash_lmo0902.txt: veridical-correlation = 0.9818588255257426 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0903_db.csv and mash_lmo0903.txt: veridical-correlation = 0.9277208389396024 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0904_db.csv and mash_lmo0904.txt: veridical-correlation = 0.9593647938536952 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0905_db.csv and mash_lmo0905.txt: veridical-correlation = 0.9595274433179417 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0906_db.csv and mash_lmo0906.txt: veridical-correlation = 0.9803798266933877 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0907_db.csv and mash_lmo0907.txt: veridical-correlation = 0.9744003479904124 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0908_db.csv and mash_lmo0908.txt: veridical-correlation = 0.9810707877688101 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0909_db.csv and mash_lmo0909.txt: veridical-correlation = 0.9161706971001106 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0910_db.csv and mash_lmo0910.txt: veridical-correlation = 0.9305330778357712 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0912_db.csv and mash_lmo0912.txt: veridical-correlation = 0.9846002654515765 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0913_db.csv and mash_lmo0913.txt: veridical-correlation = 0.9727276745428788 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0914_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0915_db.csv and mash_lmo0915.txt: veridical-correlation = 0.9769461528260855 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0916_db.csv and mash_lmo0916.txt: veridical-correlation = 0.9750101110467773 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0917_db.csv and mash_lmo0917.txt: veridical-correlation = 0.9789515361125233 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0918_db.csv and mash_lmo0918.txt: veridical-correlation = 0.9962634323731367 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0919_db.csv and mash_lmo0919.txt: veridical-correlation = 0.9800986004616666 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0921_db.csv and mash_lmo0921.txt: veridical-correlation = 0.9736201261784685 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0922_db.csv and mash_lmo0922.txt: veridical-correlation = 0.983208290152699 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0925_db.csv and mash_lmo0925.txt: veridical-correlation = 0.9629337116949936 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0926_db.csv and mash_lmo0926.txt: veridical-correlation = 0.9023539837015371 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0927_db.csv and mash_lmo0927.txt: veridical-correlation = 0.9868158446016373 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0929_db.csv and mash_lmo0929.txt: veridical-correlation = 0.9777894213820747 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0930_db.csv and mash_lmo0930.txt: veridical-correlation = 0.9800722291152172 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0931_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0932_db.csv and mash_lmo0932.txt: veridical-correlation = 0.9800000739912134 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0934_db.csv and mash_lmo0934.txt: veridical-correlation = 0.9826677269327889 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0935_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0936_db.csv and mash_lmo0936.txt: veridical-correlation = 0.9460360978629606 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0937_db.csv and mash_lmo0937.txt: veridical-correlation = 0.6646742566117237 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0943_db.csv and mash_lmo0943.txt: veridical-correlation = 0.9770989803445168 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0944_db.csv and mash_lmo0944.txt: veridical-correlation = 0.9594198035818804 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0945_db.csv and mash_lmo0945.txt: veridical-correlation = 0.9677932196253172 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0946_db.csv and mash_lmo0946.txt: veridical-correlation = 0.6578958479399307 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0947_db.csv and mash_lmo0947.txt: veridical-correlation = 0.9840603307641057 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0951_db.csv and mash_lmo0951.txt: veridical-correlation = 0.9840967714119302 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0953_db.csv and mash_lmo0953.txt: veridical-correlation = 0.9484412419205983 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0954_db.csv and mash_lmo0954.txt: veridical-correlation = 0.9577194235450799 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0955_db.csv and mash_lmo0955.txt: veridical-correlation = 0.9675586523948871 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0956_db.csv and mash_lmo0956.txt: veridical-correlation = 0.9822374996159998 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0957_db.csv and mash_lmo0957.txt: veridical-correlation = 0.9779157571814926 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0958_db.csv and mash_lmo0958.txt: veridical-correlation = 0.9805727569003148 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0959_db.csv and mash_lmo0959.txt: veridical-correlation = 0.9676447895316037 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0960_db.csv and mash_lmo0960.txt: veridical-correlation = 0.978447248963725 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0961_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo0962_db.csv and mash_lmo0962.txt: veridical-correlation = 0.9762696980894652 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0963_db.csv and mash_lmo0963.txt: veridical-correlation = 0.9775843641527954 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0964_db.csv and mash_lmo0964.txt: veridical-correlation = 0.9863896258927684 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0966_db.csv and mash_lmo0966.txt: veridical-correlation = 0.6122042605373257 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0967_db.csv and mash_lmo0967.txt: veridical-correlation = 0.9549457633372344 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0968_db.csv and mash_lmo0968.txt: veridical-correlation = 0.9760700223099212 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0970_db.csv and mash_lmo0970.txt: veridical-correlation = 0.9857954730398218 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0972_db.csv and mash_lmo0972.txt: veridical-correlation = 0.9332850399181041 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0973_db.csv and mash_lmo0973.txt: veridical-correlation = 0.9838800886432221 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0974_db.csv and mash_lmo0974.txt: veridical-correlation = 0.9841313757388869 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0977_db.csv and mash_lmo0977.txt: veridical-correlation = 0.9784353354972082 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0978_db.csv and mash_lmo0978.txt: veridical-correlation = 0.9780822509310911 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0979_db.csv and mash_lmo0979.txt: veridical-correlation = 0.9688723684190628 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0980_db.csv and mash_lmo0980.txt: veridical-correlation = 0.9773618851871925 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0981_db.csv and mash_lmo0981.txt: veridical-correlation = 0.9828207206392734 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0982_db.csv and mash_lmo0982.txt: veridical-correlation = 0.9884779590382456 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0983_db.csv and mash_lmo0983.txt: veridical-correlation = 0.9787117514723475 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0988_db.csv and mash_lmo0988.txt: veridical-correlation = 0.9876490701676952 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0989_db.csv and mash_lmo0989.txt: veridical-correlation = 0.9640766780076367 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0990_db.csv and mash_lmo0990.txt: veridical-correlation = 0.9840695411358343 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0991_db.csv and mash_lmo0991.txt: veridical-correlation = 0.9768270036969939 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0992_db.csv and mash_lmo0992.txt: veridical-correlation = 0.957000411617831 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0993_db.csv and mash_lmo0993.txt: veridical-correlation = 0.95414588795091 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0994_db.csv and mash_lmo0994.txt: veridical-correlation = 0.9783017310895602 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0995_db.csv and mash_lmo0995.txt: veridical-correlation = 0.980028324444054 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0997_db.csv and mash_lmo0997.txt: veridical-correlation = 0.9924043489657125 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0998_db.csv and mash_lmo0998.txt: veridical-correlation = 0.9581312035522992 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo0999_db.csv and mash_lmo0999.txt: veridical-correlation = 0.9831560718379461 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1002_db.csv and mash_lmo1002.txt: veridical-correlation = 0.9704626076554597 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1003_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1007_db.csv and mash_lmo1007.txt: veridical-correlation = 0.787517784312388 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1008_db.csv and mash_lmo1008.txt: veridical-correlation = 0.8929099856277186 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1009_db.csv and mash_lmo1009.txt: veridical-correlation = 0.9771990802444251 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1010_db.csv and mash_lmo1010.txt: veridical-correlation = 0.9919407312162296 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1011_db.csv and mash_lmo1011.txt: veridical-correlation = 0.9629611502184868 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1012_db.csv and mash_lmo1012.txt: veridical-correlation = 0.9868153352251351 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1013_db.csv and mash_lmo1013.txt: veridical-correlation = 0.987501503045448 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1014_db.csv and mash_lmo1014.txt: veridical-correlation = 0.9864339798160136 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1016_db.csv and mash_lmo1016.txt: veridical-correlation = 0.977503850458563 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1017_db.csv and mash_lmo1017.txt: veridical-correlation = 0.9790450569752586 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1019_db.csv and mash_lmo1019.txt: veridical-correlation = 0.9453412423153995 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1020_db.csv and mash_lmo1020.txt: veridical-correlation = 0.9799192754377584 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1021_db.csv and mash_lmo1021.txt: veridical-correlation = 0.9794739422282472 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1022_db.csv and mash_lmo1022.txt: veridical-correlation = 0.9501459749760461 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1023_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1027_db.csv and mash_lmo1027.txt: veridical-correlation = 0.9871229931563875 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1028_db.csv and mash_lmo1028.txt: veridical-correlation = 0.8972945585213267 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1029_db.csv and mash_lmo1029.txt: veridical-correlation = 0.9760700658786041 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1037_db.csv and mash_lmo1037.txt: veridical-correlation = 0.9487362077987055 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1039_db.csv and mash_lmo1039.txt: veridical-correlation = 0.9789535536000515 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1040_db.csv and mash_lmo1040.txt: veridical-correlation = 0.9633549488682147 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1046_db.csv and mash_lmo1046.txt: veridical-correlation = 0.974679568430164 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1047_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1049_db.csv and mash_lmo1049.txt: veridical-correlation = 0.9409218821319977 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1050_db.csv and mash_lmo1050.txt: veridical-correlation = 0.7177560592810417 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1051_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1052_db.csv and mash_lmo1052.txt: veridical-correlation = 0.9825282824338194 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1053_db.csv and mash_lmo1053.txt: veridical-correlation = 0.9859987694644928 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1054_db.csv and mash_lmo1054.txt: veridical-correlation = 0.9894051033884906 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1055_db.csv and mash_lmo1055.txt: veridical-correlation = 0.9940817098965967 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1056_db.csv and mash_lmo1056.txt: veridical-correlation = 0.9320291523225464 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1058_db.csv and mash_lmo1058.txt: veridical-correlation = 0.9507779433936385 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1064_db.csv and mash_lmo1064.txt: veridical-correlation = 0.9736457203059463 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1067_db.csv and mash_lmo1067.txt: veridical-correlation = 0.9865075500878844 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1070_db.csv and mash_lmo1070.txt: veridical-correlation = 0.9161641806683952 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1071_db.csv and mash_lmo1071.txt: veridical-correlation = 0.979484947408433 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1072_db.csv and mash_lmo1072.txt: veridical-correlation = 0.994963285530998 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1092_db.csv and mash_lmo1092.txt: veridical-correlation = 0.9689909858969236 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1093_db.csv and mash_lmo1093.txt: veridical-correlation = 0.956675749995726 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1096_db.csv and mash_lmo1096.txt: veridical-correlation = 0.9680510778584529 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1142_db.csv and mash_lmo1142.txt: veridical-correlation = 0.981068993253704 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1144_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1145_db.csv and mash_lmo1145.txt: veridical-correlation = 0.9236428706591022 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1148_db.csv and mash_lmo1148.txt: veridical-correlation = 0.954521281363506 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1150_db.csv and mash_lmo1150.txt: veridical-correlation = 0.9853848811016535 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1151_db.csv and mash_lmo1151.txt: veridical-correlation = 0.8739903248886002 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1152_db.csv and mash_lmo1152.txt: veridical-correlation = 0.9756363385481409 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1153_db.csv and mash_lmo1153.txt: veridical-correlation = 0.9812137376850112 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1154_db.csv and mash_lmo1154.txt: veridical-correlation = 0.9711566137606193 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1155_db.csv and mash_lmo1155.txt: veridical-correlation = 0.9842549978866928 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1156_db.csv and mash_lmo1156.txt: veridical-correlation = 0.978741864764935 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1157_db.csv and mash_lmo1157.txt: veridical-correlation = 0.9121250712729864 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1158_db.csv and mash_lmo1158.txt: veridical-correlation = 0.9679901420527558 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1159_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1160_db.csv and mash_lmo1160.txt: veridical-correlation = 0.9695706415286198 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1162_db.csv and mash_lmo1162.txt: veridical-correlation = 0.9311516099435461 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1163_db.csv and mash_lmo1163.txt: veridical-correlation = 0.7768431213530609 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1164_db.csv and mash_lmo1164.txt: veridical-correlation = 0.9734451057710879 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1165_db.csv and mash_lmo1165.txt: veridical-correlation = 0.9847717873211047 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1166_db.csv and mash_lmo1166.txt: veridical-correlation = 0.9766821862588662 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1167_db.csv and mash_lmo1167.txt: veridical-correlation = 0.9577402272350248 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1168_db.csv and mash_lmo1168.txt: veridical-correlation = 0.9866740069887119 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1171_db.csv and mash_lmo1171.txt: veridical-correlation = 0.9883142081561388 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1173_db.csv and mash_lmo1173.txt: veridical-correlation = 0.9843293664952221 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1174_db.csv and mash_lmo1174.txt: veridical-correlation = 0.9870375838078372 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1175_db.csv and mash_lmo1175.txt: veridical-correlation = 0.991697242912581 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1176_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1177_db.csv and mash_lmo1177.txt: veridical-correlation = 0.9911425862610271 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1178_db.csv and mash_lmo1178.txt: veridical-correlation = 0.9620682367743126 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1179_db.csv and mash_lmo1179.txt: veridical-correlation = 0.9873529368152286 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1180_db.csv and mash_lmo1180.txt: veridical-correlation = 0.8630881537975443 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1181_db.csv and mash_lmo1181.txt: veridical-correlation = 0.9742128404506897 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1182_db.csv and mash_lmo1182.txt: veridical-correlation = 0.9862281783639522 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1183_db.csv and mash_lmo1183.txt: veridical-correlation = 0.9613613051775964 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1185_db.csv and mash_lmo1185.txt: veridical-correlation = 0.9466107843198429 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1186_db.csv and mash_lmo1186.txt: veridical-correlation = 0.981649470628579 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1190_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1191_db.csv and mash_lmo1191.txt: veridical-correlation = 0.815007661758302 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1192_db.csv and mash_lmo1192.txt: veridical-correlation = 0.9527934697630843 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1193_db.csv and mash_lmo1193.txt: veridical-correlation = 0.9590733072367787 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1194_db.csv and mash_lmo1194.txt: veridical-correlation = 0.9738086525547878 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1198_db.csv and mash_lmo1198.txt: veridical-correlation = 0.6609432294910719 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1199_db.csv and mash_lmo1199.txt: veridical-correlation = 0.9659628611852665 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1200_db.csv and mash_lmo1200.txt: veridical-correlation = 0.978574324404893 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1202_db.csv and mash_lmo1202.txt: veridical-correlation = 0.6790355269045787 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1204_db.csv and mash_lmo1204.txt: veridical-correlation = 0.9771141582568003 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1206_db.csv and mash_lmo1206.txt: veridical-correlation = 0.9820252465175641 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1208_db.csv and mash_lmo1208.txt: veridical-correlation = 0.9777312929018428 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1210_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1211_db.csv and mash_lmo1211.txt: veridical-correlation = 0.9310998355912109 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1216_db.csv and mash_lmo1216.txt: veridical-correlation = 0.9838702583406426 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1217_db.csv and mash_lmo1217.txt: veridical-correlation = 0.9658474098539404 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1218_db.csv and mash_lmo1218.txt: veridical-correlation = 0.9683573645174164 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1219_db.csv and mash_lmo1219.txt: veridical-correlation = 0.8456283030026445 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1220_db.csv and mash_lmo1220.txt: veridical-correlation = 0.9782207169579056 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1221_db.csv and mash_lmo1221.txt: veridical-correlation = 0.9774292068699713 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1222_db.csv and mash_lmo1222.txt: veridical-correlation = 0.9884357750359324 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1223_db.csv and mash_lmo1223.txt: veridical-correlation = 0.9640754248552731 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1225_db.csv and mash_lmo1225.txt: veridical-correlation = 0.915100837759817 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1226_db.csv and mash_lmo1226.txt: veridical-correlation = 0.9957010276952112 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1227_db.csv and mash_lmo1227.txt: veridical-correlation = 0.9846591569741004 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1228_db.csv and mash_lmo1228.txt: veridical-correlation = 0.9533621068695702 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1229_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1230_db.csv and mash_lmo1230.txt: veridical-correlation = 0.9763381160905815 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1231_db.csv and mash_lmo1231.txt: veridical-correlation = 0.9913331900845918 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1232_db.csv and mash_lmo1232.txt: veridical-correlation = 0.9792496951462225 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1233_db.csv and mash_lmo1233.txt: veridical-correlation = 0.8992926535945328 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1234_db.csv and mash_lmo1234.txt: veridical-correlation = 0.9805611443424952 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1235_db.csv and mash_lmo1235.txt: veridical-correlation = 0.9899133230743974 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1236_db.csv and mash_lmo1236.txt: veridical-correlation = 0.980617907338194 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1237_db.csv and mash_lmo1237.txt: veridical-correlation = 0.9864898822366783 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1238_db.csv and mash_lmo1238.txt: veridical-correlation = 0.9699459933853131 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1239_db.csv and mash_lmo1239.txt: veridical-correlation = 0.9669916124081039 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1240_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1241_db.csv and mash_lmo1241.txt: veridical-correlation = 0.9726805667309301 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1244_db.csv and mash_lmo1244.txt: veridical-correlation = 0.9674189596447199 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1245_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1246_db.csv and mash_lmo1246.txt: veridical-correlation = 0.987102723520319 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1247_db.csv and mash_lmo1247.txt: veridical-correlation = 0.9416931306119117 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1249_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1250_db.csv and mash_lmo1250.txt: veridical-correlation = 0.9836315759279027 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1251_db.csv is not symmetric, skipped\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1252_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1253_db.csv and mash_lmo1253.txt: veridical-correlation = 0.9739398449604989 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1254_db.csv and mash_lmo1254.txt: veridical-correlation = 0.9400783846396125 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1255_db.csv and mash_lmo1255.txt: veridical-correlation = 0.96495012675261 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1267_db.csv and mash_lmo1267.txt: veridical-correlation = 0.9918743586746896 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1268_db.csv and mash_lmo1268.txt: veridical-correlation = 0.98306185412089 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1269_db.csv and mash_lmo1269.txt: veridical-correlation = 0.9890909500023145 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1271_db.csv and mash_lmo1271.txt: veridical-correlation = 0.9903564970136052 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1272_db.csv and mash_lmo1272.txt: veridical-correlation = 0.9805565325991574 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1275_db.csv and mash_lmo1275.txt: veridical-correlation = 0.994083914587024 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1277_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1278_db.csv and mash_lmo1278.txt: veridical-correlation = 0.9646923626273349 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1279_db.csv and mash_lmo1279.txt: veridical-correlation = 0.9853027654334398 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1280_db.csv and mash_lmo1280.txt: veridical-correlation = 0.9790144895665608 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1281_db.csv and mash_lmo1281.txt: veridical-correlation = 0.9401947662710737 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1282_db.csv and mash_lmo1282.txt: veridical-correlation = 0.9356535521720306 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1283_db.csv and mash_lmo1283.txt: veridical-correlation = 0.9747527330159489 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1284_db.csv and mash_lmo1284.txt: veridical-correlation = 0.9556495798125227 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1285_db.csv and mash_lmo1285.txt: veridical-correlation = 0.9580655942755996 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1286_db.csv and mash_lmo1286.txt: veridical-correlation = 0.9873994487276169 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1287_db.csv and mash_lmo1287.txt: veridical-correlation = 0.9496982139850914 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1293_db.csv and mash_lmo1293.txt: veridical-correlation = 0.9956705330576439 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1295_db.csv and mash_lmo1295.txt: veridical-correlation = 0.9309049197661029 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1298_db.csv and mash_lmo1298.txt: veridical-correlation = 0.9550519946928406 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1299_db.csv and mash_lmo1299.txt: veridical-correlation = 0.9836349568809025 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1302_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1305_db.csv and mash_lmo1305.txt: veridical-correlation = 0.9918437821542745 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1306_db.csv and mash_lmo1306.txt: veridical-correlation = 0.9019143568557033 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1313_db.csv and mash_lmo1313.txt: veridical-correlation = 0.9707726963823764 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1314_db.csv and mash_lmo1314.txt: veridical-correlation = 0.9525190070903913 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1315_db.csv and mash_lmo1315.txt: veridical-correlation = 0.9771482710321612 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1316_db.csv and mash_lmo1316.txt: veridical-correlation = 0.9732342863639396 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1317_db.csv and mash_lmo1317.txt: veridical-correlation = 0.9844367502507235 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1318_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1319_db.csv and mash_lmo1319.txt: veridical-correlation = 0.9889164903160766 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1320_db.csv and mash_lmo1320.txt: veridical-correlation = 0.9848669195527238 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1321_db.csv and mash_lmo1321.txt: veridical-correlation = 0.9519497753987657 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1322_db.csv and mash_lmo1322.txt: veridical-correlation = 0.9828594973406246 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1324_db.csv and mash_lmo1324.txt: veridical-correlation = 0.9381484951719775 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1326_db.csv and mash_lmo1326.txt: veridical-correlation = 0.9317617946677283 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1327_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1330_db.csv and mash_lmo1330.txt: veridical-correlation = 0.9678858114942867 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1331_db.csv and mash_lmo1331.txt: veridical-correlation = 0.9883958343118786 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1333_db.csv and mash_lmo1333.txt: veridical-correlation = 0.9746019347753163 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1333a_db.csv and mash_lmo1333a.txt: veridical-correlation = 0.9623271403512059 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1335_db.csv is not symmetric, skipped\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1337_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1338_db.csv and mash_lmo1338.txt: veridical-correlation = 0.9231971494249557 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1340_db.csv and mash_lmo1340.txt: veridical-correlation = 0.9877778898454193 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1341_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1342_db.csv and mash_lmo1342.txt: veridical-correlation = 0.9731106167736684 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1344_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1345_db.csv and mash_lmo1345.txt: veridical-correlation = 0.8269498512296221 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1346_db.csv and mash_lmo1346.txt: veridical-correlation = 0.7411406834105828 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1348_db.csv and mash_lmo1348.txt: veridical-correlation = 0.982435827622033 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1349_db.csv and mash_lmo1349.txt: veridical-correlation = 0.989714913288394 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1350_db.csv and mash_lmo1350.txt: veridical-correlation = 0.98154654477516 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1351_db.csv and mash_lmo1351.txt: veridical-correlation = 0.9297621194008205 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1353_db.csv and mash_lmo1353.txt: veridical-correlation = 0.9887953225660642 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1354_db.csv and mash_lmo1354.txt: veridical-correlation = 0.9871449668267382 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1355_db.csv and mash_lmo1355.txt: veridical-correlation = 0.9720884183333194 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1357_db.csv and mash_lmo1357.txt: veridical-correlation = 0.9872086173469365 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1358_db.csv and mash_lmo1358.txt: veridical-correlation = 0.9722722829821826 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1359_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1360_db.csv and mash_lmo1360.txt: veridical-correlation = 0.9805145282004191 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1361_db.csv and mash_lmo1361.txt: veridical-correlation = 0.9927533879744913 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1363_db.csv and mash_lmo1363.txt: veridical-correlation = 0.9885376101120481 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1364_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1367_db.csv and mash_lmo1367.txt: veridical-correlation = 0.9694366167876447 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1368_db.csv and mash_lmo1368.txt: veridical-correlation = 0.9957181018103873 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1370_db.csv and mash_lmo1370.txt: veridical-correlation = 0.9798545850890817 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1371_db.csv and mash_lmo1371.txt: veridical-correlation = 0.9924835726765897 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1372_db.csv and mash_lmo1372.txt: veridical-correlation = 0.9755766813417472 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1373_db.csv and mash_lmo1373.txt: veridical-correlation = 0.9687186525149055 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1374_db.csv and mash_lmo1374.txt: veridical-correlation = 0.9649461510432595 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1376_db.csv is not symmetric, skipped\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1377_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1378_db.csv and mash_lmo1378.txt: veridical-correlation = 0.9776027513719332 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1379_db.csv and mash_lmo1379.txt: veridical-correlation = 0.9675176890113822 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1380_db.csv and mash_lmo1380.txt: veridical-correlation = 0.9194804397649017 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1381_db.csv and mash_lmo1381.txt: veridical-correlation = 0.859302401743699 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1383_db.csv and mash_lmo1383.txt: veridical-correlation = 0.9867292275331473 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1384_db.csv and mash_lmo1384.txt: veridical-correlation = 0.9909126018022865 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1385_db.csv and mash_lmo1385.txt: veridical-correlation = 0.991089806458175 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1386_db.csv and mash_lmo1386.txt: veridical-correlation = 0.9937882047530828 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1388_db.csv and mash_lmo1388.txt: veridical-correlation = 0.9928936597873717 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1389_db.csv and mash_lmo1389.txt: veridical-correlation = 0.9858338585232934 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1391_db.csv and mash_lmo1391.txt: veridical-correlation = 0.9867789983822597 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1392_db.csv and mash_lmo1392.txt: veridical-correlation = 0.9894173938954411 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1394_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1395_db.csv and mash_lmo1395.txt: veridical-correlation = 0.9762687594691997 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1396_db.csv and mash_lmo1396.txt: veridical-correlation = 0.9761863833580768 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1397_db.csv and mash_lmo1397.txt: veridical-correlation = 0.7309863321325262 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1398_db.csv and mash_lmo1398.txt: veridical-correlation = 0.9875630678057552 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1399_db.csv and mash_lmo1399.txt: veridical-correlation = 0.9890391980967436 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1400_db.csv and mash_lmo1400.txt: veridical-correlation = 0.9635839200581032 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1401_db.csv and mash_lmo1401.txt: veridical-correlation = 0.9749363118442844 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1402_db.csv and mash_lmo1402.txt: veridical-correlation = 0.5536716991456204 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1406_db.csv and mash_lmo1406.txt: veridical-correlation = 0.993400082559206 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1407_db.csv and mash_lmo1407.txt: veridical-correlation = 0.9724788762741523 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1408_db.csv and mash_lmo1408.txt: veridical-correlation = 0.97289723063692 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1409_db.csv and mash_lmo1409.txt: veridical-correlation = 0.9715492987851314 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1411_db.csv and mash_lmo1411.txt: veridical-correlation = 0.8924177739612771 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1412_db.csv and mash_lmo1412.txt: veridical-correlation = 0.9763175711794984 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1415_db.csv and mash_lmo1415.txt: veridical-correlation = 0.9895037016619102 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1416_db.csv and mash_lmo1416.txt: veridical-correlation = 0.9573317234327774 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1417_db.csv and mash_lmo1417.txt: veridical-correlation = 0.9757449431943775 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1419_db.csv and mash_lmo1419.txt: veridical-correlation = 0.9911585231416723 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1420_db.csv and mash_lmo1420.txt: veridical-correlation = 0.9639193473763702 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1421_db.csv and mash_lmo1421.txt: veridical-correlation = 0.9780133505509491 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1422_db.csv and mash_lmo1422.txt: veridical-correlation = 0.9889620696753444 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1423_db.csv and mash_lmo1423.txt: veridical-correlation = 0.9929370784660746 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1424_db.csv and mash_lmo1424.txt: veridical-correlation = 0.9761427101845264 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1425_db.csv and mash_lmo1425.txt: veridical-correlation = 0.9802543392251704 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1426_db.csv and mash_lmo1426.txt: veridical-correlation = 0.982793533316769 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1427_db.csv and mash_lmo1427.txt: veridical-correlation = 0.9833631012401786 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1428_db.csv and mash_lmo1428.txt: veridical-correlation = 0.9694958156524346 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1431_db.csv and mash_lmo1431.txt: veridical-correlation = 0.973355929017617 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1434_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1435_db.csv and mash_lmo1435.txt: veridical-correlation = 0.9689958013216254 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1437_db.csv and mash_lmo1437.txt: veridical-correlation = 0.9962232359178614 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1438_db.csv and mash_lmo1438.txt: veridical-correlation = 0.9979164769031863 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1439_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1440_db.csv and mash_lmo1440.txt: veridical-correlation = 0.9744205340343977 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1444_db.csv and mash_lmo1444.txt: veridical-correlation = 0.9836336146293285 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1446_db.csv and mash_lmo1446.txt: veridical-correlation = 0.978066203050144 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1448_db.csv and mash_lmo1448.txt: veridical-correlation = 0.9809574708618255 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1450_db.csv and mash_lmo1450.txt: veridical-correlation = 0.9863775216355395 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1452_db.csv and mash_lmo1452.txt: veridical-correlation = 0.9842520011706543 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1454_db.csv and mash_lmo1454.txt: veridical-correlation = 0.985878550016774 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1455_db.csv and mash_lmo1455.txt: veridical-correlation = 0.9860013010386044 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1456_db.csv and mash_lmo1456.txt: veridical-correlation = 0.9566745503726108 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1457_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1462_db.csv and mash_lmo1462.txt: veridical-correlation = 0.9938015810647597 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1463_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1465_db.csv and mash_lmo1465.txt: veridical-correlation = 0.9615733769871018 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1466_db.csv and mash_lmo1466.txt: veridical-correlation = 0.9977164802079956 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1467_db.csv and mash_lmo1467.txt: veridical-correlation = 0.9745371412205166 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1468_db.csv and mash_lmo1468.txt: veridical-correlation = 0.970129482093894 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1469_db.csv and mash_lmo1469.txt: veridical-correlation = 0.6151182801653495 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1472_db.csv and mash_lmo1472.txt: veridical-correlation = 0.9819252082212665 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1473_db.csv and mash_lmo1473.txt: veridical-correlation = 0.9892356926781575 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1474_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1475_db.csv and mash_lmo1475.txt: veridical-correlation = 0.9889538610062212 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1476_db.csv and mash_lmo1476.txt: veridical-correlation = 0.9887287622281701 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1479_db.csv and mash_lmo1479.txt: veridical-correlation = 0.984560113325921 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1480_db.csv and mash_lmo1480.txt: veridical-correlation = 0.895312830650962 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1481_db.csv and mash_lmo1481.txt: veridical-correlation = 0.9710595287640911 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1482_db.csv and mash_lmo1482.txt: veridical-correlation = 0.988891892042542 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1483_db.csv and mash_lmo1483.txt: veridical-correlation = 0.9695880604918314 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1485_db.csv and mash_lmo1485.txt: veridical-correlation = 0.6791398106620524 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1486_db.csv and mash_lmo1486.txt: veridical-correlation = 0.9348695416558765 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1487_db.csv and mash_lmo1487.txt: veridical-correlation = 0.9699414305943219 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1489_db.csv and mash_lmo1489.txt: veridical-correlation = 0.9339608644548287 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1490_db.csv and mash_lmo1490.txt: veridical-correlation = 0.9690342088667009 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1491_db.csv and mash_lmo1491.txt: veridical-correlation = 0.9773017622471672 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1492_db.csv and mash_lmo1492.txt: veridical-correlation = 0.989131820995619 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1493_db.csv and mash_lmo1493.txt: veridical-correlation = 0.987620447082035 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1494_db.csv and mash_lmo1494.txt: veridical-correlation = 0.7607037153443358 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1495_db.csv and mash_lmo1495.txt: veridical-correlation = 0.9783182270086461 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1496_db.csv and mash_lmo1496.txt: veridical-correlation = 0.9674668867491651 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1497_db.csv and mash_lmo1497.txt: veridical-correlation = 0.9804385953093978 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1498_db.csv and mash_lmo1498.txt: veridical-correlation = 0.9865515975280392 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1499_db.csv and mash_lmo1499.txt: veridical-correlation = 0.9891273294053196 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1500_db.csv is not symmetric, skipped\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1502_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1503_db.csv and mash_lmo1503.txt: veridical-correlation = 0.9492476650734795 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1504_db.csv and mash_lmo1504.txt: veridical-correlation = 0.9882610004623228 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1507_db.csv and mash_lmo1507.txt: veridical-correlation = 0.9643188630353684 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1509_db.csv and mash_lmo1509.txt: veridical-correlation = 0.9767335390109939 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1510_db.csv and mash_lmo1510.txt: veridical-correlation = 0.9729206077955105 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1511_db.csv and mash_lmo1511.txt: veridical-correlation = 0.7327109643719675 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1512_db.csv and mash_lmo1512.txt: veridical-correlation = 0.984745044191163 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1518_db.csv and mash_lmo1518.txt: veridical-correlation = 0.9811046496850896 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1519_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1520_db.csv and mash_lmo1520.txt: veridical-correlation = 0.9827727264014156 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1521_db.csv and mash_lmo1521.txt: veridical-correlation = 0.9950417630548894 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1523_db.csv and mash_lmo1523.txt: veridical-correlation = 0.9919250029901489 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1525_db.csv and mash_lmo1525.txt: veridical-correlation = 0.9964716882640604 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1526_db.csv and mash_lmo1526.txt: veridical-correlation = 0.6147603714910617 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1527_db.csv and mash_lmo1527.txt: veridical-correlation = 0.9872949900819441 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1528_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1529_db.csv and mash_lmo1529.txt: veridical-correlation = 0.9591762245226517 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1530_db.csv and mash_lmo1530.txt: veridical-correlation = 0.9488057513786359 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1531_db.csv and mash_lmo1531.txt: veridical-correlation = 0.9833701439713475 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1532_db.csv and mash_lmo1532.txt: veridical-correlation = 0.983995432792964 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1533_db.csv and mash_lmo1533.txt: veridical-correlation = 0.9806946365977947 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1534_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1535_db.csv and mash_lmo1535.txt: veridical-correlation = 0.6164930503747542 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1536_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1537_db.csv and mash_lmo1537.txt: veridical-correlation = 0.9860299058640352 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1538_db.csv and mash_lmo1538.txt: veridical-correlation = 0.9675754672350129 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1539_db.csv and mash_lmo1539.txt: veridical-correlation = 0.9726109704142457 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1540_db.csv and mash_lmo1540.txt: veridical-correlation = 0.8295856125610491 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1541_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1542_db.csv and mash_lmo1542.txt: veridical-correlation = 0.7866441241461914 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1544_db.csv and mash_lmo1544.txt: veridical-correlation = 0.9798616594830416 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1545_db.csv and mash_lmo1545.txt: veridical-correlation = 0.9757205460843867 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1546_db.csv and mash_lmo1546.txt: veridical-correlation = 0.8790927825253496 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1547_db.csv and mash_lmo1547.txt: veridical-correlation = 0.9920668300877946 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1548_db.csv and mash_lmo1548.txt: veridical-correlation = 0.9773907202068656 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1557_db.csv and mash_lmo1557.txt: veridical-correlation = 0.9790496778430917 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1558_db.csv and mash_lmo1558.txt: veridical-correlation = 0.9645324542514077 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1559_db.csv and mash_lmo1559.txt: veridical-correlation = 0.9842045997793195 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1560_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1561_db.csv and mash_lmo1561.txt: veridical-correlation = 0.983694838433948 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1562_db.csv and mash_lmo1562.txt: veridical-correlation = 0.9873287144781904 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1563_db.csv and mash_lmo1563.txt: veridical-correlation = 0.6827165175971976 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1564_db.csv and mash_lmo1564.txt: veridical-correlation = 0.9641097110780178 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1565_db.csv and mash_lmo1565.txt: veridical-correlation = 0.9890338005139132 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1566_db.csv and mash_lmo1566.txt: veridical-correlation = 0.9866069392580994 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1567_db.csv and mash_lmo1567.txt: veridical-correlation = 0.9878963021751868 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1568_db.csv and mash_lmo1568.txt: veridical-correlation = 0.9794837488598233 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1569_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1570_db.csv and mash_lmo1570.txt: veridical-correlation = 0.9908666435260233 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1571_db.csv and mash_lmo1571.txt: veridical-correlation = 0.9745333012667666 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1572_db.csv and mash_lmo1572.txt: veridical-correlation = 0.9719275259794625 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1576_db.csv and mash_lmo1576.txt: veridical-correlation = 0.9613440350390675 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1577_db.csv and mash_lmo1577.txt: veridical-correlation = 0.9546059858310891 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1578_db.csv and mash_lmo1578.txt: veridical-correlation = 0.9865303360674043 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1579_db.csv and mash_lmo1579.txt: veridical-correlation = 0.992059172563849 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1580_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1581_db.csv and mash_lmo1581.txt: veridical-correlation = 0.9851785253802271 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1582_db.csv and mash_lmo1582.txt: veridical-correlation = 0.9820035426158968 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1583_db.csv and mash_lmo1583.txt: veridical-correlation = 0.9231354357518043 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1585_db.csv and mash_lmo1585.txt: veridical-correlation = 0.9898176263905528 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1586_db.csv and mash_lmo1586.txt: veridical-correlation = 0.9769955220524283 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1588_db.csv and mash_lmo1588.txt: veridical-correlation = 0.9599090298917314 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1590_db.csv and mash_lmo1590.txt: veridical-correlation = 0.9809227503645019 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1591_db.csv and mash_lmo1591.txt: veridical-correlation = 0.9717134425285477 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1592_db.csv and mash_lmo1592.txt: veridical-correlation = 0.9885836705907299 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1593_db.csv and mash_lmo1593.txt: veridical-correlation = 0.9783106146835527 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1594_db.csv and mash_lmo1594.txt: veridical-correlation = 0.9966513836307082 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1595_db.csv and mash_lmo1595.txt: veridical-correlation = 0.9628153225754583 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1596_db.csv and mash_lmo1596.txt: veridical-correlation = 0.9065094151150457 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1598_db.csv and mash_lmo1598.txt: veridical-correlation = 0.9676365921146656 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1599_db.csv and mash_lmo1599.txt: veridical-correlation = 0.9914332761246744 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1600_db.csv and mash_lmo1600.txt: veridical-correlation = 0.9825446062888195 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1601_db.csv and mash_lmo1601.txt: veridical-correlation = 0.9617901519153486 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1602_db.csv and mash_lmo1602.txt: veridical-correlation = 0.8425684142795632 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1603_db.csv and mash_lmo1603.txt: veridical-correlation = 0.9701159824760445 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1604_db.csv and mash_lmo1604.txt: veridical-correlation = 0.9692238461553997 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1605_db.csv and mash_lmo1605.txt: veridical-correlation = 0.9922803151375544 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1606_db.csv and mash_lmo1606.txt: veridical-correlation = 0.9806512658694795 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1609_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1612_db.csv and mash_lmo1612.txt: veridical-correlation = 0.9517286334889233 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1615_db.csv and mash_lmo1615.txt: veridical-correlation = 0.9830976639675677 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1616_db.csv and mash_lmo1616.txt: veridical-correlation = 0.9808650848457616 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1617_db.csv and mash_lmo1617.txt: veridical-correlation = 0.9915792825933142 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1618_db.csv and mash_lmo1618.txt: veridical-correlation = 0.9577307514799844 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1620_db.csv and mash_lmo1620.txt: veridical-correlation = 0.9922133785192613 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1621_db.csv and mash_lmo1621.txt: veridical-correlation = 0.959464798374786 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1624_db.csv and mash_lmo1624.txt: veridical-correlation = 0.9888482432999987 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1625_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1626_db.csv and mash_lmo1626.txt: veridical-correlation = 0.9835257406137585 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1628_db.csv and mash_lmo1628.txt: veridical-correlation = 0.9763225617749947 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1629_db.csv and mash_lmo1629.txt: veridical-correlation = 0.981373834141175 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1630_db.csv and mash_lmo1630.txt: veridical-correlation = 0.9884482624779439 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1631_db.csv and mash_lmo1631.txt: veridical-correlation = 0.993857894946872 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1634_db.csv and mash_lmo1634.txt: veridical-correlation = 0.9931254354772577 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1635_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1636_db.csv and mash_lmo1636.txt: veridical-correlation = 0.9843032566428481 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1637_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1638_db.csv and mash_lmo1638.txt: veridical-correlation = 0.9068888889479181 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1639_db.csv and mash_lmo1639.txt: veridical-correlation = 0.895799733210762 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1641_db.csv and mash_lmo1641.txt: veridical-correlation = 0.9864257549313392 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1643_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1644_db.csv and mash_lmo1644.txt: veridical-correlation = 0.9768293538776626 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1646_db.csv and mash_lmo1646.txt: veridical-correlation = 0.9627178297585993 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1647_db.csv and mash_lmo1647.txt: veridical-correlation = 0.9655935116689159 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1650_db.csv and mash_lmo1650.txt: veridical-correlation = 0.9720131239524212 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1651_db.csv and mash_lmo1651.txt: veridical-correlation = 0.9781131728958619 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1657_db.csv and mash_lmo1657.txt: veridical-correlation = 0.9908823850432318 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1658_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1660_db.csv and mash_lmo1660.txt: veridical-correlation = 0.9900198004730398 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1661_db.csv and mash_lmo1661.txt: veridical-correlation = 0.975623653768295 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1663_db.csv and mash_lmo1663.txt: veridical-correlation = 0.9866245034825818 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1665_db.csv and mash_lmo1665.txt: veridical-correlation = 0.964902051681286 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1668_db.csv and mash_lmo1668.txt: veridical-correlation = 0.5334748005411019 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1673_db.csv and mash_lmo1673.txt: veridical-correlation = 0.974881716655901 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1675_db.csv and mash_lmo1675.txt: veridical-correlation = 0.9909994589020598 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1676_db.csv and mash_lmo1676.txt: veridical-correlation = 0.984429419343435 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1677_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1678_db.csv and mash_lmo1678.txt: veridical-correlation = 0.9643885958831897 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1679_db.csv and mash_lmo1679.txt: veridical-correlation = 0.9774493309382737 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1682_db.csv and mash_lmo1682.txt: veridical-correlation = 0.9845917361423961 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1683_db.csv and mash_lmo1683.txt: veridical-correlation = 0.9480671171524772 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1685_db.csv and mash_lmo1685.txt: veridical-correlation = 0.9532359182219465 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1686_db.csv and mash_lmo1686.txt: veridical-correlation = 0.9931703443667929 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1687_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1689_db.csv and mash_lmo1689.txt: veridical-correlation = 0.9704420177196152 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1690_db.csv and mash_lmo1690.txt: veridical-correlation = 0.9526340833534256 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1692_db.csv and mash_lmo1692.txt: veridical-correlation = 0.8840084753116548 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1694_db.csv and mash_lmo1694.txt: veridical-correlation = 0.9565669489835317 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1695_db.csv and mash_lmo1695.txt: veridical-correlation = 0.9918756227617351 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1696_db.csv and mash_lmo1696.txt: veridical-correlation = 0.9783792614591393 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1697_db.csv and mash_lmo1697.txt: veridical-correlation = 0.6706271803527499 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1698_db.csv and mash_lmo1698.txt: veridical-correlation = 0.9303314872342022 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1699_db.csv and mash_lmo1699.txt: veridical-correlation = 0.9718930543253588 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1700_db.csv and mash_lmo1700.txt: veridical-correlation = 0.9119972269863388 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1702_db.csv and mash_lmo1702.txt: veridical-correlation = 0.9373818293106884 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1704_db.csv and mash_lmo1704.txt: veridical-correlation = 0.9453708323482216 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1705_db.csv and mash_lmo1705.txt: veridical-correlation = 0.9736777926161124 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1706_db.csv and mash_lmo1706.txt: veridical-correlation = 0.9841563556642139 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1707_db.csv and mash_lmo1707.txt: veridical-correlation = 0.9460135584130772 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1708_db.csv and mash_lmo1708.txt: veridical-correlation = 0.9728873451937896 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1709_db.csv and mash_lmo1709.txt: veridical-correlation = 0.9898600129434313 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1710_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1711_db.csv and mash_lmo1711.txt: veridical-correlation = 0.986652285832003 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1712_db.csv and mash_lmo1712.txt: veridical-correlation = 0.9735983911537649 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1713_db.csv and mash_lmo1713.txt: veridical-correlation = 0.9265653510477729 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1718_db.csv and mash_lmo1718.txt: veridical-correlation = 0.9775801369146031 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1719_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1720_db.csv and mash_lmo1720.txt: veridical-correlation = 0.963546376994456 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1721_db.csv and mash_lmo1721.txt: veridical-correlation = 0.9913005665989072 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1722_db.csv and mash_lmo1722.txt: veridical-correlation = 0.9804595562624256 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1726_db.csv and mash_lmo1726.txt: veridical-correlation = 0.9711411239409017 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1727_db.csv and mash_lmo1727.txt: veridical-correlation = 0.9691385349897719 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1730_db.csv and mash_lmo1730.txt: veridical-correlation = 0.9742874299923983 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1731_db.csv and mash_lmo1731.txt: veridical-correlation = 0.9720556511864492 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1732_db.csv and mash_lmo1732.txt: veridical-correlation = 0.9714067266464443 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1733_db.csv and mash_lmo1733.txt: veridical-correlation = 0.9874124663859073 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1734_db.csv and mash_lmo1734.txt: veridical-correlation = 0.9683395430215378 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1735_db.csv and mash_lmo1735.txt: veridical-correlation = 0.9800267816963879 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1736_db.csv and mash_lmo1736.txt: veridical-correlation = 0.9247259822209645 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1737_db.csv and mash_lmo1737.txt: veridical-correlation = 0.9554012464345099 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1738_db.csv and mash_lmo1738.txt: veridical-correlation = 0.9834580517826889 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1739_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1740_db.csv and mash_lmo1740.txt: veridical-correlation = 0.9723769626577644 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1745_db.csv and mash_lmo1745.txt: veridical-correlation = 0.9821199407670466 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1749_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1752_db.csv and mash_lmo1752.txt: veridical-correlation = 0.9778813672150463 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1753_db.csv and mash_lmo1753.txt: veridical-correlation = 0.994246930329115 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1754_db.csv and mash_lmo1754.txt: veridical-correlation = 0.9837010133761009 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1755_db.csv and mash_lmo1755.txt: veridical-correlation = 0.9685922787466068 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1756_db.csv and mash_lmo1756.txt: veridical-correlation = 0.9220499851264589 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1757_db.csv and mash_lmo1757.txt: veridical-correlation = 0.9829752404021659 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1758_db.csv and mash_lmo1758.txt: veridical-correlation = 0.9880016136618097 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1759_db.csv and mash_lmo1759.txt: veridical-correlation = 0.9754704795218908 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1760_db.csv is not symmetric, skipped\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1761_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1762_db.csv and mash_lmo1762.txt: veridical-correlation = 0.8127629577935191 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo1763_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo1764_db.csv and mash_lmo1764.txt: veridical-correlation = 0.9916625579038103 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1765_db.csv and mash_lmo1765.txt: veridical-correlation = 0.9810135350785013 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1766_db.csv and mash_lmo1766.txt: veridical-correlation = 0.9774572206039903 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1767_db.csv and mash_lmo1767.txt: veridical-correlation = 0.9695199571183166 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1769_db.csv and mash_lmo1769.txt: veridical-correlation = 0.9837113799202883 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1771_db.csv and mash_lmo1771.txt: veridical-correlation = 0.8803238645770507 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1772_db.csv and mash_lmo1772.txt: veridical-correlation = 0.9759567699924611 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1773_db.csv and mash_lmo1773.txt: veridical-correlation = 0.9732556954015229 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1774_db.csv and mash_lmo1774.txt: veridical-correlation = 0.9846941051117242 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1776_db.csv and mash_lmo1776.txt: veridical-correlation = 0.9752239300133226 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1779_db.csv and mash_lmo1779.txt: veridical-correlation = 0.9331149965797259 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1780_db.csv and mash_lmo1780.txt: veridical-correlation = 0.9873599267002438 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1782_db.csv and mash_lmo1782.txt: veridical-correlation = 0.9510943390422412 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1783_db.csv and mash_lmo1783.txt: veridical-correlation = 0.9703283895272787 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1784_db.csv and mash_lmo1784.txt: veridical-correlation = 0.9233870564921064 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1785_db.csv and mash_lmo1785.txt: veridical-correlation = 0.9190062746898615 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1787_db.csv and mash_lmo1787.txt: veridical-correlation = 0.8331153570568373 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1788_db.csv and mash_lmo1788.txt: veridical-correlation = 0.6556544933913387 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1791_db.csv and mash_lmo1791.txt: veridical-correlation = 0.9144516424502922 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1792_db.csv and mash_lmo1792.txt: veridical-correlation = 0.9707755835747388 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1793_db.csv and mash_lmo1793.txt: veridical-correlation = 0.9548575246119405 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1794_db.csv and mash_lmo1794.txt: veridical-correlation = 0.9616020753535263 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1795_db.csv and mash_lmo1795.txt: veridical-correlation = 0.9698346212622668 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1796_db.csv and mash_lmo1796.txt: veridical-correlation = 0.7645055881954251 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1797_db.csv and mash_lmo1797.txt: veridical-correlation = 0.9635404406764556 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1801_db.csv and mash_lmo1801.txt: veridical-correlation = 0.9932874996981911 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1802_db.csv and mash_lmo1802.txt: veridical-correlation = 0.8740604730362906 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1803_db.csv and mash_lmo1803.txt: veridical-correlation = 0.9829950021290884 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1804_db.csv and mash_lmo1804.txt: veridical-correlation = 0.9970601327865362 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1805_db.csv and mash_lmo1805.txt: veridical-correlation = 0.9803328573060792 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1806_db.csv and mash_lmo1806.txt: veridical-correlation = 0.9047854070288922 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1807_db.csv and mash_lmo1807.txt: veridical-correlation = 0.9746306677016361 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1809_db.csv and mash_lmo1809.txt: veridical-correlation = 0.9912883504838723 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1810_db.csv and mash_lmo1810.txt: veridical-correlation = 0.9915671065402407 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1811_db.csv and mash_lmo1811.txt: veridical-correlation = 0.9865730634778108 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1813_db.csv and mash_lmo1813.txt: veridical-correlation = 0.9843428617093487 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1815_db.csv and mash_lmo1815.txt: veridical-correlation = 0.9731116071638121 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1816_db.csv and mash_lmo1816.txt: veridical-correlation = 0.6695936166042916 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1818_db.csv and mash_lmo1818.txt: veridical-correlation = 0.989408348604682 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1819_db.csv and mash_lmo1819.txt: veridical-correlation = 0.9625375775737312 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1820_db.csv and mash_lmo1820.txt: veridical-correlation = 0.9761250536379666 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1821_db.csv and mash_lmo1821.txt: veridical-correlation = 0.9746365755172334 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1822_db.csv and mash_lmo1822.txt: veridical-correlation = 0.9855262884509679 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1823_db.csv and mash_lmo1823.txt: veridical-correlation = 0.9652766183749194 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1824_db.csv and mash_lmo1824.txt: veridical-correlation = 0.9812717661151352 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1825_db.csv and mash_lmo1825.txt: veridical-correlation = 0.9808047307030512 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1826_db.csv and mash_lmo1826.txt: veridical-correlation = 0.9248394924684451 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1827_db.csv and mash_lmo1827.txt: veridical-correlation = 0.9538951362681337 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1828_db.csv and mash_lmo1828.txt: veridical-correlation = 0.9811657275294169 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1830_db.csv and mash_lmo1830.txt: veridical-correlation = 0.981210274552928 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1833_db.csv and mash_lmo1833.txt: veridical-correlation = 0.9681318444137282 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1834_db.csv and mash_lmo1834.txt: veridical-correlation = 0.9832160085922038 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1835_db.csv and mash_lmo1835.txt: veridical-correlation = 0.9937512064518568 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1837_db.csv and mash_lmo1837.txt: veridical-correlation = 0.9662169975125903 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1839_db.csv and mash_lmo1839.txt: veridical-correlation = 0.9563597213196303 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1840_db.csv and mash_lmo1840.txt: veridical-correlation = 0.9586363743776936 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1841_db.csv and mash_lmo1841.txt: veridical-correlation = 0.9253126966421046 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1844_db.csv and mash_lmo1844.txt: veridical-correlation = 0.9527069143923349 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1845_db.csv and mash_lmo1845.txt: veridical-correlation = 0.9927746004858415 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1848_db.csv and mash_lmo1848.txt: veridical-correlation = 0.981017526782877 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1850_db.csv and mash_lmo1850.txt: veridical-correlation = 0.9750136003177691 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1851_db.csv and mash_lmo1851.txt: veridical-correlation = 0.9927747807415366 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1852_db.csv and mash_lmo1852.txt: veridical-correlation = 0.6516726066230037 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1853_db.csv and mash_lmo1853.txt: veridical-correlation = 0.9909913335663463 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1854_db.csv and mash_lmo1854.txt: veridical-correlation = 0.9294728182945818 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1856_db.csv and mash_lmo1856.txt: veridical-correlation = 0.9792860155111648 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1857_db.csv and mash_lmo1857.txt: veridical-correlation = 0.9104206507566333 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1858_db.csv and mash_lmo1858.txt: veridical-correlation = 0.8109187356055325 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1859_db.csv and mash_lmo1859.txt: veridical-correlation = 0.9702061506314222 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1860_db.csv and mash_lmo1860.txt: veridical-correlation = 0.9799942894239358 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1861_db.csv and mash_lmo1861.txt: veridical-correlation = 0.9716822360024671 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1862_db.csv and mash_lmo1862.txt: veridical-correlation = 0.9639512423203369 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1863_db.csv and mash_lmo1863.txt: veridical-correlation = 0.9856059955952466 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1864_db.csv and mash_lmo1864.txt: veridical-correlation = 0.9739517395238784 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1866_db.csv and mash_lmo1866.txt: veridical-correlation = 0.9873549956494767 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1867_db.csv and mash_lmo1867.txt: veridical-correlation = 0.9679878330955891 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1868_db.csv and mash_lmo1868.txt: veridical-correlation = 0.6614027151784166 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1869_db.csv and mash_lmo1869.txt: veridical-correlation = 0.9835469259627224 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1870_db.csv and mash_lmo1870.txt: veridical-correlation = 0.9341489124650408 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1871_db.csv and mash_lmo1871.txt: veridical-correlation = 0.985477939129619 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1872_db.csv and mash_lmo1872.txt: veridical-correlation = 0.9847986977614807 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1873_db.csv and mash_lmo1873.txt: veridical-correlation = 0.9824790004449635 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1874_db.csv and mash_lmo1874.txt: veridical-correlation = 0.98175427092396 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1875_db.csv and mash_lmo1875.txt: veridical-correlation = 0.9901638447503853 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1878_db.csv and mash_lmo1878.txt: veridical-correlation = 0.9520820062870162 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1879_db.csv and mash_lmo1879.txt: veridical-correlation = 0.8395942253869585 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1880_db.csv and mash_lmo1880.txt: veridical-correlation = 0.9814237561426455 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1881_db.csv and mash_lmo1881.txt: veridical-correlation = 0.9756977786447042 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1882_db.csv and mash_lmo1882.txt: veridical-correlation = 0.9510415026138064 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1883_db.csv and mash_lmo1883.txt: veridical-correlation = 0.9799366835637164 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1884_db.csv and mash_lmo1884.txt: veridical-correlation = 0.9873321039072773 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1885_db.csv and mash_lmo1885.txt: veridical-correlation = 0.9744575611071279 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1886_db.csv and mash_lmo1886.txt: veridical-correlation = 0.9959233074268331 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1887_db.csv and mash_lmo1887.txt: veridical-correlation = 0.970031831781401 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1888_db.csv and mash_lmo1888.txt: veridical-correlation = 0.8621643321182947 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1890_db.csv and mash_lmo1890.txt: veridical-correlation = 0.9762828100708383 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1891_db.csv and mash_lmo1891.txt: veridical-correlation = 0.9891514954628703 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1892_db.csv and mash_lmo1892.txt: veridical-correlation = 0.9901234824150094 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1894_db.csv and mash_lmo1894.txt: veridical-correlation = 0.9904539106814779 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1895_db.csv and mash_lmo1895.txt: veridical-correlation = 0.7673511475796274 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1896_db.csv and mash_lmo1896.txt: veridical-correlation = 0.984544375741068 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1897_db.csv and mash_lmo1897.txt: veridical-correlation = 0.9914920451985 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1898_db.csv and mash_lmo1898.txt: veridical-correlation = 0.986216994007058 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1899_db.csv and mash_lmo1899.txt: veridical-correlation = 0.9889157680977773 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1900_db.csv and mash_lmo1900.txt: veridical-correlation = 0.9611640309191011 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1902_db.csv and mash_lmo1902.txt: veridical-correlation = 0.988038395309793 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1903_db.csv and mash_lmo1903.txt: veridical-correlation = 0.584542998190989 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1907_db.csv and mash_lmo1907.txt: veridical-correlation = 0.9830550077015783 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1908_db.csv and mash_lmo1908.txt: veridical-correlation = 0.912631724933409 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1911_db.csv and mash_lmo1911.txt: veridical-correlation = 0.9960950994012752 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1912_db.csv and mash_lmo1912.txt: veridical-correlation = 0.9903998014298571 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1915_db.csv and mash_lmo1915.txt: veridical-correlation = 0.9323410270125001 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1916_db.csv and mash_lmo1916.txt: veridical-correlation = 0.7151439092443399 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1917_db.csv and mash_lmo1917.txt: veridical-correlation = 0.9875649945308834 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1918_db.csv and mash_lmo1918.txt: veridical-correlation = 0.9500983352494168 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1919_db.csv and mash_lmo1919.txt: veridical-correlation = 0.9801286183968901 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1920_db.csv and mash_lmo1920.txt: veridical-correlation = 0.9696306834264485 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1921_db.csv and mash_lmo1921.txt: veridical-correlation = 0.9629677890348973 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1922_db.csv and mash_lmo1922.txt: veridical-correlation = 0.988750834963154 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1924_db.csv and mash_lmo1924.txt: veridical-correlation = 0.9893922680685224 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1927_db.csv and mash_lmo1927.txt: veridical-correlation = 0.9839922500721237 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1928_db.csv and mash_lmo1928.txt: veridical-correlation = 0.9874217660649139 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1929_db.csv and mash_lmo1929.txt: veridical-correlation = 0.9607004907373681 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1930_db.csv and mash_lmo1930.txt: veridical-correlation = 0.9764152646266392 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1931_db.csv and mash_lmo1931.txt: veridical-correlation = 0.9585853805108433 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1932_db.csv and mash_lmo1932.txt: veridical-correlation = 0.97316852740297 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1933_db.csv and mash_lmo1933.txt: veridical-correlation = 0.9465412376312176 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1934_db.csv and mash_lmo1934.txt: veridical-correlation = 0.9778893740378062 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1935_db.csv and mash_lmo1935.txt: veridical-correlation = 0.9857924233497676 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1936_db.csv and mash_lmo1936.txt: veridical-correlation = 0.9878540863409649 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1937_db.csv and mash_lmo1937.txt: veridical-correlation = 0.9841029161113695 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1938_db.csv and mash_lmo1938.txt: veridical-correlation = 0.9840975678860108 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1940_db.csv and mash_lmo1940.txt: veridical-correlation = 0.9887313006411226 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1941_db.csv and mash_lmo1941.txt: veridical-correlation = 0.990155232162272 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1944_db.csv and mash_lmo1944.txt: veridical-correlation = 0.7939910110086129 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1946_db.csv and mash_lmo1946.txt: veridical-correlation = 0.7962636479719543 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1947_db.csv and mash_lmo1947.txt: veridical-correlation = 0.9806520891411385 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1948_db.csv and mash_lmo1948.txt: veridical-correlation = 0.9898714157959925 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1949_db.csv and mash_lmo1949.txt: veridical-correlation = 0.9890628044818611 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1950_db.csv and mash_lmo1950.txt: veridical-correlation = 0.9758758344284066 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1951_db.csv and mash_lmo1951.txt: veridical-correlation = 0.9808938812394653 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1952_db.csv and mash_lmo1952.txt: veridical-correlation = 0.9790070390438887 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1953_db.csv and mash_lmo1953.txt: veridical-correlation = 0.9793081670055448 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1954_db.csv and mash_lmo1954.txt: veridical-correlation = 0.976445563033218 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1955_db.csv and mash_lmo1955.txt: veridical-correlation = 0.9794693780706945 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1956_db.csv and mash_lmo1956.txt: veridical-correlation = 0.9518879097626738 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1957_db.csv and mash_lmo1957.txt: veridical-correlation = 0.9899273206821726 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1958_db.csv and mash_lmo1958.txt: veridical-correlation = 0.9856219838418575 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1962_db.csv and mash_lmo1962.txt: veridical-correlation = 0.9641677436546419 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1963_db.csv and mash_lmo1963.txt: veridical-correlation = 0.9869360346424595 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1964_db.csv and mash_lmo1964.txt: veridical-correlation = 0.9824538558840018 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1965_db.csv and mash_lmo1965.txt: veridical-correlation = 0.9881150811668897 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1967_db.csv and mash_lmo1967.txt: veridical-correlation = 0.9818815700897773 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1978_db.csv and mash_lmo1978.txt: veridical-correlation = 0.9865677393591444 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1979_db.csv and mash_lmo1979.txt: veridical-correlation = 0.9569266824870809 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1982_db.csv and mash_lmo1982.txt: veridical-correlation = 0.9421359992010342 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1983_db.csv and mash_lmo1983.txt: veridical-correlation = 0.9855533036840903 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1985_db.csv and mash_lmo1985.txt: veridical-correlation = 0.927038778022127 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1986_db.csv and mash_lmo1986.txt: veridical-correlation = 0.9776574768315879 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1987_db.csv and mash_lmo1987.txt: veridical-correlation = 0.9925069709171481 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1989_db.csv and mash_lmo1989.txt: veridical-correlation = 0.9856919343887468 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1991_db.csv and mash_lmo1991.txt: veridical-correlation = 0.9661692830637364 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1992_db.csv and mash_lmo1992.txt: veridical-correlation = 0.9525571445596188 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1993_db.csv and mash_lmo1993.txt: veridical-correlation = 0.9848148838636669 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1994_db.csv and mash_lmo1994.txt: veridical-correlation = 0.9743396590734615 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1995_db.csv and mash_lmo1995.txt: veridical-correlation = 0.960512661337894 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1996_db.csv and mash_lmo1996.txt: veridical-correlation = 0.9845753014066908 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1997_db.csv and mash_lmo1997.txt: veridical-correlation = 0.9645229185183097 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1998_db.csv and mash_lmo1998.txt: veridical-correlation = 0.9908405771646686 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo1999_db.csv and mash_lmo1999.txt: veridical-correlation = 0.6581782636754787 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2000_db.csv and mash_lmo2000.txt: veridical-correlation = 0.9889415962916108 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2002_db.csv and mash_lmo2002.txt: veridical-correlation = 0.9764372627808166 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2003_db.csv and mash_lmo2003.txt: veridical-correlation = 0.9761806542652708 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2004_db.csv and mash_lmo2004.txt: veridical-correlation = 0.6680711418788309 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2006_db.csv and mash_lmo2006.txt: veridical-correlation = 0.9789221800723867 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2007_db.csv and mash_lmo2007.txt: veridical-correlation = 0.9925169901346252 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2008_db.csv and mash_lmo2008.txt: veridical-correlation = 0.9856836273843862 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2009_db.csv and mash_lmo2009.txt: veridical-correlation = 0.9903744170067703 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2010_db.csv and mash_lmo2010.txt: veridical-correlation = 0.9826517498662338 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2012_db.csv and mash_lmo2012.txt: veridical-correlation = 0.9714345019376982 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2013_db.csv and mash_lmo2013.txt: veridical-correlation = 0.970123554676852 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2014_db.csv and mash_lmo2014.txt: veridical-correlation = 0.9761922638196117 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2015_db.csv and mash_lmo2015.txt: veridical-correlation = 0.987050438219415 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2016_db.csv and mash_lmo2016.txt: veridical-correlation = 0.8425746576242468 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2017_db.csv and mash_lmo2017.txt: veridical-correlation = 0.966813822138646 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2019_db.csv and mash_lmo2019.txt: veridical-correlation = 0.979117081657158 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2020_db.csv and mash_lmo2020.txt: veridical-correlation = 0.96955399677799 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2028_db.csv and mash_lmo2028.txt: veridical-correlation = 0.9626670411572024 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2029_db.csv and mash_lmo2029.txt: veridical-correlation = 0.9290489109297975 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2030_db.csv and mash_lmo2030.txt: veridical-correlation = 0.98183897792448 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2031_db.csv and mash_lmo2031.txt: veridical-correlation = 0.9737796984635985 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2034_db.csv and mash_lmo2034.txt: veridical-correlation = 0.956601720185061 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2035_db.csv and mash_lmo2035.txt: veridical-correlation = 0.9610654758288493 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2036_db.csv and mash_lmo2036.txt: veridical-correlation = 0.980199615426919 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2037_db.csv and mash_lmo2037.txt: veridical-correlation = 0.9786661715423788 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2040_db.csv and mash_lmo2040.txt: veridical-correlation = 0.96754775932505 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2041_db.csv and mash_lmo2041.txt: veridical-correlation = 0.9842096918303019 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2042_db.csv and mash_lmo2042.txt: veridical-correlation = 0.9589228895032058 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2047_db.csv and mash_lmo2047.txt: veridical-correlation = 0.8187417518162952 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2048_db.csv and mash_lmo2048.txt: veridical-correlation = 0.9820871956135496 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2049_db.csv and mash_lmo2049.txt: veridical-correlation = 0.9694192931765995 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2050_db.csv and mash_lmo2050.txt: veridical-correlation = 0.9842773827528138 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2051_db.csv and mash_lmo2051.txt: veridical-correlation = 0.9698471268711936 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2052_db.csv and mash_lmo2052.txt: veridical-correlation = 0.9756286042492295 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2053_db.csv and mash_lmo2053.txt: veridical-correlation = 0.9689418772392513 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2054_db.csv and mash_lmo2054.txt: veridical-correlation = 0.9415793904040687 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2055_db.csv and mash_lmo2055.txt: veridical-correlation = 0.9499084202475939 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2056_db.csv and mash_lmo2056.txt: veridical-correlation = 0.991674929876851 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2057_db.csv and mash_lmo2057.txt: veridical-correlation = 0.984239205758454 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2058_db.csv and mash_lmo2058.txt: veridical-correlation = 0.977902998260201 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2059_db.csv and mash_lmo2059.txt: veridical-correlation = 0.9734578992893905 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2060_db.csv and mash_lmo2060.txt: veridical-correlation = 0.6911401968712997 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2061_db.csv and mash_lmo2061.txt: veridical-correlation = 0.933593261381366 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2062_db.csv and mash_lmo2062.txt: veridical-correlation = 0.9768191575173368 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2063_db.csv and mash_lmo2063.txt: veridical-correlation = 0.6896740681816081 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2064_db.csv and mash_lmo2064.txt: veridical-correlation = 0.9151380839941909 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2065_db.csv and mash_lmo2065.txt: veridical-correlation = 0.943156173881533 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2066_db.csv and mash_lmo2066.txt: veridical-correlation = 0.7812004680806932 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2067_db.csv and mash_lmo2067.txt: veridical-correlation = 0.9657134091969559 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2068_db.csv and mash_lmo2068.txt: veridical-correlation = 0.9895220946856523 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2069_db.csv and mash_lmo2069.txt: veridical-correlation = 0.7913733322341263 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2070_db.csv and mash_lmo2070.txt: veridical-correlation = 0.9803358724145469 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2071_db.csv and mash_lmo2071.txt: veridical-correlation = 0.9412792296053895 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2072_db.csv and mash_lmo2072.txt: veridical-correlation = 0.9794522325433631 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2075_db.csv and mash_lmo2075.txt: veridical-correlation = 0.9802056429455056 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2076_db.csv and mash_lmo2076.txt: veridical-correlation = 0.965032426095958 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2083_db.csv and mash_lmo2083.txt: veridical-correlation = 0.9679119680415011 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2084_db.csv and mash_lmo2084.txt: veridical-correlation = 0.9473746899638747 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2088_db.csv and mash_lmo2088.txt: veridical-correlation = 0.9649249685725209 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2089_db.csv and mash_lmo2089.txt: veridical-correlation = 0.9842111289919427 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2090_db.csv and mash_lmo2090.txt: veridical-correlation = 0.9874470950743662 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2091_db.csv and mash_lmo2091.txt: veridical-correlation = 0.9515302613296306 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2092_db.csv and mash_lmo2092.txt: veridical-correlation = 0.9763185186766938 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2093_db.csv and mash_lmo2093.txt: veridical-correlation = 0.9290083700352759 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2094_db.csv and mash_lmo2094.txt: veridical-correlation = 0.9788883078919757 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2095_db.csv and mash_lmo2095.txt: veridical-correlation = 0.9730871041553703 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2096_db.csv and mash_lmo2096.txt: veridical-correlation = 0.9874746907337074 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2097_db.csv and mash_lmo2097.txt: veridical-correlation = 0.8566215379753102 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2098_db.csv and mash_lmo2098.txt: veridical-correlation = 0.9583447769436658 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2099_db.csv and mash_lmo2099.txt: veridical-correlation = 0.9876149812607468 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2101_db.csv and mash_lmo2101.txt: veridical-correlation = 0.9560857044356093 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2103_db.csv and mash_lmo2103.txt: veridical-correlation = 0.9745779881564318 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2104_db.csv and mash_lmo2104.txt: veridical-correlation = 0.963312376417519 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2111_db.csv and mash_lmo2111.txt: veridical-correlation = 0.947268142620702 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2112_db.csv and mash_lmo2112.txt: veridical-correlation = 0.9509156911551806 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2113_db.csv and mash_lmo2113.txt: veridical-correlation = 0.9751340735823544 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2118_db.csv and mash_lmo2118.txt: veridical-correlation = 0.9613939310802142 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2119_db.csv and mash_lmo2119.txt: veridical-correlation = 0.9717839529703485 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2120_db.csv and mash_lmo2120.txt: veridical-correlation = 0.9692068748327328 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2122_db.csv and mash_lmo2122.txt: veridical-correlation = 0.932895673786987 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2123_db.csv and mash_lmo2123.txt: veridical-correlation = 0.9579217541594727 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2124_db.csv and mash_lmo2124.txt: veridical-correlation = 0.9745316789484997 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2125_db.csv and mash_lmo2125.txt: veridical-correlation = 0.9699891889283756 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2126_db.csv and mash_lmo2126.txt: veridical-correlation = 0.987325059824688 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2130_db.csv and mash_lmo2130.txt: veridical-correlation = 0.9946374765991737 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2141_db.csv and mash_lmo2141.txt: veridical-correlation = 0.9740291236734565 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2142_db.csv and mash_lmo2142.txt: veridical-correlation = 0.9934738469312713 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2145_db.csv and mash_lmo2145.txt: veridical-correlation = 0.95539273024852 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2152_db.csv and mash_lmo2152.txt: veridical-correlation = 0.9509182221022198 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2153_db.csv and mash_lmo2153.txt: veridical-correlation = 0.9451922363556842 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2154_db.csv and mash_lmo2154.txt: veridical-correlation = 0.9855986334392564 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2155_db.csv and mash_lmo2155.txt: veridical-correlation = 0.9900780749610923 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2156_db.csv and mash_lmo2156.txt: veridical-correlation = 0.7816325129509063 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2158_db.csv and mash_lmo2158.txt: veridical-correlation = 0.917916555488686 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2159_db.csv and mash_lmo2159.txt: veridical-correlation = 0.9759123376022808 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2160_db.csv and mash_lmo2160.txt: veridical-correlation = 0.9615082929489966 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2161_db.csv and mash_lmo2161.txt: veridical-correlation = 0.9510316145790897 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2162_db.csv and mash_lmo2162.txt: veridical-correlation = 0.986886627794715 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2163_db.csv and mash_lmo2163.txt: veridical-correlation = 0.9875043863550258 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2164_db.csv and mash_lmo2164.txt: veridical-correlation = 0.9768786664872258 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2165_db.csv and mash_lmo2165.txt: veridical-correlation = 0.9735139548362746 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2166_db.csv and mash_lmo2166.txt: veridical-correlation = 0.9405994969380468 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2167_db.csv and mash_lmo2167.txt: veridical-correlation = 0.9903036369090901 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2168_db.csv and mash_lmo2168.txt: veridical-correlation = 0.9741643456243749 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2170_db.csv and mash_lmo2170.txt: veridical-correlation = 0.9797272479988374 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2171_db.csv and mash_lmo2171.txt: veridical-correlation = 0.9820975792825807 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2172_db.csv and mash_lmo2172.txt: veridical-correlation = 0.9712568092904442 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2173_db.csv and mash_lmo2173.txt: veridical-correlation = 0.9866784830771158 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2188_db.csv and mash_lmo2188.txt: veridical-correlation = 0.9911088735240294 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2190_db.csv and mash_lmo2190.txt: veridical-correlation = 0.9735931508668109 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2191_db.csv and mash_lmo2191.txt: veridical-correlation = 0.972495302653902 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2192_db.csv and mash_lmo2192.txt: veridical-correlation = 0.9651272993313257 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2193_db.csv and mash_lmo2193.txt: veridical-correlation = 0.9899720014586272 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2194_db.csv and mash_lmo2194.txt: veridical-correlation = 0.9357514587367208 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2195_db.csv and mash_lmo2195.txt: veridical-correlation = 0.9843381303897608 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2196_db.csv and mash_lmo2196.txt: veridical-correlation = 0.9929687945977343 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2198_db.csv and mash_lmo2198.txt: veridical-correlation = 0.9833398399427034 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2200_db.csv and mash_lmo2200.txt: veridical-correlation = 0.6243069007095345 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2201_db.csv and mash_lmo2201.txt: veridical-correlation = 0.9932704492037328 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2202_db.csv and mash_lmo2202.txt: veridical-correlation = 0.9937723978393403 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2203_db.csv and mash_lmo2203.txt: veridical-correlation = 0.9953967478713946 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2204_db.csv and mash_lmo2204.txt: veridical-correlation = 0.9153857663068742 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2205_db.csv and mash_lmo2205.txt: veridical-correlation = 0.9836359087753036 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2206_db.csv and mash_lmo2206.txt: veridical-correlation = 0.991452077167312 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2207_db.csv and mash_lmo2207.txt: veridical-correlation = 0.9679751201305483 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2208_db.csv and mash_lmo2208.txt: veridical-correlation = 0.9661516325785654 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2210_db.csv and mash_lmo2210.txt: veridical-correlation = 0.9355915948845678 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2211_db.csv and mash_lmo2211.txt: veridical-correlation = 0.9713104465481006 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2212_db.csv and mash_lmo2212.txt: veridical-correlation = 0.9946213523243185 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2213_db.csv and mash_lmo2213.txt: veridical-correlation = 0.9756620658625625 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2214_db.csv and mash_lmo2214.txt: veridical-correlation = 0.9736968863680897 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2216_db.csv and mash_lmo2216.txt: veridical-correlation = 0.966699907322372 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2217_db.csv and mash_lmo2217.txt: veridical-correlation = 0.9553532837412068 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2218_db.csv and mash_lmo2218.txt: veridical-correlation = 0.9794567234043822 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2219_db.csv and mash_lmo2219.txt: veridical-correlation = 0.9780814204178543 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2220_db.csv and mash_lmo2220.txt: veridical-correlation = 0.9718736968462424 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2223_db.csv and mash_lmo2223.txt: veridical-correlation = 0.9444419730357729 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2225_db.csv and mash_lmo2225.txt: veridical-correlation = 0.894594762095679 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2227_db.csv and mash_lmo2227.txt: veridical-correlation = 0.9360340341050812 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2229_db.csv and mash_lmo2229.txt: veridical-correlation = 0.9948062224344786 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2230_db.csv and mash_lmo2230.txt: veridical-correlation = 0.9708256346189232 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2232_db.csv and mash_lmo2232.txt: veridical-correlation = 0.9745913921042033 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2233_db.csv and mash_lmo2233.txt: veridical-correlation = 0.9706772305166558 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2234_db.csv and mash_lmo2234.txt: veridical-correlation = 0.9579137871260529 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2235_db.csv and mash_lmo2235.txt: veridical-correlation = 0.9634870626988763 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2236_db.csv and mash_lmo2236.txt: veridical-correlation = 0.9638458197296629 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2237_db.csv and mash_lmo2237.txt: veridical-correlation = 0.9898737182482179 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2238_db.csv and mash_lmo2238.txt: veridical-correlation = 0.9924170440073768 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2244_db.csv and mash_lmo2244.txt: veridical-correlation = 0.9864084014631623 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2246_db.csv and mash_lmo2246.txt: veridical-correlation = 0.6859293112338521 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2247_db.csv and mash_lmo2247.txt: veridical-correlation = 0.9785496776969616 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2248_db.csv and mash_lmo2248.txt: veridical-correlation = 0.9874382388410756 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2249_db.csv and mash_lmo2249.txt: veridical-correlation = 0.9739866372037632 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2250_db.csv and mash_lmo2250.txt: veridical-correlation = 0.9861877664890016 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2252_db.csv and mash_lmo2252.txt: veridical-correlation = 0.9829340151758053 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2253_db.csv and mash_lmo2253.txt: veridical-correlation = 0.976963765987717 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2254_db.csv and mash_lmo2254.txt: veridical-correlation = 0.9756850149816785 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2255_db.csv and mash_lmo2255.txt: veridical-correlation = 0.5183190238688892 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2256_db.csv and mash_lmo2256.txt: veridical-correlation = 0.9490041223535917 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2257_db.csv and mash_lmo2257.txt: veridical-correlation = 0.8590544587088853 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2258_db.csv and mash_lmo2258.txt: veridical-correlation = 0.5928032786866712 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2261_db.csv and mash_lmo2261.txt: veridical-correlation = 0.8604161898405511 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2262_db.csv and mash_lmo2262.txt: veridical-correlation = 0.94099788023055 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2263_db.csv and mash_lmo2263.txt: veridical-correlation = 0.9899163272901786 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2264_db.csv and mash_lmo2264.txt: veridical-correlation = 0.9508294194997736 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2265_db.csv and mash_lmo2265.txt: veridical-correlation = 0.8656815561551789 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2269_db.csv and mash_lmo2269.txt: veridical-correlation = 0.957261682985355 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2334_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2336_db.csv and mash_lmo2336.txt: veridical-correlation = 0.9693755626704927 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2337_db.csv and mash_lmo2337.txt: veridical-correlation = 0.9914378173838395 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2338_db.csv and mash_lmo2338.txt: veridical-correlation = 0.9882438269991134 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2340_db.csv and mash_lmo2340.txt: veridical-correlation = 0.9547768423488808 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2341_db.csv and mash_lmo2341.txt: veridical-correlation = 0.9847818718766211 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2345_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2346_db.csv and mash_lmo2346.txt: veridical-correlation = 0.9639377321500191 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2347_db.csv and mash_lmo2347.txt: veridical-correlation = 0.9625581793473998 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2348_db.csv and mash_lmo2348.txt: veridical-correlation = 0.9693443773136107 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2349_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2350_db.csv and mash_lmo2350.txt: veridical-correlation = 0.9773523983074803 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2351_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2352_db.csv and mash_lmo2352.txt: veridical-correlation = 0.9686520048696642 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2353_db.csv and mash_lmo2353.txt: veridical-correlation = 0.9785243216680874 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2354_db.csv and mash_lmo2354.txt: veridical-correlation = 0.6475481168861745 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2355_db.csv and mash_lmo2355.txt: veridical-correlation = 0.9932648682328417 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2358_db.csv and mash_lmo2358.txt: veridical-correlation = 0.9746418885267032 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2360_db.csv and mash_lmo2360.txt: veridical-correlation = 0.9752963687815792 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2361_db.csv and mash_lmo2361.txt: veridical-correlation = 0.9783094803272934 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2362_db.csv and mash_lmo2362.txt: veridical-correlation = 0.9848986385947254 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2363_db.csv and mash_lmo2363.txt: veridical-correlation = 0.9781498371259894 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2366_db.csv and mash_lmo2366.txt: veridical-correlation = 0.9637223953144597 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2367_db.csv and mash_lmo2367.txt: veridical-correlation = 0.9925119025681874 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2368_db.csv and mash_lmo2368.txt: veridical-correlation = 0.9748182348537652 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2369_db.csv and mash_lmo2369.txt: veridical-correlation = 0.9690454738388983 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2370_db.csv and mash_lmo2370.txt: veridical-correlation = 0.9843754175130786 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2371_db.csv and mash_lmo2371.txt: veridical-correlation = 0.973668782616909 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2372_db.csv and mash_lmo2372.txt: veridical-correlation = 0.9632412979708587 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2373_db.csv and mash_lmo2373.txt: veridical-correlation = 0.9450526597181643 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2374_db.csv and mash_lmo2374.txt: veridical-correlation = 0.9686391166911551 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2376_db.csv and mash_lmo2376.txt: veridical-correlation = 0.9797091620523837 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2378_db.csv and mash_lmo2378.txt: veridical-correlation = 0.9783691531504689 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2381_db.csv and mash_lmo2381.txt: veridical-correlation = 0.9679936641716327 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2382_db.csv and mash_lmo2382.txt: veridical-correlation = 0.9808587178909017 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2384_db.csv and mash_lmo2384.txt: veridical-correlation = 0.968724551507918 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2386_db.csv and mash_lmo2386.txt: veridical-correlation = 0.9579397607050788 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2389_db.csv and mash_lmo2389.txt: veridical-correlation = 0.9713964241047109 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2390_db.csv and mash_lmo2390.txt: veridical-correlation = 0.9900148722252936 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2391_db.csv and mash_lmo2391.txt: veridical-correlation = 0.980469791717764 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2392_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2393_db.csv and mash_lmo2393.txt: veridical-correlation = 0.9508298539272216 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2397_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2398_db.csv and mash_lmo2398.txt: veridical-correlation = 0.7288644021588931 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2399_db.csv and mash_lmo2399.txt: veridical-correlation = 0.9702008668857115 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2401_db.csv and mash_lmo2401.txt: veridical-correlation = 0.9794656155242427 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2402_db.csv and mash_lmo2402.txt: veridical-correlation = 0.9427229896961437 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2404_db.csv is not symmetric, skipped\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2405_db.csv is not symmetric, skipped\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2406_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2411_db.csv and mash_lmo2411.txt: veridical-correlation = 0.9908591366800762 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2412_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2413_db.csv and mash_lmo2413.txt: veridical-correlation = 0.9803146222862343 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2414_db.csv and mash_lmo2414.txt: veridical-correlation = 0.9826802057603755 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2415_db.csv and mash_lmo2415.txt: veridical-correlation = 0.9841080196676949 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2417_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2418_db.csv and mash_lmo2418.txt: veridical-correlation = 0.9744008227751348 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2419_db.csv and mash_lmo2419.txt: veridical-correlation = 0.9908445777906949 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2420_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2421_db.csv and mash_lmo2421.txt: veridical-correlation = 0.9730682029621859 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2423_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2425_db.csv and mash_lmo2425.txt: veridical-correlation = 0.9386593147922029 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2426_db.csv and mash_lmo2426.txt: veridical-correlation = 0.9377018376458255 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2427_db.csv and mash_lmo2427.txt: veridical-correlation = 0.9904567388746937 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2428_db.csv and mash_lmo2428.txt: veridical-correlation = 0.9916038428378643 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2429_db.csv and mash_lmo2429.txt: veridical-correlation = 0.9475543930456972 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2430_db.csv and mash_lmo2430.txt: veridical-correlation = 0.9694095955573673 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2431_db.csv and mash_lmo2431.txt: veridical-correlation = 0.9849435949354101 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2432_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2433_db.csv and mash_lmo2433.txt: veridical-correlation = 0.9811380743975837 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2434_db.csv and mash_lmo2434.txt: veridical-correlation = 0.9849938780587576 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2436_db.csv and mash_lmo2436.txt: veridical-correlation = 0.9873562296317883 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2437_db.csv and mash_lmo2437.txt: veridical-correlation = 0.8739383514217687 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2441_db.csv and mash_lmo2441.txt: veridical-correlation = 0.9861249557470086 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2448_db.csv and mash_lmo2448.txt: veridical-correlation = 0.9758393100190841 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2449_db.csv and mash_lmo2449.txt: veridical-correlation = 0.9870891444347589 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2450_db.csv and mash_lmo2450.txt: veridical-correlation = 0.9814859949640001 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2451_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2452_db.csv and mash_lmo2452.txt: veridical-correlation = 0.9709070636608894 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2453_db.csv and mash_lmo2453.txt: veridical-correlation = 0.971298856262962 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2454_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2455_db.csv and mash_lmo2455.txt: veridical-correlation = 0.983879053927522 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2456_db.csv and mash_lmo2456.txt: veridical-correlation = 0.9884149693198462 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2457_db.csv is not symmetric, skipped\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2458_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2459_db.csv and mash_lmo2459.txt: veridical-correlation = 0.9493204840563715 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2460_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2462_db.csv and mash_lmo2462.txt: veridical-correlation = 0.9760282516242261 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2465_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2468_db.csv and mash_lmo2468.txt: veridical-correlation = 0.9713082155858058 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2469_db.csv and mash_lmo2469.txt: veridical-correlation = 0.9923825940542434 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2471_db.csv and mash_lmo2471.txt: veridical-correlation = 0.9338279163962165 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2472_db.csv and mash_lmo2472.txt: veridical-correlation = 0.9836206996888073 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2473_db.csv and mash_lmo2473.txt: veridical-correlation = 0.9732423966820449 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2474_db.csv and mash_lmo2474.txt: veridical-correlation = 0.9786718046516839 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2475_db.csv and mash_lmo2475.txt: veridical-correlation = 0.9808396994079815 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2477_db.csv and mash_lmo2477.txt: veridical-correlation = 0.983877155754243 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2478_db.csv and mash_lmo2478.txt: veridical-correlation = 0.9865345908812063 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2479_db.csv and mash_lmo2479.txt: veridical-correlation = 0.9916715054937878 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2480_db.csv and mash_lmo2480.txt: veridical-correlation = 0.8112790734280486 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2481_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2482_db.csv and mash_lmo2482.txt: veridical-correlation = 0.9842667373905433 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2483_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2484_db.csv and mash_lmo2484.txt: veridical-correlation = 0.9758673623586113 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2486_db.csv and mash_lmo2486.txt: veridical-correlation = 0.9827415249812879 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2487_db.csv and mash_lmo2487.txt: veridical-correlation = 0.9749755062978833 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2488_db.csv and mash_lmo2488.txt: veridical-correlation = 0.9773504372000201 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2489_db.csv and mash_lmo2489.txt: veridical-correlation = 0.9922222984554047 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2490_db.csv and mash_lmo2490.txt: veridical-correlation = 0.7948300029145784 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2491_db.csv and mash_lmo2491.txt: veridical-correlation = 0.9830999649953194 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2492_db.csv is not symmetric, skipped\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2493_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2494_db.csv and mash_lmo2494.txt: veridical-correlation = 0.981692380508251 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2496_db.csv is not symmetric, skipped\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2497_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2498_db.csv and mash_lmo2498.txt: veridical-correlation = 0.9638226646148977 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2499_db.csv and mash_lmo2499.txt: veridical-correlation = 0.9827895527417132 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2501_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2503_db.csv and mash_lmo2503.txt: veridical-correlation = 0.9875464104495929 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2504_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2505_db.csv and mash_lmo2505.txt: veridical-correlation = 0.9878229283824032 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2506_db.csv and mash_lmo2506.txt: veridical-correlation = 0.9891012560257234 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2509_db.csv and mash_lmo2509.txt: veridical-correlation = 0.9766947031395619 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2510_db.csv and mash_lmo2510.txt: veridical-correlation = 0.9914566082749463 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2511_db.csv and mash_lmo2511.txt: veridical-correlation = 0.9665197017151497 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2514_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2515_db.csv and mash_lmo2515.txt: veridical-correlation = 0.9860894086778669 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2516_db.csv and mash_lmo2516.txt: veridical-correlation = 0.9814437762201071 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2518_db.csv and mash_lmo2518.txt: veridical-correlation = 0.992389683107677 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2524_db.csv and mash_lmo2524.txt: veridical-correlation = 0.9700987372916668 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2525_db.csv and mash_lmo2525.txt: veridical-correlation = 0.9938206391078226 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2526_db.csv and mash_lmo2526.txt: veridical-correlation = 0.9882032179702851 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2527_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2528_db.csv and mash_lmo2528.txt: veridical-correlation = 0.9374518663189173 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2529_db.csv and mash_lmo2529.txt: veridical-correlation = 0.9935802258600192 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2530_db.csv and mash_lmo2530.txt: veridical-correlation = 0.9717704614616481 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2531_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2532_db.csv and mash_lmo2532.txt: veridical-correlation = 0.9718837965741772 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2533_db.csv and mash_lmo2533.txt: veridical-correlation = 0.9436730982314138 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2534_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2535_db.csv and mash_lmo2535.txt: veridical-correlation = 0.9585397145999438 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2536_db.csv and mash_lmo2536.txt: veridical-correlation = 0.9454679895807705 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2536a_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2537_db.csv and mash_lmo2537.txt: veridical-correlation = 0.9517125158098595 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2538_db.csv and mash_lmo2538.txt: veridical-correlation = 0.9781257315496792 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2539_db.csv and mash_lmo2539.txt: veridical-correlation = 0.9915982324438455 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2543_db.csv and mash_lmo2543.txt: veridical-correlation = 0.964690182710379 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2544_db.csv and mash_lmo2544.txt: veridical-correlation = 0.9638019962407633 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2545_db.csv is not symmetric, skipped\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2546_db.csv is not symmetric, skipped\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2547_db.csv is not symmetric, skipped\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2548_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2555_db.csv and mash_lmo2555.txt: veridical-correlation = 0.9916224531901586 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2556_db.csv and mash_lmo2556.txt: veridical-correlation = 0.984754512127369 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2559_db.csv and mash_lmo2559.txt: veridical-correlation = 0.9860718114080526 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2560_db.csv and mash_lmo2560.txt: veridical-correlation = 0.9592975613739367 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2561_db.csv and mash_lmo2561.txt: veridical-correlation = 0.9782522034999471 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2562_db.csv and mash_lmo2562.txt: veridical-correlation = 0.9699624236553963 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2564_db.csv and mash_lmo2564.txt: veridical-correlation = 0.6914258217622531 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2566_db.csv and mash_lmo2566.txt: veridical-correlation = 0.9274860599697066 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2567_db.csv and mash_lmo2567.txt: veridical-correlation = 0.8059299042338737 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2568_db.csv and mash_lmo2568.txt: veridical-correlation = 0.9554420445469309 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2569_db.csv and mash_lmo2569.txt: veridical-correlation = 0.9932121251280315 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2570_db.csv and mash_lmo2570.txt: veridical-correlation = 0.9781884179541108 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2571_db.csv and mash_lmo2571.txt: veridical-correlation = 0.9751270105043702 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2572_db.csv and mash_lmo2572.txt: veridical-correlation = 0.8204115428629353 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2574_db.csv and mash_lmo2574.txt: veridical-correlation = 0.8457174472951149 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2575_db.csv and mash_lmo2575.txt: veridical-correlation = 0.9880559471215442 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2581_db.csv and mash_lmo2581.txt: veridical-correlation = 0.9724085157308343 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2582_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2583_db.csv and mash_lmo2583.txt: veridical-correlation = 0.8279732622048582 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2584_db.csv and mash_lmo2584.txt: veridical-correlation = 0.9643333310061698 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2585_db.csv and mash_lmo2585.txt: veridical-correlation = 0.8988889845945166 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2586_db.csv and mash_lmo2586.txt: veridical-correlation = 0.9732227580702291 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2588_db.csv and mash_lmo2588.txt: veridical-correlation = 0.9800575227767604 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2596_db.csv and mash_lmo2596.txt: veridical-correlation = 0.9604337818192388 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2597_db.csv and mash_lmo2597.txt: veridical-correlation = 0.9203463003138902 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2598_db.csv and mash_lmo2598.txt: veridical-correlation = 0.9666973221298155 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2599_db.csv and mash_lmo2599.txt: veridical-correlation = 0.9687676102325073 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2600_db.csv and mash_lmo2600.txt: veridical-correlation = 0.9739742430264307 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2605_db.csv and mash_lmo2605.txt: veridical-correlation = 0.8408624230118333 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2606_db.csv and mash_lmo2606.txt: veridical-correlation = 0.9901310171854344 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2607_db.csv and mash_lmo2607.txt: veridical-correlation = 0.9735376081058817 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2608_db.csv and mash_lmo2608.txt: veridical-correlation = 0.9916938200579888 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2609_db.csv and mash_lmo2609.txt: veridical-correlation = 0.5134366207034569 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2610_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2611_db.csv and mash_lmo2611.txt: veridical-correlation = 0.9745094748514809 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2612_db.csv and mash_lmo2612.txt: veridical-correlation = 0.9874884241901164 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2613_db.csv and mash_lmo2613.txt: veridical-correlation = 0.9500782612680276 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2614_db.csv and mash_lmo2614.txt: veridical-correlation = 0.9634697118810209 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2615_db.csv is not symmetric, skipped\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2616_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2617_db.csv and mash_lmo2617.txt: veridical-correlation = 0.9788651034464056 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2618_db.csv and mash_lmo2618.txt: veridical-correlation = 0.9738797046872558 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2619_db.csv and mash_lmo2619.txt: veridical-correlation = 0.9109539488144358 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2620_db.csv and mash_lmo2620.txt: veridical-correlation = 0.8042995291449276 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2621_db.csv and mash_lmo2621.txt: veridical-correlation = 0.9801958068051573 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2622_db.csv and mash_lmo2622.txt: veridical-correlation = 0.9825932032616854 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2623_db.csv and mash_lmo2623.txt: veridical-correlation = 0.7653334188095477 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2625_db.csv and mash_lmo2625.txt: veridical-correlation = 0.9896151757123166 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2626_db.csv is not symmetric, skipped\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2627_db.csv is not symmetric, skipped\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2628_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2629_db.csv and mash_lmo2629.txt: veridical-correlation = 0.9939430653728878 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2630_db.csv is not symmetric, skipped\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2631_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2632_db.csv and mash_lmo2632.txt: veridical-correlation = 0.9908555414297194 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2633_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2634_db.csv and mash_lmo2634.txt: veridical-correlation = 0.8120032703670521 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2636_db.csv and mash_lmo2636.txt: veridical-correlation = 0.9835375381393783 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2637_db.csv and mash_lmo2637.txt: veridical-correlation = 0.9901391726159948 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2638_db.csv and mash_lmo2638.txt: veridical-correlation = 0.9943092641202956 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2639_db.csv and mash_lmo2639.txt: veridical-correlation = 0.982622518066796 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2640_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2641_db.csv and mash_lmo2641.txt: veridical-correlation = 0.9695887979404195 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2642_db.csv and mash_lmo2642.txt: veridical-correlation = 0.9686927851379559 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2643_db.csv and mash_lmo2643.txt: veridical-correlation = 0.9545678395793364 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2644a_db.csv and mash_lmo2644a.txt: veridical-correlation = 0.9652897416962875 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2648_db.csv and mash_lmo2648.txt: veridical-correlation = 0.9552210445671377 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2649_db.csv and mash_lmo2649.txt: veridical-correlation = 0.9812549813567766 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2650_db.csv and mash_lmo2650.txt: veridical-correlation = 0.6984450822527032 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2651_db.csv and mash_lmo2651.txt: veridical-correlation = 0.9442928276448644 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2653_db.csv and mash_lmo2653.txt: veridical-correlation = 0.9798943634131384 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2654_db.csv and mash_lmo2654.txt: veridical-correlation = 0.993807293638317 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2655_db.csv and mash_lmo2655.txt: veridical-correlation = 0.9014686111630174 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2656_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2657_db.csv and mash_lmo2657.txt: veridical-correlation = 0.9811098375763699 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2658_db.csv and mash_lmo2658.txt: veridical-correlation = 0.9576709424683145 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2659_db.csv and mash_lmo2659.txt: veridical-correlation = 0.9181337054534693 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2660_db.csv and mash_lmo2660.txt: veridical-correlation = 0.9865292575280632 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2661_db.csv and mash_lmo2661.txt: veridical-correlation = 0.9692067313924722 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2662_db.csv and mash_lmo2662.txt: veridical-correlation = 0.944320545687006 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2663_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2664_db.csv and mash_lmo2664.txt: veridical-correlation = 0.9724409570141622 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2665_db.csv and mash_lmo2665.txt: veridical-correlation = 0.9960417826205324 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2666_db.csv and mash_lmo2666.txt: veridical-correlation = 0.9333986895461587 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2667_db.csv and mash_lmo2667.txt: veridical-correlation = 0.9798952371029033 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2668_db.csv and mash_lmo2668.txt: veridical-correlation = 0.9968972160591886 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2669_db.csv and mash_lmo2669.txt: veridical-correlation = 0.9701314637607384 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2670_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2673_db.csv and mash_lmo2673.txt: veridical-correlation = 0.9685603398598858 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2674_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2677_db.csv and mash_lmo2677.txt: veridical-correlation = 0.9660590581385778 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2678_db.csv and mash_lmo2678.txt: veridical-correlation = 0.9594052385808421 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2680_db.csv and mash_lmo2680.txt: veridical-correlation = 0.9843406594477687 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2681_db.csv and mash_lmo2681.txt: veridical-correlation = 0.9778867671702661 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2682_db.csv and mash_lmo2682.txt: veridical-correlation = 0.9688378658690487 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2683_db.csv and mash_lmo2683.txt: veridical-correlation = 0.9346886705135106 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2685_db.csv and mash_lmo2685.txt: veridical-correlation = 0.921879470548221 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2689a_db.csv and mash_lmo2689a.txt: veridical-correlation = 0.8153986931745925 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2692_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2693_db.csv and mash_lmo2693.txt: veridical-correlation = 0.9593025267293839 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2694_db.csv and mash_lmo2694.txt: veridical-correlation = 0.9722240798343658 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2695_db.csv and mash_lmo2695.txt: veridical-correlation = 0.9800214227370141 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2697_db.csv and mash_lmo2697.txt: veridical-correlation = 0.6582614695364564 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2698_db.csv and mash_lmo2698.txt: veridical-correlation = 0.9282702387102818 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2699_db.csv and mash_lmo2699.txt: veridical-correlation = 0.9636670641098626 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2700_db.csv and mash_lmo2700.txt: veridical-correlation = 0.9715652741241306 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2701_db.csv and mash_lmo2701.txt: veridical-correlation = 0.9543734274933168 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2702_db.csv and mash_lmo2702.txt: veridical-correlation = 0.9827209610756834 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2703_db.csv and mash_lmo2703.txt: veridical-correlation = 0.9618645240211211 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2704_db.csv and mash_lmo2704.txt: veridical-correlation = 0.9643427628595497 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2706_db.csv and mash_lmo2706.txt: veridical-correlation = 0.9373552534830137 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2707_db.csv and mash_lmo2707.txt: veridical-correlation = 0.9034985965132082 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2708_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2710_db.csv and mash_lmo2710.txt: veridical-correlation = 0.9802060244248793 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2711_db.csv and mash_lmo2711.txt: veridical-correlation = 0.9245237666942778 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2712_db.csv and mash_lmo2712.txt: veridical-correlation = 0.9846874028933367 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2713_db.csv and mash_lmo2713.txt: veridical-correlation = 0.9647091907014235 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2716_db.csv and mash_lmo2716.txt: veridical-correlation = 0.9752759503267249 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2718_db.csv and mash_lmo2718.txt: veridical-correlation = 0.9924217139418476 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2719_db.csv and mash_lmo2719.txt: veridical-correlation = 0.9547296540756849 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2720_db.csv and mash_lmo2720.txt: veridical-correlation = 0.9756821660421964 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2721_db.csv and mash_lmo2721.txt: veridical-correlation = 0.9465905892000862 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2722_db.csv and mash_lmo2722.txt: veridical-correlation = 0.9396898947850492 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2723_db.csv and mash_lmo2723.txt: veridical-correlation = 0.9551438953551135 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2724_db.csv and mash_lmo2724.txt: veridical-correlation = 0.9146493355875396 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2725_db.csv and mash_lmo2725.txt: veridical-correlation = 0.9793435780813186 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2726_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2727_db.csv and mash_lmo2727.txt: veridical-correlation = 0.9786807572346528 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2730_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2738_db.csv and mash_lmo2738.txt: veridical-correlation = 0.9895443380320954 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2741_db.csv and mash_lmo2741.txt: veridical-correlation = 0.9813420939783437 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2742_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2743_db.csv and mash_lmo2743.txt: veridical-correlation = 0.9751784520660857 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2744_db.csv and mash_lmo2744.txt: veridical-correlation = 0.9877642883144281 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2745_db.csv and mash_lmo2745.txt: veridical-correlation = 0.9902056764155096 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2746_db.csv and mash_lmo2746.txt: veridical-correlation = 0.8999620095341763 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2747_db.csv and mash_lmo2747.txt: veridical-correlation = 0.9545355388943894 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2751_db.csv and mash_lmo2751.txt: veridical-correlation = 0.990713576116783 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2752_db.csv and mash_lmo2752.txt: veridical-correlation = 0.9854949665634158 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2754_db.csv and mash_lmo2754.txt: veridical-correlation = 0.9880074536069239 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2755_db.csv and mash_lmo2755.txt: veridical-correlation = 0.7389867371317743 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2756_db.csv and mash_lmo2756.txt: veridical-correlation = 0.9801934449792533 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2757_db.csv and mash_lmo2757.txt: veridical-correlation = 0.9917744648842087 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2758_db.csv and mash_lmo2758.txt: veridical-correlation = 0.9781114038635053 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2759_db.csv and mash_lmo2759.txt: veridical-correlation = 0.9439945516102874 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2761_db.csv is not symmetric, skipped\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2762_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2763_db.csv and mash_lmo2763.txt: veridical-correlation = 0.9916200035103238 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2764_db.csv and mash_lmo2764.txt: veridical-correlation = 0.9675605409242231 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2766_db.csv and mash_lmo2766.txt: veridical-correlation = 0.9728059826145152 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2767_db.csv and mash_lmo2767.txt: veridical-correlation = 0.9633546032562417 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2769_db.csv and mash_lmo2769.txt: veridical-correlation = 0.9903059691341806 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2770_db.csv and mash_lmo2770.txt: veridical-correlation = 0.98804869303004 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2779_db.csv and mash_lmo2779.txt: veridical-correlation = 0.9725089512253702 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2785_db.csv and mash_lmo2785.txt: veridical-correlation = 0.9862568482319474 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2789_db.csv and mash_lmo2789.txt: veridical-correlation = 0.7630045698487326 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2790_db.csv and mash_lmo2790.txt: veridical-correlation = 0.981185882566048 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2792_db.csv and mash_lmo2792.txt: veridical-correlation = 0.9803003502491852 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2793_db.csv and mash_lmo2793.txt: veridical-correlation = 0.9274229911692732 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2794_db.csv and mash_lmo2794.txt: veridical-correlation = 0.9660056223144173 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2796_db.csv and mash_lmo2796.txt: veridical-correlation = 0.9636521048838761 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2797_db.csv and mash_lmo2797.txt: veridical-correlation = 0.96925998564361 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2798_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2799_db.csv and mash_lmo2799.txt: veridical-correlation = 0.9840057116243229 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2800_db.csv and mash_lmo2800.txt: veridical-correlation = 0.9766520092560841 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2801_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2802_db.csv and mash_lmo2802.txt: veridical-correlation = 0.9497852110297698 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2810_db.csv and mash_lmo2810.txt: veridical-correlation = 0.9844183745526496 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2819_db.csv and mash_lmo2819.txt: veridical-correlation = 0.9800736400334623 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2822_db.csv and mash_lmo2822.txt: veridical-correlation = 0.8983854237770272 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2823_db.csv and mash_lmo2823.txt: veridical-correlation = 0.9748719101268142 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2824_db.csv and mash_lmo2824.txt: veridical-correlation = 0.9888808864115785 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2826_db.csv and mash_lmo2826.txt: veridical-correlation = 0.9485302567058566 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2829_db.csv and mash_lmo2829.txt: veridical-correlation = 0.9649011603905193 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2830_db.csv and mash_lmo2830.txt: veridical-correlation = 0.9532466639517093 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2831_db.csv and mash_lmo2831.txt: veridical-correlation = 0.9476299340944822 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2832_db.csv and mash_lmo2832.txt: veridical-correlation = 0.9770715472299697 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2833_db.csv and mash_lmo2833.txt: veridical-correlation = 0.9850661379124609 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2834_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2835_db.csv and mash_lmo2835.txt: veridical-correlation = 0.9724686711071843 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2836_db.csv and mash_lmo2836.txt: veridical-correlation = 0.9784094029570778 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2838_db.csv and mash_lmo2838.txt: veridical-correlation = 0.9829668024805531 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2839_db.csv and mash_lmo2839.txt: veridical-correlation = 0.9855213530483747 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2840_db.csv and mash_lmo2840.txt: veridical-correlation = 0.9831872899398854 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2842_db.csv and mash_lmo2842.txt: veridical-correlation = 0.9523050825972756 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2853_db.csv and mash_lmo2853.txt: veridical-correlation = 0.9814152516113063 | p-value = 0.01\n", + "Results from mantel test between pident_matrix_lmo2854_db.csv and mash_lmo2854.txt: veridical-correlation = 0.9629433230504976 | p-value = 0.01\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2855_db.csv is not symmetric, skipped\n", + "/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo2856_db.csv is not symmetric, skipped\n", + "Results from mantel test between pident_matrix_lmo2857_db.csv and mash_lmo2857.txt: veridical-correlation = 0.9735985373097789 | p-value = 0.01\n" + ] + } + ], + "source": [ + "blast_paths = sorted(glob.glob(\"/home/pmata/pruebas_tests/distance_matrix/blast/*.csv\"))\n", + "mash_paths = sorted(glob.glob(\"/home/pmata/pruebas_tests/distance_matrix/mash/mash*.txt\"))\n", + "mantel_summary = mantel_tester(blast_paths, mash_paths, pval=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 272, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "with open(\"mantel_test_pval001.json\", \"w\") as f:\n", + " json.dump(mantel_summary, f)" + ] + }, + { + "cell_type": "code", + "execution_count": 273, + "metadata": {}, + "outputs": [], + "source": [ + "with open(\"mantel_test_pval001.json\", \"r\") as f:\n", + " mantel_pval001 = json.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 275, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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OVFFRkcrKyjRmzBg5nU4VFxcrNTVVZWVlRhyAxoHl4AGYqrS0VE6nUxUVFReN8fHxUXFxsfz9/U3MDEB9Yzl4ANeldevWGYXIxXbtraio0Lp160zPDYA1KEYAmOqHH36QdH4zvAt7RyoqKhQaGuoRB8D7MWcEgKkqd+vNzc1VWFiYHnjgARUWFqpJkyb6xz/+YUxgvdSuvgC8Cz0jAEzVokULSZK/v7/279+vhIQENW3aVAkJCdq/f78xT6QyDoD3o2cEgKkq954pLS1Vy5Ytjd1558yZI6fTqdLSUiNu3LhxluUJwDz0jAAwVdUb+CoLkZrOG8CNfgDqCcUIAFNV3QDvYnfTXBgHwLtRjAAwVadOnYzjmu6mqSkOgHejGAFgqs8//9w49vPzU7du3dSxY0d169ZNfn5+NcYB8G5MYAVgqoMHD0qSAgICdO7cOW3dutWjvfJ6ZRwA70cxAsAS586dU0hIiMLDw3X8+HG1bNlSR48eZX0RoBGiGAFgqjZt2hjHeXl5RvFx4YqrVeMAeDfmjAAwVUFBQb3GAWj4KEYAmKq264ewzgjQeFCMADBV1eEYp9Pp0Vb1nI3ygMaDYgSAqY4fPy7p/F0zwcHBHm0hISEKCAjwiAPg/ZjACsBUNptN0vm7aS7s/fj++++Nhc8q4wB4P3pGAJgqMTHROL7UCqxV4wB4N5u7AcwSKygoUPPmzXX69GkFBQVZnQ6Aq1BcXKzAwEBJ51dgve2223Tu3DkFBATom2++UVlZmSSpqKio2pwSAA1LbX+/GaYBYKp169YZx2VlZdVWYK0aFx8fb1JWAKx0RcM08+bNU1RUlAICAhQbG6tNmzZdMn7u3Lnq0KGDnE6nIiMj9dRTT+ncuXNXlDCAhi0rK6te4wA0fHUuRhYvXqzk5GRNmzZNX375pbp27aohQ4YoNze3xvh//OMfeu655zRt2jTt2LFDb7/9thYvXqzf/OY3V508gIancl5Ir169dPbsWU2cOFHdunXTxIkTdfbsWcXGxnrEAfB+dR6mmTNnjiZMmKDx48dLkhYsWKDly5dr4cKFeu6556rFr1u3Tvfcc48eeOABSVJUVJTuv/9+bdy48SpTB9AQ3XjjjZKko0ePqlOnTjp06JAkaevWrUpLSzPuoqmMA+D96lSMlJaWasuWLZoyZYpxzcfHRwMHDtT69etrfEzv3r3197//XZs2bdJdd92l7777TmlpaXrooYcu+jolJSUqKSkxziuXhS4rKzMmtwFomCrXFjlw4EC1tsrCpDKOzzvQsNX2M1ynYiQ/P18ul0thYWEe18PCwrRz584aH/PAAw8oPz9fffr0kdvtVnl5uSZOnHjJYZqZM2dq+vTp1a6np6cbs/ABNEwHDx6sdVxaWto1zgbAtVRUVFSruGt+N01WVpZmzJihN998U7Gxsdq7d6+efPJJ/fa3v9WLL75Y42OmTJmi5ORk47ygoECRkZEaPHgwt/YCDZzdbjeOnU6niouLazzv0aOHhgwZYnp+AOpPbTe8rFMxEhwcLLvdrpycHI/rOTk5atWqVY2PefHFF/XQQw/pkUcekSR17txZhYWFevTRR/X888/Lx6f6HFqHwyGHw1Htup+fn/z8/OqSMoDrzPvvv28cX7jMUdXz999/X8OGDTMtLwD1r7a/2XW6m8bf3189e/ZUZmamca2iokKZmZnq1atXjY8pKiqqVnBU/suoAay3BqCeVZ0rcuGS71XPa5pTAsA71fnW3uTkZP35z3/W3/72N+3YsUOTJk1SYWGhcXfN2LFjPSa4Dh8+XPPnz9f777+v/fv3KyMjQy+++KKGDx/u0V0LoHG46aabJNW8UV5wcLCxUV5lHADvV+c5I2PGjFFeXp6mTp2qY8eOqVu3blqxYoUxqfXQoUMePSEvvPCCbDabXnjhBf3www8KCQnR8OHD9corr9TfuwDQYHTv3l3vvfeezp07p++//96j7fvvvzd6TLt3725FegAswN40AEy1aNEijR079rJx//u//3vJJQAAXP9q+/vNrr0ATHWx1ZqvNA5Aw0cxAsBUJ06ckCSFh4dXm9zu4+Oj8PBwjzgA3o9iBICpKguQo0eP1theeb2m2/4BeCc+7QBM1bdvX+PY39/fo63q+kJV4wB4t2u+AisAVFV1LZEBAwYoJiZGu3fvVvv27bVv3z59+umn1eIAeDeKEQCmWrNmjXG8YsUK41be9PR0jwJkzZo1GjRokOn5ATAfwzQAAMBS9IwAMFXVuSA/+tGP1K5dO+3atUsdOnTQ3r17jWEa5owAjQfFCADLZGVlGcVHenq6nE6nxRkBsALDNABMlZ2dbRyXlJR4tJWWltYYB8C7UYwAMFVFRYUkqX379mrTpo1HW5s2bdS+fXuPOADej2EaAKZq2bKlJOnkyZPKz8/3aDt06JCxk29lHADvRzECwFSVO3zn5eVVa3O73cb1yjgA3o9hGgCmCgkJqdc4AA0fxQgAU3399dfG8YWrrFY9rxoHwLtRjAAw1dq1a43jkJAQjR49Wvfee69Gjx7t0RtSNQ6Ad2POCABTFRYWSpKaN2+u48ePKyUlxWiz2+1q3ry5Tp8+bcQB8H70jAAwVWhoqCTp9OnT8vPz82jz8/PT6dOnPeIAeD96RgCY6qabbjKOXS6Xfv3rXysqKkoHDhzQ3Llza4wD4N0oRgCY6sYbbzSOy8rK9OqrrxrnVSewVo0D4N0YpgFgqlOnThnHAQEBHm0Oh6PGOADejWIEgKl8fP79tXOpW3urxgHwbnzaAZiqf//+kqSOHTtWW2W1VatW6tixo0ccAO9nc7vdbquTuJyCggLjdr+goCCr0wFwFVwul8LDw5WXl6f77rtPgwcP1p49e3TLLbcoPT1dy5cvV2hoqI4cOSK73W51ugCuQm1/v5nACsBUdrtdCxYs0KhRo7Ry5UotX77caAsMDJQkzZ8/n0IEaEQYpgFguqSkJC1ZsqTa/jMhISFasmSJkpKSLMoMgBUoRgBY5sJJqhdOaAXQOFCMADBdamqqRo8erc6dOys7O1vvvfeesrOz1blzZ40ePVqpqalWpwjARBQjAEzlcrn09NNPa9iwYfrggw+0ceNGLVq0SBs3btQHH3ygYcOGafLkyXK5XFanCsAkFCMATJWdna0DBw4oKChITZs21eTJk5WWlqbJkyeradOmatasmfbv36/s7GyrUwVgEooRAKY6evSoJOndd9/VhSsLuN1u/eMf//CIA+D9KEYAmKply5bGcUJCgseckYSEhBrjAHg3ihEApvr6668lSc2aNdNHH32k2NhYOZ1OxcbG6qOPPlKzZs084gB4P4oRAKZat26dJOnMmTNKSkrShg0bVFxcrA0bNigpKUlnzpzxiAPg/ViBFYCpKns+HnnkEWVkZCguLs5oi4qK0sMPP6yFCxcacQC8Hz0jAEz10EMPSZLee++9am1ut1uLFy/2iAPg/dgoD4CpXC6XgoKCVFRUdNGYwMBAFRQUsD8N0MDV9vebnhEApvP1vfQI8eXaAXgXihEApsrKylJBQYEkyel0erRVnhcUFCgrK8vs1ABYhGIEgKlWrlwpSWrfvn2Nu/a2b9/eIw6A96MYAWCqQ4cOSZJ2796trl27eix61rVrV+3evdsjDoD3Y2AWgKnatGkjSWrRooVSU1Pldrt1/PhxxcbGKjU1VSEhITp16pQRB8D70TMCwFTBwcGSpJMnT2rkyJEei56NHDlSp06d8ogD4P3oGQFgqlatWhnHn332mZYtW2acV53QWjUOgHejZwSAqSIiIozjc+fOebQVFxfXGAfAu1GMADBV3759jbtobDabR1vleWhoqPr27Wt6bgCswTANANOVlJRIOj8vpF+/fjp58qRatGih1atXKy8vr1qPCQDvRjECwFSVi55FRETo2LFjSklJMdp8fX0VERGhH374QVlZWYqPj7cwUwBmoRgBYKrKlVWPHDmi++67T4MGDdKePXt0yy23KCMjQ8uXLzfiKEaAxoFiBICpKioqJEl33323li5dKpfLpbS0NCUkJOjxxx/XPffcow0bNhhxALwfE1gBmOrGG2+UJBUWFtbYXnm9Mg6A96MYAWCqyvVD/vWvfykxMdFj0bPExERt27bNIw6A92OYBoCpqq4fkpmZ6bHoWWBgYI1xALwbxQgAU/Xt21dRUVEKDg5Wbm6ux4Z4ISEhCgkJ0fHjx1lnBGhEKEYAmMput2v27NkaNWpUtbaDBw/q4MGDWrJkiex2uwXZAbACc0YAmG7Dhg2SJB8fz6+gygKksh1A42Bzu91uq5O4nIKCAjVv3lynT59WUFCQ1ekAuAqlpaVq0qSJWrZsqYMHDyo7O1uffvqphg4dqr59+6pt27Y6fvy4CgsL5e/vb3W6AK5CbX+/r6hnZN68eYqKilJAQIBiY2O1adOmi8b2799fNput2p/77rvvSl4aQAP35ptvqry8XL/73e/k6+s5Uuzr66uXX35Z5eXlevPNNy3KEIDZ6lyMLF68WMnJyZo2bZq+/PJLde3aVUOGDFFubm6N8ampqTp69KjxZ/v27bLb7frJT35y1ckDaHj27dsn6fymeO3atdOgQYM0Z84cDRo0SO3atTOGbirjAHi/Ok9gnTNnjiZMmKDx48dLkhYsWKDly5dr4cKFeu6556rFX7hw0fvvv6/AwECKEaCRiomJkSRNmDBBCQkJGj58uHbt2qUOHTrou+++06OPPuoRB8D71WnOSGlpqQIDA5WSkqIRI0YY18eNG6dTp05p6dKll32Ozp07q1evXvrTn/500ZiSkhJjV0/p/JhTZGSk8vPzmTMCNHDFxcVq3ry57Ha73G63x7LvPj4+stlscrlcOn36tJxOp4WZArhaBQUFCg4OvuyckTr1jOTn58vlciksLMzjelhYmHbu3HnZx2/atEnbt2/X22+/fcm4mTNnavr06dWup6eneyyKBKDhqVxh1eVyyWazqX///vrxj3+sTz75RKtXrzaKkz/84Q/q3LmzlakCuEpFRUW1ijN1nZG3335bnTt31l133XXJuClTpig5Odk4r+wZGTx4MD0jQAN34sQJSZLT6VRxcbGysrKMnXxtNptxPSIiQgkJCRZmCuBqFRQU1CquTsVIcHCw7Ha7cnJyPK7n5ORcdh+JwsJCvf/++3r55Zcv+zoOh0MOh6PadT8/P/n5+dUlZQDXmS1btkiSunTpoi+++EIul8to8/Hx0e23367Nmzdry5Ytxtw0AA1TbX+z63Q3jb+/v3r27KnMzEzjWkVFhTIzM9WrV69LPvbDDz9USUmJfvazn9XlJQF4mcppahs3bvSYLyKd/z7ZvHmzRxwA71fnYZrk5GSNGzdOd9xxh+666y7NnTtXhYWFxr9gxo4dq4iICM2cOdPjcW+//bZGjBihli1b1k/mABqk6Oho4zgkJETTp0+Xw+FQSUmJpk2bZiwTUDUOgHerczEyZswY5eXlaerUqTp27Ji6deumFStWGJNaDx06VG2J5127dunzzz9Xenp6/WQNoMGq7A3x8fGR0+nUpEmTjLaoqCj5+PiooqKiWq8JAO/FcvAATDVy5Eh9/PHHks5PWK36FVT1fMSIEfroo4+sSBFAPbmmy8EDwJVq2rSpcXzhv4WqnleNA+DdKEYAmOqBBx6QdH6H3jZt2ni0tWnTxti5tzIOgPczdZ0RAKjcidflcik3N1cDBgyQy+WS3W7X2rVrjVt92bEXaDwoRgCY6tixY8ZxaWmpVq1addk4AN6NYRoApsrLyzOObTabR1vV86pxALwbxQgAU7Vo0UKS5Ovrq4iICI+2iIgI+fr6esQB8H4UIwBMVbnCanl5ucrKyjR//nwtXLhQ8+fPV1lZmcrLyz3iAHg/5owAMFXlBNVmzZpVW/QsOjpazZo105kzZzz2rAHg3ShGAJiq8tbdM2fOKC4uTsOGDdPu3bvVvn177d+/X8uXL/eIA+D9KEYAmCo2Nlbz5s2T0+nUihUrjB6Q9PR02e12OZ1OFRcXKzY21uJMAZiFYgSAqSIjIyVJxcXF1dpcLpdxvTIOgPdjAisAU/Xu3bvaZpoX8vHxUe/evU3KCIDVKEYAmCo7O9vYkTc0NFRxcXHq1KmT4uLiFBoaKun8zr7Z2dlWpgnARAzTADDVypUrJUmtW7dWTk6O1qxZY7TZ7Xa1bt1aR44c0cqVKxUfH29VmgBMRDECwFSHDh2SJB05ckT33XefoqOja7ybpjIOgPejGAFgqsqJqYGBgdq+fbtRfKSnp6tt27YKDAxUUVERE1iBRoRiBICpWrZsKUkqKirS4cOHPdoOHz5szCepjAPg/ZjACsBUlZNUJRmFR03nVeMAeDeKEQCmys3Nrdc4AA0fxQgAU504caJe4wA0fMwZAWCqyl15JSk4OFgPPfSQioqKFBgYqEWLFik/P79aHADvRjECwFQ7duyQJPn5+cnpdOq1114z2m666Sb5+vqqvLzciAPg/RimAWCqI0eOSJLKysqMXpBKeXl5Ro9IZRwA70cxAsBULVq0MI5LSko82kpLS2uMA+DdKEYAmOrJJ580jsPDwz3aqp5XjQPg3ShGAJjK6XQaxz/88INH2/fff19jHADvRjECwFSsMwLgQhQjAExVubLqrbfeqjZt2ni0RUZGqmPHjh5xALwfxQgAS7jdbvn4eH4F2Ww2i7IBYCXWGQFgqsrhl507dyosLEzz58+Xw+FQSUmJXnrpJR06dMgjDoD3oxgBYKrK4ZeOHTvq3LlzmjRpktEWHR2tjh07aufOnQzTAI0IwzQALBEcHKzdu3crIyNDycnJysjI0K5duxQcHGx1agBMRjECwFSVwy9r167VqFGj5HA4dOedd8rhcGjUqFFau3atRxwA70cxAsBUlQubzZgxQ9u2bVNcXJzuv/9+xcXFafv27XrllVc84gB4P4oRAKbq27evoqKitG7duhqHadavX6/o6Gj17dvX6lQBmIRiBICp7Ha7Zs+erWXLltU4TLNs2TLNmjVLdrvd6lQBmIS7aQCYLikpSSkpKXr66acVFxdnXI+OjlZKSoqSkpIszA6A2Wxut9ttdRKXU1BQoObNm+v06dMKCgqyOh0A9cTlcmnVqlX69NNPNXToUA0YMIAeEcCL1Pb3m54RAJax2+3q16+fCgsL1a9fPwoRoJFizggAy7hcLq1evVpr1qzR6tWr5XK5rE4JgAUoRgBYIjU1VTExMRo0aJDmzJmjQYMGKSYmRqmpqVanBsBkFCMATJeamqpRo0ZVW9gsNzdXo0aNoiABGhmKEQCmcrlcmjhxoiQpPj5e2dnZeu+995Sdna34+HhJ0qRJkxiyARoRihEApsrKylJeXp769OmjpUuXKjY2Vk6nU7GxsVq6dKn69Omj3NxcZWVlWZ0qAJNQjAAwVWWRMX36dPn4eH4F+fj4aNq0aR5xALwfxQgAy3A3DQCJRc8AmCwzM1MDBw5Ux44dVVxcrIMHDxptbdu2ldPp1M6dO/XZZ58Zc0gANEy1/f2mGAFgKpfLpZYtW+r06dMXjWnevLmOHz/OImhAA1fb32+GaQCYrqys7KraAXgXihEAplq5cqWKiorkcDiq9XzY7XY5HA4VFRVp5cqVFmUIwGzsTQPAVIsWLZIklZSUaNiwYRo8eLB2796t9u3bKz09XcuWLTPiBg0aZGWqAExCMQLAVAUFBZKkO++8U0uXLpXL5VJaWpoSEhL02GOP6e6779bmzZuNOADej2EaAKZq3bq1JOnkyZM1tp84ccIjDoD3oxgBYKpevXpJkvbu3avExERt2LBBxcXF2rBhgxITE7Vv3z6POADej2EaAKaKjIw0jtPS0ow5IpI8JrRWjQPg3ShGAJiqb9++ioqKkt1u14EDB6q1x8TEqKKiQn379jU/OQCWoBgBYCq73a7Zs2dr9OjRSkhIUHR0tHE3zf79+5WWlqaUlBQWPAMakSuaMzJv3jxFRUUpICBAsbGx2rRp0yXjT506pccee0zh4eFyOBxq37690tLSrihhAA1fUlKSUlJS9M033+iNN95Qenq63njjDX377bdKSUlRUlKS1SkCMFGde0YWL16s5ORkLViwQLGxsZo7d66GDBmiXbt2KTQ0tFp8aWmpBg0apNDQUKWkpCgiIkIHDx7UDTfcUB/5A2igkpKSlJiYqFWrVunTTz/V0KFDNWDAAHpEgEaoznvTxMbG6s4779Qbb7whSaqoqFBkZKSeeOIJPffcc9XiFyxYoFdffVU7d+6Un5/fFSXJ3jSA9yorKzPWGbnS7wgA16fa/n7XqWektLRUW7Zs0ZQpU4xrPj4+GjhwoNavX1/jYz755BP16tVLjz32mJYuXaqQkBA98MADevbZZy/6L6CSkhKVlJR4vBnp/JcWe1YA3sPlcikrK0tr1qyRw+FQ//796RkBvEhtf7PrVIzk5+fL5XIpLCzM43pYWJh27txZ42O+++47rVy5Ug8++KDS0tK0d+9e/eIXv1BZWZmmTZtW42Nmzpyp6dOnV7uenp6uwMDAuqQM4Dq1fv16LVy4UHl5eZKkOXPmKCQkRA8//DBrjABeoqioqFZxdRqmOXLkiCIiIrRu3TqPL4tnnnlGq1ev1saNG6s9pn379jp37pz2799v/Itnzpw5evXVV3X06NEaX6emnpHIyEjl5+czTAN4gY8++khjxoyR0+lUcXGxcb3yfPHixRo5cqSFGQKoDwUFBQoODq7fYZrg4GDZ7Xbl5OR4XM/JyVGrVq1qfEx4eLj8/Pw8ul5vvfVWHTt2TKWlpfL396/2GIfDIYfDUe26n58fY8pAA+dyufT4449LkuLj4/Xss8/q+++/V5s2bfT73/9ey5Yt0xNPPKFRo0YxZAM0cLX9za7Trb3+/v7q2bOnMjMzjWsVFRXKzMy8aLfqPffco71796qiosK4tnv3boWHh9dYiADwbllZWcrLy1OfPn20dOlSxcbGyul0KjY2VkuXLlWfPn2Um5urrKwsq1MFYJI6rzOSnJysP//5z/rb3/6mHTt2aNKkSSosLNT48eMlSWPHjvWY4Dpp0iSdOHFCTz75pHbv3q3ly5drxowZeuyxx+rvXQBoMCqLjOnTp8vHx/MryMfHx5hLRjECNB51XmdkzJgxysvL09SpU3Xs2DF169ZNK1asMCa1Hjp0yOMLJjIyUv/85z/11FNPqUuXLoqIiNCTTz6pZ599tv7eBQAAaLDqvM6IFVhnBPAemZmZGjhwoPr06aOVK1dq9erVxqJn/fr104ABA7R27Vp99tlnio+PtzpdAFehtr/fFCMATOVyudS6dWvl5uYqICBA586dM9oqz0NDQ3XkyBEmsAINXG1/v69obxoAuFJ2u13jxo2TdH4hxaoqF0gaN24chQjQiFCMADCVy+XShx9+qDvuuEM33XSTR9tNN92kO+64QykpKXK5XBZlCMBsFCMATJWdna0DBw7oj3/8o/bu3auMjAwlJycrIyNDe/bs0R/+8Aft379f2dnZVqcKwCR1vpsGAK5G5crLt99+u+x2u/r166fCwkL169dPdrtdt99+u0ccAO9HzwgAU4WHh0uStm/fXmN75fXKOADej2IEgKn69u2rqKgozZgxw2NlZun8is4zZ85UdHS0+vbta1GGAMxGMQLAVHa7XbNnz9ayZcs0YsQIbdiwQcXFxdqwYYNGjBihZcuWadasWdxNAzQizBkBYLqkpCSlpKTo6aefVlxcnHE9OjpaKSkpSkpKsjA7AGZj0TMAlnG5XFq1apWxAuuAAQPoEQG8SG1/v+kZAWCZmu6mAdD4MGcEAABYimIEAABYimIEAABYimIEAABYimIEAABYimIEAABYimIEAABYimIEAABYimIEgGVcLpdWr16tNWvWaPXq1XK5XFanBMACFCMALJGamqqYmBgNGjRIc+bM0aBBgxQTE6PU1FSrUwNgMooRAKZLTU3VqFGjlJub63E9NzdXo0aNoiABGhmKEQCmcrlcmjhxoiQpPj5e2dnZeu+995Sdna34+HhJ0qRJkxiyARoRihEApsrKylJeXp769OmjpUuXKjY2Vk6nU7GxsVq6dKn69Omj3NxcZWVlWZ0qAJNQjAAwVWWRMX36dPn4eH4F+fj4aNq0aR5xALwfxQgAALAUxQgAU/Xv31+SNG3aNFVUVHi0VVRU6KWXXvKIA+D9KEYAmKp///4KDQ3V559/rsTERG3YsEHFxcXasGGDEhMTtXbtWoWGhlKMAI2Ir9UJAGhc7Ha75s+fr9GjRyszM1PLli0z2gIDA2Wz2TR//nzZ7XYLswRgJnpGAJguKSlJKSkpCgsL87geFhamlJQUJSUlWZQZACvY3G632+okLqegoEDNmzfX6dOnFRQUZHU6AOqJy+XSqlWr9Omnn2ro0KEaMGAAPSKAF6nt7zc9IwAAwFIUIwAskZqaqnbt2nnsTdOuXTuWggcaIYoRAKZLTU3V6NGj1blzZ4/l4Dt37qzRo0dTkACNDMUIAFO5XC49/fTTGjZsmJYsWaJz585p8+bNOnfunJYsWaJhw4Zp8uTJ7E0DNCIUIwBMlZ2drQMHDqh3795q3769xzBN+/bt1atXL+3fv1/Z2dlWpwrAJKwzAsBUR48elST95je/0X333aennnpKe/bs0S233KKMjAw9//zzHnEAvB/FCABThYaGSpI6duyobdu2eSx61rZtW3Xo0EE7d+404gB4P4ZpAFhix44dNU5g3blzp9WpATAZxQgAUx07dszjvHLdxQvXX7wwDoD3YpgGgKny8vIkSZMmTdKnn36quLg4oy06OloTJ07UggULjDgA3o+eEQCmCgkJkSQdOHBAu3fvVkZGhpKTk5WRkaFdu3bp4MGDHnEAvB/FCABTRURESJJWrFihUaNGyeFw6M4775TD4dCoUaO0YsUKjzgA3o+N8gCYyuVyqV27dgoODlZ+fr4OHDhgtEVHR6tly5Y6fvy49uzZw6Z5QANX299v5owAMJXdbtfs2bM1evRoJSQkaNiwYdq9e7fat2+v/fv3Ky0tTSkpKRQiQCNCMQLAdElJSZo8ebJee+01lZeXS5LS09Pl6+uryZMnKykpyeIMAZiJYgSA6VJTUzVr1izdd999GjRokMcKrLNmzdLdd99NQQI0IswZAWCqyjkjnTt31scffyyXy6W0tDQlJCTIbrdrxIgR2r59O3NGAC9Q299v7qYBYKrKjfJ+85vfyO12a/Xq1VqzZo1Wr14tt9utKVOmsFEe0MhQjAAwVeUGePv27VO7du08du1t166dvvvuO484AN6PYgSAqcLDwyVJDz30UI170zz00EMecQC8H3NGAJiqtLRUTZo0UcuWLfX999/L7XYbc0ZsNpvatGmj48ePq7CwUP7+/lanC+AqsM4IgOvSunXrVF5erpycHI0cOdK4m+bgwYPKyMhQTk6OEde/f39rkwVgCooRAKaqnAvy5JNPat68eVq2bJnR5uvrqyeffFKvv/46c0aARoRiBICpKueCvP766xo2bJgGDx5srMCanp6u119/3SMOgPdjzggAUzFnBGg8WGcEwHWpcs5Ibm6ukpKStGHDBhUXF2vDhg1KSkpSbm6uysvLtW7dOqtTBWASihEApqqcC7Jo0SJt27ZNcXFxuv/++xUXF6ft27dr0aJFHnEAvN8VFSPz5s1TVFSUAgICFBsbq02bNl009p133pHNZvP4ExAQcMUJA2jYKueCxMTEaO/evcrIyFBycrIyMjK0Z88e3XzzzR5xALxfnYuRxYsXKzk5WdOmTdOXX36prl27asiQIcrNzb3oY4KCgnT06FHjz8GDB68qaQANV9++fRUVFaUZM2bIZrOpX79+iouLU79+/WSz2TRz5kxFR0erb9++VqcKwCR1LkbmzJmjCRMmaPz48erUqZMWLFigwMBALVy48KKPsdlsatWqlfEnLCzsqpIG0HDZ7XbNnj1by5Yt04gRIzzmjIwYMULLli3TrFmz2CQPaETqdGtvaWmptmzZoilTphjXfHx8NHDgQK1fv/6ijzt79qzatm2riooK9ejRQzNmzNBtt9120fiSkhKVlJQY5wUFBZKksrIylZWV1SVlANeh4cOH6/3339ezzz6ruLg443p0dLTef/99DR8+nM864AVq+zmuUzGSn58vl8tVrWcjLCxMO3furPExHTp00MKFC9WlSxedPn1as2bNUu/evfXNN9+oTZs2NT5m5syZmj59erXr6enpCgwMrEvKAK5TDodDs2fP1rfffquTJ0+qRYsW6tSpk+x2u9LS0qxOD0A9KCoqqlVcndYZOXLkiCIiIrRu3Tr16tXLuP7MM89o9erV2rhx42Wfo6ysTLfeeqvuv/9+/fa3v60xpqaekcjISOXn57POCOBlysrKlJGRoUGDBsnPz8/qdADUo4KCAgUHB9fv3jTBwcGy2+3G3hGVcnJy1KpVq1o9h5+fn7p37669e/deNMbhcMjhcNT4WL6sAO/E5xvwPrX9TNdpAqu/v7969uypzMxM41pFRYUyMzM9ekouxeVyadu2bdy2BwAAJF3B3jTJyckaN26c7rjjDt11112aO3euCgsLNX78eEnS2LFjFRERoZkzZ0qSXn75Zd19991q166dTp06pVdffVUHDx7UI488Ur/vBAAANEh1LkbGjBmjvLw8TZ06VceOHVO3bt20YsUKY1LroUOH5OPz7w6XkydPasKECTp27JhatGihnj17at26derUqVP9vQsAANBgsVEeAEuVlZUZG+UxZwTwLmyUBwAAGgSKEQAAYCmKEQAAYCmKEQAAYKk6301jhco5tpV71ADwHmVlZSoqKlJBQQETWAEvU/m7fbl7ZRpEMXLmzBlJUmRkpMWZAACAujpz5oyaN29+0fYGcWtvRUWFjhw5ombNmslms1mdDoB6VLn31OHDh7l1H/AybrdbZ86cUevWrT3WILtQgyhGAHgv1hECwARWAABgKYoRAABgKYoRAJZyOByaNm2aHA6H1akAsAhzRgAAgKXoGQEAAJaiGAEAAJaiGAEAAJaiGAG8QFZWlmw2m06dOnXRmHfeeUc33HCDcf7SSy+pW7du9ZbDhc/fkERFRWnu3LnXzfMAjQ3FCOAFevfuraNHj15yueULTZ48WZmZmdcwK+91scJr8+bNevTRR81PCGjgGsTeNAAurqysTP7+/mrVqlWdHte0aVM1bdr0GmVlLpfLJZvNVm256dLSUvn7+5uWR0hIiGmvBXgTekYAE/3pT39S69atVVFR4XE9MTFRDz/8sCRp6dKl6tGjhwICAnTzzTdr+vTpKi8vN2JtNpvmz5+vH//4x2rSpIleeeWVGodp3nnnHd10000KDAzUyJEjdfz4cY/XrGmYZuHChbrtttvkcDgUHh6uxx9/3GibM2eOOnfurCZNmigyMlK/+MUvdPbs2Sv+u/i///s/3XnnnQoICFBwcLBGjhxptJ08eVJjx45VixYtFBgYqKFDh2rPnj0e7+2GG27QJ598ok6dOsnhcOjQoUOKiorSb3/7W40dO1ZBQUFGL8Xnn3+uvn37yul0KjIyUr/85S9VWFh40dwu9V6zsrI0fvx4nT59WjabTTabTS+99JKk6sM0hw4dUmJiopo2baqgoCD9x3/8h3Jycoz2yv8HixYtUlRUlJo3b66f/vSnxuagQGNBMQKY6Cc/+YmOHz+uVatWGddOnDihFStW6MEHH1R2drbGjh2rJ598Ut9++63eeustvfPOO3rllVc8nuell17SyJEjtW3bNqOIqWrjxo36+c9/rscff1xbt27VgAED9Lvf/e6Suc2fP1+PPfaYHn30UW3btk2ffPKJ2rVrZ7T7+PjoD3/4g7755hv97W9/08qVK/XMM89c0d/D8uXLNXLkSCUkJOirr75SZmam7rrrLqP9P//zP/XFF1/ok08+0fr16+V2u5WQkKCysjIjpqioSL///e/1l7/8Rd98841CQ0MlSbNmzVLXrl311Vdf6cUXX9S+ffv0ox/9SKNGjdK//vUvLV68WJ9//rlHoXWhS73X3r17a+7cuQoKCtLRo0d19OhRTZ48udpzVFRUKDExUSdOnNDq1auVkZGh7777TmPGjPGI27dvnz7++GMtW7ZMy5Yt0+rVq/Xf//3fV/T3CjRYbgCmSkxMdD/88MPG+VtvveVu3bq12+VyuePj490zZszwiF+0aJE7PDzcOJfk/tWvfuURs2rVKrck98mTJ91ut9t9//33uxMSEjxixowZ427evLlxPm3aNHfXrl2N89atW7uff/75Wr+PDz/80N2yZUvj/K9//avH819Kr1693A8++GCNbbt373ZLcq9du9a4lp+f73Y6ne4PPvjAeC1J7q1bt3o8tm3btu4RI0Z4XPv5z3/ufvTRRz2uZWdnu318fNzFxcXG41577bWL5lvb91r1edLT0912u9196NAho/2bb75xS3Jv2rTJ7Xaf/38QGBjoLigoMGJ+/etfu2NjYy+aC+CN6BkBTPbggw9qyZIlKikpkSS9++67+ulPfyofHx99/fXXevnll435HE2bNtWECRN09OhRFRUVGc9xxx13XPI1duzYodjYWI9rvXr1umh8bm6ujhw5ovj4+IvGfPbZZ4qPj1dERISaNWumhx56SMePH/fIq7a2bt160dfasWOHfH19PfJv2bKlOnTooB07dhjX/P391aVLl2qPv/Dv5uuvv9Y777zj8Xc6ZMgQVVRUaP/+/dfsve7YsUORkZGKjIw0rnXq1Ek33HCDx/uIiopSs2bNjPPw8HDl5ubW+nUAb0AxAphs+PDhcrvdWr58uQ4fPqzs7Gw9+OCDkqSzZ89q+vTp2rp1q/Fn27Zt2rNnjwICAoznaNKkSb3m5HQ6L9l+4MABDRs2TF26dNGSJUu0ZcsWzZs3T9L5SaL1/Xq1fQ6bzVbt+oV/N2fPntV//dd/efydfv3119qzZ49iYmKqPb6+3+vl+Pn5eZzbbLZqc4oAb8fdNIDJAgIClJSUpHfffVd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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "mantel_df_pval001 = pd.DataFrame.from_dict(mantel_pval001)\n", + "mantel_df_pval001_tr = mantel_df_pval001.T\n", + "mantel_df_pval001_tr.boxplot(column=[\"veridical_correlation\"], return_type=\"axes\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 280, + "metadata": {}, + "outputs": [], + "source": [ + "def get_df_outliers(mantel_df, column):\n", + " q1 = mantel_df[column].quantile(0.25)\n", + " q3 = mantel_df[column].quantile(0.75)\n", + " iqr = q3 - q1\n", + "\n", + " outliers = (mantel_df[column] < (q1 - 1.5 * iqr)) | (mantel_df[column] > (q3 + 1.5 * iqr))\n", + " outliers_df = mantel_df[outliers]\n", + " return outliers_df, outliers.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 283, + "metadata": {}, + "outputs": [], + "source": [ + "outliers_df = get_df_outliers(mantel_df_pval001_tr, \"veridical_correlation\")[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 290, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "137" + ] + }, + "execution_count": 290, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(outliers_df[outliers_df[\"veridical_correlation\"] < 0.90])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# More tests (not relevant)" + ] + }, + { + "cell_type": "code", + "execution_count": 218, + "metadata": {}, + "outputs": [], + "source": [ + "with open (\"mantel_test_part2.json\", \"r\") as f:\n", + " contentjson2 = json.load(f)\n", + "with open (\"mantel_test_part1.json\", \"r\") as f:\n", + " contentjson1 = json.load(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 219, + "metadata": {}, + "outputs": [], + "source": [ + "merged_mantel_dicts = contentjson1 | contentjson2" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'veridical_correlation': 0.9629433230504976,\n", + " 'p_value': 0.0001,\n", + " 'z_score': 37.26985950504366}" + ] + }, + "execution_count": 227, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged_mantel_dicts[\"_lmo2854\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 228, + "metadata": {}, + "outputs": [], + "source": [ + "dict(sorted(merged_mantel_dicts.items()))\n", + "import pandas as pd\n", + "\n", + "mantel_df = pd.DataFrame.from_dict(merged_mantel_dicts)" + ] + }, + { + "cell_type": "code", + "execution_count": 257, + "metadata": {}, + "outputs": [], + "source": [ + "mantel_df_transp = mantel_df.T" + ] + }, + { + "cell_type": "code", + "execution_count": 258, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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veridical_correlationp_valuez_score
_lmo00010.9944610.000129.314388
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_lmo00040.9202670.000112.597377
_lmo00050.9904020.000135.187808
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............
_lmo28400.9831870.000136.689192
_lmo28420.9523050.000154.484262
_lmo28530.9814150.000122.089173
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1526 rows × 3 columns

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" + ], + "text/plain": [ + " veridical_correlation p_value z_score\n", + "_lmo0001 0.994461 0.0001 29.314388\n", + "_lmo0002 0.991776 0.0001 39.949286\n", + "_lmo0004 0.920267 0.0001 12.597377\n", + "_lmo0005 0.990402 0.0001 35.187808\n", + "_lmo0006 0.992188 0.0001 39.453992\n", + "... ... ... ...\n", + "_lmo2840 0.983187 0.0001 36.689192\n", + "_lmo2842 0.952305 0.0001 54.484262\n", + "_lmo2853 0.981415 0.0001 22.089173\n", + "_lmo2854 0.962943 0.0001 37.269860\n", + "_lmo2857 0.973599 0.0001 34.797613\n", + "\n", + "[1526 rows x 3 columns]" + ] + }, + "execution_count": 258, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mantel_df_transp" + ] + }, + { + "cell_type": "code", + "execution_count": 251, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "mantel_df_transp.boxplot(column=[\"veridical_correlation\"], return_type=\"axes\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "get_df_outliers(mantel)" + ] + }, + { + "cell_type": "code", + "execution_count": 264, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.10222804718217562" + ] + }, + "execution_count": 264, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "156/len(mantel_df_transp)" + ] + }, + { + "cell_type": "code", + "execution_count": 208, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'_lmo0001': {'veridical_correlation': 0.9944607850255069,\n", + " 'p_value': 0.0001,\n", + " 'z_score': 29.314387624554765},\n", + " '_lmo0002': {'veridical_correlation': 0.9917758624883196,\n", + " 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62.719803391734395},\n", + " '_lmo1675': {'veridical_correlation': 0.9909994589020598,\n", + " 'p_value': 0.0001,\n", + " 'z_score': 92.37150547542909},\n", + " '_lmo1676': {'veridical_correlation': 0.984429419343435,\n", + " 'p_value': 0.0001,\n", + " 'z_score': 35.21023294060893},\n", + " '_lmo1678': {'veridical_correlation': 0.9643885958831897,\n", + " 'p_value': 0.0001,\n", + " 'z_score': 73.87986581699401},\n", + " '_lmo1679': {'veridical_correlation': 0.9774493309382737,\n", + " 'p_value': 0.0001,\n", + " 'z_score': 94.29687484759903},\n", + " '_lmo1682': {'veridical_correlation': 0.9845917361423961,\n", + " 'p_value': 0.0001,\n", + " 'z_score': 79.79655170929455},\n", + " '_lmo1683': {'veridical_correlation': 0.9480671171524772,\n", + " 'p_value': 0.0001,\n", + " 'z_score': 23.05416953296509},\n", + " '_lmo1685': {'veridical_correlation': 0.9532359182219465,\n", + " 'p_value': 0.0001,\n", + " 'z_score': 72.91008399064285},\n", + " '_lmo1686': {'veridical_correlation': 0.9931703443667929,\n", + " 'p_value': 0.0001,\n", + " 'z_score': 44.4274861608015},\n", + " '_lmo1689': {'veridical_correlation': 0.9704420177196152,\n", + " 'p_value': 0.0001,\n", + " 'z_score': 81.42074656009765},\n", + " '_lmo1690': {'veridical_correlation': 0.9526340833534256,\n", + " 'p_value': 0.0001,\n", + " 'z_score': 49.80372019703752},\n", + " '_lmo1692': {'veridical_correlation': 0.8840084753116548,\n", + " 'p_value': 0.0001,\n", + " 'z_score': 27.088557307315302},\n", + " '_lmo1694': {'veridical_correlation': 0.9565669489835317,\n", + " 'p_value': 0.0001,\n", + " 'z_score': 94.85785398548505},\n", + " '_lmo1695': {'veridical_correlation': 0.9918756227617351,\n", + " 'p_value': 0.0001,\n", + " 'z_score': 76.8687256986481}}" + ] + }, + "execution_count": 208, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with open (\"mantel_test_part1.json\", \"r\") as f:\n", + " mantelsum = json.load(f)\n", + "mantelsum" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Non symmetrical pairs for locus pident_matrix_lmo0202_db.csv: 3.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0284_db.csv: 7.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0152_db.csv: 202.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0343_db.csv: 13.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0387_db.csv: 15.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0289_db.csv: 880.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0232_db.csv: 2914.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0097_db.csv: 287.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0454_db.csv: 722.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0455_db.csv: 18547.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0206_db.csv: 1.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0293_db.csv: 725.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0046_db.csv: 266.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0374_db.csv: 28.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0153_db.csv: 515.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0344_db.csv: 318.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0029_db.csv: 1.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0006_db.csv: 924.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0214_db.csv: 104.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0221_db.csv: 12.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0115_db.csv: 8.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0288_db.csv: 78.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0114_db.csv: 4.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0033_db.csv: 140021.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0227_db.csv: 52.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0048_db.csv: 4.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0358_db.csv: 891.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0217_db.csv: 45.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0314_db.csv: 303.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0370_db.csv: 579.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0439_db.csv: 9851.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0240_db.csv: 0.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0426_db.csv: 261.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0352_db.csv: 2529.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0008_db.csv: 1902.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0056_db.csv: 128.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0241_db.csv: 4613.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0177_db.csv: 1867.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0076_db.csv: 7767.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0131_db.csv: 119.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0407_db.csv: 10.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0235_db.csv: 739.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0427_db.csv: 10.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0377_db.csv: 16.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0219_db.csv: 4257.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0218_db.csv: 1.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0075_db.csv: 6559.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0292_db.csv: 852.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0025_db.csv: 228.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0055_db.csv: 187.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0346_db.csv: 3275.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0030_db.csv: 397.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0243_db.csv: 166.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0428_db.csv: 655.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0398_db.csv: 145.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0272_db.csv: 343.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0185_db.csv: 1436.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0401_db.csv: 26287.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0239_db.csv: 847.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0026_db.csv: 44.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0205_db.csv: 1705.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0273_db.csv: 16.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0296_db.csv: 82.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0162_db.csv: 3474.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0249_db.csv: 1.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0137_db.csv: 1875.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0107_db.csv: 2403.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0103_db.csv: 1067.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0229_db.csv: 4.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0021_db.csv: 324.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0394_db.csv: 9.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0429_db.csv: 49024.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0210_db.csv: 47.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0238_db.csv: 1.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0191_db.csv: 55.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0007_db.csv: 26.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0193_db.csv: 74.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0001_db.csv: 3.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0418_db.csv: 0.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0267_db.csv: 1210.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0355_db.csv: 88.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0158_db.csv: 3645.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0223_db.csv: 184.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0164_db.csv: 348.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0299_db.csv: 1.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0281_db.csv: 905.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0136_db.csv: 2.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0226_db.csv: 479.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0405_db.csv: 1845.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0317_db.csv: 239.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0391_db.csv: 5.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0375_db.csv: 3.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0213_db.csv: 385.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0360_db.csv: 552.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0392_db.csv: 695.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0203_db.csv: 12649.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0231_db.csv: 1847.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0350_db.csv: 246.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0053_db.csv: 315.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0044_db.csv: 0.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0012_db.csv: 60.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0002_db.csv: 5.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0251_db.csv: 155.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0458_db.csv: 897.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0132_db.csv: 22.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0208_db.csv: 3.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0096_db.csv: 790.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0349_db.csv: 1942.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0371_db.csv: 106.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0105_db.csv: 5732.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0155_db.csv: 1.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0297_db.csv: 6538.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0451_db.csv: 482.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0042_db.csv: 2668.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0449_db.csv: 6.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0234_db.csv: 2726.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0211_db.csv: 590.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0443_db.csv: 12.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0176_db.csv: 14.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0054_db.csv: 16.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0018_db.csv: 994.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0077_db.csv: 5046.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0198_db.csv: 238.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0005_db.csv: 49.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0242_db.csv: 4.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0442_db.csv: 421.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0188_db.csv: 698.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0354_db.csv: 1534.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0245_db.csv: 0.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0347_db.csv: 366.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0300_db.csv: 11950.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0399_db.csv: 6.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0282_db.csv: 9.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0246_db.csv: 0.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0111_db.csv: 919.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0034_db.csv: 67.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0316_db.csv: 1836.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0271_db.csv: 41.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0009_db.csv: 223.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0013_db.csv: 41.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0031_db.csv: 773.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0291_db.csv: 926.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0167_db.csv: 3656.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0400_db.csv: 542.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0098_db.csv: 23915.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0319_db.csv: 1250.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0372_db.csv: 62.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0109_db.csv: 371.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0386_db.csv: 2709.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0163_db.csv: 478.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0250_db.csv: 1.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0216_db.csv: 0.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0256_db.csv: 2.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0269_db.csv: 80209.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0134_db.csv: 0.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0351_db.csv: 7.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0052_db.csv: 135.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0286_db.csv: 195360.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0437_db.csv: 2402.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0003_db.csv: 66854.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0285_db.csv: 1522.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0348_db.csv: 4193.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0135_db.csv: 3.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0194_db.csv: 1109.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0298_db.csv: 15.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0376_db.csv: 946.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0258_db.csv: 10.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0020_db.csv: 415.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0016_db.csv: 6.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0248_db.csv: 0.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0287_db.csv: 5.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0201_db.csv: 9345.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0099_db.csv: 9290.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0357_db.csv: 238.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0035_db.csv: 3394.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0169_db.csv: 6.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0051_db.csv: 179.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0244_db.csv: 60.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0290_db.csv: 902.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0356_db.csv: 242.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0024_db.csv: 3.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0088_db.csv: 36.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0359_db.csv: 39.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0266_db.csv: 94.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0014_db.csv: 4.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0190_db.csv: 0.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0480_db.csv: 371.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0450_db.csv: 27.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0408_db.csv: 2.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0233_db.csv: 204.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0116_db.csv: 3.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0383_db.csv: 573.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0022_db.csv: 14864.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0189_db.csv: 0.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0222_db.csv: 26.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0102_db.csv: 82.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0078_db.csv: 1013.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0388_db.csv: 7.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0113_db.csv: 18.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0433_db.csv: 67.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0195_db.csv: 766.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0342_db.csv: 15865.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0424_db.csv: 48.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0382_db.csv: 7167.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0283_db.csv: 40.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0004_db.csv: 0.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0369_db.csv: 5.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0345_db.csv: 263.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0259_db.csv: 15.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0110_db.csv: 795.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0453_db.csv: 1239.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0133_db.csv: 31.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0045_db.csv: 317.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0027_db.csv: 22173.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0278_db.csv: 681.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0212_db.csv: 1069.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0261_db.csv: 48398.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0199_db.csv: 24.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0268_db.csv: 27.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0192_db.csv: 12.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0406_db.csv: 465.0\n", + "Non symmetrical pairs for locus pident_matrix_lmo0441_db.csv: 74742.0\n" + ] + } + ], + "source": [ + "def check_nonsym_pairs(table_path):\n", + " blastable = pd.read_csv(table_path, sep=\",\")\n", + " blastable_clean = blastable.drop(blastable.columns[0], axis=1)\n", + " blastarray_full = blastable_clean.to_numpy()\n", + " # Distance matrix should be symmetrical, this line gets the number of non-symmetrical pairs\n", + " non_sym_pairs = len(blastarray_full[np.where(np.abs(blastarray_full-blastarray_full.T) != 0)])/2\n", + " return non_sym_pairs\n", + "\n", + "all_paths = glob.glob(\"/home/pmata/pruebas_tests/distance_matrix/blast/*.csv\")\n", + "sym_blastmats = []\n", + "for path in all_paths:\n", + " non_sym_pairs = check_nonsym_pairs(path)\n", + " print(f\"Non symmetrical pairs for locus {os.path.basename(path)}: {non_sym_pairs}\")\n", + " if non_sym_pairs == 0:\n", + " sym_blastmats.append(path)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(array([ 1, 1, 1, ..., 1308, 1308, 1308]), array([ 4, 10, 18, ..., 1103, 1139, 1259]))\n" + ] + } + ], + "source": [ + "table_path = \"/home/pmata/pruebas_tests/distance_matrix/blast/pident_matrix_lmo0033_db.csv\"\n", + "blastable = pd.read_csv(table_path, sep=\",\")\n", + "blastable_clean = blastable.drop(blastable.columns[0], axis=1)\n", + "blastarray_full = blastable_clean.to_numpy()\n", + "blastarray_full[np.where(np.abs(blastarray_full-blastarray_full.T) != 0)]\n", + "print(np.where(np.abs(blastarray_full-blastarray_full.T) != 0))" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.0\n", + "(array([ 48, 111, 180, 180, 180, 554]), array([180, 180, 48, 111, 554, 180]))\n" + ] + } + ], + "source": [ + "print(len(blastarray_full[np.where(np.abs(blastarray_full-blastarray_full.T) != 0)])/2)\n", + "print(np.where(np.abs(blastarray_full-blastarray_full.T) != 0))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "data_analysis", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.15" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/assets/mash_blast_correlation.py b/assets/mash_blast_correlation.py new file mode 100644 index 0000000..4528b4b --- /dev/null +++ b/assets/mash_blast_correlation.py @@ -0,0 +1,100 @@ +import numpy as np +import pandas as pd +import glob +import os +import mantel +import scipy +from difflib import SequenceMatcher +import json +import matplotlib.pyplot as plt + + +def fill_triangle_matrix(mash_tabpath): + with open(mash_tabpath, "r") as file: + mashvals = [list(map(float, line.split())) for line in file] + + matrix_size = len(mashvals) + + for i in range(matrix_size): + for j in range(i + 1): # Only fill values up to the diagonal + mashvals[i][j] = mashvals[i][j] + full_mashtab = mashvals + tri_mashtable = pd.DataFrame(full_mashtab).fillna(0) + tri_mashtable_clean = tri_mashtable.drop(tri_mashtable.columns[0], axis=1) + tri_mashtable_clean[tri_mashtable_clean.columns[-1] + 1] = float(0) + tri_masharray = tri_mashtable_clean.values + masharray_transraw = tri_masharray.T + masharray_clean = np.nan_to_num(masharray_transraw, nan=0.0) + masharray_full = ( + tri_masharray + masharray_transraw - np.diag(np.diag(masharray_clean)) + ) + return masharray_full + + +def take_upper_tri_and_dup(full_dist_matrix): + upper_triangle_matrix = np.triu(full_dist_matrix) + full_matrix = ( + upper_triangle_matrix + + upper_triangle_matrix.T + - np.diag(np.diag(upper_triangle_matrix)) + ) + return full_matrix + + +def mantel_tester(blast_paths, mash_paths, pval=0.01): + mantel_summary = {} + failed_tabs = [] + for blast_tabpath, mash_tabpath in zip(blast_paths, mash_paths): + blast_filename = os.path.basename(blast_tabpath) + mash_filename = os.path.basename(mash_tabpath) + match = SequenceMatcher( + None, blast_filename, mash_filename + ).find_longest_match() + common_name = blast_filename[match.a : match.a + match.size].strip(".") + + blastable = pd.read_csv(blast_tabpath) + blastarray = blastable.drop(blastable.columns[0], axis=1).to_numpy() + mirror_blastarray = take_upper_tri_and_dup(blastarray) + inverted_blast = 100 - mirror_blastarray + + masharray_full = fill_triangle_matrix(mash_tabpath) + + condensed_mash = scipy.spatial.distance.squareform( + masharray_full, force="tovector", checks=True + ) + try: + condensed_blast = scipy.spatial.distance.squareform( + inverted_blast, force="tovector", checks=True + ) + except ValueError: + print(f"{blast_tabpath} is not symmetric, skipped") + failed_tabs.append(blast_tabpath) + continue + permutations = int(1 / pval) + result = mantel.test( + condensed_mash, condensed_blast, perms=permutations, method="pearson" + ) + print( + f"Results from mantel test between {blast_filename} and {mash_filename}:", + f"veridical-correlation = {result.r} | p-value = {result.p}", + ) + mantel_summary[common_name] = { + "veridical_correlation": result.r, + "p_value": result.p, + "z_score": result.z, + } + print(f"{len(failed_tabs)} blast matrixes where non-symmetrical: {failed_tabs}") + return mantel_summary + + +blast_paths = sorted(glob.glob("blast/*.csv")) +mash_paths = sorted(glob.glob("mash/mash*.txt")) +mantel_summary = mantel_tester(blast_paths, mash_paths, pval=0.01) + +with open("mantel_test_pval001.json", "w") as f: + json.dump(mantel_summary, f) + +mantel_df_pval001 = pd.DataFrame.from_dict(mantel_summary) +mantel_df_pval001_tr = mantel_df_pval001.T +mantel_df_pval001_tr.boxplot(column=["veridical_correlation"], return_type="axes") +plt.show() From 48f48db920f16c155a5077f5a1616dc0a48ee00b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Mon, 29 Apr 2024 12:51:25 +0200 Subject: [PATCH 177/214] adapted mash blast correlation script --- assets/mash_blast_correlation.py | 68 ++++++++++++++++++++++---------- 1 file changed, 48 insertions(+), 20 deletions(-) diff --git a/assets/mash_blast_correlation.py b/assets/mash_blast_correlation.py index 4528b4b..ddfd1bc 100644 --- a/assets/mash_blast_correlation.py +++ b/assets/mash_blast_correlation.py @@ -1,22 +1,22 @@ -import numpy as np -import pandas as pd +import argparse import glob +import json import os -import mantel -import scipy from difflib import SequenceMatcher -import json + +import mantel import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import scipy def fill_triangle_matrix(mash_tabpath): with open(mash_tabpath, "r") as file: mashvals = [list(map(float, line.split())) for line in file] - matrix_size = len(mashvals) - for i in range(matrix_size): - for j in range(i + 1): # Only fill values up to the diagonal + for j in range(i + 1): mashvals[i][j] = mashvals[i][j] full_mashtab = mashvals tri_mashtable = pd.DataFrame(full_mashtab).fillna(0) @@ -51,14 +51,11 @@ def mantel_tester(blast_paths, mash_paths, pval=0.01): None, blast_filename, mash_filename ).find_longest_match() common_name = blast_filename[match.a : match.a + match.size].strip(".") - blastable = pd.read_csv(blast_tabpath) blastarray = blastable.drop(blastable.columns[0], axis=1).to_numpy() mirror_blastarray = take_upper_tri_and_dup(blastarray) inverted_blast = 100 - mirror_blastarray - masharray_full = fill_triangle_matrix(mash_tabpath) - condensed_mash = scipy.spatial.distance.squareform( masharray_full, force="tovector", checks=True ) @@ -87,14 +84,45 @@ def mantel_tester(blast_paths, mash_paths, pval=0.01): return mantel_summary -blast_paths = sorted(glob.glob("blast/*.csv")) -mash_paths = sorted(glob.glob("mash/mash*.txt")) -mantel_summary = mantel_tester(blast_paths, mash_paths, pval=0.01) +# Argument parser setup +parser = argparse.ArgumentParser(description='Process the Mantel test for genetic data.') +parser.add_argument('root_path', type=str, help='The root directory containing the datasets.') +args = parser.parse_args() + +# Use the root_path argument +root_path = args.root_path + +datasets = ["brucella", "listeria", "mtuberculosis"] +all_results = {} + +for dataset in datasets: + blast_paths = sorted(glob.glob(os.path.join(root_path, dataset, "blast", "*.csv"))) + mash_paths = sorted(glob.glob(os.path.join(root_path, dataset, "mash", "*.txt"))) + mantel_summary = mantel_tester(blast_paths, mash_paths, pval=0.01) + all_results[dataset] = mantel_summary + with open(f"mantel_test_pval001_{dataset}.json", "w") as f: + json.dump(mantel_summary, f) + +# Create DataFrame for visualization +results_df = pd.DataFrame( + { + (dataset, key): value["veridical_correlation"] + for dataset, results in all_results.items() + for key, value in results.items() + } +).T.reset_index() +results_df.columns = ["Dataset", "Comparison", "Veridical Correlation"] -with open("mantel_test_pval001.json", "w") as f: - json.dump(mantel_summary, f) +# Crea el boxplot +fig, ax = plt.subplots(figsize=(10, 6)) # Dimensiones en pulgadas (ancho, alto) +results_df.boxplot(by="Dataset", column=["Veridical Correlation"], grid=False, ax=ax) +plt.title("Dataset mash-blast correlation comparison") # Título opcional +plt.suptitle("") # Elimina el título por defecto +plt.xlabel("Dataset") # Etiqueta para el eje x +plt.ylabel("Mantel correlation value") # Etiqueta para el eje y -mantel_df_pval001 = pd.DataFrame.from_dict(mantel_summary) -mantel_df_pval001_tr = mantel_df_pval001.T -mantel_df_pval001_tr.boxplot(column=["veridical_correlation"], return_type="axes") -plt.show() +# Guarda el boxplot como PNG +plt.savefig( + "boxplot_mantel_test.png", dpi=300, bbox_inches="tight" +) # Guarda con alta resolución y ajusta el borde +plt.close() # Cierra la figura para liberar memoria From f3a6476d1bf6064a678f7d8d5a4953272c8cc0b6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Mon, 29 Apr 2024 12:53:32 +0200 Subject: [PATCH 178/214] added requiremens for assets --- assets/requirements_assets.txt | 5 +++++ 1 file changed, 5 insertions(+) create mode 100644 assets/requirements_assets.txt diff --git a/assets/requirements_assets.txt b/assets/requirements_assets.txt new file mode 100644 index 0000000..dd668dc --- /dev/null +++ b/assets/requirements_assets.txt @@ -0,0 +1,5 @@ +numpy +pandas +matplotlib +scipy +mantel \ No newline at end of file From c08483080ff1cf578e84ae1289f21c669d874cad Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Mon, 29 Apr 2024 16:55:52 +0200 Subject: [PATCH 179/214] a couple of fixes in mash_blast_correlation script --- assets/mash_blast_correlation.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/assets/mash_blast_correlation.py b/assets/mash_blast_correlation.py index ddfd1bc..2883326 100644 --- a/assets/mash_blast_correlation.py +++ b/assets/mash_blast_correlation.py @@ -13,7 +13,7 @@ def fill_triangle_matrix(mash_tabpath): with open(mash_tabpath, "r") as file: - mashvals = [list(map(float, line.split())) for line in file] + mashvals = [list(map(float, line.split())) for i, line in enumerate(file) if i > 0] matrix_size = len(mashvals) for i in range(matrix_size): for j in range(i + 1): @@ -59,6 +59,8 @@ def mantel_tester(blast_paths, mash_paths, pval=0.01): condensed_mash = scipy.spatial.distance.squareform( masharray_full, force="tovector", checks=True ) + if condensed_mash.shape[0] <= 3: + continue try: condensed_blast = scipy.spatial.distance.squareform( inverted_blast, force="tovector", checks=True From 26bc4a6fed47eace5a454ab3aac82bd938e4c158 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Mon, 29 Apr 2024 18:32:47 +0200 Subject: [PATCH 180/214] fix result df creation in mash blat correlation script --- assets/mash_blast_correlation.py | 20 ++++++++++++++------ 1 file changed, 14 insertions(+), 6 deletions(-) diff --git a/assets/mash_blast_correlation.py b/assets/mash_blast_correlation.py index 2883326..be9fa19 100644 --- a/assets/mash_blast_correlation.py +++ b/assets/mash_blast_correlation.py @@ -60,6 +60,8 @@ def mantel_tester(blast_paths, mash_paths, pval=0.01): masharray_full, force="tovector", checks=True ) if condensed_mash.shape[0] <= 3: + print(f"Locus in file {blast_filename} has less than 3 alleles.\n") + failed_tabs.append(blast_tabpath) continue try: condensed_blast = scipy.spatial.distance.squareform( @@ -70,6 +72,10 @@ def mantel_tester(blast_paths, mash_paths, pval=0.01): failed_tabs.append(blast_tabpath) continue permutations = int(1 / pval) + if condensed_mash.shape != condensed_blast.shape: + print("Blast and mash matrizes have different shape.\n") + failed_tabs.append(blast_tabpath) + continue result = mantel.test( condensed_mash, condensed_blast, perms=permutations, method="pearson" ) @@ -82,7 +88,7 @@ def mantel_tester(blast_paths, mash_paths, pval=0.01): "p_value": result.p, "z_score": result.z, } - print(f"{len(failed_tabs)} blast matrixes where non-symmetrical: {failed_tabs}") + print(f"{len(failed_tabs)} blast matrixes could not be analyzed due to non-symmetrical, less than three alleles or different shape: {failed_tabs}") return mantel_summary @@ -100,25 +106,27 @@ def mantel_tester(blast_paths, mash_paths, pval=0.01): for dataset in datasets: blast_paths = sorted(glob.glob(os.path.join(root_path, dataset, "blast", "*.csv"))) mash_paths = sorted(glob.glob(os.path.join(root_path, dataset, "mash", "*.txt"))) - mantel_summary = mantel_tester(blast_paths, mash_paths, pval=0.01) + mantel_summary = mantel_tester(blast_paths, mash_paths, pval=0.01) all_results[dataset] = mantel_summary with open(f"mantel_test_pval001_{dataset}.json", "w") as f: json.dump(mantel_summary, f) # Create DataFrame for visualization -results_df = pd.DataFrame( +results_series = pd.Series( { (dataset, key): value["veridical_correlation"] for dataset, results in all_results.items() for key, value in results.items() } -).T.reset_index() -results_df.columns = ["Dataset", "Comparison", "Veridical Correlation"] +) + +results_df = pd.DataFrame(results_series).reset_index() +results_df.columns = ["Dataset", "Locus", "Veridical Correlation"] # Crea el boxplot fig, ax = plt.subplots(figsize=(10, 6)) # Dimensiones en pulgadas (ancho, alto) results_df.boxplot(by="Dataset", column=["Veridical Correlation"], grid=False, ax=ax) -plt.title("Dataset mash-blast correlation comparison") # Título opcional +plt.title("Dataset mash-blast correlation") # Título opcional plt.suptitle("") # Elimina el título por defecto plt.xlabel("Dataset") # Etiqueta para el eje x plt.ylabel("Mantel correlation value") # Etiqueta para el eje y From 62786a1ab2db76c1335b56491ff54888d0a2ff34 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Mon, 29 Apr 2024 18:39:55 +0200 Subject: [PATCH 181/214] linting mash blast correlation script --- assets/mash_blast_correlation.py | 18 +++++++++++++----- requirements.txt | 4 ++-- 2 files changed, 15 insertions(+), 7 deletions(-) diff --git a/assets/mash_blast_correlation.py b/assets/mash_blast_correlation.py index be9fa19..a4cf505 100644 --- a/assets/mash_blast_correlation.py +++ b/assets/mash_blast_correlation.py @@ -13,7 +13,9 @@ def fill_triangle_matrix(mash_tabpath): with open(mash_tabpath, "r") as file: - mashvals = [list(map(float, line.split())) for i, line in enumerate(file) if i > 0] + mashvals = [ + list(map(float, line.split())) for i, line in enumerate(file) if i > 0 + ] matrix_size = len(mashvals) for i in range(matrix_size): for j in range(i + 1): @@ -88,13 +90,19 @@ def mantel_tester(blast_paths, mash_paths, pval=0.01): "p_value": result.p, "z_score": result.z, } - print(f"{len(failed_tabs)} blast matrixes could not be analyzed due to non-symmetrical, less than three alleles or different shape: {failed_tabs}") + print( + f"{len(failed_tabs)} blast matrixes could not be analyzed due to non-symmetrical, less than three alleles or different shape: {failed_tabs}" + ) return mantel_summary # Argument parser setup -parser = argparse.ArgumentParser(description='Process the Mantel test for genetic data.') -parser.add_argument('root_path', type=str, help='The root directory containing the datasets.') +parser = argparse.ArgumentParser( + description="Process the Mantel test for genetic data." +) +parser.add_argument( + "root_path", type=str, help="The root directory containing the datasets." +) args = parser.parse_args() # Use the root_path argument @@ -106,7 +114,7 @@ def mantel_tester(blast_paths, mash_paths, pval=0.01): for dataset in datasets: blast_paths = sorted(glob.glob(os.path.join(root_path, dataset, "blast", "*.csv"))) mash_paths = sorted(glob.glob(os.path.join(root_path, dataset, "mash", "*.txt"))) - mantel_summary = mantel_tester(blast_paths, mash_paths, pval=0.01) + mantel_summary = mantel_tester(blast_paths, mash_paths, pval=0.01) all_results[dataset] = mantel_summary with open(f"mantel_test_pval001_{dataset}.json", "w") as f: json.dump(mantel_summary, f) diff --git a/requirements.txt b/requirements.txt index 6ea2be1..b24e6b9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,6 +5,6 @@ click leidenalg questionary bio -scikit-learn +scikit - learn plotly -kaleido \ No newline at end of file +kaleido From 8163d802e63b87f70cdcf757a4cfaeaf92df4f15 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Tue, 30 Apr 2024 16:24:07 +0200 Subject: [PATCH 182/214] fix typo in requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index b24e6b9..5b97e97 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,6 +5,6 @@ click leidenalg questionary bio -scikit - learn +scikit-learn plotly kaleido From 578a838dff0233758b5e27629d3ffe9953eb137c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Tue, 30 Apr 2024 16:24:50 +0200 Subject: [PATCH 183/214] renaming and cleaning main --- taranis/__main__.py | 33 +++++++++++++++------------------ 1 file changed, 15 insertions(+), 18 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index 7b18c30..093157a 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -18,7 +18,6 @@ import taranis.inferred_alleles -# import pdb log = logging.getLogger() # Set up rich stderr console @@ -657,28 +656,26 @@ def distance_matrix( start = time.perf_counter() # filter the alleles matrix according to the thresholds and filters allele_matrix = pd.read_csv(alleles, sep=",", index_col=0, header=0) - filtering_string = ["ASM", "ALM"] + to_mask = ["ASM", "ALM", "TPR", "PAMA"] if paralog_filter: - filtering_string.append("NIPH") - filtering_string.append("NIPHEM") + to_mask.append("NIPH") + to_mask.append("NIPHEM") if lnf_filter: - filtering_string.append("LNF") + to_mask.append("LNF") if plot_filter: - filtering_string.append("PLOT") + to_mask.append("PLOT") # pdb.set_trace() - filtered_allele = taranis.utils.filter_data_frame_by_parameters( - allele_matrix, - locus_missing_threshold, - sample_missing_threshold, - filtering_string, - replaced_by_zero=False, - ) + # filtered_allele = taranis.utils.filter_df( + # allele_matrix, + # sample_missing_threshold, + # to_mask, + # ) + allele_matrix_fil = allele_matrix # Create the distance matrix - # pdb.set_trace() - d_matrix_obj = taranis.distance.HammingDistance(filtered_allele) - distance_matrix = d_matrix_obj.create_matrix() - # pdb.set_trace() - print(distance_matrix) + d_matrix_obj = taranis.distance.HammingDistance(allele_matrix_fil) + distance_matrix = d_matrix_obj.create_matrix(to_mask) + distance_matrix.to_csv(f"{output}/distance_matrix.csv") + finish = time.perf_counter() print(f"Distance matrix finish in {round((finish-start)/60, 2)} minutes") log.info("Distance matrix finish in %s minutes", round((finish - start) / 60, 2)) From 3f1a489f91f3be13919002e33c8f9778353de9dc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Tue, 30 Apr 2024 16:25:26 +0200 Subject: [PATCH 184/214] rewriten hamming distance, tested and fixed bugs --- taranis/distance.py | 62 ++++++++++++++++++++++++++------------------- 1 file changed, 36 insertions(+), 26 deletions(-) diff --git a/taranis/distance.py b/taranis/distance.py index 67283b1..87c3b31 100644 --- a/taranis/distance.py +++ b/taranis/distance.py @@ -1,6 +1,7 @@ import io import logging import pandas as pd +import numpy as np import subprocess import rich import sys @@ -72,63 +73,72 @@ def create_matrix(self) -> pd.DataFrame: out_data = out.decode("UTF-8").split("\n") allele_names = [item.split("\t")[0] for item in out_data[1:-1]] # create file in memory to increase speed - dist_matrix = io.StringIO() - dist_matrix.write("alleles\t" + "\t".join(allele_names) + "\n") - dist_matrix.write("\n".join(out_data[1:])) - dist_matrix.seek(0) + self.allele_matrix = io.StringIO() + self.allele_matrix.write("alleles\t" + "\t".join(allele_names) + "\n") + self.allele_matrix.write("\n".join(out_data[1:])) + self.allele_matrix.seek(0) matrix_pd = pd.read_csv( - dist_matrix, sep="\t", index_col="alleles", engine="python" + self.allele_matrix, sep="\t", index_col="alleles", engine="python" ).fillna(0) # Close object and discard memory buffer - dist_matrix.close() + self.allele_matrix.close() log.debug(f"create distance for {allele_name}") return matrix_pd class HammingDistance: - def __init__(self, dist_matrix: pd.DataFrame) -> "HammingDistance": + def __init__(self, allele_matrix: pd.DataFrame) -> "HammingDistance": """HammingDistance instance creation Args: - dist_matrix (pd.DataFrame): Distance matrix + self.allele_matrix (pd.DataFrame): Distance matrix Returns: HammingDistance: created hamming distance """ - self.dist_matrix = dist_matrix + self.allele_matrix = allele_matrix - def create_matrix(self) -> pd.DataFrame: - """Create hamming distance matrix using external program called mash + def create_matrix(self, mask_values: list) -> pd.DataFrame: + """Create hamming distance matrix + + Args: + mask_values: list of values to mask p.e ["ASM", "LNF"] Returns: pd.DataFrame: Hamming distance matrix as panda DataFrame """ + # Mask unwanted values directly in the DataFrame + regex_pattern = '|'.join([f".*{value}.*" for value in mask_values]) + self.allele_matrix.replace(regex_pattern, np.nan, regex=True, inplace=True) + # Get unique values excluding NaN unique_values = pd.unique( - self.dist_matrix[list(self.dist_matrix.keys())].values.ravel("K") + self.allele_matrix.values.ravel("K") ) + unique_values = unique_values[~pd.isna(unique_values)] # Exclude NaNs from unique values + # Create binary matrix ('1' or '0' ) matching the input matrix vs the unique_values[0] # astype(int) is used to transform the boolean matrix into integer - U = self.dist_matrix.eq(unique_values[0]).astype(int) + U = self.allele_matrix.eq(unique_values[0]).astype(int) # multiply the matrix with the transpose H = U.dot(U.T) # Repeat for each unique value for unique_val in range(1, len(unique_values)): - U = self.dist_matrix.eq(unique_values[unique_val]).astype(int) + U = self.allele_matrix.eq(unique_values[unique_val]).astype(int) # Add the value of the binary matrix with the previous stored values H = H.add(U.dot(U.T)) - return len(self.dist_matrix.columns) - H + # Convert to Boolean where True is not NaN (valid) + valid_data = self.allele_matrix.notna() - """ - dist_matrix = self.dist_matrix - allele_names = dist_matrix.index - hamming_matrix = pd.DataFrame(index=allele_names, columns=allele_names) - for i in allele_names: - for j in allele_names: - hamming_matrix.at[i, j] = sum( - dist_matrix.loc[i] != dist_matrix.loc[j] - ) - return hamming_matrix - """ + # Use broadcasting to find pairwise non-NaN entries + # valid_data[:, None] adds a new axis, making it a 3D array where each 2D slice is one sample's valid data + # We then logical AND across all pairs of samples + pairwise_valid = valid_data.values[:, None] & valid_data.values + + # Sum along the third dimension to get pairwise counts of non-NaN positions + pairwise_valid_counts = pairwise_valid.sum(axis=2) + distance_matrix = pairwise_valid_counts - H + + return pd.DataFrame(distance_matrix, index=self.allele_matrix.index, columns=self.allele_matrix.index) From 3c4877210e52419e735394738578d36512d19c49 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Tue, 30 Apr 2024 16:25:36 +0200 Subject: [PATCH 185/214] renaming def in utils --- taranis/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/taranis/utils.py b/taranis/utils.py index ea7405a..161d404 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -332,7 +332,7 @@ def find_nearest_numpy_value(array, value): """ -def filter_data_frame_by_parameters( +def filter_df( data_frame: pd.DataFrame, column_thr: int, row_thr: int, From c4b911030203d2ed97d3ed9b81ecb9dc716cdf24 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Tue, 30 Apr 2024 17:09:30 +0200 Subject: [PATCH 186/214] rewritten filter_df function, now filtering both rows and columns, matrix is printed as well as two distance matrices --- taranis/__main__.py | 23 +++++++++++++------- taranis/utils.py | 51 +++++++++++++++------------------------------ 2 files changed, 32 insertions(+), 42 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index 093157a..1ef42b4 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -664,18 +664,25 @@ def distance_matrix( to_mask.append("LNF") if plot_filter: to_mask.append("PLOT") - # pdb.set_trace() - # filtered_allele = taranis.utils.filter_df( - # allele_matrix, - # sample_missing_threshold, - # to_mask, - # ) - allele_matrix_fil = allele_matrix + + allele_matrix_fil = taranis.utils.filter_df( + allele_matrix, + locus_missing_threshold, + sample_missing_threshold, + to_mask, + ) + allele_matrix_fil.to_csv(f"{output}/allele_matrix_fil.csv") + # Create the distance matrix - d_matrix_obj = taranis.distance.HammingDistance(allele_matrix_fil) + d_matrix_obj = taranis.distance.HammingDistance(allele_matrix) distance_matrix = d_matrix_obj.create_matrix(to_mask) distance_matrix.to_csv(f"{output}/distance_matrix.csv") + # Create the filtered distance matrix + d_matrix_obj = taranis.distance.HammingDistance(allele_matrix_fil) + distance_matrix = d_matrix_obj.create_matrix(to_mask) + distance_matrix.to_csv(f"{output}/distance_matrix_core.csv") + finish = time.perf_counter() print(f"Distance matrix finish in {round((finish-start)/60, 2)} minutes") log.info("Distance matrix finish in %s minutes", round((finish - start) / 60, 2)) diff --git a/taranis/utils.py b/taranis/utils.py index 161d404..bdc7c19 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -323,45 +323,28 @@ def file_exists(file_to_check): return False -""" -def find_nearest_numpy_value(array, value): - array = np.asarray(array) - idx = (np.abs(array - value)).argmin() - return array[idx] - - """ - - def filter_df( data_frame: pd.DataFrame, column_thr: int, row_thr: int, - filter_str: list[str], - replaced_by_zero: bool, + filter_values: list[str], ) -> pd.DataFrame: - # get the number of columns and rows - num_rows, num_columns = data_frame.shape - # remove the columns which the filter strings are higher than the threshold - column_threshold = column_thr * num_rows / 100 - # Condition: Check if any string in the filter list is present in each cell of the DataFrame - f_condition = data_frame.apply( - lambda column: column.astype(str).str.contains("|".join(filter_str), na=False) - ) - if replaced_by_zero: - new_data_frame = data_frame.mask(f_condition, 0) - else: - # Count the number of hits per column - hits_per_column = f_condition.sum() - # pdb.set_trace() - # Filter for removing columns where the count of hits is higher than the threshold - to_be_removed_columns = hits_per_column[ - hits_per_column > column_threshold - ].index - new_data_frame = data_frame.drop(columns=to_be_removed_columns) - # pdb.set_trace() - # remove the rows which the filter strings are higher than the threshold - # row_threshold = row_thr * num_columns - return new_data_frame + # Convert percentages to proportions for easier calculation + column_thr /= 100 + row_thr /= 100 + + # Identify filter values and create a mask for the DataFrame + mask = data_frame.isin(filter_values) + + # Filter rows: Drop rows where the count of true in mask / total columns >= row_thr + rows_to_drop = mask.sum(axis=1) / len(data_frame.columns) >= row_thr + filtered_data_frame = data_frame.loc[~rows_to_drop, :] + + # Filter columns: Drop columns where the count of true in mask / total rows >= column_thr + cols_to_drop = mask.sum(axis=0) / len(data_frame) >= column_thr + filtered_data_frame = filtered_data_frame.loc[:, ~cols_to_drop] + + return filtered_data_frame def folder_exists(folder_to_check): From 8f46223ba416436da68108b8d410c23709be171f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Wed, 1 May 2024 12:02:00 +0200 Subject: [PATCH 187/214] rename variable for clarity, add dtype to read_csv --- taranis/__main__.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index 1ef42b4..8df438d 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -655,7 +655,7 @@ def distance_matrix( _ = taranis.utils.prompt_user_if_folder_exists(output) start = time.perf_counter() # filter the alleles matrix according to the thresholds and filters - allele_matrix = pd.read_csv(alleles, sep=",", index_col=0, header=0) + allele_matrix = pd.read_csv(alleles, sep=",", index_col=0, header=0, dtype=str) to_mask = ["ASM", "ALM", "TPR", "PAMA"] if paralog_filter: to_mask.append("NIPH") @@ -679,9 +679,9 @@ def distance_matrix( distance_matrix.to_csv(f"{output}/distance_matrix.csv") # Create the filtered distance matrix - d_matrix_obj = taranis.distance.HammingDistance(allele_matrix_fil) - distance_matrix = d_matrix_obj.create_matrix(to_mask) - distance_matrix.to_csv(f"{output}/distance_matrix_core.csv") + d_matrix_core_obj = taranis.distance.HammingDistance(allele_matrix_fil) + distance_matrix_core = d_matrix_core_obj.create_matrix(to_mask) + distance_matrix_core.to_csv(f"{output}/distance_matrix_core.csv") finish = time.perf_counter() print(f"Distance matrix finish in {round((finish-start)/60, 2)} minutes") From 4d4607c5fad05294784e07a87358cb769071ff0a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Wed, 1 May 2024 12:08:35 +0200 Subject: [PATCH 188/214] clarified help messages, fixed default value for keeping a locus in distance matrix fil --- taranis/__main__.py | 6 +++--- taranis/utils.py | 1 + 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/taranis/__main__.py b/taranis/__main__.py index 8df438d..ce7dcb5 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -599,8 +599,8 @@ def allele_calling( required=False, multiple=False, type=int, - default=100, - help="Threshold for missing alleles in locus, which loci is excluded from distance matrix", + default=0, + help="Maximum percentaje of missing values a locus can have, otherwise is filtered. By default core genome is calculated, locus must be found in all samples.", ) @click.option( "-s", @@ -609,7 +609,7 @@ def allele_calling( multiple=False, type=int, default=20, - help="Threshold for missing samples, which sample is excluded from distance matrix", + help="Maximum percentaje for missing values a sample can have, otherwise it is filtered", ) @click.option( "--paralog-filter/--no-paralog-filter", diff --git a/taranis/utils.py b/taranis/utils.py index bdc7c19..d614626 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -333,6 +333,7 @@ def filter_df( column_thr /= 100 row_thr /= 100 + import pdb; pdb.set_trace() # Identify filter values and create a mask for the DataFrame mask = data_frame.isin(filter_values) From dbfda2828a8d7c8eecb6d187c36a09baf2c3ec52 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Wed, 1 May 2024 12:09:24 +0200 Subject: [PATCH 189/214] removed missing pdb --- taranis/utils.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/taranis/utils.py b/taranis/utils.py index d614626..8ee657a 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -333,12 +333,11 @@ def filter_df( column_thr /= 100 row_thr /= 100 - import pdb; pdb.set_trace() # Identify filter values and create a mask for the DataFrame mask = data_frame.isin(filter_values) # Filter rows: Drop rows where the count of true in mask / total columns >= row_thr - rows_to_drop = mask.sum(axis=1) / len(data_frame.columns) >= row_thr + rows_to_drop = len(data_frame.columns) filtered_data_frame = data_frame.loc[~rows_to_drop, :] # Filter columns: Drop columns where the count of true in mask / total rows >= column_thr From 317a99abda0127d6a0f21bcee064ba3d07a2f818 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Wed, 1 May 2024 12:13:56 +0200 Subject: [PATCH 190/214] fixed big fingers typo --- taranis/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/taranis/utils.py b/taranis/utils.py index 8ee657a..bdc7c19 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -337,7 +337,7 @@ def filter_df( mask = data_frame.isin(filter_values) # Filter rows: Drop rows where the count of true in mask / total columns >= row_thr - rows_to_drop = len(data_frame.columns) + rows_to_drop = mask.sum(axis=1) / len(data_frame.columns) >= row_thr filtered_data_frame = data_frame.loc[~rows_to_drop, :] # Filter columns: Drop columns where the count of true in mask / total rows >= column_thr From 2e7ce828a2371666f7cfabdb394e34ab4e8b488e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Wed, 1 May 2024 12:21:52 +0200 Subject: [PATCH 191/214] variable renaming and fixed bounds for condition --- taranis/utils.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/taranis/utils.py b/taranis/utils.py index bdc7c19..4c46423 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -324,7 +324,7 @@ def file_exists(file_to_check): def filter_df( - data_frame: pd.DataFrame, + df: pd.DataFrame, column_thr: int, row_thr: int, filter_values: list[str], @@ -334,17 +334,17 @@ def filter_df( row_thr /= 100 # Identify filter values and create a mask for the DataFrame - mask = data_frame.isin(filter_values) + mask = df.isin(filter_values) # Filter rows: Drop rows where the count of true in mask / total columns >= row_thr - rows_to_drop = mask.sum(axis=1) / len(data_frame.columns) >= row_thr - filtered_data_frame = data_frame.loc[~rows_to_drop, :] + rows_to_drop = mask.sum(axis=1) / len(df.columns) > row_thr + filtered_df = df.loc[~rows_to_drop, :] # Filter columns: Drop columns where the count of true in mask / total rows >= column_thr - cols_to_drop = mask.sum(axis=0) / len(data_frame) >= column_thr - filtered_data_frame = filtered_data_frame.loc[:, ~cols_to_drop] + cols_to_drop = mask.sum(axis=0) / len(df) > column_thr + filtered_df = filtered_df.loc[:, ~cols_to_drop] - return filtered_data_frame + return filtered_df def folder_exists(folder_to_check): From f805814850caf5db198251d82cc29a185e447819 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Wed, 1 May 2024 20:38:35 +0200 Subject: [PATCH 192/214] minor modifications in graph for mash blast corr script --- assets/mash_blast_correlation.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/assets/mash_blast_correlation.py b/assets/mash_blast_correlation.py index a4cf505..8663805 100644 --- a/assets/mash_blast_correlation.py +++ b/assets/mash_blast_correlation.py @@ -108,7 +108,7 @@ def mantel_tester(blast_paths, mash_paths, pval=0.01): # Use the root_path argument root_path = args.root_path -datasets = ["brucella", "listeria", "mtuberculosis"] +datasets = ["bmelitensis", "lmonocytogenes", "mtuberculosis"] all_results = {} for dataset in datasets: @@ -138,6 +138,7 @@ def mantel_tester(blast_paths, mash_paths, pval=0.01): plt.suptitle("") # Elimina el título por defecto plt.xlabel("Dataset") # Etiqueta para el eje x plt.ylabel("Mantel correlation value") # Etiqueta para el eje y +ax.set_xticklabels([ticklabel.get_text().capitalize() for ticklabel in ax.get_xticklabels()]) # Guarda el boxplot como PNG plt.savefig( From 769758fc3d229262a6c901d66cb23a8fe15708b2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Wed, 1 May 2024 20:38:53 +0200 Subject: [PATCH 193/214] added filter with regex --- taranis/utils.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/taranis/utils.py b/taranis/utils.py index 4c46423..86918f0 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -334,7 +334,10 @@ def filter_df( row_thr /= 100 # Identify filter values and create a mask for the DataFrame - mask = df.isin(filter_values) + regex_pattern = '|'.join(filter_values) # This creates 'ASM|LNF|EXC' + + # Apply regex across the DataFrame to create a mask + mask = df.applymap(lambda x: bool(re.search(regex_pattern, str(x)))) # Filter rows: Drop rows where the count of true in mask / total columns >= row_thr rows_to_drop = mask.sum(axis=1) / len(df.columns) > row_thr From 418c4f2db15d8ab8c557b265cba1868dda2ac40f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Wed, 1 May 2024 20:39:16 +0200 Subject: [PATCH 194/214] added notebook for benchmarking analysis --- assets/benchmark.ipynb | 27040 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 27040 insertions(+) create mode 100644 assets/benchmark.ipynb diff --git a/assets/benchmark.ipynb b/assets/benchmark.ipynb new file mode 100644 index 0000000..921a4f9 --- /dev/null +++ b/assets/benchmark.ipynb @@ -0,0 +1,27040 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we are going to benchmark chewbbaca, taranis and seqshpere using three different datasets:\n", + "- ECDC EQA - mtuberculosis\n", + "- Halbedel et al. 2019 - lmonocytogenes\n", + "- UNSGM PT3 - bmelitensis" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Defaulting to user installation because normal site-packages is not writeable\n", + "Requirement already satisfied: pandas in /home/smonzon/.local/lib/python3.10/site-packages (1.5.3)\n", + "Requirement already satisfied: matplotlib in /home/smonzon/.local/lib/python3.10/site-packages (3.8.4)\n", + "Requirement already satisfied: seaborn in /home/smonzon/.local/lib/python3.10/site-packages (0.13.2)\n", + "Requirement already satisfied: numpy in /home/smonzon/.local/lib/python3.10/site-packages (1.25.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /home/smonzon/.local/lib/python3.10/site-packages (from pandas) (2022.6)\n", + "Requirement already satisfied: python-dateutil>=2.8.1 in /home/smonzon/.local/lib/python3.10/site-packages (from pandas) (2.8.2)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/lib/python3/dist-packages (from matplotlib) (2.4.7)\n", + "Requirement already satisfied: kiwisolver>=1.3.1 in /home/smonzon/.local/lib/python3.10/site-packages (from matplotlib) (1.4.5)\n", + "Requirement already satisfied: pillow>=8 in /usr/lib/python3/dist-packages (from matplotlib) (9.0.1)\n", + "Requirement already satisfied: cycler>=0.10 in /home/smonzon/.local/lib/python3.10/site-packages (from matplotlib) (0.12.1)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /home/smonzon/.local/lib/python3.10/site-packages (from matplotlib) (1.2.1)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /home/smonzon/.local/lib/python3.10/site-packages (from matplotlib) (4.51.0)\n", + "Requirement already satisfied: packaging>=20.0 in /home/smonzon/.local/lib/python3.10/site-packages (from matplotlib) (23.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n" + ] + } + ], + "source": [ + "! pip install pandas matplotlib seaborn numpy" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "├── .~lock.summary_comparison.csv#\n", + "├── bmelitensis\n", + "│ ├── .~lock.results_alleles_chewbbaca.tsv#\n", + "│ ├── .~lock.summary_taranis.csv#\n", + "│ ├── distance_chewbbaca\n", + "│ │ ├── allele_matrix_fil.csv\n", + "│ │ ├── distance_matrix.csv\n", + "│ │ └── distance_matrix_core.csv\n", + "│ ├── distance_seqsphere\n", + "│ │ └── distance_matrix.csv\n", + "│ ├── distance_taranis\n", + "│ │ ├── allele_matrix_fil.csv\n", + "│ │ ├── distance_matrix.csv\n", + "│ │ └── distance_matrix_core.csv\n", + "│ ├── results_alleles_chewbbaca.csv\n", + "│ ├── results_alleles_chewbbaca.tsv\n", + "│ ├── results_alleles_chewbbaca_masked.tsv\n", + "│ ├── results_alleles_taranis.csv\n", + "│ ├── summary_chewbbaca.csv\n", + "│ ├── summary_chewbbaca.tsv\n", + "│ └── summary_taranis.csv\n", + "├── datasets.txt\n", + "├── distance_comparison.png\n", + "├── lablog\n", + "├── lmonocytogenes\n", + "│ ├── distance_chewbbaca\n", + "│ │ ├── allele_matrix_fil.csv\n", + "│ │ ├── distance_matrix.csv\n", + "│ │ └── distance_matrix_core.csv\n", + "│ ├── distance_seqsphere\n", + "│ │ └── distance_matrix.csv\n", + "│ ├── distance_taranis\n", + "│ │ ├── allele_matrix_fil.csv\n", + "│ │ ├── distance_matrix.csv\n", + "│ │ └── distance_matrix_core.csv\n", + "│ ├── results_alleles_chewbbaca.csv\n", + "│ ├── results_alleles_chewbbaca.tsv\n", + "│ ├── results_alleles_chewbbaca_masked.tsv\n", + "│ ├── results_alleles_taranis.csv\n", + "│ ├── summary_chewbbaca.csv\n", + "│ ├── summary_chewbbaca.tsv\n", + "│ └── summary_taranis.csv\n", + "├── mtuberculosis\n", + "│ ├── .venv\n", + "│ │ ├── .gitignore\n", + "│ │ ├── bin\n", + "│ │ │ ├── Activate.ps1\n", + "│ │ │ ├── activate\n", + "│ │ │ ├── activate.csh\n", + "│ │ │ ├── activate.fish\n", + "│ │ │ ├── f2py\n", + "│ │ │ ├── ipython\n", + "│ │ │ ├── ipython3\n", + "│ │ │ ├── jupyter\n", + "│ │ │ ├── jupyter-kernel\n", + "│ │ │ ├── jupyter-kernelspec\n", + "│ │ │ ├── jupyter-migrate\n", + "│ │ │ ├── jupyter-run\n", + "│ │ │ ├── jupyter-troubleshoot\n", + "│ │ │ ├── pip\n", + "│ │ │ ├── pip3\n", + "│ │ │ ├── pip3.10\n", + "│ │ │ ├── pygmentize\n", + "│ │ │ ├── python\n", + "│ │ │ ├── python3\n", + "│ │ │ └── python3.10\n", + "│ │ ├── include\n", + "│ │ ├── lib\n", + "│ │ │ └── python3.10\n", + "│ │ │ └── site-packages\n", + "│ │ │ ├── IPython\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __main__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ ├── consoleapp.cpython-310.pyc\n", + "│ │ │ │ │ ├── display.cpython-310.pyc\n", + "│ │ │ │ │ └── paths.cpython-310.pyc\n", + "│ │ │ │ ├── conftest.py\n", + "│ │ │ │ ├── consoleapp.py\n", + "│ │ │ │ ├── core\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── alias.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── application.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── async_helpers.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── autocall.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── builtin_trap.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── compilerop.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── completer.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── completerlib.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── crashhandler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── debugger.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── display.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── display_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── display_trap.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── displayhook.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── displaypub.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── error.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── events.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── excolors.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── extensions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── formatters.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── getipython.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── guarded_eval.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── history.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── historyapp.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── hooks.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── inputsplitter.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── inputtransformer.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── inputtransformer2.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── interactiveshell.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── latex_symbols.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── logger.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── macro.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── magic.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── magic_arguments.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── oinspect.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── page.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── payload.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── payloadpage.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── prefilter.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── profileapp.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── profiledir.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── prompts.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pylabtools.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── release.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── shellapp.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── splitinput.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ultratb.cpython-310.pyc\n", + "│ │ │ │ │ │ └── usage.cpython-310.pyc\n", + "│ │ │ │ │ ├── alias.py\n", + "│ │ │ │ │ ├── application.py\n", + "│ │ │ │ │ ├── async_helpers.py\n", + "│ │ │ │ │ ├── autocall.py\n", + "│ │ │ │ │ ├── builtin_trap.py\n", + "│ │ │ │ │ ├── compilerop.py\n", + "│ │ │ │ │ ├── completer.py\n", + "│ │ │ │ │ ├── completerlib.py\n", + "│ │ │ │ │ ├── crashhandler.py\n", + "│ │ │ │ │ ├── debugger.py\n", + "│ │ │ │ │ ├── display.py\n", + "│ │ │ │ │ ├── display_functions.py\n", + "│ │ │ │ │ ├── display_trap.py\n", + "│ │ │ │ │ ├── displayhook.py\n", + "│ │ │ │ │ ├── displaypub.py\n", + "│ │ │ │ │ ├── error.py\n", + "│ │ │ │ │ ├── events.py\n", + "│ │ │ │ │ ├── excolors.py\n", + "│ │ │ │ │ ├── extensions.py\n", + "│ │ │ │ │ ├── formatters.py\n", + "│ │ │ │ │ ├── getipython.py\n", + "│ │ │ │ │ ├── guarded_eval.py\n", + "│ │ │ │ │ ├── history.py\n", + "│ │ │ │ │ ├── historyapp.py\n", + "│ │ │ │ │ ├── hooks.py\n", + "│ │ │ │ │ ├── inputsplitter.py\n", + "│ │ │ │ │ ├── inputtransformer.py\n", + "│ │ │ │ │ ├── inputtransformer2.py\n", + "│ │ │ │ │ ├── interactiveshell.py\n", + "│ │ │ │ │ ├── latex_symbols.py\n", + "│ │ │ │ │ ├── logger.py\n", + "│ │ │ │ │ ├── macro.py\n", + "│ │ │ │ │ ├── magic.py\n", + "│ │ │ │ │ ├── magic_arguments.py\n", + "│ │ │ │ │ ├── magics\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── ast_mod.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── auto.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── basic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── code.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── config.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── display.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── execution.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── extension.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── history.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── logging.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── namespace.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── osm.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── packaging.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pylab.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── script.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ast_mod.py\n", + "│ │ │ │ │ │ ├── auto.py\n", + "│ │ │ │ │ │ ├── basic.py\n", + "│ │ │ │ │ │ ├── code.py\n", + "│ │ │ │ │ │ ├── config.py\n", + "│ │ │ │ │ │ ├── display.py\n", + "│ │ │ │ │ │ ├── execution.py\n", + "│ │ │ │ │ │ ├── extension.py\n", + "│ │ │ │ │ │ ├── history.py\n", + "│ │ │ │ │ │ ├── logging.py\n", + "│ │ │ │ │ │ ├── namespace.py\n", + "│ │ │ │ │ │ ├── osm.py\n", + "│ │ │ │ │ │ ├── packaging.py\n", + "│ │ │ │ │ │ ├── pylab.py\n", + "│ │ │ │ │ │ └── script.py\n", + "│ │ │ │ │ ├── oinspect.py\n", + "│ │ │ │ │ ├── page.py\n", + "│ │ │ │ │ ├── payload.py\n", + "│ │ │ │ │ ├── payloadpage.py\n", + "│ │ │ │ │ ├── prefilter.py\n", + "│ │ │ │ │ ├── profile\n", + "│ │ │ │ │ │ └── README_STARTUP\n", + "│ │ │ │ │ ├── profileapp.py\n", + "│ │ │ │ │ ├── profiledir.py\n", + "│ │ │ │ │ ├── prompts.py\n", + "│ │ │ │ │ ├── pylabtools.py\n", + "│ │ │ │ │ ├── release.py\n", + "│ │ │ │ │ ├── shellapp.py\n", + "│ │ │ │ │ ├── splitinput.py\n", + "│ │ │ │ │ ├── tests\n", + "│ │ │ │ │ │ ├── 2x2.jpg\n", + "│ │ │ │ │ │ ├── 2x2.png\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── bad_all.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── nonascii.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── nonascii2.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── print_argv.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── refbug.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── simpleerr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── tclass.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_alias.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_application.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_async_helpers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_autocall.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_compilerop.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_completer.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_completerlib.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_debugger.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_display.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_displayhook.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_events.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_exceptiongroup_tb.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_extension.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_formatters.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_guarded_eval.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_handlers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_history.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_hooks.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_imports.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_inputsplitter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_inputtransformer.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_inputtransformer2.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_inputtransformer2_line.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_interactiveshell.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_iplib.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_logger.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_magic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_magic_arguments.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_magic_terminal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_oinspect.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_page.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_paths.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_prefilter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_profile.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_prompts.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_pylabtools.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_run.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_shellapp.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_splitinput.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_ultratb.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── bad_all.py\n", + "│ │ │ │ │ │ ├── daft_extension\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ └── daft_extension.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── daft_extension.py\n", + "│ │ │ │ │ │ ├── nonascii.py\n", + "│ │ │ │ │ │ ├── nonascii2.py\n", + "│ │ │ │ │ │ ├── print_argv.py\n", + "│ │ │ │ │ │ ├── refbug.py\n", + "│ │ │ │ │ │ ├── simpleerr.py\n", + "│ │ │ │ │ │ ├── tclass.py\n", + "│ │ │ │ │ │ ├── test_alias.py\n", + "│ │ │ │ │ │ ├── test_application.py\n", + "│ │ │ │ │ │ ├── test_async_helpers.py\n", + "│ │ │ │ │ │ ├── test_autocall.py\n", + "│ │ │ │ │ │ ├── test_compilerop.py\n", + "│ │ │ │ │ │ ├── test_completer.py\n", + "│ │ │ │ │ │ ├── test_completerlib.py\n", + "│ │ │ │ │ │ ├── test_debugger.py\n", + "│ │ │ │ │ │ ├── test_display.py\n", + "│ │ │ │ │ │ ├── test_displayhook.py\n", + "│ │ │ │ │ │ ├── test_events.py\n", + "│ │ │ │ │ │ ├── test_exceptiongroup_tb.py\n", + "│ │ │ │ │ │ ├── test_extension.py\n", + "│ │ │ │ │ │ ├── test_formatters.py\n", + "│ │ │ │ │ │ ├── test_guarded_eval.py\n", + "│ │ │ │ │ │ ├── test_handlers.py\n", + "│ │ │ │ │ │ ├── test_history.py\n", + "│ │ │ │ │ │ ├── test_hooks.py\n", + "│ │ │ │ │ │ ├── test_imports.py\n", + "│ │ │ │ │ │ ├── test_inputsplitter.py\n", + "│ │ │ │ │ │ ├── test_inputtransformer.py\n", + "│ │ │ │ │ │ ├── test_inputtransformer2.py\n", + "│ │ │ │ │ │ ├── test_inputtransformer2_line.py\n", + "│ │ │ │ │ │ ├── test_interactiveshell.py\n", + "│ │ │ │ │ │ ├── test_iplib.py\n", + "│ │ │ │ │ │ ├── test_logger.py\n", + "│ │ │ │ │ │ ├── test_magic.py\n", + "│ │ │ │ │ │ ├── test_magic_arguments.py\n", + "│ │ │ │ │ │ ├── test_magic_terminal.py\n", + "│ │ │ │ │ │ ├── test_oinspect.py\n", + "│ │ │ │ │ │ ├── test_page.py\n", + "│ │ │ │ │ │ ├── test_paths.py\n", + "│ │ │ │ │ │ ├── test_prefilter.py\n", + "│ │ │ │ │ │ ├── test_profile.py\n", + "│ │ │ │ │ │ ├── test_prompts.py\n", + "│ │ │ │ │ │ ├── test_pylabtools.py\n", + "│ │ │ │ │ │ ├── test_run.py\n", + "│ │ │ │ │ │ ├── test_shellapp.py\n", + "│ │ │ │ │ │ ├── test_splitinput.py\n", + "│ │ │ │ │ │ └── test_ultratb.py\n", + "│ │ │ │ │ ├── ultratb.py\n", + "│ │ │ │ │ └── usage.py\n", + "│ │ │ │ ├── display.py\n", + "│ │ │ │ ├── extensions\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── autoreload.cpython-310.pyc\n", + "│ │ │ │ │ │ └── storemagic.cpython-310.pyc\n", + "│ │ │ │ │ ├── autoreload.py\n", + "│ │ │ │ │ ├── storemagic.py\n", + "│ │ │ │ │ └── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_autoreload.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_storemagic.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_autoreload.py\n", + "│ │ │ │ │ └── test_storemagic.py\n", + "│ │ │ │ ├── external\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── qt_for_kernel.cpython-310.pyc\n", + "│ │ │ │ │ │ └── qt_loaders.cpython-310.pyc\n", + "│ │ │ │ │ ├── qt_for_kernel.py\n", + "│ │ │ │ │ ├── qt_loaders.py\n", + "│ │ │ │ │ └── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_qt_loaders.cpython-310.pyc\n", + "│ │ │ │ │ └── test_qt_loaders.py\n", + "│ │ │ │ ├── lib\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── backgroundjobs.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── clipboard.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── deepreload.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── demo.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── display.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── editorhooks.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── guisupport.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── latextools.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── lexers.cpython-310.pyc\n", + "│ │ │ │ │ │ └── pretty.cpython-310.pyc\n", + "│ │ │ │ │ ├── backgroundjobs.py\n", + "│ │ │ │ │ ├── clipboard.py\n", + "│ │ │ │ │ ├── deepreload.py\n", + "│ │ │ │ │ ├── demo.py\n", + "│ │ │ │ │ ├── display.py\n", + "│ │ │ │ │ ├── editorhooks.py\n", + "│ │ │ │ │ ├── guisupport.py\n", + "│ │ │ │ │ ├── latextools.py\n", + "│ │ │ │ │ ├── lexers.py\n", + "│ │ │ │ │ ├── pretty.py\n", + "│ │ │ │ │ └── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_backgroundjobs.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_clipboard.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_deepreload.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_display.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_editorhooks.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_imports.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_latextools.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_lexers.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_pretty.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_pygments.cpython-310.pyc\n", + "│ │ │ │ │ ├── test.wav\n", + "│ │ │ │ │ ├── test_backgroundjobs.py\n", + "│ │ │ │ │ ├── test_clipboard.py\n", + "│ │ │ │ │ ├── test_deepreload.py\n", + "│ │ │ │ │ ├── test_display.py\n", + "│ │ │ │ │ ├── test_editorhooks.py\n", + "│ │ │ │ │ ├── test_imports.py\n", + "│ │ │ │ │ ├── test_latextools.py\n", + "│ │ │ │ │ ├── test_lexers.py\n", + "│ │ │ │ │ ├── test_pretty.py\n", + "│ │ │ │ │ └── test_pygments.py\n", + "│ │ │ │ ├── paths.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ ├── sphinxext\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── custom_doctests.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ipython_console_highlighting.cpython-310.pyc\n", + "│ │ │ │ │ │ └── ipython_directive.cpython-310.pyc\n", + "│ │ │ │ │ ├── custom_doctests.py\n", + "│ │ │ │ │ ├── ipython_console_highlighting.py\n", + "│ │ │ │ │ └── ipython_directive.py\n", + "│ │ │ │ ├── terminal\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── console.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── debugger.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── embed.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── interactiveshell.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ipapp.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── magics.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── prompts.cpython-310.pyc\n", + "│ │ │ │ │ │ └── ptutils.cpython-310.pyc\n", + "│ │ │ │ │ ├── console.py\n", + "│ │ │ │ │ ├── debugger.py\n", + "│ │ │ │ │ ├── embed.py\n", + "│ │ │ │ │ ├── interactiveshell.py\n", + "│ │ │ │ │ ├── ipapp.py\n", + "│ │ │ │ │ ├── magics.py\n", + "│ │ │ │ │ ├── prompts.py\n", + "│ │ │ │ │ ├── pt_inputhooks\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── asyncio.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── glut.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── gtk.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── gtk3.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── gtk4.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── osx.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pyglet.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── qt.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── tk.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── wx.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── asyncio.py\n", + "│ │ │ │ │ │ ├── glut.py\n", + "│ │ │ │ │ │ ├── gtk.py\n", + "│ │ │ │ │ │ ├── gtk3.py\n", + "│ │ │ │ │ │ ├── gtk4.py\n", + "│ │ │ │ │ │ ├── osx.py\n", + "│ │ │ │ │ │ ├── pyglet.py\n", + "│ │ │ │ │ │ ├── qt.py\n", + "│ │ │ │ │ │ ├── tk.py\n", + "│ │ │ │ │ │ └── wx.py\n", + "│ │ │ │ │ ├── ptutils.py\n", + "│ │ │ │ │ ├── shortcuts\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── auto_match.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── auto_suggest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── filters.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── auto_match.py\n", + "│ │ │ │ │ │ ├── auto_suggest.py\n", + "│ │ │ │ │ │ └── filters.py\n", + "│ │ │ │ │ └── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_debug_magic.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_embed.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_help.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_interactivshell.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_pt_inputhooks.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_shortcuts.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_debug_magic.py\n", + "│ │ │ │ │ ├── test_embed.py\n", + "│ │ │ │ │ ├── test_help.py\n", + "│ │ │ │ │ ├── test_interactivshell.py\n", + "│ │ │ │ │ ├── test_pt_inputhooks.py\n", + "│ │ │ │ │ └── test_shortcuts.py\n", + "│ │ │ │ ├── testing\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── decorators.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── globalipapp.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ipunittest.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── skipdoctest.cpython-310.pyc\n", + "│ │ │ │ │ │ └── tools.cpython-310.pyc\n", + "│ │ │ │ │ ├── decorators.py\n", + "│ │ │ │ │ ├── globalipapp.py\n", + "│ │ │ │ │ ├── ipunittest.py\n", + "│ │ │ │ │ ├── plugin\n", + "│ │ │ │ │ │ ├── README.txt\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── dtexample.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── ipdoctest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pytest_ipdoctest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── setup.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── simple.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── simplevars.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_ipdoctest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_refs.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dtexample.py\n", + "│ │ │ │ │ │ ├── ipdoctest.py\n", + "│ │ │ │ │ │ ├── pytest_ipdoctest.py\n", + "│ │ │ │ │ │ ├── setup.py\n", + "│ │ │ │ │ │ ├── simple.py\n", + "│ │ │ │ │ │ ├── simplevars.py\n", + "│ │ │ │ │ │ ├── test_combo.txt\n", + "│ │ │ │ │ │ ├── test_example.txt\n", + "│ │ │ │ │ │ ├── test_exampleip.txt\n", + "│ │ │ │ │ │ ├── test_ipdoctest.py\n", + "│ │ │ │ │ │ └── test_refs.py\n", + "│ │ │ │ │ ├── skipdoctest.py\n", + "│ │ │ │ │ ├── tests\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_decorators.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_ipunittest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_tools.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_decorators.py\n", + "│ │ │ │ │ │ ├── test_ipunittest.py\n", + "│ │ │ │ │ │ └── test_tools.py\n", + "│ │ │ │ │ └── tools.py\n", + "│ │ │ │ └── utils\n", + "│ │ │ │ ├── PyColorize.py\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── PyColorize.cpython-310.pyc\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _process_cli.cpython-310.pyc\n", + "│ │ │ │ │ ├── _process_common.cpython-310.pyc\n", + "│ │ │ │ │ ├── _process_emscripten.cpython-310.pyc\n", + "│ │ │ │ │ ├── _process_posix.cpython-310.pyc\n", + "│ │ │ │ │ ├── _process_win32.cpython-310.pyc\n", + "│ │ │ │ │ ├── _process_win32_controller.cpython-310.pyc\n", + "│ │ │ │ │ ├── _sysinfo.cpython-310.pyc\n", + "│ │ │ │ │ ├── capture.cpython-310.pyc\n", + "│ │ │ │ │ ├── colorable.cpython-310.pyc\n", + "│ │ │ │ │ ├── coloransi.cpython-310.pyc\n", + "│ │ │ │ │ ├── contexts.cpython-310.pyc\n", + "│ │ │ │ │ ├── daemonize.cpython-310.pyc\n", + "│ │ │ │ │ ├── data.cpython-310.pyc\n", + "│ │ │ │ │ ├── decorators.cpython-310.pyc\n", + "│ │ │ │ │ ├── dir2.cpython-310.pyc\n", + "│ │ │ │ │ ├── docs.cpython-310.pyc\n", + "│ │ │ │ │ ├── encoding.cpython-310.pyc\n", + "│ │ │ │ │ ├── eventful.cpython-310.pyc\n", + "│ │ │ │ │ ├── frame.cpython-310.pyc\n", + "│ │ │ │ │ ├── generics.cpython-310.pyc\n", + "│ │ │ │ │ ├── importstring.cpython-310.pyc\n", + "│ │ │ │ │ ├── io.cpython-310.pyc\n", + "│ │ │ │ │ ├── ipstruct.cpython-310.pyc\n", + "│ │ │ │ │ ├── jsonutil.cpython-310.pyc\n", + "│ │ │ │ │ ├── localinterfaces.cpython-310.pyc\n", + "│ │ │ │ │ ├── log.cpython-310.pyc\n", + "│ │ │ │ │ ├── module_paths.cpython-310.pyc\n", + "│ │ │ │ │ ├── openpy.cpython-310.pyc\n", + "│ │ │ │ │ ├── path.cpython-310.pyc\n", + "│ │ │ │ │ ├── process.cpython-310.pyc\n", + "│ │ │ │ │ ├── py3compat.cpython-310.pyc\n", + "│ │ │ │ │ ├── sentinel.cpython-310.pyc\n", + "│ │ │ │ │ ├── shimmodule.cpython-310.pyc\n", + "│ │ │ │ │ ├── signatures.cpython-310.pyc\n", + "│ │ │ │ │ ├── strdispatch.cpython-310.pyc\n", + "│ │ │ │ │ ├── sysinfo.cpython-310.pyc\n", + "│ │ │ │ │ ├── syspathcontext.cpython-310.pyc\n", + "│ │ │ │ │ ├── tempdir.cpython-310.pyc\n", + "│ │ │ │ │ ├── terminal.cpython-310.pyc\n", + "│ │ │ │ │ ├── text.cpython-310.pyc\n", + "│ │ │ │ │ ├── timing.cpython-310.pyc\n", + "│ │ │ │ │ ├── tokenutil.cpython-310.pyc\n", + "│ │ │ │ │ ├── traitlets.cpython-310.pyc\n", + "│ │ │ │ │ ├── tz.cpython-310.pyc\n", + "│ │ │ │ │ ├── ulinecache.cpython-310.pyc\n", + "│ │ │ │ │ ├── version.cpython-310.pyc\n", + "│ │ │ │ │ └── wildcard.cpython-310.pyc\n", + "│ │ │ │ ├── _process_cli.py\n", + "│ │ │ │ ├── _process_common.py\n", + "│ │ │ │ ├── _process_emscripten.py\n", + "│ │ │ │ ├── _process_posix.py\n", + "│ │ │ │ ├── _process_win32.py\n", + "│ │ │ │ ├── _process_win32_controller.py\n", + "│ │ │ │ ├── _sysinfo.py\n", + "│ │ │ │ ├── capture.py\n", + "│ │ │ │ ├── colorable.py\n", + "│ │ │ │ ├── coloransi.py\n", + "│ │ │ │ ├── contexts.py\n", + "│ │ │ │ ├── daemonize.py\n", + "│ │ │ │ ├── data.py\n", + "│ │ │ │ ├── decorators.py\n", + "│ │ │ │ ├── dir2.py\n", + "│ │ │ │ ├── docs.py\n", + "│ │ │ │ ├── encoding.py\n", + "│ │ │ │ ├── eventful.py\n", + "│ │ │ │ ├── frame.py\n", + "│ │ │ │ ├── generics.py\n", + "│ │ │ │ ├── importstring.py\n", + "│ │ │ │ ├── io.py\n", + "│ │ │ │ ├── ipstruct.py\n", + "│ │ │ │ ├── jsonutil.py\n", + "│ │ │ │ ├── localinterfaces.py\n", + "│ │ │ │ ├── log.py\n", + "│ │ │ │ ├── module_paths.py\n", + "│ │ │ │ ├── openpy.py\n", + "│ │ │ │ ├── path.py\n", + "│ │ │ │ ├── process.py\n", + "│ │ │ │ ├── py3compat.py\n", + "│ │ │ │ ├── sentinel.py\n", + "│ │ │ │ ├── shimmodule.py\n", + "│ │ │ │ ├── signatures.py\n", + "│ │ │ │ ├── strdispatch.py\n", + "│ │ │ │ ├── sysinfo.py\n", + "│ │ │ │ ├── syspathcontext.py\n", + "│ │ │ │ ├── tempdir.py\n", + "│ │ │ │ ├── terminal.py\n", + "│ │ │ │ ├── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_capture.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_decorators.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_deprecated.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_dir2.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_imports.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_importstring.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_io.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_module_paths.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_openpy.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_path.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_process.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_pycolorize.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_shimmodule.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_sysinfo.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_tempdir.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_text.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_tokenutil.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_wildcard.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_capture.py\n", + "│ │ │ │ │ ├── test_decorators.py\n", + "│ │ │ │ │ ├── test_deprecated.py\n", + "│ │ │ │ │ ├── test_dir2.py\n", + "│ │ │ │ │ ├── test_imports.py\n", + "│ │ │ │ │ ├── test_importstring.py\n", + "│ │ │ │ │ ├── test_io.py\n", + "│ │ │ │ │ ├── test_module_paths.py\n", + "│ │ │ │ │ ├── test_openpy.py\n", + "│ │ │ │ │ ├── test_path.py\n", + "│ │ │ │ │ ├── test_process.py\n", + "│ │ │ │ │ ├── test_pycolorize.py\n", + "│ │ │ │ │ ├── test_shimmodule.py\n", + "│ │ │ │ │ ├── test_sysinfo.py\n", + "│ │ │ │ │ ├── test_tempdir.py\n", + "│ │ │ │ │ ├── test_text.py\n", + "│ │ │ │ │ ├── test_tokenutil.py\n", + "│ │ │ │ │ └── test_wildcard.py\n", + "│ │ │ │ ├── text.py\n", + "│ │ │ │ ├── timing.py\n", + "│ │ │ │ ├── tokenutil.py\n", + "│ │ │ │ ├── traitlets.py\n", + "│ │ │ │ ├── tz.py\n", + "│ │ │ │ ├── ulinecache.py\n", + "│ │ │ │ ├── version.py\n", + "│ │ │ │ └── wildcard.py\n", + "│ │ │ ├── __pycache__\n", + "│ │ │ │ ├── decorator.cpython-310.pyc\n", + "│ │ │ │ ├── ipykernel_launcher.cpython-310.pyc\n", + "│ │ │ │ ├── jupyter.cpython-310.pyc\n", + "│ │ │ │ ├── nest_asyncio.cpython-310.pyc\n", + "│ │ │ │ ├── six.cpython-310.pyc\n", + "│ │ │ │ └── typing_extensions.cpython-310.pyc\n", + "│ │ │ ├── _distutils_hack\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ └── override.cpython-310.pyc\n", + "│ │ │ │ └── override.py\n", + "│ │ │ ├── asttokens\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── astroid_compat.cpython-310.pyc\n", + "│ │ │ │ │ ├── asttokens.cpython-310.pyc\n", + "│ │ │ │ │ ├── line_numbers.cpython-310.pyc\n", + "│ │ │ │ │ ├── mark_tokens.cpython-310.pyc\n", + "│ │ │ │ │ ├── util.cpython-310.pyc\n", + "│ │ │ │ │ └── version.cpython-310.pyc\n", + "│ │ │ │ ├── astroid_compat.py\n", + "│ │ │ │ ├── asttokens.py\n", + "│ │ │ │ ├── line_numbers.py\n", + "│ │ │ │ ├── mark_tokens.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ ├── util.py\n", + "│ │ │ │ └── version.py\n", + "│ │ │ ├── asttokens-2.4.1.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── comm\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ └── base_comm.cpython-310.pyc\n", + "│ │ │ │ ├── base_comm.py\n", + "│ │ │ │ └── py.typed\n", + "│ │ │ ├── comm-0.2.2.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── licenses\n", + "│ │ │ │ └── LICENSE\n", + "│ │ │ ├── dateutil\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _common.cpython-310.pyc\n", + "│ │ │ │ │ ├── _version.cpython-310.pyc\n", + "│ │ │ │ │ ├── easter.cpython-310.pyc\n", + "│ │ │ │ │ ├── relativedelta.cpython-310.pyc\n", + "│ │ │ │ │ ├── rrule.cpython-310.pyc\n", + "│ │ │ │ │ ├── tzwin.cpython-310.pyc\n", + "│ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ ├── _common.py\n", + "│ │ │ │ ├── _version.py\n", + "│ │ │ │ ├── easter.py\n", + "│ │ │ │ ├── parser\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _parser.cpython-310.pyc\n", + "│ │ │ │ │ │ └── isoparser.cpython-310.pyc\n", + "│ │ │ │ │ ├── _parser.py\n", + "│ │ │ │ │ └── isoparser.py\n", + "│ │ │ │ ├── relativedelta.py\n", + "│ │ │ │ ├── rrule.py\n", + "│ │ │ │ ├── tz\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _common.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _factories.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── tz.cpython-310.pyc\n", + "│ │ │ │ │ │ └── win.cpython-310.pyc\n", + "│ │ │ │ │ ├── _common.py\n", + "│ │ │ │ │ ├── _factories.py\n", + "│ │ │ │ │ ├── tz.py\n", + "│ │ │ │ │ └── win.py\n", + "│ │ │ │ ├── tzwin.py\n", + "│ │ │ │ ├── utils.py\n", + "│ │ │ │ └── zoneinfo\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ └── rebuild.cpython-310.pyc\n", + "│ │ │ │ ├── dateutil-zoneinfo.tar.gz\n", + "│ │ │ │ └── rebuild.py\n", + "│ │ │ ├── debugpy\n", + "│ │ │ │ ├── ThirdPartyNotices.txt\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __main__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _version.cpython-310.pyc\n", + "│ │ │ │ │ └── public_api.cpython-310.pyc\n", + "│ │ │ │ ├── _vendored\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _pydevd_packaging.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _util.cpython-310.pyc\n", + "│ │ │ │ │ │ └── force_pydevd.cpython-310.pyc\n", + "│ │ │ │ │ ├── _pydevd_packaging.py\n", + "│ │ │ │ │ ├── _util.py\n", + "│ │ │ │ │ ├── force_pydevd.py\n", + "│ │ │ │ │ └── pydevd\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── pydev_app_engine_debug_startup.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pydev_coverage.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pydev_pysrc.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pydev_run_in_console.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pydevconsole.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pydevd.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pydevd_file_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pydevd_tracing.cpython-310.pyc\n", + "│ │ │ │ │ │ └── setup_pydevd_cython.cpython-310.pyc\n", + "│ │ │ │ │ ├── _pydev_bundle\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _pydev_calltip_util.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _pydev_completer.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _pydev_execfile.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _pydev_filesystem_encoding.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _pydev_getopt.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _pydev_imports_tipper.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _pydev_jy_imports_tipper.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _pydev_log.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _pydev_saved_modules.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _pydev_sys_patch.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _pydev_tipper_common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_console_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_import_hook.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_imports.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_ipython_console.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_ipython_console_011.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_is_thread_alive.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_localhost.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_log.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_monkey.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_monkey_qt.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_override.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_umd.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── pydev_versioncheck.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _pydev_calltip_util.py\n", + "│ │ │ │ │ │ ├── _pydev_completer.py\n", + "│ │ │ │ │ │ ├── _pydev_execfile.py\n", + "│ │ │ │ │ │ ├── _pydev_filesystem_encoding.py\n", + "│ │ │ │ │ │ ├── _pydev_getopt.py\n", + "│ │ │ │ │ │ ├── _pydev_imports_tipper.py\n", + "│ │ │ │ │ │ ├── _pydev_jy_imports_tipper.py\n", + "│ │ │ │ │ │ ├── _pydev_log.py\n", + "│ │ │ │ │ │ ├── _pydev_saved_modules.py\n", + "│ │ │ │ │ │ ├── _pydev_sys_patch.py\n", + "│ │ │ │ │ │ ├── _pydev_tipper_common.py\n", + "│ │ │ │ │ │ ├── fsnotify\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pydev_console_utils.py\n", + "│ │ │ │ │ │ ├── pydev_import_hook.py\n", + "│ │ │ │ │ │ ├── pydev_imports.py\n", + "│ │ │ │ │ │ ├── pydev_ipython_console.py\n", + "│ │ │ │ │ │ ├── pydev_ipython_console_011.py\n", + "│ │ │ │ │ │ ├── pydev_is_thread_alive.py\n", + "│ │ │ │ │ │ ├── pydev_localhost.py\n", + "│ │ │ │ │ │ ├── pydev_log.py\n", + "│ │ │ │ │ │ ├── pydev_monkey.py\n", + "│ │ │ │ │ │ ├── pydev_monkey_qt.py\n", + "│ │ │ │ │ │ ├── pydev_override.py\n", + "│ │ │ │ │ │ ├── pydev_umd.py\n", + "│ │ │ │ │ │ └── pydev_versioncheck.py\n", + "│ │ │ │ │ ├── _pydev_runfiles\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_runfiles.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_runfiles_coverage.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_runfiles_nose.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_runfiles_parallel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_runfiles_parallel_client.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_runfiles_pytest2.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_runfiles_unittest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── pydev_runfiles_xml_rpc.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pydev_runfiles.py\n", + "│ │ │ │ │ │ ├── pydev_runfiles_coverage.py\n", + "│ │ │ │ │ │ ├── pydev_runfiles_nose.py\n", + "│ │ │ │ │ │ ├── pydev_runfiles_parallel.py\n", + "│ │ │ │ │ │ ├── pydev_runfiles_parallel_client.py\n", + "│ │ │ │ │ │ ├── pydev_runfiles_pytest2.py\n", + "│ │ │ │ │ │ ├── pydev_runfiles_unittest.py\n", + "│ │ │ │ │ │ └── pydev_runfiles_xml_rpc.py\n", + "│ │ │ │ │ ├── _pydevd_bundle\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevconsole_code.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_additional_thread_info.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_additional_thread_info_regular.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_breakpoints.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_bytecode_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_code_to_source.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_collect_bytecode_info.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_comm.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_comm_constants.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_command_line_handling.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_console.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_constants.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_custom_frames.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_cython_wrapper.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_daemon_thread.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_defaults.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_dont_trace.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_dont_trace_files.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_exec2.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_extension_api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_extension_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_filtering.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_frame.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_frame_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_gevent_integration.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_import_class.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_io.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_json_debug_options.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_net_command.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_net_command_factory_json.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_net_command_factory_xml.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_plugin_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_process_net_command.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_process_net_command_json.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_referrers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_reload.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_resolver.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_runpy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_safe_repr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_save_locals.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_signature.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_source_mapping.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_stackless.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_suspended_frames.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_thread_lifecycle.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_timeout.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_trace_api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_trace_dispatch.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_trace_dispatch_regular.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_traceproperty.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_vars.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_vm_type.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── pydevd_xml.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _debug_adapter\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __main__pydevd_gen_debug_adapter_protocol.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── __main__pydevd_gen_debug_adapter_protocol.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── pydevd_base_schema.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── pydevd_schema.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── pydevd_schema_log.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── debugProtocol.json\n", + "│ │ │ │ │ │ │ ├── debugProtocolCustom.json\n", + "│ │ │ │ │ │ │ ├── pydevd_base_schema.py\n", + "│ │ │ │ │ │ │ ├── pydevd_schema.py\n", + "│ │ │ │ │ │ │ └── pydevd_schema_log.py\n", + "│ │ │ │ │ │ ├── pydevconsole_code.py\n", + "│ │ │ │ │ │ ├── pydevd_additional_thread_info.py\n", + "│ │ │ │ │ │ ├── pydevd_additional_thread_info_regular.py\n", + "│ │ │ │ │ │ ├── pydevd_api.py\n", + "│ │ │ │ │ │ ├── pydevd_breakpoints.py\n", + "│ │ │ │ │ │ ├── pydevd_bytecode_utils.py\n", + "│ │ │ │ │ │ ├── pydevd_code_to_source.py\n", + "│ │ │ │ │ │ ├── pydevd_collect_bytecode_info.py\n", + "│ │ │ │ │ │ ├── pydevd_comm.py\n", + "│ │ │ │ │ │ ├── pydevd_comm_constants.py\n", + "│ │ │ │ │ │ ├── pydevd_command_line_handling.py\n", + "│ │ │ │ │ │ ├── pydevd_concurrency_analyser\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── pydevd_concurrency_logger.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── pydevd_thread_wrappers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_concurrency_logger.py\n", + "│ │ │ │ │ │ │ └── pydevd_thread_wrappers.py\n", + "│ │ │ │ │ │ ├── pydevd_console.py\n", + "│ │ │ │ │ │ ├── pydevd_constants.py\n", + "│ │ │ │ │ │ ├── pydevd_custom_frames.py\n", + "│ │ │ │ │ │ ├── pydevd_cython.c\n", + "│ │ │ │ │ │ ├── pydevd_cython.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── pydevd_cython.pxd\n", + "│ │ │ │ │ │ ├── pydevd_cython.pyx\n", + "│ │ │ │ │ │ ├── pydevd_cython_wrapper.py\n", + "│ │ │ │ │ │ ├── pydevd_daemon_thread.py\n", + "│ │ │ │ │ │ ├── pydevd_defaults.py\n", + "│ │ │ │ │ │ ├── pydevd_dont_trace.py\n", + "│ │ │ │ │ │ ├── pydevd_dont_trace_files.py\n", + "│ │ │ │ │ │ ├── pydevd_exec2.py\n", + "│ │ │ │ │ │ ├── pydevd_extension_api.py\n", + "│ │ │ │ │ │ ├── pydevd_extension_utils.py\n", + "│ │ │ │ │ │ ├── pydevd_filtering.py\n", + "│ │ │ │ │ │ ├── pydevd_frame.py\n", + "│ │ │ │ │ │ ├── pydevd_frame_utils.py\n", + "│ │ │ │ │ │ ├── pydevd_gevent_integration.py\n", + "│ │ │ │ │ │ ├── pydevd_import_class.py\n", + "│ │ │ │ │ │ ├── pydevd_io.py\n", + "│ │ │ │ │ │ ├── pydevd_json_debug_options.py\n", + "│ │ │ │ │ │ ├── pydevd_net_command.py\n", + "│ │ │ │ │ │ ├── pydevd_net_command_factory_json.py\n", + "│ │ │ │ │ │ ├── pydevd_net_command_factory_xml.py\n", + "│ │ │ │ │ │ ├── pydevd_plugin_utils.py\n", + "│ │ │ │ │ │ ├── pydevd_process_net_command.py\n", + "│ │ │ │ │ │ ├── pydevd_process_net_command_json.py\n", + "│ │ │ │ │ │ ├── pydevd_referrers.py\n", + "│ │ │ │ │ │ ├── pydevd_reload.py\n", + "│ │ │ │ │ │ ├── pydevd_resolver.py\n", + "│ │ │ │ │ │ ├── pydevd_runpy.py\n", + "│ │ │ │ │ │ ├── pydevd_safe_repr.py\n", + "│ │ │ │ │ │ ├── pydevd_save_locals.py\n", + "│ │ │ │ │ │ ├── pydevd_signature.py\n", + "│ │ │ │ │ │ ├── pydevd_source_mapping.py\n", + "│ │ │ │ │ │ ├── pydevd_stackless.py\n", + "│ │ │ │ │ │ ├── pydevd_suspended_frames.py\n", + "│ │ │ │ │ │ ├── pydevd_thread_lifecycle.py\n", + "│ │ │ │ │ │ ├── pydevd_timeout.py\n", + "│ │ │ │ │ │ ├── pydevd_trace_api.py\n", + "│ │ │ │ │ │ ├── pydevd_trace_dispatch.py\n", + "│ │ │ │ │ │ ├── pydevd_trace_dispatch_regular.py\n", + "│ │ │ │ │ │ ├── pydevd_traceproperty.py\n", + "│ │ │ │ │ │ ├── pydevd_utils.py\n", + "│ │ │ │ │ │ ├── pydevd_vars.py\n", + "│ │ │ │ │ │ ├── pydevd_vm_type.py\n", + "│ │ │ │ │ │ └── pydevd_xml.py\n", + "│ │ │ │ │ ├── _pydevd_frame_eval\n", + "│ │ │ │ │ │ ├── .gitignore\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_frame_eval_cython_wrapper.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_frame_eval_main.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_frame_tracing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── pydevd_modify_bytecode.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pydevd_frame_eval_cython_wrapper.py\n", + "│ │ │ │ │ │ ├── pydevd_frame_eval_main.py\n", + "│ │ │ │ │ │ ├── pydevd_frame_evaluator.c\n", + "│ │ │ │ │ │ ├── pydevd_frame_evaluator.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── pydevd_frame_evaluator.pxd\n", + "│ │ │ │ │ │ ├── pydevd_frame_evaluator.pyx\n", + "│ │ │ │ │ │ ├── pydevd_frame_evaluator.template.pyx\n", + "│ │ │ │ │ │ ├── pydevd_frame_tracing.py\n", + "│ │ │ │ │ │ ├── pydevd_modify_bytecode.py\n", + "│ │ │ │ │ │ ├── release_mem.h\n", + "│ │ │ │ │ │ └── vendored\n", + "│ │ │ │ │ │ ├── README.txt\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── pydevd_fix_code.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── bytecode\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── bytecode.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── cfg.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── concrete.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── flags.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── instr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── peephole_opt.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── bytecode.py\n", + "│ │ │ │ │ │ │ ├── cfg.py\n", + "│ │ │ │ │ │ │ ├── concrete.py\n", + "│ │ │ │ │ │ │ ├── flags.py\n", + "│ │ │ │ │ │ │ ├── instr.py\n", + "│ │ │ │ │ │ │ ├── peephole_opt.py\n", + "│ │ │ │ │ │ │ └── tests\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_bytecode.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_cfg.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_code.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_concrete.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_flags.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_instr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_misc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_peephole_opt.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── util_annotation.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_bytecode.py\n", + "│ │ │ │ │ │ │ ├── test_cfg.py\n", + "│ │ │ │ │ │ │ ├── test_code.py\n", + "│ │ │ │ │ │ │ ├── test_concrete.py\n", + "│ │ │ │ │ │ │ ├── test_flags.py\n", + "│ │ │ │ │ │ │ ├── test_instr.py\n", + "│ │ │ │ │ │ │ ├── test_misc.py\n", + "│ │ │ │ │ │ │ ├── test_peephole_opt.py\n", + "│ │ │ │ │ │ │ └── util_annotation.py\n", + "│ │ │ │ │ │ ├── bytecode-0.13.0.dev0.dist-info\n", + "│ │ │ │ │ │ │ ├── COPYING\n", + "│ │ │ │ │ │ │ ├── INSTALLER\n", + "│ │ │ │ │ │ │ ├── METADATA\n", + "│ │ │ │ │ │ │ ├── RECORD\n", + "│ │ │ │ │ │ │ ├── REQUESTED\n", + "│ │ │ │ │ │ │ ├── WHEEL\n", + "│ │ │ │ │ │ │ ├── direct_url.json\n", + "│ │ │ │ │ │ │ └── top_level.txt\n", + "│ │ │ │ │ │ └── pydevd_fix_code.py\n", + "│ │ │ │ │ ├── pydev_app_engine_debug_startup.py\n", + "│ │ │ │ │ ├── pydev_coverage.py\n", + "│ │ │ │ │ ├── pydev_ipython\n", + "│ │ │ │ │ │ ├── README\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── inputhook.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── inputhookglut.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── inputhookgtk.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── inputhookgtk3.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── inputhookpyglet.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── inputhookqt4.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── inputhookqt5.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── inputhooktk.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── inputhookwx.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── matplotlibtools.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── qt.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── qt_for_kernel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── qt_loaders.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── version.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── inputhook.py\n", + "│ │ │ │ │ │ ├── inputhookglut.py\n", + "│ │ │ │ │ │ ├── inputhookgtk.py\n", + "│ │ │ │ │ │ ├── inputhookgtk3.py\n", + "│ │ │ │ │ │ ├── inputhookpyglet.py\n", + "│ │ │ │ │ │ ├── inputhookqt4.py\n", + "│ │ │ │ │ │ ├── inputhookqt5.py\n", + "│ │ │ │ │ │ ├── inputhooktk.py\n", + "│ │ │ │ │ │ ├── inputhookwx.py\n", + "│ │ │ │ │ │ ├── matplotlibtools.py\n", + "│ │ │ │ │ │ ├── qt.py\n", + "│ │ │ │ │ │ ├── qt_for_kernel.py\n", + "│ │ │ │ │ │ ├── qt_loaders.py\n", + "│ │ │ │ │ │ └── version.py\n", + "│ │ │ │ │ ├── pydev_pysrc.py\n", + "│ │ │ │ │ ├── pydev_run_in_console.py\n", + "│ │ │ │ │ ├── pydev_sitecustomize\n", + "│ │ │ │ │ │ ├── __not_in_default_pythonpath.txt\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ └── sitecustomize.cpython-310.pyc\n", + "│ │ │ │ │ │ └── sitecustomize.py\n", + "│ │ │ │ │ ├── pydevconsole.py\n", + "│ │ │ │ │ ├── pydevd.py\n", + "│ │ │ │ │ ├── pydevd_attach_to_process\n", + "│ │ │ │ │ │ ├── README.txt\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── _always_live_program.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _check.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _test_attach_to_process.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _test_attach_to_process_linux.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── add_code_to_python_process.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── attach_pydevd.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── attach_script.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _always_live_program.py\n", + "│ │ │ │ │ │ ├── _check.py\n", + "│ │ │ │ │ │ ├── _test_attach_to_process.py\n", + "│ │ │ │ │ │ ├── _test_attach_to_process_linux.py\n", + "│ │ │ │ │ │ ├── add_code_to_python_process.py\n", + "│ │ │ │ │ │ ├── attach_linux_amd64.so\n", + "│ │ │ │ │ │ ├── attach_pydevd.py\n", + "│ │ │ │ │ │ ├── attach_script.py\n", + "│ │ │ │ │ │ ├── common\n", + "│ │ │ │ │ │ │ ├── py_custom_pyeval_settrace.hpp\n", + "│ │ │ │ │ │ │ ├── py_custom_pyeval_settrace_310.hpp\n", + "│ │ │ │ │ │ │ ├── py_custom_pyeval_settrace_311.hpp\n", + "│ │ │ │ │ │ │ ├── py_custom_pyeval_settrace_common.hpp\n", + "│ │ │ │ │ │ │ ├── py_settrace.hpp\n", + "│ │ │ │ │ │ │ ├── py_utils.hpp\n", + "│ │ │ │ │ │ │ ├── py_version.hpp\n", + "│ │ │ │ │ │ │ ├── python.h\n", + "│ │ │ │ │ │ │ └── ref_utils.hpp\n", + "│ │ │ │ │ │ ├── linux_and_mac\n", + "│ │ │ │ │ │ │ ├── .gitignore\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ └── lldb_prepare.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── attach.cpp\n", + "│ │ │ │ │ │ │ ├── compile_linux.sh\n", + "│ │ │ │ │ │ │ ├── compile_mac.sh\n", + "│ │ │ │ │ │ │ ├── compile_manylinux.cmd\n", + "│ │ │ │ │ │ │ └── lldb_prepare.py\n", + "│ │ │ │ │ │ ├── winappdbg\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── breakpoint.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── crash.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── debug.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── disasm.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── event.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── interactive.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── module.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── process.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── registry.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── search.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── sql.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── system.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── textio.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── thread.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── util.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── window.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── breakpoint.py\n", + "│ │ │ │ │ │ │ ├── compat.py\n", + "│ │ │ │ │ │ │ ├── crash.py\n", + "│ │ │ │ │ │ │ ├── debug.py\n", + "│ │ │ │ │ │ │ ├── disasm.py\n", + "│ │ │ │ │ │ │ ├── event.py\n", + "│ │ │ │ │ │ │ ├── interactive.py\n", + "│ │ │ │ │ │ │ ├── module.py\n", + "│ │ │ │ │ │ │ ├── process.py\n", + "│ │ │ │ │ │ │ ├── registry.py\n", + "│ │ │ │ │ │ │ ├── search.py\n", + "│ │ │ │ │ │ │ ├── sql.py\n", + "│ │ │ │ │ │ │ ├── system.py\n", + "│ │ │ │ │ │ │ ├── textio.py\n", + "│ │ │ │ │ │ │ ├── thread.py\n", + "│ │ │ │ │ │ │ ├── util.py\n", + "│ │ │ │ │ │ │ ├── win32\n", + "│ │ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── advapi32.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── context_amd64.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── context_i386.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── dbghelp.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── defines.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── gdi32.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── kernel32.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── ntdll.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── peb_teb.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── psapi.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── shell32.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── shlwapi.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── user32.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── version.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ └── wtsapi32.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── advapi32.py\n", + "│ │ │ │ │ │ │ │ ├── context_amd64.py\n", + "│ │ │ │ │ │ │ │ ├── context_i386.py\n", + "│ │ │ │ │ │ │ │ ├── dbghelp.py\n", + "│ │ │ │ │ │ │ │ ├── defines.py\n", + "│ │ │ │ │ │ │ │ ├── gdi32.py\n", + "│ │ │ │ │ │ │ │ ├── kernel32.py\n", + "│ │ │ │ │ │ │ │ ├── ntdll.py\n", + "│ │ │ │ │ │ │ │ ├── peb_teb.py\n", + "│ │ │ │ │ │ │ │ ├── psapi.py\n", + "│ │ │ │ │ │ │ │ ├── shell32.py\n", + "│ │ │ │ │ │ │ │ ├── shlwapi.py\n", + "│ │ │ │ │ │ │ │ ├── user32.py\n", + "│ │ │ │ │ │ │ │ ├── version.py\n", + "│ │ │ │ │ │ │ │ └── wtsapi32.py\n", + "│ │ │ │ │ │ │ └── window.py\n", + "│ │ │ │ │ │ └── windows\n", + "│ │ │ │ │ │ ├── attach.cpp\n", + "│ │ │ │ │ │ ├── attach.h\n", + "│ │ │ │ │ │ ├── compile_windows.bat\n", + "│ │ │ │ │ │ ├── inject_dll.cpp\n", + "│ │ │ │ │ │ ├── py_win_helpers.hpp\n", + "│ │ │ │ │ │ ├── run_code_in_memory.hpp\n", + "│ │ │ │ │ │ ├── run_code_on_dllmain.cpp\n", + "│ │ │ │ │ │ ├── stdafx.cpp\n", + "│ │ │ │ │ │ ├── stdafx.h\n", + "│ │ │ │ │ │ └── targetver.h\n", + "│ │ │ │ │ ├── pydevd_file_utils.py\n", + "│ │ │ │ │ ├── pydevd_plugins\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── django_debug.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── jinja2_debug.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── pydevd_line_validation.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── django_debug.py\n", + "│ │ │ │ │ │ ├── extensions\n", + "│ │ │ │ │ │ │ ├── README.md\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── types\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── pydevd_helpers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── pydevd_plugin_numpy_types.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── pydevd_plugin_pandas_types.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── pydevd_plugins_django_form_str.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_helpers.py\n", + "│ │ │ │ │ │ │ ├── pydevd_plugin_numpy_types.py\n", + "│ │ │ │ │ │ │ ├── pydevd_plugin_pandas_types.py\n", + "│ │ │ │ │ │ │ └── pydevd_plugins_django_form_str.py\n", + "│ │ │ │ │ │ ├── jinja2_debug.py\n", + "│ │ │ │ │ │ └── pydevd_line_validation.py\n", + "│ │ │ │ │ ├── pydevd_tracing.py\n", + "│ │ │ │ │ └── setup_pydevd_cython.py\n", + "│ │ │ │ ├── _version.py\n", + "│ │ │ │ ├── adapter\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __main__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── clients.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── components.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── launchers.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── servers.cpython-310.pyc\n", + "│ │ │ │ │ │ └── sessions.cpython-310.pyc\n", + "│ │ │ │ │ ├── clients.py\n", + "│ │ │ │ │ ├── components.py\n", + "│ │ │ │ │ ├── launchers.py\n", + "│ │ │ │ │ ├── servers.py\n", + "│ │ │ │ │ └── sessions.py\n", + "│ │ │ │ ├── common\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── json.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── log.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── messaging.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── singleton.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── sockets.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── stacks.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── timestamp.cpython-310.pyc\n", + "│ │ │ │ │ │ └── util.cpython-310.pyc\n", + "│ │ │ │ │ ├── json.py\n", + "│ │ │ │ │ ├── log.py\n", + "│ │ │ │ │ ├── messaging.py\n", + "│ │ │ │ │ ├── singleton.py\n", + "│ │ │ │ │ ├── sockets.py\n", + "│ │ │ │ │ ├── stacks.py\n", + "│ │ │ │ │ ├── timestamp.py\n", + "│ │ │ │ │ └── util.py\n", + "│ │ │ │ ├── launcher\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __main__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── debuggee.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── handlers.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── output.cpython-310.pyc\n", + "│ │ │ │ │ │ └── winapi.cpython-310.pyc\n", + "│ │ │ │ │ ├── debuggee.py\n", + "│ │ │ │ │ ├── handlers.py\n", + "│ │ │ │ │ ├── output.py\n", + "│ │ │ │ │ └── winapi.py\n", + "│ │ │ │ ├── public_api.py\n", + "│ │ │ │ └── server\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── api.cpython-310.pyc\n", + "│ │ │ │ │ ├── attach_pid_injected.cpython-310.pyc\n", + "│ │ │ │ │ └── cli.cpython-310.pyc\n", + "│ │ │ │ ├── api.py\n", + "│ │ │ │ ├── attach_pid_injected.py\n", + "│ │ │ │ └── cli.py\n", + "│ │ │ ├── debugpy-1.8.1.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── decorator-5.1.1.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE.txt\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ ├── pbr.json\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── decorator.py\n", + "│ │ │ ├── distutils-precedence.pth\n", + "│ │ │ ├── exceptiongroup\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _catch.cpython-310.pyc\n", + "│ │ │ │ │ ├── _exceptions.cpython-310.pyc\n", + "│ │ │ │ │ ├── _formatting.cpython-310.pyc\n", + "│ │ │ │ │ ├── _suppress.cpython-310.pyc\n", + "│ │ │ │ │ └── _version.cpython-310.pyc\n", + "│ │ │ │ ├── _catch.py\n", + "│ │ │ │ ├── _exceptions.py\n", + "│ │ │ │ ├── _formatting.py\n", + "│ │ │ │ ├── _suppress.py\n", + "│ │ │ │ ├── _version.py\n", + "│ │ │ │ └── py.typed\n", + "│ │ │ ├── exceptiongroup-1.2.1.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ └── WHEEL\n", + "│ │ │ ├── executing\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _exceptions.cpython-310.pyc\n", + "│ │ │ │ │ ├── _position_node_finder.cpython-310.pyc\n", + "│ │ │ │ │ ├── executing.cpython-310.pyc\n", + "│ │ │ │ │ └── version.cpython-310.pyc\n", + "│ │ │ │ ├── _exceptions.py\n", + "│ │ │ │ ├── _position_node_finder.py\n", + "│ │ │ │ ├── executing.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ └── version.py\n", + "│ │ │ ├── executing-2.0.1.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE.txt\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── ipykernel\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __main__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _eventloop_macos.cpython-310.pyc\n", + "│ │ │ │ │ ├── _version.cpython-310.pyc\n", + "│ │ │ │ │ ├── compiler.cpython-310.pyc\n", + "│ │ │ │ │ ├── connect.cpython-310.pyc\n", + "│ │ │ │ │ ├── control.cpython-310.pyc\n", + "│ │ │ │ │ ├── datapub.cpython-310.pyc\n", + "│ │ │ │ │ ├── debugger.cpython-310.pyc\n", + "│ │ │ │ │ ├── displayhook.cpython-310.pyc\n", + "│ │ │ │ │ ├── embed.cpython-310.pyc\n", + "│ │ │ │ │ ├── eventloops.cpython-310.pyc\n", + "│ │ │ │ │ ├── heartbeat.cpython-310.pyc\n", + "│ │ │ │ │ ├── iostream.cpython-310.pyc\n", + "│ │ │ │ │ ├── ipkernel.cpython-310.pyc\n", + "│ │ │ │ │ ├── jsonutil.cpython-310.pyc\n", + "│ │ │ │ │ ├── kernelapp.cpython-310.pyc\n", + "│ │ │ │ │ ├── kernelbase.cpython-310.pyc\n", + "│ │ │ │ │ ├── kernelspec.cpython-310.pyc\n", + "│ │ │ │ │ ├── log.cpython-310.pyc\n", + "│ │ │ │ │ ├── parentpoller.cpython-310.pyc\n", + "│ │ │ │ │ ├── pickleutil.cpython-310.pyc\n", + "│ │ │ │ │ ├── serialize.cpython-310.pyc\n", + "│ │ │ │ │ ├── trio_runner.cpython-310.pyc\n", + "│ │ │ │ │ └── zmqshell.cpython-310.pyc\n", + "│ │ │ │ ├── _eventloop_macos.py\n", + "│ │ │ │ ├── _version.py\n", + "│ │ │ │ ├── comm\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── comm.cpython-310.pyc\n", + "│ │ │ │ │ │ └── manager.cpython-310.pyc\n", + "│ │ │ │ │ ├── comm.py\n", + "│ │ │ │ │ └── manager.py\n", + "│ │ │ │ ├── compiler.py\n", + "│ │ │ │ ├── connect.py\n", + "│ │ │ │ ├── control.py\n", + "│ │ │ │ ├── datapub.py\n", + "│ │ │ │ ├── debugger.py\n", + "│ │ │ │ ├── displayhook.py\n", + "│ │ │ │ ├── embed.py\n", + "│ │ │ │ ├── eventloops.py\n", + "│ │ │ │ ├── gui\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── gtk3embed.cpython-310.pyc\n", + "│ │ │ │ │ │ └── gtkembed.cpython-310.pyc\n", + "│ │ │ │ │ ├── gtk3embed.py\n", + "│ │ │ │ │ └── gtkembed.py\n", + "│ │ │ │ ├── heartbeat.py\n", + "│ │ │ │ ├── inprocess\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── blocking.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── channels.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── client.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── constants.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ipkernel.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── manager.cpython-310.pyc\n", + "│ │ │ │ │ │ └── socket.cpython-310.pyc\n", + "│ │ │ │ │ ├── blocking.py\n", + "│ │ │ │ │ ├── channels.py\n", + "│ │ │ │ │ ├── client.py\n", + "│ │ │ │ │ ├── constants.py\n", + "│ │ │ │ │ ├── ipkernel.py\n", + "│ │ │ │ │ ├── manager.py\n", + "│ │ │ │ │ └── socket.py\n", + "│ │ │ │ ├── iostream.py\n", + "│ │ │ │ ├── ipkernel.py\n", + "│ │ │ │ ├── jsonutil.py\n", + "│ │ │ │ ├── kernelapp.py\n", + "│ │ │ │ ├── kernelbase.py\n", + "│ │ │ │ ├── kernelspec.py\n", + "│ │ │ │ ├── log.py\n", + "│ │ │ │ ├── parentpoller.py\n", + "│ │ │ │ ├── pickleutil.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ ├── pylab\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── backend_inline.cpython-310.pyc\n", + "│ │ │ │ │ │ └── config.cpython-310.pyc\n", + "│ │ │ │ │ ├── backend_inline.py\n", + "│ │ │ │ │ └── config.py\n", + "│ │ │ │ ├── resources\n", + "│ │ │ │ │ ├── logo-32x32.png\n", + "│ │ │ │ │ ├── logo-64x64.png\n", + "│ │ │ │ │ └── logo-svg.svg\n", + "│ │ │ │ ├── serialize.py\n", + "│ │ │ │ ├── trio_runner.py\n", + "│ │ │ │ └── zmqshell.py\n", + "│ │ │ ├── ipykernel-6.29.4.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── REQUESTED\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── licenses\n", + "│ │ │ │ └── LICENSE\n", + "│ │ │ ├── ipykernel_launcher.py\n", + "│ │ │ ├── ipython-8.24.0.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ ├── entry_points.txt\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── jedi\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __main__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _compatibility.cpython-310.pyc\n", + "│ │ │ │ │ ├── cache.cpython-310.pyc\n", + "│ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ ├── debug.cpython-310.pyc\n", + "│ │ │ │ │ ├── file_io.cpython-310.pyc\n", + "│ │ │ │ │ ├── parser_utils.cpython-310.pyc\n", + "│ │ │ │ │ ├── settings.cpython-310.pyc\n", + "│ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ ├── _compatibility.py\n", + "│ │ │ │ ├── api\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── classes.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── completion.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── completion_cache.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── environment.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── errors.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── exceptions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── file_name.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── helpers.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── interpreter.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── keywords.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── project.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── replstartup.cpython-310.pyc\n", + "│ │ │ │ │ │ └── strings.cpython-310.pyc\n", + "│ │ │ │ │ ├── classes.py\n", + "│ │ │ │ │ ├── completion.py\n", + "│ │ │ │ │ ├── completion_cache.py\n", + "│ │ │ │ │ ├── environment.py\n", + "│ │ │ │ │ ├── errors.py\n", + "│ │ │ │ │ ├── exceptions.py\n", + "│ │ │ │ │ ├── file_name.py\n", + "│ │ │ │ │ ├── helpers.py\n", + "│ │ │ │ │ ├── interpreter.py\n", + "│ │ │ │ │ ├── keywords.py\n", + "│ │ │ │ │ ├── project.py\n", + "│ │ │ │ │ ├── refactoring\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── extract.cpython-310.pyc\n", + "│ │ │ │ │ │ └── extract.py\n", + "│ │ │ │ │ ├── replstartup.py\n", + "│ │ │ │ │ └── strings.py\n", + "│ │ │ │ ├── cache.py\n", + "│ │ │ │ ├── common.py\n", + "│ │ │ │ ├── debug.py\n", + "│ │ │ │ ├── file_io.py\n", + "│ │ │ │ ├── inference\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── analysis.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── arguments.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base_value.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cache.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── context.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── docstring_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── docstrings.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dynamic_params.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── filters.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── finder.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── flow_analysis.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── helpers.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── imports.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── lazy_value.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── names.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── param.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── parser_cache.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── recursion.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── references.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── signature.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── star_args.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── syntax_tree.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── sys_path.cpython-310.pyc\n", + "│ │ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ │ ├── analysis.py\n", + "│ │ │ │ │ ├── arguments.py\n", + "│ │ │ │ │ ├── base_value.py\n", + "│ │ │ │ │ ├── cache.py\n", + "│ │ │ │ │ ├── compiled\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── access.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── getattr_static.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── mixed.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── value.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── access.py\n", + "│ │ │ │ │ │ ├── getattr_static.py\n", + "│ │ │ │ │ │ ├── mixed.py\n", + "│ │ │ │ │ │ ├── subprocess\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __main__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── functions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── functions.py\n", + "│ │ │ │ │ │ └── value.py\n", + "│ │ │ │ │ ├── context.py\n", + "│ │ │ │ │ ├── docstring_utils.py\n", + "│ │ │ │ │ ├── docstrings.py\n", + "│ │ │ │ │ ├── dynamic_params.py\n", + "│ │ │ │ │ ├── filters.py\n", + "│ │ │ │ │ ├── finder.py\n", + "│ │ │ │ │ ├── flow_analysis.py\n", + "│ │ │ │ │ ├── gradual\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── annotation.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conversion.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── generics.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── stub_value.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── type_var.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── typeshed.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── typing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── annotation.py\n", + "│ │ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ │ ├── conversion.py\n", + "│ │ │ │ │ │ ├── generics.py\n", + "│ │ │ │ │ │ ├── stub_value.py\n", + "│ │ │ │ │ │ ├── type_var.py\n", + "│ │ │ │ │ │ ├── typeshed.py\n", + "│ │ │ │ │ │ ├── typing.py\n", + "│ │ │ │ │ │ └── utils.py\n", + "│ │ │ │ │ ├── helpers.py\n", + "│ │ │ │ │ ├── imports.py\n", + "│ │ │ │ │ ├── lazy_value.py\n", + "│ │ │ │ │ ├── names.py\n", + "│ │ │ │ │ ├── param.py\n", + "│ │ │ │ │ ├── parser_cache.py\n", + "│ │ │ │ │ ├── recursion.py\n", + "│ │ │ │ │ ├── references.py\n", + "│ │ │ │ │ ├── signature.py\n", + "│ │ │ │ │ ├── star_args.py\n", + "│ │ │ │ │ ├── syntax_tree.py\n", + "│ │ │ │ │ ├── sys_path.py\n", + "│ │ │ │ │ ├── utils.py\n", + "│ │ │ │ │ └── value\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── decorator.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dynamic_arrays.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── function.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── instance.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── iterable.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── klass.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── module.cpython-310.pyc\n", + "│ │ │ │ │ │ └── namespace.cpython-310.pyc\n", + "│ │ │ │ │ ├── decorator.py\n", + "│ │ │ │ │ ├── dynamic_arrays.py\n", + "│ │ │ │ │ ├── function.py\n", + "│ │ │ │ │ ├── instance.py\n", + "│ │ │ │ │ ├── iterable.py\n", + "│ │ │ │ │ ├── klass.py\n", + "│ │ │ │ │ ├── module.py\n", + "│ │ │ │ │ └── namespace.py\n", + "│ │ │ │ ├── parser_utils.py\n", + "│ │ │ │ ├── plugins\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── django.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── flask.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pytest.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── registry.cpython-310.pyc\n", + "│ │ │ │ │ │ └── stdlib.cpython-310.pyc\n", + "│ │ │ │ │ ├── django.py\n", + "│ │ │ │ │ ├── flask.py\n", + "│ │ │ │ │ ├── pytest.py\n", + "│ │ │ │ │ ├── registry.py\n", + "│ │ │ │ │ └── stdlib.py\n", + "│ │ │ │ ├── settings.py\n", + "│ │ │ │ ├── third_party\n", + "│ │ │ │ │ ├── django-stubs\n", + "│ │ │ │ │ │ ├── LICENSE.txt\n", + "│ │ │ │ │ │ └── django-stubs\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ ├── apps\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── config.pyi\n", + "│ │ │ │ │ │ │ └── registry.pyi\n", + "│ │ │ │ │ │ ├── conf\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── global_settings.pyi\n", + "│ │ │ │ │ │ │ ├── locale\n", + "│ │ │ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ │ │ └── urls\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── i18n.pyi\n", + "│ │ │ │ │ │ │ └── static.pyi\n", + "│ │ │ │ │ │ ├── contrib\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── admin\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── actions.pyi\n", + "│ │ │ │ │ │ │ │ ├── apps.pyi\n", + "│ │ │ │ │ │ │ │ ├── checks.pyi\n", + "│ │ │ │ │ │ │ │ ├── decorators.pyi\n", + "│ │ │ │ │ │ │ │ ├── filters.pyi\n", + "│ │ │ │ │ │ │ │ ├── forms.pyi\n", + "│ │ │ │ │ │ │ │ ├── helpers.pyi\n", + "│ │ │ │ │ │ │ │ ├── models.pyi\n", + "│ │ │ │ │ │ │ │ ├── options.pyi\n", + "│ │ │ │ │ │ │ │ ├── sites.pyi\n", + "│ │ │ │ │ │ │ │ ├── templatetags\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── admin_list.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── admin_modify.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── admin_static.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── admin_urls.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ │ └── log.pyi\n", + "│ │ │ │ │ │ │ │ ├── tests.pyi\n", + "│ │ │ │ │ │ │ │ ├── utils.pyi\n", + "│ │ │ │ │ │ │ │ ├── views\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── autocomplete.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── decorators.pyi\n", + "│ │ │ │ │ │ │ │ │ └── main.pyi\n", + "│ │ │ │ │ │ │ │ └── widgets.pyi\n", + "│ │ │ │ │ │ │ ├── admindocs\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── middleware.pyi\n", + "│ │ │ │ │ │ │ │ ├── urls.pyi\n", + "│ │ │ │ │ │ │ │ ├── utils.pyi\n", + "│ │ │ │ │ │ │ │ └── views.pyi\n", + "│ │ │ │ │ │ │ ├── auth\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── admin.pyi\n", + "│ │ │ │ │ │ │ │ ├── apps.pyi\n", + "│ │ │ │ │ │ │ │ ├── backends.pyi\n", + "│ │ │ │ │ │ │ │ ├── base_user.pyi\n", + "│ │ │ │ │ │ │ │ ├── checks.pyi\n", + "│ │ │ │ │ │ │ │ ├── context_processors.pyi\n", + "│ │ │ │ │ │ │ │ ├── decorators.pyi\n", + "│ │ │ │ │ │ │ │ ├── forms.pyi\n", + "│ │ │ │ │ │ │ │ ├── handlers\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── modwsgi.pyi\n", + "│ │ │ │ │ │ │ │ ├── hashers.pyi\n", + "│ │ │ │ │ │ │ │ ├── management\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── commands\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── changepassword.pyi\n", + "│ │ │ │ │ │ │ │ │ └── createsuperuser.pyi\n", + "│ │ │ │ │ │ │ │ ├── middleware.pyi\n", + "│ │ │ │ │ │ │ │ ├── mixins.pyi\n", + "│ │ │ │ │ │ │ │ ├── models.pyi\n", + "│ │ │ │ │ │ │ │ ├── password_validation.pyi\n", + "│ │ │ │ │ │ │ │ ├── signals.pyi\n", + "│ │ │ │ │ │ │ │ ├── tokens.pyi\n", + "│ │ │ │ │ │ │ │ ├── urls.pyi\n", + "│ │ │ │ │ │ │ │ ├── validators.pyi\n", + "│ │ │ │ │ │ │ │ └── views.pyi\n", + "│ │ │ │ │ │ │ ├── contenttypes\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── admin.pyi\n", + "│ │ │ │ │ │ │ │ ├── apps.pyi\n", + "│ │ │ │ │ │ │ │ ├── checks.pyi\n", + "│ │ │ │ │ │ │ │ ├── fields.pyi\n", + "│ │ │ │ │ │ │ │ ├── forms.pyi\n", + "│ │ │ │ │ │ │ │ ├── management\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── commands\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── remove_stale_contenttypes.pyi\n", + "│ │ │ │ │ │ │ │ ├── models.pyi\n", + "│ │ │ │ │ │ │ │ └── views.pyi\n", + "│ │ │ │ │ │ │ ├── flatpages\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── forms.pyi\n", + "│ │ │ │ │ │ │ │ ├── middleware.pyi\n", + "│ │ │ │ │ │ │ │ ├── models.pyi\n", + "│ │ │ │ │ │ │ │ ├── sitemaps.pyi\n", + "│ │ │ │ │ │ │ │ ├── templatetags\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── flatpages.pyi\n", + "│ │ │ │ │ │ │ │ ├── urls.pyi\n", + "│ │ │ │ │ │ │ │ └── views.pyi\n", + "│ │ │ │ │ │ │ ├── gis\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── db\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── models\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── fields.pyi\n", + "│ │ │ │ │ │ │ ├── humanize\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── templatetags\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── humanize.pyi\n", + "│ │ │ │ │ │ │ ├── messages\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── api.pyi\n", + "│ │ │ │ │ │ │ │ ├── constants.pyi\n", + "│ │ │ │ │ │ │ │ ├── context_processors.pyi\n", + "│ │ │ │ │ │ │ │ ├── middleware.pyi\n", + "│ │ │ │ │ │ │ │ ├── storage\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── cookie.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── fallback.pyi\n", + "│ │ │ │ │ │ │ │ │ └── session.pyi\n", + "│ │ │ │ │ │ │ │ ├── utils.pyi\n", + "│ │ │ │ │ │ │ │ └── views.pyi\n", + "│ │ │ │ │ │ │ ├── postgres\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── aggregates\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── general.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── mixins.pyi\n", + "│ │ │ │ │ │ │ │ │ └── statistics.pyi\n", + "│ │ │ │ │ │ │ │ ├── constraints.pyi\n", + "│ │ │ │ │ │ │ │ ├── fields\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── array.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── citext.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── hstore.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── jsonb.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── mixins.pyi\n", + "│ │ │ │ │ │ │ │ │ └── ranges.pyi\n", + "│ │ │ │ │ │ │ │ ├── functions.pyi\n", + "│ │ │ │ │ │ │ │ ├── indexes.pyi\n", + "│ │ │ │ │ │ │ │ ├── lookups.pyi\n", + "│ │ │ │ │ │ │ │ ├── operations.pyi\n", + "│ │ │ │ │ │ │ │ ├── search.pyi\n", + "│ │ │ │ │ │ │ │ ├── signals.pyi\n", + "│ │ │ │ │ │ │ │ └── validators.pyi\n", + "│ │ │ │ │ │ │ ├── redirects\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── middleware.pyi\n", + "│ │ │ │ │ │ │ │ └── models.pyi\n", + "│ │ │ │ │ │ │ ├── sessions\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── backends\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── cache.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── cached_db.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── db.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── file.pyi\n", + "│ │ │ │ │ │ │ │ │ └── signed_cookies.pyi\n", + "│ │ │ │ │ │ │ │ ├── base_session.pyi\n", + "│ │ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ │ ├── management\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── commands\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── clearsessions.pyi\n", + "│ │ │ │ │ │ │ │ ├── middleware.pyi\n", + "│ │ │ │ │ │ │ │ ├── models.pyi\n", + "│ │ │ │ │ │ │ │ └── serializers.pyi\n", + "│ │ │ │ │ │ │ ├── sitemaps\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── management\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── commands\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── ping_google.pyi\n", + "│ │ │ │ │ │ │ │ └── views.pyi\n", + "│ │ │ │ │ │ │ ├── sites\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── apps.pyi\n", + "│ │ │ │ │ │ │ │ ├── management.pyi\n", + "│ │ │ │ │ │ │ │ ├── managers.pyi\n", + "│ │ │ │ │ │ │ │ ├── middleware.pyi\n", + "│ │ │ │ │ │ │ │ ├── models.pyi\n", + "│ │ │ │ │ │ │ │ ├── requests.pyi\n", + "│ │ │ │ │ │ │ │ └── shortcuts.pyi\n", + "│ │ │ │ │ │ │ ├── staticfiles\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── apps.pyi\n", + "│ │ │ │ │ │ │ │ ├── checks.pyi\n", + "│ │ │ │ │ │ │ │ ├── finders.pyi\n", + "│ │ │ │ │ │ │ │ ├── handlers.pyi\n", + "│ │ │ │ │ │ │ │ ├── management\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── commands\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── collectstatic.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── findstatic.pyi\n", + "│ │ │ │ │ │ │ │ │ └── runserver.pyi\n", + "│ │ │ │ │ │ │ │ ├── storage.pyi\n", + "│ │ │ │ │ │ │ │ ├── templatetags\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── staticfiles.pyi\n", + "│ │ │ │ │ │ │ │ ├── testing.pyi\n", + "│ │ │ │ │ │ │ │ ├── urls.pyi\n", + "│ │ │ │ │ │ │ │ ├── utils.pyi\n", + "│ │ │ │ │ │ │ │ └── views.pyi\n", + "│ │ │ │ │ │ │ └── syndication\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ └── views.pyi\n", + "│ │ │ │ │ │ ├── core\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── cache\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── backends\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── db.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── dummy.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── filebased.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── locmem.pyi\n", + "│ │ │ │ │ │ │ │ │ └── memcached.pyi\n", + "│ │ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ │ ├── checks\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── caches.pyi\n", + "│ │ │ │ │ │ │ │ ├── database.pyi\n", + "│ │ │ │ │ │ │ │ ├── messages.pyi\n", + "│ │ │ │ │ │ │ │ ├── model_checks.pyi\n", + "│ │ │ │ │ │ │ │ ├── registry.pyi\n", + "│ │ │ │ │ │ │ │ ├── security\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── csrf.pyi\n", + "│ │ │ │ │ │ │ │ │ └── sessions.pyi\n", + "│ │ │ │ │ │ │ │ ├── templates.pyi\n", + "│ │ │ │ │ │ │ │ ├── translation.pyi\n", + "│ │ │ │ │ │ │ │ └── urls.pyi\n", + "│ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ ├── files\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ ├── images.pyi\n", + "│ │ │ │ │ │ │ │ ├── locks.pyi\n", + "│ │ │ │ │ │ │ │ ├── move.pyi\n", + "│ │ │ │ │ │ │ │ ├── storage.pyi\n", + "│ │ │ │ │ │ │ │ ├── temp.pyi\n", + "│ │ │ │ │ │ │ │ ├── uploadedfile.pyi\n", + "│ │ │ │ │ │ │ │ ├── uploadhandler.pyi\n", + "│ │ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ │ ├── handlers\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ ├── exception.pyi\n", + "│ │ │ │ │ │ │ │ └── wsgi.pyi\n", + "│ │ │ │ │ │ │ ├── mail\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── backends\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── console.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── dummy.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── filebased.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── locmem.pyi\n", + "│ │ │ │ │ │ │ │ │ └── smtp.pyi\n", + "│ │ │ │ │ │ │ │ ├── message.pyi\n", + "│ │ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ │ ├── management\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ ├── color.pyi\n", + "│ │ │ │ │ │ │ │ ├── commands\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── dumpdata.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── loaddata.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── makemessages.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── runserver.pyi\n", + "│ │ │ │ │ │ │ │ │ └── testserver.pyi\n", + "│ │ │ │ │ │ │ │ ├── sql.pyi\n", + "│ │ │ │ │ │ │ │ ├── templates.pyi\n", + "│ │ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ │ ├── paginator.pyi\n", + "│ │ │ │ │ │ │ ├── serializers\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ ├── json.pyi\n", + "│ │ │ │ │ │ │ │ └── python.pyi\n", + "│ │ │ │ │ │ │ ├── servers\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── basehttp.pyi\n", + "│ │ │ │ │ │ │ ├── signals.pyi\n", + "│ │ │ │ │ │ │ ├── signing.pyi\n", + "│ │ │ │ │ │ │ ├── validators.pyi\n", + "│ │ │ │ │ │ │ └── wsgi.pyi\n", + "│ │ │ │ │ │ ├── db\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── backends\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── base\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── client.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── creation.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── features.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── introspection.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── operations.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── schema.pyi\n", + "│ │ │ │ │ │ │ │ │ └── validation.pyi\n", + "│ │ │ │ │ │ │ │ ├── ddl_references.pyi\n", + "│ │ │ │ │ │ │ │ ├── dummy\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── base.pyi\n", + "│ │ │ │ │ │ │ │ ├── mysql\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── client.pyi\n", + "│ │ │ │ │ │ │ │ ├── postgresql\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── client.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── creation.pyi\n", + "│ │ │ │ │ │ │ │ │ └── operations.pyi\n", + "│ │ │ │ │ │ │ │ ├── signals.pyi\n", + "│ │ │ │ │ │ │ │ ├── sqlite3\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── creation.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── features.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── introspection.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── operations.pyi\n", + "│ │ │ │ │ │ │ │ │ └── schema.pyi\n", + "│ │ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ │ ├── migrations\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── autodetector.pyi\n", + "│ │ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ │ ├── executor.pyi\n", + "│ │ │ │ │ │ │ │ ├── graph.pyi\n", + "│ │ │ │ │ │ │ │ ├── loader.pyi\n", + "│ │ │ │ │ │ │ │ ├── migration.pyi\n", + "│ │ │ │ │ │ │ │ ├── operations\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── fields.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── models.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── special.pyi\n", + "│ │ │ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ │ │ ├── optimizer.pyi\n", + "│ │ │ │ │ │ │ │ ├── questioner.pyi\n", + "│ │ │ │ │ │ │ │ ├── recorder.pyi\n", + "│ │ │ │ │ │ │ │ ├── serializer.pyi\n", + "│ │ │ │ │ │ │ │ ├── state.pyi\n", + "│ │ │ │ │ │ │ │ ├── topological_sort.pyi\n", + "│ │ │ │ │ │ │ │ ├── utils.pyi\n", + "│ │ │ │ │ │ │ │ └── writer.pyi\n", + "│ │ │ │ │ │ │ ├── models\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── aggregates.pyi\n", + "│ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ ├── constraints.pyi\n", + "│ │ │ │ │ │ │ │ ├── deletion.pyi\n", + "│ │ │ │ │ │ │ │ ├── enums.pyi\n", + "│ │ │ │ │ │ │ │ ├── expressions.pyi\n", + "│ │ │ │ │ │ │ │ ├── fields\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── files.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── mixins.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── proxy.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── related.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── related_descriptors.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── related_lookups.pyi\n", + "│ │ │ │ │ │ │ │ │ └── reverse_related.pyi\n", + "│ │ │ │ │ │ │ │ ├── functions\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── comparison.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── datetime.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── math.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── mixins.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── text.pyi\n", + "│ │ │ │ │ │ │ │ │ └── window.pyi\n", + "│ │ │ │ │ │ │ │ ├── indexes.pyi\n", + "│ │ │ │ │ │ │ │ ├── lookups.pyi\n", + "│ │ │ │ │ │ │ │ ├── manager.pyi\n", + "│ │ │ │ │ │ │ │ ├── options.pyi\n", + "│ │ │ │ │ │ │ │ ├── query.pyi\n", + "│ │ │ │ │ │ │ │ ├── query_utils.pyi\n", + "│ │ │ │ │ │ │ │ ├── signals.pyi\n", + "│ │ │ │ │ │ │ │ ├── sql\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── compiler.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── constants.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── datastructures.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── query.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── subqueries.pyi\n", + "│ │ │ │ │ │ │ │ │ └── where.pyi\n", + "│ │ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ │ ├── transaction.pyi\n", + "│ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ ├── dispatch\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ └── dispatcher.pyi\n", + "│ │ │ │ │ │ ├── forms\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── boundfield.pyi\n", + "│ │ │ │ │ │ │ ├── fields.pyi\n", + "│ │ │ │ │ │ │ ├── forms.pyi\n", + "│ │ │ │ │ │ │ ├── formsets.pyi\n", + "│ │ │ │ │ │ │ ├── models.pyi\n", + "│ │ │ │ │ │ │ ├── renderers.pyi\n", + "│ │ │ │ │ │ │ ├── utils.pyi\n", + "│ │ │ │ │ │ │ └── widgets.pyi\n", + "│ │ │ │ │ │ ├── http\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── cookie.pyi\n", + "│ │ │ │ │ │ │ ├── multipartparser.pyi\n", + "│ │ │ │ │ │ │ ├── request.pyi\n", + "│ │ │ │ │ │ │ └── response.pyi\n", + "│ │ │ │ │ │ ├── middleware\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── cache.pyi\n", + "│ │ │ │ │ │ │ ├── clickjacking.pyi\n", + "│ │ │ │ │ │ │ ├── common.pyi\n", + "│ │ │ │ │ │ │ ├── csrf.pyi\n", + "│ │ │ │ │ │ │ ├── gzip.pyi\n", + "│ │ │ │ │ │ │ ├── http.pyi\n", + "│ │ │ │ │ │ │ ├── locale.pyi\n", + "│ │ │ │ │ │ │ └── security.pyi\n", + "│ │ │ │ │ │ ├── shortcuts.pyi\n", + "│ │ │ │ │ │ ├── template\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── backends\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ ├── django.pyi\n", + "│ │ │ │ │ │ │ │ ├── dummy.pyi\n", + "│ │ │ │ │ │ │ │ ├── jinja2.pyi\n", + "│ │ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ ├── context.pyi\n", + "│ │ │ │ │ │ │ ├── context_processors.pyi\n", + "│ │ │ │ │ │ │ ├── defaultfilters.pyi\n", + "│ │ │ │ │ │ │ ├── defaulttags.pyi\n", + "│ │ │ │ │ │ │ ├── engine.pyi\n", + "│ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ ├── library.pyi\n", + "│ │ │ │ │ │ │ ├── loader.pyi\n", + "│ │ │ │ │ │ │ ├── loader_tags.pyi\n", + "│ │ │ │ │ │ │ ├── loaders\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── app_directories.pyi\n", + "│ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ ├── cached.pyi\n", + "│ │ │ │ │ │ │ │ ├── filesystem.pyi\n", + "│ │ │ │ │ │ │ │ └── locmem.pyi\n", + "│ │ │ │ │ │ │ ├── response.pyi\n", + "│ │ │ │ │ │ │ ├── smartif.pyi\n", + "│ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ ├── templatetags\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── cache.pyi\n", + "│ │ │ │ │ │ │ ├── i18n.pyi\n", + "│ │ │ │ │ │ │ ├── l10n.pyi\n", + "│ │ │ │ │ │ │ ├── static.pyi\n", + "│ │ │ │ │ │ │ └── tz.pyi\n", + "│ │ │ │ │ │ ├── test\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── client.pyi\n", + "│ │ │ │ │ │ │ ├── html.pyi\n", + "│ │ │ │ │ │ │ ├── runner.pyi\n", + "│ │ │ │ │ │ │ ├── selenium.pyi\n", + "│ │ │ │ │ │ │ ├── signals.pyi\n", + "│ │ │ │ │ │ │ ├── testcases.pyi\n", + "│ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ ├── urls\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ ├── conf.pyi\n", + "│ │ │ │ │ │ │ ├── converters.pyi\n", + "│ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ ├── resolvers.pyi\n", + "│ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ ├── utils\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── _os.pyi\n", + "│ │ │ │ │ │ │ ├── archive.pyi\n", + "│ │ │ │ │ │ │ ├── autoreload.pyi\n", + "│ │ │ │ │ │ │ ├── baseconv.pyi\n", + "│ │ │ │ │ │ │ ├── cache.pyi\n", + "│ │ │ │ │ │ │ ├── crypto.pyi\n", + "│ │ │ │ │ │ │ ├── datastructures.pyi\n", + "│ │ │ │ │ │ │ ├── dateformat.pyi\n", + "│ │ │ │ │ │ │ ├── dateparse.pyi\n", + "│ │ │ │ │ │ │ ├── dates.pyi\n", + "│ │ │ │ │ │ │ ├── datetime_safe.pyi\n", + "│ │ │ │ │ │ │ ├── deconstruct.pyi\n", + "│ │ │ │ │ │ │ ├── decorators.pyi\n", + "│ │ │ │ │ │ │ ├── deprecation.pyi\n", + "│ │ │ │ │ │ │ ├── duration.pyi\n", + "│ │ │ │ │ │ │ ├── encoding.pyi\n", + "│ │ │ │ │ │ │ ├── feedgenerator.pyi\n", + "│ │ │ │ │ │ │ ├── formats.pyi\n", + "│ │ │ │ │ │ │ ├── functional.pyi\n", + "│ │ │ │ │ │ │ ├── hashable.pyi\n", + "│ │ │ │ │ │ │ ├── html.pyi\n", + "│ │ │ │ │ │ │ ├── http.pyi\n", + "│ │ │ │ │ │ │ ├── inspect.pyi\n", + "│ │ │ │ │ │ │ ├── ipv6.pyi\n", + "│ │ │ │ │ │ │ ├── itercompat.pyi\n", + "│ │ │ │ │ │ │ ├── jslex.pyi\n", + "│ │ │ │ │ │ │ ├── log.pyi\n", + "│ │ │ │ │ │ │ ├── lorem_ipsum.pyi\n", + "│ │ │ │ │ │ │ ├── module_loading.pyi\n", + "│ │ │ │ │ │ │ ├── numberformat.pyi\n", + "│ │ │ │ │ │ │ ├── regex_helper.pyi\n", + "│ │ │ │ │ │ │ ├── safestring.pyi\n", + "│ │ │ │ │ │ │ ├── six.pyi\n", + "│ │ │ │ │ │ │ ├── termcolors.pyi\n", + "│ │ │ │ │ │ │ ├── text.pyi\n", + "│ │ │ │ │ │ │ ├── timesince.pyi\n", + "│ │ │ │ │ │ │ ├── timezone.pyi\n", + "│ │ │ │ │ │ │ ├── topological_sort.pyi\n", + "│ │ │ │ │ │ │ ├── translation\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── reloader.pyi\n", + "│ │ │ │ │ │ │ │ ├── template.pyi\n", + "│ │ │ │ │ │ │ │ ├── trans_null.pyi\n", + "│ │ │ │ │ │ │ │ └── trans_real.pyi\n", + "│ │ │ │ │ │ │ ├── tree.pyi\n", + "│ │ │ │ │ │ │ ├── version.pyi\n", + "│ │ │ │ │ │ │ └── xmlutils.pyi\n", + "│ │ │ │ │ │ └── views\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ ├── csrf.pyi\n", + "│ │ │ │ │ │ ├── debug.pyi\n", + "│ │ │ │ │ │ ├── decorators\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── cache.pyi\n", + "│ │ │ │ │ │ │ ├── clickjacking.pyi\n", + "│ │ │ │ │ │ │ ├── csrf.pyi\n", + "│ │ │ │ │ │ │ ├── debug.pyi\n", + "│ │ │ │ │ │ │ ├── gzip.pyi\n", + "│ │ │ │ │ │ │ ├── http.pyi\n", + "│ │ │ │ │ │ │ └── vary.pyi\n", + "│ │ │ │ │ │ ├── defaults.pyi\n", + "│ │ │ │ │ │ ├── generic\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ ├── dates.pyi\n", + "│ │ │ │ │ │ │ ├── detail.pyi\n", + "│ │ │ │ │ │ │ ├── edit.pyi\n", + "│ │ │ │ │ │ │ └── list.pyi\n", + "│ │ │ │ │ │ ├── i18n.pyi\n", + "│ │ │ │ │ │ └── static.pyi\n", + "│ │ │ │ │ └── typeshed\n", + "│ │ │ │ │ ├── LICENSE\n", + "│ │ │ │ │ ├── stdlib\n", + "│ │ │ │ │ │ ├── 2\n", + "│ │ │ │ │ │ │ ├── BaseHTTPServer.pyi\n", + "│ │ │ │ │ │ │ ├── CGIHTTPServer.pyi\n", + "│ │ │ │ │ │ │ ├── ConfigParser.pyi\n", + "│ │ │ │ │ │ │ ├── Cookie.pyi\n", + "│ │ │ │ │ │ │ ├── HTMLParser.pyi\n", + "│ │ │ │ │ │ │ ├── Queue.pyi\n", + "│ │ │ │ │ │ │ ├── SimpleHTTPServer.pyi\n", + "│ │ │ │ │ │ │ ├── SocketServer.pyi\n", + "│ │ │ │ │ │ │ ├── StringIO.pyi\n", + "│ │ │ │ │ │ │ ├── UserDict.pyi\n", + "│ │ │ │ │ │ │ ├── UserList.pyi\n", + "│ │ │ │ │ │ │ ├── UserString.pyi\n", + "│ │ │ │ │ │ │ ├── __builtin__.pyi\n", + "│ │ │ │ │ │ │ ├── _ast.pyi\n", + "│ │ │ │ │ │ │ ├── _collections.pyi\n", + "│ │ │ │ │ │ │ ├── _functools.pyi\n", + "│ │ │ │ │ │ │ ├── _hotshot.pyi\n", + "│ │ │ │ │ │ │ ├── _io.pyi\n", + "│ │ │ │ │ │ │ ├── _json.pyi\n", + "│ │ │ │ │ │ │ ├── _md5.pyi\n", + "│ │ │ │ │ │ │ ├── _sha.pyi\n", + "│ │ │ │ │ │ │ ├── _sha256.pyi\n", + "│ │ │ │ │ │ │ ├── _sha512.pyi\n", + "│ │ │ │ │ │ │ ├── _socket.pyi\n", + "│ │ │ │ │ │ │ ├── _sre.pyi\n", + "│ │ │ │ │ │ │ ├── _struct.pyi\n", + "│ │ │ │ │ │ │ ├── _symtable.pyi\n", + "│ │ │ │ │ │ │ ├── _threading_local.pyi\n", + "│ │ │ │ │ │ │ ├── _winreg.pyi\n", + "│ │ │ │ │ │ │ ├── abc.pyi\n", + "│ │ │ │ │ │ │ ├── ast.pyi\n", + "│ │ │ │ │ │ │ ├── atexit.pyi\n", + "│ │ │ │ │ │ │ ├── builtins.pyi\n", + "│ │ │ │ │ │ │ ├── cPickle.pyi\n", + "│ │ │ │ │ │ │ ├── cStringIO.pyi\n", + "│ │ │ │ │ │ │ ├── collections.pyi\n", + "│ │ │ │ │ │ │ ├── commands.pyi\n", + "│ │ │ │ │ │ │ ├── compileall.pyi\n", + "│ │ │ │ │ │ │ ├── cookielib.pyi\n", + "│ │ │ │ │ │ │ ├── copy_reg.pyi\n", + "│ │ │ │ │ │ │ ├── dircache.pyi\n", + "│ │ │ │ │ │ │ ├── distutils\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── archive_util.pyi\n", + "│ │ │ │ │ │ │ │ ├── bcppcompiler.pyi\n", + "│ │ │ │ │ │ │ │ ├── ccompiler.pyi\n", + "│ │ │ │ │ │ │ │ ├── cmd.pyi\n", + "│ │ │ │ │ │ │ │ ├── command\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── bdist.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── bdist_dumb.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── bdist_msi.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── bdist_packager.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── bdist_rpm.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── bdist_wininst.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── build.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── build_clib.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── build_ext.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── build_py.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── build_scripts.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── check.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── clean.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── config.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── install.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── install_data.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── install_egg_info.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── install_headers.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── install_lib.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── install_scripts.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── register.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── sdist.pyi\n", + "│ │ │ │ │ │ │ │ │ └── upload.pyi\n", + "│ │ │ │ │ │ │ │ ├── config.pyi\n", + "│ │ │ │ │ │ │ │ ├── core.pyi\n", + "│ │ │ │ │ │ │ │ ├── cygwinccompiler.pyi\n", + "│ │ │ │ │ │ │ │ ├── debug.pyi\n", + "│ │ │ │ │ │ │ │ ├── dep_util.pyi\n", + "│ │ │ │ │ │ │ │ ├── dir_util.pyi\n", + "│ │ │ │ │ │ │ │ ├── dist.pyi\n", + "│ │ │ │ │ │ │ │ ├── emxccompiler.pyi\n", + "│ │ │ │ │ │ │ │ ├── errors.pyi\n", + "│ │ │ │ │ │ │ │ ├── extension.pyi\n", + "│ │ │ │ │ │ │ │ ├── fancy_getopt.pyi\n", + "│ │ │ │ │ │ │ │ ├── file_util.pyi\n", + "│ │ │ │ │ │ │ │ ├── filelist.pyi\n", + "│ │ │ │ │ │ │ │ ├── log.pyi\n", + "│ │ │ │ │ │ │ │ ├── msvccompiler.pyi\n", + "│ │ │ │ │ │ │ │ ├── spawn.pyi\n", + "│ │ │ │ │ │ │ │ ├── sysconfig.pyi\n", + "│ │ │ │ │ │ │ │ ├── text_file.pyi\n", + "│ │ │ │ │ │ │ │ ├── unixccompiler.pyi\n", + "│ │ │ │ │ │ │ │ ├── util.pyi\n", + "│ │ │ │ │ │ │ │ └── version.pyi\n", + "│ │ │ │ │ │ │ ├── dummy_thread.pyi\n", + "│ │ │ │ │ │ │ ├── email\n", + "│ │ │ │ │ │ │ │ ├── MIMEText.pyi\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── _parseaddr.pyi\n", + "│ │ │ │ │ │ │ │ ├── base64mime.pyi\n", + "│ │ │ │ │ │ │ │ ├── charset.pyi\n", + "│ │ │ │ │ │ │ │ ├── encoders.pyi\n", + "│ │ │ │ │ │ │ │ ├── feedparser.pyi\n", + "│ │ │ │ │ │ │ │ ├── generator.pyi\n", + "│ │ │ │ │ │ │ │ ├── header.pyi\n", + "│ │ │ │ │ │ │ │ ├── iterators.pyi\n", + "│ │ │ │ │ │ │ │ ├── message.pyi\n", + "│ │ │ │ │ │ │ │ ├── mime\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── application.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── audio.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── image.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── message.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── multipart.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── nonmultipart.pyi\n", + "│ │ │ │ │ │ │ │ │ └── text.pyi\n", + "│ │ │ │ │ │ │ │ ├── parser.pyi\n", + "│ │ │ │ │ │ │ │ ├── quoprimime.pyi\n", + "│ │ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ │ ├── encodings\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── utf_8.pyi\n", + "│ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ ├── fcntl.pyi\n", + "│ │ │ │ │ │ │ ├── fnmatch.pyi\n", + "│ │ │ │ │ │ │ ├── functools.pyi\n", + "│ │ │ │ │ │ │ ├── future_builtins.pyi\n", + "│ │ │ │ │ │ │ ├── gc.pyi\n", + "│ │ │ │ │ │ │ ├── getopt.pyi\n", + "│ │ │ │ │ │ │ ├── getpass.pyi\n", + "│ │ │ │ │ │ │ ├── gettext.pyi\n", + "│ │ │ │ │ │ │ ├── glob.pyi\n", + "│ │ │ │ │ │ │ ├── gzip.pyi\n", + "│ │ │ │ │ │ │ ├── hashlib.pyi\n", + "│ │ │ │ │ │ │ ├── heapq.pyi\n", + "│ │ │ │ │ │ │ ├── htmlentitydefs.pyi\n", + "│ │ │ │ │ │ │ ├── httplib.pyi\n", + "│ │ │ │ │ │ │ ├── imp.pyi\n", + "│ │ │ │ │ │ │ ├── importlib.pyi\n", + "│ │ │ │ │ │ │ ├── inspect.pyi\n", + "│ │ │ │ │ │ │ ├── io.pyi\n", + "│ │ │ │ │ │ │ ├── itertools.pyi\n", + "│ │ │ │ │ │ │ ├── json.pyi\n", + "│ │ │ │ │ │ │ ├── markupbase.pyi\n", + "│ │ │ │ │ │ │ ├── md5.pyi\n", + "│ │ │ │ │ │ │ ├── mimetools.pyi\n", + "│ │ │ │ │ │ │ ├── multiprocessing\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── dummy\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── connection.pyi\n", + "│ │ │ │ │ │ │ │ ├── pool.pyi\n", + "│ │ │ │ │ │ │ │ ├── process.pyi\n", + "│ │ │ │ │ │ │ │ └── util.pyi\n", + "│ │ │ │ │ │ │ ├── mutex.pyi\n", + "│ │ │ │ │ │ │ ├── ntpath.pyi\n", + "│ │ │ │ │ │ │ ├── nturl2path.pyi\n", + "│ │ │ │ │ │ │ ├── os\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── path.pyi\n", + "│ │ │ │ │ │ │ ├── os2emxpath.pyi\n", + "│ │ │ │ │ │ │ ├── pipes.pyi\n", + "│ │ │ │ │ │ │ ├── platform.pyi\n", + "│ │ │ │ │ │ │ ├── popen2.pyi\n", + "│ │ │ │ │ │ │ ├── posix.pyi\n", + "│ │ │ │ │ │ │ ├── posixpath.pyi\n", + "│ │ │ │ │ │ │ ├── random.pyi\n", + "│ │ │ │ │ │ │ ├── re.pyi\n", + "│ │ │ │ │ │ │ ├── repr.pyi\n", + "│ │ │ │ │ │ │ ├── resource.pyi\n", + "│ │ │ │ │ │ │ ├── rfc822.pyi\n", + "│ │ │ │ │ │ │ ├── robotparser.pyi\n", + "│ │ │ │ │ │ │ ├── runpy.pyi\n", + "│ │ │ │ │ │ │ ├── sets.pyi\n", + "│ │ │ │ │ │ │ ├── sha.pyi\n", + "│ │ │ │ │ │ │ ├── shelve.pyi\n", + "│ │ │ │ │ │ │ ├── shlex.pyi\n", + "│ │ │ │ │ │ │ ├── signal.pyi\n", + "│ │ │ │ │ │ │ ├── smtplib.pyi\n", + "│ │ │ │ │ │ │ ├── spwd.pyi\n", + "│ │ │ │ │ │ │ ├── sre_constants.pyi\n", + "│ │ │ │ │ │ │ ├── sre_parse.pyi\n", + "│ │ │ │ │ │ │ ├── stat.pyi\n", + "│ │ │ │ │ │ │ ├── string.pyi\n", + "│ │ │ │ │ │ │ ├── stringold.pyi\n", + "│ │ │ │ │ │ │ ├── strop.pyi\n", + "│ │ │ │ │ │ │ ├── subprocess.pyi\n", + "│ │ │ │ │ │ │ ├── symbol.pyi\n", + "│ │ │ │ │ │ │ ├── sys.pyi\n", + "│ │ │ │ │ │ │ ├── tempfile.pyi\n", + "│ │ │ │ │ │ │ ├── textwrap.pyi\n", + "│ │ │ │ │ │ │ ├── thread.pyi\n", + "│ │ │ │ │ │ │ ├── toaiff.pyi\n", + "│ │ │ │ │ │ │ ├── tokenize.pyi\n", + "│ │ │ │ │ │ │ ├── types.pyi\n", + "│ │ │ │ │ │ │ ├── typing.pyi\n", + "│ │ │ │ │ │ │ ├── unittest.pyi\n", + "│ │ │ │ │ │ │ ├── urllib.pyi\n", + "│ │ │ │ │ │ │ ├── urllib2.pyi\n", + "│ │ │ │ │ │ │ ├── urlparse.pyi\n", + "│ │ │ │ │ │ │ ├── user.pyi\n", + "│ │ │ │ │ │ │ ├── whichdb.pyi\n", + "│ │ │ │ │ │ │ └── xmlrpclib.pyi\n", + "│ │ │ │ │ │ ├── 2and3\n", + "│ │ │ │ │ │ │ ├── __future__.pyi\n", + "│ │ │ │ │ │ │ ├── _bisect.pyi\n", + "│ │ │ │ │ │ │ ├── _codecs.pyi\n", + "│ │ │ │ │ │ │ ├── _csv.pyi\n", + "│ │ │ │ │ │ │ ├── _curses.pyi\n", + "│ │ │ │ │ │ │ ├── _dummy_threading.pyi\n", + "│ │ │ │ │ │ │ ├── _heapq.pyi\n", + "│ │ │ │ │ │ │ ├── _msi.pyi\n", + "│ │ │ │ │ │ │ ├── _random.pyi\n", + "│ │ │ │ │ │ │ ├── _typeshed\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── wsgi.pyi\n", + "│ │ │ │ │ │ │ │ └── xml.pyi\n", + "│ │ │ │ │ │ │ ├── _warnings.pyi\n", + "│ │ │ │ │ │ │ ├── _weakref.pyi\n", + "│ │ │ │ │ │ │ ├── _weakrefset.pyi\n", + "│ │ │ │ │ │ │ ├── aifc.pyi\n", + "│ │ │ │ │ │ │ ├── antigravity.pyi\n", + "│ │ │ │ │ │ │ ├── argparse.pyi\n", + "│ │ │ │ │ │ │ ├── array.pyi\n", + "│ │ │ │ │ │ │ ├── asynchat.pyi\n", + "│ │ │ │ │ │ │ ├── asyncore.pyi\n", + "│ │ │ │ │ │ │ ├── audioop.pyi\n", + "│ │ │ │ │ │ │ ├── base64.pyi\n", + "│ │ │ │ │ │ │ ├── bdb.pyi\n", + "│ │ │ │ │ │ │ ├── binascii.pyi\n", + "│ │ │ │ │ │ │ ├── binhex.pyi\n", + "│ │ │ │ │ │ │ ├── bisect.pyi\n", + "│ │ │ │ │ │ │ ├── bz2.pyi\n", + "│ │ │ │ │ │ │ ├── cProfile.pyi\n", + "│ │ │ │ │ │ │ ├── calendar.pyi\n", + "│ │ │ │ │ │ │ ├── cgi.pyi\n", + "│ │ │ │ │ │ │ ├── cgitb.pyi\n", + "│ │ │ │ │ │ │ ├── chunk.pyi\n", + "│ │ │ │ │ │ │ ├── cmath.pyi\n", + "│ │ │ │ │ │ │ ├── cmd.pyi\n", + "│ │ │ │ │ │ │ ├── code.pyi\n", + "│ │ │ │ │ │ │ ├── codecs.pyi\n", + "│ │ │ │ │ │ │ ├── codeop.pyi\n", + "│ │ │ │ │ │ │ ├── colorsys.pyi\n", + "│ │ │ │ │ │ │ ├── contextlib.pyi\n", + "│ │ │ │ │ │ │ ├── copy.pyi\n", + "│ │ │ │ │ │ │ ├── crypt.pyi\n", + "│ │ │ │ │ │ │ ├── csv.pyi\n", + "│ │ │ │ │ │ │ ├── ctypes\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── util.pyi\n", + "│ │ │ │ │ │ │ │ └── wintypes.pyi\n", + "│ │ │ │ │ │ │ ├── curses\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── ascii.pyi\n", + "│ │ │ │ │ │ │ │ ├── panel.pyi\n", + "│ │ │ │ │ │ │ │ └── textpad.pyi\n", + "│ │ │ │ │ │ │ ├── datetime.pyi\n", + "│ │ │ │ │ │ │ ├── decimal.pyi\n", + "│ │ │ │ │ │ │ ├── difflib.pyi\n", + "│ │ │ │ │ │ │ ├── dis.pyi\n", + "│ │ │ │ │ │ │ ├── doctest.pyi\n", + "│ │ │ │ │ │ │ ├── dummy_threading.pyi\n", + "│ │ │ │ │ │ │ ├── ensurepip\n", + "│ │ │ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── errno.pyi\n", + "│ │ │ │ │ │ │ ├── filecmp.pyi\n", + "│ │ │ │ │ │ │ ├── fileinput.pyi\n", + "│ │ │ │ │ │ │ ├── formatter.pyi\n", + "│ │ │ │ │ │ │ ├── fractions.pyi\n", + "│ │ │ │ │ │ │ ├── ftplib.pyi\n", + "│ │ │ │ │ │ │ ├── genericpath.pyi\n", + "│ │ │ │ │ │ │ ├── grp.pyi\n", + "│ │ │ │ │ │ │ ├── hmac.pyi\n", + "│ │ │ │ │ │ │ ├── imaplib.pyi\n", + "│ │ │ │ │ │ │ ├── imghdr.pyi\n", + "│ │ │ │ │ │ │ ├── keyword.pyi\n", + "│ │ │ │ │ │ │ ├── lib2to3\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── pgen2\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── driver.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── grammar.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── literals.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── parse.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── pgen.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── token.pyi\n", + "│ │ │ │ │ │ │ │ │ └── tokenize.pyi\n", + "│ │ │ │ │ │ │ │ ├── pygram.pyi\n", + "│ │ │ │ │ │ │ │ └── pytree.pyi\n", + "│ │ │ │ │ │ │ ├── linecache.pyi\n", + "│ │ │ │ │ │ │ ├── locale.pyi\n", + "│ │ │ │ │ │ │ ├── logging\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── config.pyi\n", + "│ │ │ │ │ │ │ │ └── handlers.pyi\n", + "│ │ │ │ │ │ │ ├── macpath.pyi\n", + "│ │ │ │ │ │ │ ├── mailbox.pyi\n", + "│ │ │ │ │ │ │ ├── mailcap.pyi\n", + "│ │ │ │ │ │ │ ├── marshal.pyi\n", + "│ │ │ │ │ │ │ ├── math.pyi\n", + "│ │ │ │ │ │ │ ├── mimetypes.pyi\n", + "│ │ │ │ │ │ │ ├── mmap.pyi\n", + "│ │ │ │ │ │ │ ├── modulefinder.pyi\n", + "│ │ │ │ │ │ │ ├── msilib\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── schema.pyi\n", + "│ │ │ │ │ │ │ │ ├── sequence.pyi\n", + "│ │ │ │ │ │ │ │ └── text.pyi\n", + "│ │ │ │ │ │ │ ├── msvcrt.pyi\n", + "│ │ │ │ │ │ │ ├── netrc.pyi\n", + "│ │ │ │ │ │ │ ├── nis.pyi\n", + "│ │ │ │ │ │ │ ├── numbers.pyi\n", + "│ │ │ │ │ │ │ ├── opcode.pyi\n", + "│ │ │ │ │ │ │ ├── operator.pyi\n", + "│ │ │ │ │ │ │ ├── optparse.pyi\n", + "│ │ │ │ │ │ │ ├── parser.pyi\n", + "│ │ │ │ │ │ │ ├── pdb.pyi\n", + "│ │ │ │ │ │ │ ├── pickle.pyi\n", + "│ │ │ │ │ │ │ ├── pickletools.pyi\n", + "│ │ │ │ │ │ │ ├── pkgutil.pyi\n", + "│ │ │ │ │ │ │ ├── plistlib.pyi\n", + "│ │ │ │ │ │ │ ├── poplib.pyi\n", + "│ │ │ │ │ │ │ ├── pprint.pyi\n", + "│ │ │ │ │ │ │ ├── profile.pyi\n", + "│ │ │ │ │ │ │ ├── pstats.pyi\n", + "│ │ │ │ │ │ │ ├── pty.pyi\n", + "│ │ │ │ │ │ │ ├── pwd.pyi\n", + "│ │ │ │ │ │ │ ├── py_compile.pyi\n", + "│ │ │ │ │ │ │ ├── pyclbr.pyi\n", + "│ │ │ │ │ │ │ ├── pydoc.pyi\n", + "│ │ │ │ │ │ │ ├── pydoc_data\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── topics.pyi\n", + "│ │ │ │ │ │ │ ├── pyexpat\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── errors.pyi\n", + "│ │ │ │ │ │ │ │ └── model.pyi\n", + "│ │ │ │ │ │ │ ├── quopri.pyi\n", + "│ │ │ │ │ │ │ ├── readline.pyi\n", + "│ │ │ │ │ │ │ ├── rlcompleter.pyi\n", + "│ │ │ │ │ │ │ ├── sched.pyi\n", + "│ │ │ │ │ │ │ ├── select.pyi\n", + "│ │ │ │ │ │ │ ├── shutil.pyi\n", + "│ │ │ │ │ │ │ ├── site.pyi\n", + "│ │ │ │ │ │ │ ├── smtpd.pyi\n", + "│ │ │ │ │ │ │ ├── sndhdr.pyi\n", + "│ │ │ │ │ │ │ ├── socket.pyi\n", + "│ │ │ │ │ │ │ ├── sqlite3\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── dbapi2.pyi\n", + "│ │ │ │ │ │ │ ├── sre_compile.pyi\n", + "│ │ │ │ │ │ │ ├── ssl.pyi\n", + "│ │ │ │ │ │ │ ├── stringprep.pyi\n", + "│ │ │ │ │ │ │ ├── struct.pyi\n", + "│ │ │ │ │ │ │ ├── sunau.pyi\n", + "│ │ │ │ │ │ │ ├── symtable.pyi\n", + "│ │ │ │ │ │ │ ├── sysconfig.pyi\n", + "│ │ │ │ │ │ │ ├── syslog.pyi\n", + "│ │ │ │ │ │ │ ├── tabnanny.pyi\n", + "│ │ │ │ │ │ │ ├── tarfile.pyi\n", + "│ │ │ │ │ │ │ ├── telnetlib.pyi\n", + "│ │ │ │ │ │ │ ├── termios.pyi\n", + "│ │ │ │ │ │ │ ├── this.pyi\n", + "│ │ │ │ │ │ │ ├── threading.pyi\n", + "│ │ │ │ │ │ │ ├── time.pyi\n", + "│ │ │ │ │ │ │ ├── timeit.pyi\n", + "│ │ │ │ │ │ │ ├── token.pyi\n", + "│ │ │ │ │ │ │ ├── trace.pyi\n", + "│ │ │ │ │ │ │ ├── traceback.pyi\n", + "│ │ │ │ │ │ │ ├── tty.pyi\n", + "│ │ │ │ │ │ │ ├── turtle.pyi\n", + "│ │ │ │ │ │ │ ├── unicodedata.pyi\n", + "│ │ │ │ │ │ │ ├── uu.pyi\n", + "│ │ │ │ │ │ │ ├── uuid.pyi\n", + "│ │ │ │ │ │ │ ├── warnings.pyi\n", + "│ │ │ │ │ │ │ ├── wave.pyi\n", + "│ │ │ │ │ │ │ ├── weakref.pyi\n", + "│ │ │ │ │ │ │ ├── webbrowser.pyi\n", + "│ │ │ │ │ │ │ ├── winsound.pyi\n", + "│ │ │ │ │ │ │ ├── wsgiref\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── handlers.pyi\n", + "│ │ │ │ │ │ │ │ ├── headers.pyi\n", + "│ │ │ │ │ │ │ │ ├── simple_server.pyi\n", + "│ │ │ │ │ │ │ │ ├── types.pyi\n", + "│ │ │ │ │ │ │ │ ├── util.pyi\n", + "│ │ │ │ │ │ │ │ └── validate.pyi\n", + "│ │ │ │ │ │ │ ├── xdrlib.pyi\n", + "│ │ │ │ │ │ │ ├── xml\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── dom\n", + "│ │ │ │ │ │ │ │ │ ├── NodeFilter.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── domreg.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── expatbuilder.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── minicompat.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── minidom.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── pulldom.pyi\n", + "│ │ │ │ │ │ │ │ │ └── xmlbuilder.pyi\n", + "│ │ │ │ │ │ │ │ ├── etree\n", + "│ │ │ │ │ │ │ │ │ ├── ElementInclude.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── ElementPath.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── ElementTree.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── cElementTree.pyi\n", + "│ │ │ │ │ │ │ │ ├── parsers\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── expat\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── errors.pyi\n", + "│ │ │ │ │ │ │ │ │ └── model.pyi\n", + "│ │ │ │ │ │ │ │ └── sax\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── handler.pyi\n", + "│ │ │ │ │ │ │ │ ├── saxutils.pyi\n", + "│ │ │ │ │ │ │ │ └── xmlreader.pyi\n", + "│ │ │ │ │ │ │ ├── zipfile.pyi\n", + "│ │ │ │ │ │ │ ├── zipimport.pyi\n", + "│ │ │ │ │ │ │ └── zlib.pyi\n", + "│ │ │ │ │ │ ├── 3\n", + "│ │ │ │ │ │ │ ├── _ast.pyi\n", + "│ │ │ │ │ │ │ ├── _bootlocale.pyi\n", + "│ │ │ │ │ │ │ ├── _compat_pickle.pyi\n", + "│ │ │ │ │ │ │ ├── _compression.pyi\n", + "│ │ │ │ │ │ │ ├── _decimal.pyi\n", + "│ │ │ │ │ │ │ ├── _dummy_thread.pyi\n", + "│ │ │ │ │ │ │ ├── _imp.pyi\n", + "│ │ │ │ │ │ │ ├── _importlib_modulespec.pyi\n", + "│ │ │ │ │ │ │ ├── _json.pyi\n", + "│ │ │ │ │ │ │ ├── _markupbase.pyi\n", + "│ │ │ │ │ │ │ ├── _operator.pyi\n", + "│ │ │ │ │ │ │ ├── _osx_support.pyi\n", + "│ │ │ │ │ │ │ ├── _posixsubprocess.pyi\n", + "│ │ │ │ │ │ │ ├── _pydecimal.pyi\n", + "│ │ │ │ │ │ │ ├── _sitebuiltins.pyi\n", + "│ │ │ │ │ │ │ ├── _stat.pyi\n", + "│ │ │ │ │ │ │ ├── _thread.pyi\n", + "│ │ │ │ │ │ │ ├── _threading_local.pyi\n", + "│ │ │ │ │ │ │ ├── _tkinter.pyi\n", + "│ │ │ │ │ │ │ ├── _tracemalloc.pyi\n", + "│ │ │ │ │ │ │ ├── _winapi.pyi\n", + "│ │ │ │ │ │ │ ├── abc.pyi\n", + "│ │ │ │ │ │ │ ├── ast.pyi\n", + "│ │ │ │ │ │ │ ├── asyncio\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── base_events.pyi\n", + "│ │ │ │ │ │ │ │ ├── base_futures.pyi\n", + "│ │ │ │ │ │ │ │ ├── base_subprocess.pyi\n", + "│ │ │ │ │ │ │ │ ├── base_tasks.pyi\n", + "│ │ │ │ │ │ │ │ ├── compat.pyi\n", + "│ │ │ │ │ │ │ │ ├── constants.pyi\n", + "│ │ │ │ │ │ │ │ ├── coroutines.pyi\n", + "│ │ │ │ │ │ │ │ ├── events.pyi\n", + "│ │ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ │ ├── format_helpers.pyi\n", + "│ │ │ │ │ │ │ │ ├── futures.pyi\n", + "│ │ │ │ │ │ │ │ ├── locks.pyi\n", + "│ │ │ │ │ │ │ │ ├── log.pyi\n", + "│ │ │ │ │ │ │ │ ├── proactor_events.pyi\n", + "│ │ │ │ │ │ │ │ ├── protocols.pyi\n", + "│ │ │ │ │ │ │ │ ├── queues.pyi\n", + "│ │ │ │ │ │ │ │ ├── runners.pyi\n", + "│ │ │ │ │ │ │ │ ├── selector_events.pyi\n", + "│ │ │ │ │ │ │ │ ├── sslproto.pyi\n", + "│ │ │ │ │ │ │ │ ├── staggered.pyi\n", + "│ │ │ │ │ │ │ │ ├── streams.pyi\n", + "│ │ │ │ │ │ │ │ ├── subprocess.pyi\n", + "│ │ │ │ │ │ │ │ ├── tasks.pyi\n", + "│ │ │ │ │ │ │ │ ├── threads.pyi\n", + "│ │ │ │ │ │ │ │ ├── transports.pyi\n", + "│ │ │ │ │ │ │ │ ├── trsock.pyi\n", + "│ │ │ │ │ │ │ │ ├── unix_events.pyi\n", + "│ │ │ │ │ │ │ │ ├── windows_events.pyi\n", + "│ │ │ │ │ │ │ │ └── windows_utils.pyi\n", + "│ │ │ │ │ │ │ ├── atexit.pyi\n", + "│ │ │ │ │ │ │ ├── builtins.pyi\n", + "│ │ │ │ │ │ │ ├── collections\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── abc.pyi\n", + "│ │ │ │ │ │ │ ├── compileall.pyi\n", + "│ │ │ │ │ │ │ ├── concurrent\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── futures\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── _base.pyi\n", + "│ │ │ │ │ │ │ │ ├── process.pyi\n", + "│ │ │ │ │ │ │ │ └── thread.pyi\n", + "│ │ │ │ │ │ │ ├── configparser.pyi\n", + "│ │ │ │ │ │ │ ├── copyreg.pyi\n", + "│ │ │ │ │ │ │ ├── dbm\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── dumb.pyi\n", + "│ │ │ │ │ │ │ │ ├── gnu.pyi\n", + "│ │ │ │ │ │ │ │ └── ndbm.pyi\n", + "│ │ │ │ │ │ │ ├── distutils\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── archive_util.pyi\n", + "│ │ │ │ │ │ │ │ ├── bcppcompiler.pyi\n", + "│ │ │ │ │ │ │ │ ├── ccompiler.pyi\n", + "│ │ │ │ │ │ │ │ ├── cmd.pyi\n", + "│ │ │ │ │ │ │ │ ├── command\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── bdist.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── bdist_dumb.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── bdist_msi.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── bdist_packager.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── bdist_rpm.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── bdist_wininst.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── build.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── build_clib.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── build_ext.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── build_py.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── build_scripts.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── check.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── clean.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── config.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── install.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── install_data.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── install_egg_info.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── install_headers.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── install_lib.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── install_scripts.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── register.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── sdist.pyi\n", + "│ │ │ │ │ │ │ │ │ └── upload.pyi\n", + "│ │ │ │ │ │ │ │ ├── config.pyi\n", + "│ │ │ │ │ │ │ │ ├── core.pyi\n", + "│ │ │ │ │ │ │ │ ├── cygwinccompiler.pyi\n", + "│ │ │ │ │ │ │ │ ├── debug.pyi\n", + "│ │ │ │ │ │ │ │ ├── dep_util.pyi\n", + "│ │ │ │ │ │ │ │ ├── dir_util.pyi\n", + "│ │ │ │ │ │ │ │ ├── dist.pyi\n", + "│ │ │ │ │ │ │ │ ├── errors.pyi\n", + "│ │ │ │ │ │ │ │ ├── extension.pyi\n", + "│ │ │ │ │ │ │ │ ├── fancy_getopt.pyi\n", + "│ │ │ │ │ │ │ │ ├── file_util.pyi\n", + "│ │ │ │ │ │ │ │ ├── filelist.pyi\n", + "│ │ │ │ │ │ │ │ ├── log.pyi\n", + "│ │ │ │ │ │ │ │ ├── msvccompiler.pyi\n", + "│ │ │ │ │ │ │ │ ├── spawn.pyi\n", + "│ │ │ │ │ │ │ │ ├── sysconfig.pyi\n", + "│ │ │ │ │ │ │ │ ├── text_file.pyi\n", + "│ │ │ │ │ │ │ │ ├── unixccompiler.pyi\n", + "│ │ │ │ │ │ │ │ ├── util.pyi\n", + "│ │ │ │ │ │ │ │ └── version.pyi\n", + "│ │ │ │ │ │ │ ├── email\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── charset.pyi\n", + "│ │ │ │ │ │ │ │ ├── contentmanager.pyi\n", + "│ │ │ │ │ │ │ │ ├── encoders.pyi\n", + "│ │ │ │ │ │ │ │ ├── errors.pyi\n", + "│ │ │ │ │ │ │ │ ├── feedparser.pyi\n", + "│ │ │ │ │ │ │ │ ├── generator.pyi\n", + "│ │ │ │ │ │ │ │ ├── header.pyi\n", + "│ │ │ │ │ │ │ │ ├── headerregistry.pyi\n", + "│ │ │ │ │ │ │ │ ├── iterators.pyi\n", + "│ │ │ │ │ │ │ │ ├── message.pyi\n", + "│ │ │ │ │ │ │ │ ├── mime\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── application.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── audio.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── image.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── message.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── multipart.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── nonmultipart.pyi\n", + "│ │ │ │ │ │ │ │ │ └── text.pyi\n", + "│ │ │ │ │ │ │ │ ├── parser.pyi\n", + "│ │ │ │ │ │ │ │ ├── policy.pyi\n", + "│ │ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ │ ├── encodings\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── utf_8.pyi\n", + "│ │ │ │ │ │ │ ├── enum.pyi\n", + "│ │ │ │ │ │ │ ├── faulthandler.pyi\n", + "│ │ │ │ │ │ │ ├── fcntl.pyi\n", + "│ │ │ │ │ │ │ ├── fnmatch.pyi\n", + "│ │ │ │ │ │ │ ├── functools.pyi\n", + "│ │ │ │ │ │ │ ├── gc.pyi\n", + "│ │ │ │ │ │ │ ├── getopt.pyi\n", + "│ │ │ │ │ │ │ ├── getpass.pyi\n", + "│ │ │ │ │ │ │ ├── gettext.pyi\n", + "│ │ │ │ │ │ │ ├── glob.pyi\n", + "│ │ │ │ │ │ │ ├── gzip.pyi\n", + "│ │ │ │ │ │ │ ├── hashlib.pyi\n", + "│ │ │ │ │ │ │ ├── heapq.pyi\n", + "│ │ │ │ │ │ │ ├── html\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── entities.pyi\n", + "│ │ │ │ │ │ │ │ └── parser.pyi\n", + "│ │ │ │ │ │ │ ├── http\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── client.pyi\n", + "│ │ │ │ │ │ │ │ ├── cookiejar.pyi\n", + "│ │ │ │ │ │ │ │ ├── cookies.pyi\n", + "│ │ │ │ │ │ │ │ └── server.pyi\n", + "│ │ │ │ │ │ │ ├── imp.pyi\n", + "│ │ │ │ │ │ │ ├── importlib\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── abc.pyi\n", + "│ │ │ │ │ │ │ │ ├── machinery.pyi\n", + "│ │ │ │ │ │ │ │ ├── metadata.pyi\n", + "│ │ │ │ │ │ │ │ ├── resources.pyi\n", + "│ │ │ │ │ │ │ │ └── util.pyi\n", + "│ │ │ │ │ │ │ ├── inspect.pyi\n", + "│ │ │ │ │ │ │ ├── io.pyi\n", + "│ │ │ │ │ │ │ ├── ipaddress.pyi\n", + "│ │ │ │ │ │ │ ├── itertools.pyi\n", + "│ │ │ │ │ │ │ ├── json\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── decoder.pyi\n", + "│ │ │ │ │ │ │ │ ├── encoder.pyi\n", + "│ │ │ │ │ │ │ │ └── tool.pyi\n", + "│ │ │ │ │ │ │ ├── lzma.pyi\n", + "│ │ │ │ │ │ │ ├── macurl2path.pyi\n", + "│ │ │ │ │ │ │ ├── multiprocessing\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── connection.pyi\n", + "│ │ │ │ │ │ │ │ ├── context.pyi\n", + "│ │ │ │ │ │ │ │ ├── dummy\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── connection.pyi\n", + "│ │ │ │ │ │ │ │ ├── managers.pyi\n", + "│ │ │ │ │ │ │ │ ├── pool.pyi\n", + "│ │ │ │ │ │ │ │ ├── process.pyi\n", + "│ │ │ │ │ │ │ │ ├── queues.pyi\n", + "│ │ │ │ │ │ │ │ ├── shared_memory.pyi\n", + "│ │ │ │ │ │ │ │ ├── sharedctypes.pyi\n", + "│ │ │ │ │ │ │ │ ├── spawn.pyi\n", + "│ │ │ │ │ │ │ │ └── synchronize.pyi\n", + "│ │ │ │ │ │ │ ├── nntplib.pyi\n", + "│ │ │ │ │ │ │ ├── ntpath.pyi\n", + "│ │ │ │ │ │ │ ├── nturl2path.pyi\n", + "│ │ │ │ │ │ │ ├── os\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── path.pyi\n", + "│ │ │ │ │ │ │ ├── pathlib.pyi\n", + "│ │ │ │ │ │ │ ├── pipes.pyi\n", + "│ │ │ │ │ │ │ ├── platform.pyi\n", + "│ │ │ │ │ │ │ ├── posix.pyi\n", + "│ │ │ │ │ │ │ ├── posixpath.pyi\n", + "│ │ │ │ │ │ │ ├── queue.pyi\n", + "│ │ │ │ │ │ │ ├── random.pyi\n", + "│ │ │ │ │ │ │ ├── re.pyi\n", + "│ │ │ │ │ │ │ ├── reprlib.pyi\n", + "│ │ │ │ │ │ │ ├── resource.pyi\n", + "│ │ │ │ │ │ │ ├── runpy.pyi\n", + "│ │ │ │ │ │ │ ├── secrets.pyi\n", + "│ │ │ │ │ │ │ ├── selectors.pyi\n", + "│ │ │ │ │ │ │ ├── shelve.pyi\n", + "│ │ │ │ │ │ │ ├── shlex.pyi\n", + "│ │ │ │ │ │ │ ├── signal.pyi\n", + "│ │ │ │ │ │ │ ├── smtplib.pyi\n", + "│ │ │ │ │ │ │ ├── socketserver.pyi\n", + "│ │ │ │ │ │ │ ├── spwd.pyi\n", + "│ │ │ │ │ │ │ ├── sre_constants.pyi\n", + "│ │ │ │ │ │ │ ├── sre_parse.pyi\n", + "│ │ │ │ │ │ │ ├── stat.pyi\n", + "│ │ │ │ │ │ │ ├── statistics.pyi\n", + "│ │ │ │ │ │ │ ├── string.pyi\n", + "│ │ │ │ │ │ │ ├── subprocess.pyi\n", + "│ │ │ │ │ │ │ ├── symbol.pyi\n", + "│ │ │ │ │ │ │ ├── sys.pyi\n", + "│ │ │ │ │ │ │ ├── tempfile.pyi\n", + "│ │ │ │ │ │ │ ├── textwrap.pyi\n", + "│ │ │ │ │ │ │ ├── tkinter\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── commondialog.pyi\n", + "│ │ │ │ │ │ │ │ ├── constants.pyi\n", + "│ │ │ │ │ │ │ │ ├── dialog.pyi\n", + "│ │ │ │ │ │ │ │ ├── filedialog.pyi\n", + "│ │ │ │ │ │ │ │ ├── font.pyi\n", + "│ │ │ │ │ │ │ │ ├── messagebox.pyi\n", + "│ │ │ │ │ │ │ │ └── ttk.pyi\n", + "│ │ │ │ │ │ │ ├── tokenize.pyi\n", + "│ │ │ │ │ │ │ ├── tracemalloc.pyi\n", + "│ │ │ │ │ │ │ ├── types.pyi\n", + "│ │ │ │ │ │ │ ├── typing.pyi\n", + "│ │ │ │ │ │ │ ├── unittest\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── async_case.pyi\n", + "│ │ │ │ │ │ │ │ ├── case.pyi\n", + "│ │ │ │ │ │ │ │ ├── loader.pyi\n", + "│ │ │ │ │ │ │ │ ├── main.pyi\n", + "│ │ │ │ │ │ │ │ ├── mock.pyi\n", + "│ │ │ │ │ │ │ │ ├── result.pyi\n", + "│ │ │ │ │ │ │ │ ├── runner.pyi\n", + "│ │ │ │ │ │ │ │ ├── signals.pyi\n", + "│ │ │ │ │ │ │ │ ├── suite.pyi\n", + "│ │ │ │ │ │ │ │ └── util.pyi\n", + "│ │ │ │ │ │ │ ├── urllib\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── error.pyi\n", + "│ │ │ │ │ │ │ │ ├── parse.pyi\n", + "│ │ │ │ │ │ │ │ ├── request.pyi\n", + "│ │ │ │ │ │ │ │ ├── response.pyi\n", + "│ │ │ │ │ │ │ │ └── robotparser.pyi\n", + "│ │ │ │ │ │ │ ├── venv\n", + "│ │ │ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── winreg.pyi\n", + "│ │ │ │ │ │ │ ├── xmlrpc\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── client.pyi\n", + "│ │ │ │ │ │ │ │ └── server.pyi\n", + "│ │ │ │ │ │ │ ├── xxlimited.pyi\n", + "│ │ │ │ │ │ │ └── zipapp.pyi\n", + "│ │ │ │ │ │ ├── 3.7\n", + "│ │ │ │ │ │ │ ├── _py_abc.pyi\n", + "│ │ │ │ │ │ │ ├── contextvars.pyi\n", + "│ │ │ │ │ │ │ └── dataclasses.pyi\n", + "│ │ │ │ │ │ └── 3.9\n", + "│ │ │ │ │ │ ├── graphlib.pyi\n", + "│ │ │ │ │ │ └── zoneinfo\n", + "│ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ └── third_party\n", + "│ │ │ │ │ ├── 2\n", + "│ │ │ │ │ │ ├── OpenSSL\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ └── crypto.pyi\n", + "│ │ │ │ │ │ ├── concurrent\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ └── futures\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── _base.pyi\n", + "│ │ │ │ │ │ │ ├── process.pyi\n", + "│ │ │ │ │ │ │ └── thread.pyi\n", + "│ │ │ │ │ │ ├── enum.pyi\n", + "│ │ │ │ │ │ ├── fb303\n", + "│ │ │ │ │ │ │ ├── FacebookService.pyi\n", + "│ │ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ │ ├── ipaddress.pyi\n", + "│ │ │ │ │ │ ├── kazoo\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── client.pyi\n", + "│ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ └── recipe\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ └── watchers.pyi\n", + "│ │ │ │ │ │ ├── pathlib2.pyi\n", + "│ │ │ │ │ │ ├── pymssql.pyi\n", + "│ │ │ │ │ │ ├── routes\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── mapper.pyi\n", + "│ │ │ │ │ │ │ └── util.pyi\n", + "│ │ │ │ │ │ ├── scribe\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── scribe.pyi\n", + "│ │ │ │ │ │ │ └── ttypes.pyi\n", + "│ │ │ │ │ │ ├── six\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ └── moves\n", + "│ │ │ │ │ │ │ ├── BaseHTTPServer.pyi\n", + "│ │ │ │ │ │ │ ├── CGIHTTPServer.pyi\n", + "│ │ │ │ │ │ │ ├── SimpleHTTPServer.pyi\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── _dummy_thread.pyi\n", + "│ │ │ │ │ │ │ ├── _thread.pyi\n", + "│ │ │ │ │ │ │ ├── cPickle.pyi\n", + "│ │ │ │ │ │ │ ├── collections_abc.pyi\n", + "│ │ │ │ │ │ │ ├── configparser.pyi\n", + "│ │ │ │ │ │ │ ├── email_mime_base.pyi\n", + "│ │ │ │ │ │ │ ├── email_mime_multipart.pyi\n", + "│ │ │ │ │ │ │ ├── email_mime_nonmultipart.pyi\n", + "│ │ │ │ │ │ │ ├── email_mime_text.pyi\n", + "│ │ │ │ │ │ │ ├── html_entities.pyi\n", + "│ │ │ │ │ │ │ ├── html_parser.pyi\n", + "│ │ │ │ │ │ │ ├── http_client.pyi\n", + "│ │ │ │ │ │ │ ├── http_cookiejar.pyi\n", + "│ │ │ │ │ │ │ ├── http_cookies.pyi\n", + "│ │ │ │ │ │ │ ├── queue.pyi\n", + "│ │ │ │ │ │ │ ├── reprlib.pyi\n", + "│ │ │ │ │ │ │ ├── socketserver.pyi\n", + "│ │ │ │ │ │ │ ├── urllib\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── error.pyi\n", + "│ │ │ │ │ │ │ │ ├── parse.pyi\n", + "│ │ │ │ │ │ │ │ ├── request.pyi\n", + "│ │ │ │ │ │ │ │ ├── response.pyi\n", + "│ │ │ │ │ │ │ │ └── robotparser.pyi\n", + "│ │ │ │ │ │ │ ├── urllib_error.pyi\n", + "│ │ │ │ │ │ │ ├── urllib_parse.pyi\n", + "│ │ │ │ │ │ │ ├── urllib_request.pyi\n", + "│ │ │ │ │ │ │ ├── urllib_response.pyi\n", + "│ │ │ │ │ │ │ ├── urllib_robotparser.pyi\n", + "│ │ │ │ │ │ │ └── xmlrpc_client.pyi\n", + "│ │ │ │ │ │ └── tornado\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ ├── concurrent.pyi\n", + "│ │ │ │ │ │ ├── gen.pyi\n", + "│ │ │ │ │ │ ├── httpclient.pyi\n", + "│ │ │ │ │ │ ├── httpserver.pyi\n", + "│ │ │ │ │ │ ├── httputil.pyi\n", + "│ │ │ │ │ │ ├── ioloop.pyi\n", + "│ │ │ │ │ │ ├── locks.pyi\n", + "│ │ │ │ │ │ ├── netutil.pyi\n", + "│ │ │ │ │ │ ├── process.pyi\n", + "│ │ │ │ │ │ ├── tcpserver.pyi\n", + "│ │ │ │ │ │ ├── testing.pyi\n", + "│ │ │ │ │ │ ├── util.pyi\n", + "│ │ │ │ │ │ └── web.pyi\n", + "│ │ │ │ │ ├── 2and3\n", + "│ │ │ │ │ │ ├── atomicwrites\n", + "│ │ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ │ ├── attr\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── _version_info.pyi\n", + "│ │ │ │ │ │ │ ├── converters.pyi\n", + "│ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ ├── filters.pyi\n", + "│ │ │ │ │ │ │ └── validators.pyi\n", + "│ │ │ │ │ │ ├── backports\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ └── ssl_match_hostname.pyi\n", + "│ │ │ │ │ │ ├── backports_abc.pyi\n", + "│ │ │ │ │ │ ├── bleach\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── callbacks.pyi\n", + "│ │ │ │ │ │ │ ├── linkifier.pyi\n", + "│ │ │ │ │ │ │ ├── sanitizer.pyi\n", + "│ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ ├── boto\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── auth.pyi\n", + "│ │ │ │ │ │ │ ├── auth_handler.pyi\n", + "│ │ │ │ │ │ │ ├── compat.pyi\n", + "│ │ │ │ │ │ │ ├── connection.pyi\n", + "│ │ │ │ │ │ │ ├── ec2\n", + "│ │ │ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── elb\n", + "│ │ │ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── exception.pyi\n", + "│ │ │ │ │ │ │ ├── kms\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ │ └── layer1.pyi\n", + "│ │ │ │ │ │ │ ├── plugin.pyi\n", + "│ │ │ │ │ │ │ ├── regioninfo.pyi\n", + "│ │ │ │ │ │ │ ├── s3\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── acl.pyi\n", + "│ │ │ │ │ │ │ │ ├── bucket.pyi\n", + "│ │ │ │ │ │ │ │ ├── bucketlistresultset.pyi\n", + "│ │ │ │ │ │ │ │ ├── bucketlogging.pyi\n", + "│ │ │ │ │ │ │ │ ├── connection.pyi\n", + "│ │ │ │ │ │ │ │ ├── cors.pyi\n", + "│ │ │ │ │ │ │ │ ├── deletemarker.pyi\n", + "│ │ │ │ │ │ │ │ ├── key.pyi\n", + "│ │ │ │ │ │ │ │ ├── keyfile.pyi\n", + "│ │ │ │ │ │ │ │ ├── lifecycle.pyi\n", + "│ │ │ │ │ │ │ │ ├── multidelete.pyi\n", + "│ │ │ │ │ │ │ │ ├── multipart.pyi\n", + "│ │ │ │ │ │ │ │ ├── prefix.pyi\n", + "│ │ │ │ │ │ │ │ ├── tagging.pyi\n", + "│ │ │ │ │ │ │ │ ├── user.pyi\n", + "│ │ │ │ │ │ │ │ └── website.pyi\n", + "│ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ ├── cachetools\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── abc.pyi\n", + "│ │ │ │ │ │ │ ├── cache.pyi\n", + "│ │ │ │ │ │ │ ├── decorators.pyi\n", + "│ │ │ │ │ │ │ ├── func.pyi\n", + "│ │ │ │ │ │ │ ├── lfu.pyi\n", + "│ │ │ │ │ │ │ ├── lru.pyi\n", + "│ │ │ │ │ │ │ ├── rr.pyi\n", + "│ │ │ │ │ │ │ └── ttl.pyi\n", + "│ │ │ │ │ │ ├── certifi.pyi\n", + "│ │ │ │ │ │ ├── characteristic\n", + "│ │ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ │ ├── chardet\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── enums.pyi\n", + "│ │ │ │ │ │ │ ├── langbulgarianmodel.pyi\n", + "│ │ │ │ │ │ │ ├── langcyrillicmodel.pyi\n", + "│ │ │ │ │ │ │ ├── langgreekmodel.pyi\n", + "│ │ │ │ │ │ │ ├── langhebrewmodel.pyi\n", + "│ │ │ │ │ │ │ ├── langhungarianmodel.pyi\n", + "│ │ │ │ │ │ │ ├── langthaimodel.pyi\n", + "│ │ │ │ │ │ │ ├── langturkishmodel.pyi\n", + "│ │ │ │ │ │ │ ├── universaldetector.pyi\n", + "│ │ │ │ │ │ │ └── version.pyi\n", + "│ │ │ │ │ │ ├── click\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── _termui_impl.pyi\n", + "│ │ │ │ │ │ │ ├── core.pyi\n", + "│ │ │ │ │ │ │ ├── decorators.pyi\n", + "│ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ ├── formatting.pyi\n", + "│ │ │ │ │ │ │ ├── globals.pyi\n", + "│ │ │ │ │ │ │ ├── parser.pyi\n", + "│ │ │ │ │ │ │ ├── termui.pyi\n", + "│ │ │ │ │ │ │ ├── testing.pyi\n", + "│ │ │ │ │ │ │ ├── types.pyi\n", + "│ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ ├── croniter.pyi\n", + "│ │ │ │ │ │ ├── cryptography\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ ├── fernet.pyi\n", + "│ │ │ │ │ │ │ ├── hazmat\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── backends\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── interfaces.pyi\n", + "│ │ │ │ │ │ │ │ ├── bindings\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── openssl\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── binding.pyi\n", + "│ │ │ │ │ │ │ │ └── primitives\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── asymmetric\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── dh.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── dsa.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── ec.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── ed25519.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── ed448.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── padding.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── rsa.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── utils.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── x25519.pyi\n", + "│ │ │ │ │ │ │ │ │ └── x448.pyi\n", + "│ │ │ │ │ │ │ │ ├── ciphers\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── aead.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── algorithms.pyi\n", + "│ │ │ │ │ │ │ │ │ └── modes.pyi\n", + "│ │ │ │ │ │ │ │ ├── cmac.pyi\n", + "│ │ │ │ │ │ │ │ ├── constant_time.pyi\n", + "│ │ │ │ │ │ │ │ ├── hashes.pyi\n", + "│ │ │ │ │ │ │ │ ├── hmac.pyi\n", + "│ │ │ │ │ │ │ │ ├── kdf\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── concatkdf.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── hkdf.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── kbkdf.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── pbkdf2.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── scrypt.pyi\n", + "│ │ │ │ │ │ │ │ │ └── x963kdf.pyi\n", + "│ │ │ │ │ │ │ │ ├── keywrap.pyi\n", + "│ │ │ │ │ │ │ │ ├── padding.pyi\n", + "│ │ │ │ │ │ │ │ ├── poly1305.pyi\n", + "│ │ │ │ │ │ │ │ ├── serialization\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── pkcs12.pyi\n", + "│ │ │ │ │ │ │ │ └── twofactor\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── hotp.pyi\n", + "│ │ │ │ │ │ │ │ └── totp.pyi\n", + "│ │ │ │ │ │ │ └── x509\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── extensions.pyi\n", + "│ │ │ │ │ │ │ └── oid.pyi\n", + "│ │ │ │ │ │ ├── dateparser.pyi\n", + "│ │ │ │ │ │ ├── datetimerange\n", + "│ │ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ │ ├── dateutil\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── _common.pyi\n", + "│ │ │ │ │ │ │ ├── easter.pyi\n", + "│ │ │ │ │ │ │ ├── parser.pyi\n", + "│ │ │ │ │ │ │ ├── relativedelta.pyi\n", + "│ │ │ │ │ │ │ ├── rrule.pyi\n", + "│ │ │ │ │ │ │ ├── tz\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── _common.pyi\n", + "│ │ │ │ │ │ │ │ └── tz.pyi\n", + "│ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ ├── decorator.pyi\n", + "│ │ │ │ │ │ ├── deprecated\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── classic.pyi\n", + "│ │ │ │ │ │ │ └── sphinx.pyi\n", + "│ │ │ │ │ │ ├── emoji\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── core.pyi\n", + "│ │ │ │ │ │ │ └── unicode_codes.pyi\n", + "│ │ │ │ │ │ ├── first.pyi\n", + "│ │ │ │ │ │ ├── flask\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── app.pyi\n", + "│ │ │ │ │ │ │ ├── blueprints.pyi\n", + "│ │ │ │ │ │ │ ├── cli.pyi\n", + "│ │ │ │ │ │ │ ├── config.pyi\n", + "│ │ │ │ │ │ │ ├── ctx.pyi\n", + "│ │ │ │ │ │ │ ├── debughelpers.pyi\n", + "│ │ │ │ │ │ │ ├── globals.pyi\n", + "│ │ │ │ │ │ │ ├── helpers.pyi\n", + "│ │ │ │ │ │ │ ├── json\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── tag.pyi\n", + "│ │ │ │ │ │ │ ├── logging.pyi\n", + "│ │ │ │ │ │ │ ├── sessions.pyi\n", + "│ │ │ │ │ │ │ ├── signals.pyi\n", + "│ │ │ │ │ │ │ ├── templating.pyi\n", + "│ │ │ │ │ │ │ ├── testing.pyi\n", + "│ │ │ │ │ │ │ ├── views.pyi\n", + "│ │ │ │ │ │ │ └── wrappers.pyi\n", + "│ │ │ │ │ │ ├── geoip2\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── database.pyi\n", + "│ │ │ │ │ │ │ ├── errors.pyi\n", + "│ │ │ │ │ │ │ ├── mixins.pyi\n", + "│ │ │ │ │ │ │ ├── models.pyi\n", + "│ │ │ │ │ │ │ └── records.pyi\n", + "│ │ │ │ │ │ ├── gflags.pyi\n", + "│ │ │ │ │ │ ├── google\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ └── protobuf\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── any_pb2.pyi\n", + "│ │ │ │ │ │ │ ├── api_pb2.pyi\n", + "│ │ │ │ │ │ │ ├── compiler\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── plugin_pb2.pyi\n", + "│ │ │ │ │ │ │ ├── descriptor.pyi\n", + "│ │ │ │ │ │ │ ├── descriptor_pb2.pyi\n", + "│ │ │ │ │ │ │ ├── descriptor_pool.pyi\n", + "│ │ │ │ │ │ │ ├── duration_pb2.pyi\n", + "│ │ │ │ │ │ │ ├── empty_pb2.pyi\n", + "│ │ │ │ │ │ │ ├── field_mask_pb2.pyi\n", + "│ │ │ │ │ │ │ ├── internal\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── containers.pyi\n", + "│ │ │ │ │ │ │ │ ├── decoder.pyi\n", + "│ │ │ │ │ │ │ │ ├── encoder.pyi\n", + "│ │ │ │ │ │ │ │ ├── enum_type_wrapper.pyi\n", + "│ │ │ │ │ │ │ │ ├── extension_dict.pyi\n", + "│ │ │ │ │ │ │ │ ├── message_listener.pyi\n", + "│ │ │ │ │ │ │ │ ├── python_message.pyi\n", + "│ │ │ │ │ │ │ │ ├── well_known_types.pyi\n", + "│ │ │ │ │ │ │ │ └── wire_format.pyi\n", + "│ │ │ │ │ │ │ ├── json_format.pyi\n", + "│ │ │ │ │ │ │ ├── message.pyi\n", + "│ │ │ │ │ │ │ ├── message_factory.pyi\n", + "│ │ │ │ │ │ │ ├── reflection.pyi\n", + "│ │ │ │ │ │ │ ├── service.pyi\n", + "│ │ │ │ │ │ │ ├── source_context_pb2.pyi\n", + "│ │ │ │ │ │ │ ├── struct_pb2.pyi\n", + "│ │ │ │ │ │ │ ├── symbol_database.pyi\n", + "│ │ │ │ │ │ │ ├── timestamp_pb2.pyi\n", + "│ │ │ │ │ │ │ ├── type_pb2.pyi\n", + "│ │ │ │ │ │ │ ├── util\n", + "│ │ │ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ │ │ └── wrappers_pb2.pyi\n", + "│ │ │ │ │ │ ├── itsdangerous.pyi\n", + "│ │ │ │ │ │ ├── jinja2\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── _compat.pyi\n", + "│ │ │ │ │ │ │ ├── _stringdefs.pyi\n", + "│ │ │ │ │ │ │ ├── bccache.pyi\n", + "│ │ │ │ │ │ │ ├── compiler.pyi\n", + "│ │ │ │ │ │ │ ├── constants.pyi\n", + "│ │ │ │ │ │ │ ├── debug.pyi\n", + "│ │ │ │ │ │ │ ├── defaults.pyi\n", + "│ │ │ │ │ │ │ ├── environment.pyi\n", + "│ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ ├── ext.pyi\n", + "│ │ │ │ │ │ │ ├── filters.pyi\n", + "│ │ │ │ │ │ │ ├── lexer.pyi\n", + "│ │ │ │ │ │ │ ├── loaders.pyi\n", + "│ │ │ │ │ │ │ ├── meta.pyi\n", + "│ │ │ │ │ │ │ ├── nodes.pyi\n", + "│ │ │ │ │ │ │ ├── optimizer.pyi\n", + "│ │ │ │ │ │ │ ├── parser.pyi\n", + "│ │ │ │ │ │ │ ├── runtime.pyi\n", + "│ │ │ │ │ │ │ ├── sandbox.pyi\n", + "│ │ │ │ │ │ │ ├── tests.pyi\n", + "│ │ │ │ │ │ │ ├── utils.pyi\n", + "│ │ │ │ │ │ │ └── visitor.pyi\n", + "│ │ │ │ │ │ ├── markdown\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── __meta__.pyi\n", + "│ │ │ │ │ │ │ ├── blockparser.pyi\n", + "│ │ │ │ │ │ │ ├── blockprocessors.pyi\n", + "│ │ │ │ │ │ │ ├── core.pyi\n", + "│ │ │ │ │ │ │ ├── extensions\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── abbr.pyi\n", + "│ │ │ │ │ │ │ │ ├── admonition.pyi\n", + "│ │ │ │ │ │ │ │ ├── attr_list.pyi\n", + "│ │ │ │ │ │ │ │ ├── codehilite.pyi\n", + "│ │ │ │ │ │ │ │ ├── def_list.pyi\n", + "│ │ │ │ │ │ │ │ ├── extra.pyi\n", + "│ │ │ │ │ │ │ │ ├── fenced_code.pyi\n", + "│ │ │ │ │ │ │ │ ├── footnotes.pyi\n", + "│ │ │ │ │ │ │ │ ├── legacy_attrs.pyi\n", + "│ │ │ │ │ │ │ │ ├── legacy_em.pyi\n", + "│ │ │ │ │ │ │ │ ├── md_in_html.pyi\n", + "│ │ │ │ │ │ │ │ ├── meta.pyi\n", + "│ │ │ │ │ │ │ │ ├── nl2br.pyi\n", + "│ │ │ │ │ │ │ │ ├── sane_lists.pyi\n", + "│ │ │ │ │ │ │ │ ├── smarty.pyi\n", + "│ │ │ │ │ │ │ │ ├── tables.pyi\n", + "│ │ │ │ │ │ │ │ ├── toc.pyi\n", + "│ │ │ │ │ │ │ │ └── wikilinks.pyi\n", + "│ │ │ │ │ │ │ ├── inlinepatterns.pyi\n", + "│ │ │ │ │ │ │ ├── pep562.pyi\n", + "│ │ │ │ │ │ │ ├── postprocessors.pyi\n", + "│ │ │ │ │ │ │ ├── preprocessors.pyi\n", + "│ │ │ │ │ │ │ ├── serializers.pyi\n", + "│ │ │ │ │ │ │ ├── treeprocessors.pyi\n", + "│ │ │ │ │ │ │ └── util.pyi\n", + "│ │ │ │ │ │ ├── markupsafe\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── _compat.pyi\n", + "│ │ │ │ │ │ │ ├── _constants.pyi\n", + "│ │ │ │ │ │ │ ├── _native.pyi\n", + "│ │ │ │ │ │ │ └── _speedups.pyi\n", + "│ │ │ │ │ │ ├── maxminddb\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── compat.pyi\n", + "│ │ │ │ │ │ │ ├── const.pyi\n", + "│ │ │ │ │ │ │ ├── decoder.pyi\n", + "│ │ │ │ │ │ │ ├── errors.pyi\n", + "│ │ │ │ │ │ │ ├── extension.pyi\n", + "│ │ │ │ │ │ │ └── reader.pyi\n", + "│ │ │ │ │ │ ├── mock.pyi\n", + "│ │ │ │ │ │ ├── mypy_extensions.pyi\n", + "│ │ │ │ │ │ ├── nmap\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ └── nmap.pyi\n", + "│ │ │ │ │ │ ├── paramiko\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── _version.pyi\n", + "│ │ │ │ │ │ │ ├── _winapi.pyi\n", + "│ │ │ │ │ │ │ ├── agent.pyi\n", + "│ │ │ │ │ │ │ ├── auth_handler.pyi\n", + "│ │ │ │ │ │ │ ├── ber.pyi\n", + "│ │ │ │ │ │ │ ├── buffered_pipe.pyi\n", + "│ │ │ │ │ │ │ ├── channel.pyi\n", + "│ │ │ │ │ │ │ ├── client.pyi\n", + "│ │ │ │ │ │ │ ├── common.pyi\n", + "│ │ │ │ │ │ │ ├── compress.pyi\n", + "│ │ │ │ │ │ │ ├── config.pyi\n", + "│ │ │ │ │ │ │ ├── dsskey.pyi\n", + "│ │ │ │ │ │ │ ├── ecdsakey.pyi\n", + "│ │ │ │ │ │ │ ├── ed25519key.pyi\n", + "│ │ │ │ │ │ │ ├── file.pyi\n", + "│ │ │ │ │ │ │ ├── hostkeys.pyi\n", + "│ │ │ │ │ │ │ ├── kex_curve25519.pyi\n", + "│ │ │ │ │ │ │ ├── kex_ecdh_nist.pyi\n", + "│ │ │ │ │ │ │ ├── kex_gex.pyi\n", + "│ │ │ │ │ │ │ ├── kex_group1.pyi\n", + "│ │ │ │ │ │ │ ├── kex_group14.pyi\n", + "│ │ │ │ │ │ │ ├── kex_group16.pyi\n", + "│ │ │ │ │ │ │ ├── kex_gss.pyi\n", + "│ │ │ │ │ │ │ ├── message.pyi\n", + "│ │ │ │ │ │ │ ├── packet.pyi\n", + "│ │ │ │ │ │ │ ├── pipe.pyi\n", + "│ │ │ │ │ │ │ ├── pkey.pyi\n", + "│ │ │ │ │ │ │ ├── primes.pyi\n", + "│ │ │ │ │ │ │ ├── proxy.pyi\n", + "│ │ │ │ │ │ │ ├── py3compat.pyi\n", + "│ │ │ │ │ │ │ ├── rsakey.pyi\n", + "│ │ │ │ │ │ │ ├── server.pyi\n", + "│ │ │ │ │ │ │ ├── sftp.pyi\n", + "│ │ │ │ │ │ │ ├── sftp_attr.pyi\n", + "│ │ │ │ │ │ │ ├── sftp_client.pyi\n", + "│ │ │ │ │ │ │ ├── sftp_file.pyi\n", + "│ │ │ │ │ │ │ ├── sftp_handle.pyi\n", + "│ │ │ │ │ │ │ ├── sftp_server.pyi\n", + "│ │ │ │ │ │ │ ├── sftp_si.pyi\n", + "│ │ │ │ │ │ │ ├── ssh_exception.pyi\n", + "│ │ │ │ │ │ │ ├── ssh_gss.pyi\n", + "│ │ │ │ │ │ │ ├── transport.pyi\n", + "│ │ │ │ │ │ │ ├── util.pyi\n", + "│ │ │ │ │ │ │ └── win_pageant.pyi\n", + "│ │ │ │ │ │ ├── polib.pyi\n", + "│ │ │ │ │ │ ├── pyVmomi\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── vim\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── event.pyi\n", + "│ │ │ │ │ │ │ │ ├── fault.pyi\n", + "│ │ │ │ │ │ │ │ ├── option.pyi\n", + "│ │ │ │ │ │ │ │ └── view.pyi\n", + "│ │ │ │ │ │ │ └── vmodl\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── fault.pyi\n", + "│ │ │ │ │ │ │ └── query.pyi\n", + "│ │ │ │ │ │ ├── pycurl.pyi\n", + "│ │ │ │ │ │ ├── pymysql\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── charset.pyi\n", + "│ │ │ │ │ │ │ ├── connections.pyi\n", + "│ │ │ │ │ │ │ ├── constants\n", + "│ │ │ │ │ │ │ │ ├── CLIENT.pyi\n", + "│ │ │ │ │ │ │ │ ├── COMMAND.pyi\n", + "│ │ │ │ │ │ │ │ ├── ER.pyi\n", + "│ │ │ │ │ │ │ │ ├── FIELD_TYPE.pyi\n", + "│ │ │ │ │ │ │ │ ├── FLAG.pyi\n", + "│ │ │ │ │ │ │ │ ├── SERVER_STATUS.pyi\n", + "│ │ │ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── converters.pyi\n", + "│ │ │ │ │ │ │ ├── cursors.pyi\n", + "│ │ │ │ │ │ │ ├── err.pyi\n", + "│ │ │ │ │ │ │ ├── times.pyi\n", + "│ │ │ │ │ │ │ └── util.pyi\n", + "│ │ │ │ │ │ ├── pynamodb\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── attributes.pyi\n", + "│ │ │ │ │ │ │ ├── connection\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ ├── table.pyi\n", + "│ │ │ │ │ │ │ │ └── util.pyi\n", + "│ │ │ │ │ │ │ ├── constants.pyi\n", + "│ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ ├── indexes.pyi\n", + "│ │ │ │ │ │ │ ├── models.pyi\n", + "│ │ │ │ │ │ │ ├── settings.pyi\n", + "│ │ │ │ │ │ │ ├── throttle.pyi\n", + "│ │ │ │ │ │ │ └── types.pyi\n", + "│ │ │ │ │ │ ├── pyre_extensions.pyi\n", + "│ │ │ │ │ │ ├── pytz\n", + "│ │ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ │ ├── redis\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── client.pyi\n", + "│ │ │ │ │ │ │ ├── connection.pyi\n", + "│ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ ├── requests\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── adapters.pyi\n", + "│ │ │ │ │ │ │ ├── api.pyi\n", + "│ │ │ │ │ │ │ ├── auth.pyi\n", + "│ │ │ │ │ │ │ ├── compat.pyi\n", + "│ │ │ │ │ │ │ ├── cookies.pyi\n", + "│ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ ├── hooks.pyi\n", + "│ │ │ │ │ │ │ ├── models.pyi\n", + "│ │ │ │ │ │ │ ├── packages\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── urllib3\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── _collections.pyi\n", + "│ │ │ │ │ │ │ │ ├── connection.pyi\n", + "│ │ │ │ │ │ │ │ ├── connectionpool.pyi\n", + "│ │ │ │ │ │ │ │ ├── contrib\n", + "│ │ │ │ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ │ ├── fields.pyi\n", + "│ │ │ │ │ │ │ │ ├── filepost.pyi\n", + "│ │ │ │ │ │ │ │ ├── packages\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── ssl_match_hostname\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── _implementation.pyi\n", + "│ │ │ │ │ │ │ │ ├── poolmanager.pyi\n", + "│ │ │ │ │ │ │ │ ├── request.pyi\n", + "│ │ │ │ │ │ │ │ ├── response.pyi\n", + "│ │ │ │ │ │ │ │ └── util\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── connection.pyi\n", + "│ │ │ │ │ │ │ │ ├── request.pyi\n", + "│ │ │ │ │ │ │ │ ├── response.pyi\n", + "│ │ │ │ │ │ │ │ ├── retry.pyi\n", + "│ │ │ │ │ │ │ │ ├── ssl_.pyi\n", + "│ │ │ │ │ │ │ │ ├── timeout.pyi\n", + "│ │ │ │ │ │ │ │ └── url.pyi\n", + "│ │ │ │ │ │ │ ├── sessions.pyi\n", + "│ │ │ │ │ │ │ ├── status_codes.pyi\n", + "│ │ │ │ │ │ │ ├── structures.pyi\n", + "│ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ ├── retry\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ └── api.pyi\n", + "│ │ │ │ │ │ ├── simplejson\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── decoder.pyi\n", + "│ │ │ │ │ │ │ ├── encoder.pyi\n", + "│ │ │ │ │ │ │ └── scanner.pyi\n", + "│ │ │ │ │ │ ├── singledispatch.pyi\n", + "│ │ │ │ │ │ ├── slugify\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── slugify.pyi\n", + "│ │ │ │ │ │ │ └── special.pyi\n", + "│ │ │ │ │ │ ├── tabulate.pyi\n", + "│ │ │ │ │ │ ├── termcolor.pyi\n", + "│ │ │ │ │ │ ├── toml.pyi\n", + "│ │ │ │ │ │ ├── typing_extensions.pyi\n", + "│ │ │ │ │ │ ├── tzlocal\n", + "│ │ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ │ ├── ujson.pyi\n", + "│ │ │ │ │ │ ├── werkzeug\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── _compat.pyi\n", + "│ │ │ │ │ │ │ ├── _internal.pyi\n", + "│ │ │ │ │ │ │ ├── _reloader.pyi\n", + "│ │ │ │ │ │ │ ├── contrib\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── atom.pyi\n", + "│ │ │ │ │ │ │ │ ├── cache.pyi\n", + "│ │ │ │ │ │ │ │ ├── fixers.pyi\n", + "│ │ │ │ │ │ │ │ ├── iterio.pyi\n", + "│ │ │ │ │ │ │ │ ├── jsrouting.pyi\n", + "│ │ │ │ │ │ │ │ ├── limiter.pyi\n", + "│ │ │ │ │ │ │ │ ├── lint.pyi\n", + "│ │ │ │ │ │ │ │ ├── profiler.pyi\n", + "│ │ │ │ │ │ │ │ ├── securecookie.pyi\n", + "│ │ │ │ │ │ │ │ ├── sessions.pyi\n", + "│ │ │ │ │ │ │ │ ├── testtools.pyi\n", + "│ │ │ │ │ │ │ │ └── wrappers.pyi\n", + "│ │ │ │ │ │ │ ├── datastructures.pyi\n", + "│ │ │ │ │ │ │ ├── debug\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── console.pyi\n", + "│ │ │ │ │ │ │ │ ├── repr.pyi\n", + "│ │ │ │ │ │ │ │ └── tbtools.pyi\n", + "│ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ ├── filesystem.pyi\n", + "│ │ │ │ │ │ │ ├── formparser.pyi\n", + "│ │ │ │ │ │ │ ├── http.pyi\n", + "│ │ │ │ │ │ │ ├── local.pyi\n", + "│ │ │ │ │ │ │ ├── middleware\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── dispatcher.pyi\n", + "│ │ │ │ │ │ │ │ ├── http_proxy.pyi\n", + "│ │ │ │ │ │ │ │ ├── lint.pyi\n", + "│ │ │ │ │ │ │ │ ├── profiler.pyi\n", + "│ │ │ │ │ │ │ │ ├── proxy_fix.pyi\n", + "│ │ │ │ │ │ │ │ └── shared_data.pyi\n", + "│ │ │ │ │ │ │ ├── posixemulation.pyi\n", + "│ │ │ │ │ │ │ ├── routing.pyi\n", + "│ │ │ │ │ │ │ ├── script.pyi\n", + "│ │ │ │ │ │ │ ├── security.pyi\n", + "│ │ │ │ │ │ │ ├── serving.pyi\n", + "│ │ │ │ │ │ │ ├── test.pyi\n", + "│ │ │ │ │ │ │ ├── testapp.pyi\n", + "│ │ │ │ │ │ │ ├── urls.pyi\n", + "│ │ │ │ │ │ │ ├── useragents.pyi\n", + "│ │ │ │ │ │ │ ├── utils.pyi\n", + "│ │ │ │ │ │ │ ├── wrappers.pyi\n", + "│ │ │ │ │ │ │ └── wsgi.pyi\n", + "│ │ │ │ │ │ └── yaml\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ ├── composer.pyi\n", + "│ │ │ │ │ │ ├── constructor.pyi\n", + "│ │ │ │ │ │ ├── cyaml.pyi\n", + "│ │ │ │ │ │ ├── dumper.pyi\n", + "│ │ │ │ │ │ ├── emitter.pyi\n", + "│ │ │ │ │ │ ├── error.pyi\n", + "│ │ │ │ │ │ ├── events.pyi\n", + "│ │ │ │ │ │ ├── loader.pyi\n", + "│ │ │ │ │ │ ├── nodes.pyi\n", + "│ │ │ │ │ │ ├── parser.pyi\n", + "│ │ │ │ │ │ ├── reader.pyi\n", + "│ │ │ │ │ │ ├── representer.pyi\n", + "│ │ │ │ │ │ ├── resolver.pyi\n", + "│ │ │ │ │ │ ├── scanner.pyi\n", + "│ │ │ │ │ │ ├── serializer.pyi\n", + "│ │ │ │ │ │ └── tokens.pyi\n", + "│ │ │ │ │ └── 3\n", + "│ │ │ │ │ ├── aiofiles\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ ├── os.pyi\n", + "│ │ │ │ │ │ └── threadpool\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ ├── binary.pyi\n", + "│ │ │ │ │ │ └── text.pyi\n", + "│ │ │ │ │ ├── contextvars.pyi\n", + "│ │ │ │ │ ├── dataclasses.pyi\n", + "│ │ │ │ │ ├── docutils\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ ├── examples.pyi\n", + "│ │ │ │ │ │ ├── nodes.pyi\n", + "│ │ │ │ │ │ └── parsers\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ └── rst\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ ├── nodes.pyi\n", + "│ │ │ │ │ │ ├── roles.pyi\n", + "│ │ │ │ │ │ └── states.pyi\n", + "│ │ │ │ │ ├── filelock\n", + "│ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ ├── freezegun\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ └── api.pyi\n", + "│ │ │ │ │ ├── frozendict.pyi\n", + "│ │ │ │ │ ├── jwt\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ ├── algorithms.pyi\n", + "│ │ │ │ │ │ └── contrib\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ └── algorithms\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ ├── py_ecdsa.pyi\n", + "│ │ │ │ │ │ └── pycrypto.pyi\n", + "│ │ │ │ │ ├── orjson.pyi\n", + "│ │ │ │ │ ├── pkg_resources\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ └── py31compat.pyi\n", + "│ │ │ │ │ ├── pyrfc3339\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ ├── generator.pyi\n", + "│ │ │ │ │ │ ├── parser.pyi\n", + "│ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ ├── six\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ └── moves\n", + "│ │ │ │ │ │ ├── BaseHTTPServer.pyi\n", + "│ │ │ │ │ │ ├── CGIHTTPServer.pyi\n", + "│ │ │ │ │ │ ├── SimpleHTTPServer.pyi\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ ├── _dummy_thread.pyi\n", + "│ │ │ │ │ │ ├── _thread.pyi\n", + "│ │ │ │ │ │ ├── builtins.pyi\n", + "│ │ │ │ │ │ ├── cPickle.pyi\n", + "│ │ │ │ │ │ ├── collections_abc.pyi\n", + "│ │ │ │ │ │ ├── configparser.pyi\n", + "│ │ │ │ │ │ ├── email_mime_base.pyi\n", + "│ │ │ │ │ │ ├── email_mime_multipart.pyi\n", + "│ │ │ │ │ │ ├── email_mime_nonmultipart.pyi\n", + "│ │ │ │ │ │ ├── email_mime_text.pyi\n", + "│ │ │ │ │ │ ├── html_entities.pyi\n", + "│ │ │ │ │ │ ├── html_parser.pyi\n", + "│ │ │ │ │ │ ├── http_client.pyi\n", + "│ │ │ │ │ │ ├── http_cookiejar.pyi\n", + "│ │ │ │ │ │ ├── http_cookies.pyi\n", + "│ │ │ │ │ │ ├── queue.pyi\n", + "│ │ │ │ │ │ ├── reprlib.pyi\n", + "│ │ │ │ │ │ ├── socketserver.pyi\n", + "│ │ │ │ │ │ ├── tkinter.pyi\n", + "│ │ │ │ │ │ ├── tkinter_commondialog.pyi\n", + "│ │ │ │ │ │ ├── tkinter_constants.pyi\n", + "│ │ │ │ │ │ ├── tkinter_dialog.pyi\n", + "│ │ │ │ │ │ ├── tkinter_filedialog.pyi\n", + "│ │ │ │ │ │ ├── tkinter_tkfiledialog.pyi\n", + "│ │ │ │ │ │ ├── tkinter_ttk.pyi\n", + "│ │ │ │ │ │ ├── urllib\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── error.pyi\n", + "│ │ │ │ │ │ │ ├── parse.pyi\n", + "│ │ │ │ │ │ │ ├── request.pyi\n", + "│ │ │ │ │ │ │ ├── response.pyi\n", + "│ │ │ │ │ │ │ └── robotparser.pyi\n", + "│ │ │ │ │ │ ├── urllib_error.pyi\n", + "│ │ │ │ │ │ ├── urllib_parse.pyi\n", + "│ │ │ │ │ │ ├── urllib_request.pyi\n", + "│ │ │ │ │ │ ├── urllib_response.pyi\n", + "│ │ │ │ │ │ └── urllib_robotparser.pyi\n", + "│ │ │ │ │ ├── typed_ast\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ ├── ast27.pyi\n", + "│ │ │ │ │ │ ├── ast3.pyi\n", + "│ │ │ │ │ │ └── conversions.pyi\n", + "│ │ │ │ │ └── waitress\n", + "│ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ ├── adjustments.pyi\n", + "│ │ │ │ │ ├── buffers.pyi\n", + "│ │ │ │ │ ├── channel.pyi\n", + "│ │ │ │ │ ├── compat.pyi\n", + "│ │ │ │ │ ├── parser.pyi\n", + "│ │ │ │ │ ├── proxy_headers.pyi\n", + "│ │ │ │ │ ├── receiver.pyi\n", + "│ │ │ │ │ ├── rfc7230.pyi\n", + "│ │ │ │ │ ├── runner.pyi\n", + "│ │ │ │ │ ├── server.pyi\n", + "│ │ │ │ │ ├── task.pyi\n", + "│ │ │ │ │ ├── trigger.pyi\n", + "│ │ │ │ │ ├── utilities.pyi\n", + "│ │ │ │ │ └── wasyncore.pyi\n", + "│ │ │ │ └── utils.py\n", + "│ │ │ ├── jedi-0.19.1.dist-info\n", + "│ │ │ │ ├── AUTHORS.txt\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE.txt\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── jupyter.py\n", + "│ │ │ ├── jupyter_client\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _version.cpython-310.pyc\n", + "│ │ │ │ │ ├── adapter.cpython-310.pyc\n", + "│ │ │ │ │ ├── channels.cpython-310.pyc\n", + "│ │ │ │ │ ├── channelsabc.cpython-310.pyc\n", + "│ │ │ │ │ ├── client.cpython-310.pyc\n", + "│ │ │ │ │ ├── clientabc.cpython-310.pyc\n", + "│ │ │ │ │ ├── connect.cpython-310.pyc\n", + "│ │ │ │ │ ├── consoleapp.cpython-310.pyc\n", + "│ │ │ │ │ ├── jsonutil.cpython-310.pyc\n", + "│ │ │ │ │ ├── kernelapp.cpython-310.pyc\n", + "│ │ │ │ │ ├── kernelspec.cpython-310.pyc\n", + "│ │ │ │ │ ├── kernelspecapp.cpython-310.pyc\n", + "│ │ │ │ │ ├── launcher.cpython-310.pyc\n", + "│ │ │ │ │ ├── localinterfaces.cpython-310.pyc\n", + "│ │ │ │ │ ├── manager.cpython-310.pyc\n", + "│ │ │ │ │ ├── managerabc.cpython-310.pyc\n", + "│ │ │ │ │ ├── multikernelmanager.cpython-310.pyc\n", + "│ │ │ │ │ ├── restarter.cpython-310.pyc\n", + "│ │ │ │ │ ├── runapp.cpython-310.pyc\n", + "│ │ │ │ │ ├── session.cpython-310.pyc\n", + "│ │ │ │ │ ├── threaded.cpython-310.pyc\n", + "│ │ │ │ │ ├── utils.cpython-310.pyc\n", + "│ │ │ │ │ └── win_interrupt.cpython-310.pyc\n", + "│ │ │ │ ├── _version.py\n", + "│ │ │ │ ├── adapter.py\n", + "│ │ │ │ ├── asynchronous\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ └── client.cpython-310.pyc\n", + "│ │ │ │ │ └── client.py\n", + "│ │ │ │ ├── blocking\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ └── client.cpython-310.pyc\n", + "│ │ │ │ │ └── client.py\n", + "│ │ │ │ ├── channels.py\n", + "│ │ │ │ ├── channelsabc.py\n", + "│ │ │ │ ├── client.py\n", + "│ │ │ │ ├── clientabc.py\n", + "│ │ │ │ ├── connect.py\n", + "│ │ │ │ ├── consoleapp.py\n", + "│ │ │ │ ├── ioloop\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── manager.cpython-310.pyc\n", + "│ │ │ │ │ │ └── restarter.cpython-310.pyc\n", + "│ │ │ │ │ ├── manager.py\n", + "│ │ │ │ │ └── restarter.py\n", + "│ │ │ │ ├── jsonutil.py\n", + "│ │ │ │ ├── kernelapp.py\n", + "│ │ │ │ ├── kernelspec.py\n", + "│ │ │ │ ├── kernelspecapp.py\n", + "│ │ │ │ ├── launcher.py\n", + "│ │ │ │ ├── localinterfaces.py\n", + "│ │ │ │ ├── manager.py\n", + "│ │ │ │ ├── managerabc.py\n", + "│ │ │ │ ├── multikernelmanager.py\n", + "│ │ │ │ ├── provisioning\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── factory.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── local_provisioner.cpython-310.pyc\n", + "│ │ │ │ │ │ └── provisioner_base.cpython-310.pyc\n", + "│ │ │ │ │ ├── factory.py\n", + "│ │ │ │ │ ├── local_provisioner.py\n", + "│ │ │ │ │ └── provisioner_base.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ ├── restarter.py\n", + "│ │ │ │ ├── runapp.py\n", + "│ │ │ │ ├── session.py\n", + "│ │ │ │ ├── ssh\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── forward.cpython-310.pyc\n", + "│ │ │ │ │ │ └── tunnel.cpython-310.pyc\n", + "│ │ │ │ │ ├── forward.py\n", + "│ │ │ │ │ └── tunnel.py\n", + "│ │ │ │ ├── threaded.py\n", + "│ │ │ │ ├── utils.py\n", + "│ │ │ │ └── win_interrupt.py\n", + "│ │ │ ├── jupyter_client-8.6.1.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ ├── entry_points.txt\n", + "│ │ │ │ └── licenses\n", + "│ │ │ │ └── LICENSE\n", + "│ │ │ ├── jupyter_core\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __main__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ ├── application.cpython-310.pyc\n", + "│ │ │ │ │ ├── command.cpython-310.pyc\n", + "│ │ │ │ │ ├── migrate.cpython-310.pyc\n", + "│ │ │ │ │ ├── paths.cpython-310.pyc\n", + "│ │ │ │ │ ├── troubleshoot.cpython-310.pyc\n", + "│ │ │ │ │ └── version.cpython-310.pyc\n", + "│ │ │ │ ├── application.py\n", + "│ │ │ │ ├── command.py\n", + "│ │ │ │ ├── migrate.py\n", + "│ │ │ │ ├── paths.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ ├── troubleshoot.py\n", + "│ │ │ │ ├── utils\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ └── version.py\n", + "│ │ │ ├── jupyter_core-5.7.2.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ ├── entry_points.txt\n", + "│ │ │ │ └── licenses\n", + "│ │ │ │ └── LICENSE\n", + "│ │ │ ├── matplotlib_inline\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── backend_inline.cpython-310.pyc\n", + "│ │ │ │ │ └── config.cpython-310.pyc\n", + "│ │ │ │ ├── backend_inline.py\n", + "│ │ │ │ └── config.py\n", + "│ │ │ ├── matplotlib_inline-0.1.7.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ ├── entry_points.txt\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── nest_asyncio-1.6.0.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── nest_asyncio.py\n", + "│ │ │ ├── numpy\n", + "│ │ │ │ ├── __config__.py\n", + "│ │ │ │ ├── __init__.cython-30.pxd\n", + "│ │ │ │ ├── __init__.pxd\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __config__.cpython-310.pyc\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _distributor_init.cpython-310.pyc\n", + "│ │ │ │ │ ├── _globals.cpython-310.pyc\n", + "│ │ │ │ │ ├── _pytesttester.cpython-310.pyc\n", + "│ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ ├── ctypeslib.cpython-310.pyc\n", + "│ │ │ │ │ ├── dtypes.cpython-310.pyc\n", + "│ │ │ │ │ ├── exceptions.cpython-310.pyc\n", + "│ │ │ │ │ ├── matlib.cpython-310.pyc\n", + "│ │ │ │ │ └── version.cpython-310.pyc\n", + "│ │ │ │ ├── _core\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _dtype.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _dtype_ctypes.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _internal.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _multiarray_umath.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── multiarray.cpython-310.pyc\n", + "│ │ │ │ │ │ └── umath.cpython-310.pyc\n", + "│ │ │ │ │ ├── _dtype.py\n", + "│ │ │ │ │ ├── _dtype_ctypes.py\n", + "│ │ │ │ │ ├── _internal.py\n", + "│ │ │ │ │ ├── _multiarray_umath.py\n", + "│ │ │ │ │ ├── multiarray.py\n", + "│ │ │ │ │ └── umath.py\n", + "│ │ │ │ ├── _distributor_init.py\n", + "│ │ │ │ ├── _globals.py\n", + "│ │ │ │ ├── _pyinstaller\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── hook-numpy.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pyinstaller-smoke.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_pyinstaller.cpython-310.pyc\n", + "│ │ │ │ │ ├── hook-numpy.py\n", + "│ │ │ │ │ ├── pyinstaller-smoke.py\n", + "│ │ │ │ │ └── test_pyinstaller.py\n", + "│ │ │ │ ├── _pytesttester.py\n", + "│ │ │ │ ├── _pytesttester.pyi\n", + "│ │ │ │ ├── _typing\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _add_docstring.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _array_like.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _char_codes.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _dtype_like.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _extended_precision.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _nbit.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _nested_sequence.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _scalars.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _shape.cpython-310.pyc\n", + "│ │ │ │ │ │ └── setup.cpython-310.pyc\n", + "│ │ │ │ │ ├── _add_docstring.py\n", + "│ │ │ │ │ ├── _array_like.py\n", + "│ │ │ │ │ ├── _callable.pyi\n", + "│ │ │ │ │ ├── _char_codes.py\n", + "│ │ │ │ │ ├── _dtype_like.py\n", + "│ │ │ │ │ ├── _extended_precision.py\n", + "│ │ │ │ │ ├── _nbit.py\n", + "│ │ │ │ │ ├── _nested_sequence.py\n", + "│ │ │ │ │ ├── _scalars.py\n", + "│ │ │ │ │ ├── _shape.py\n", + "│ │ │ │ │ ├── _ufunc.pyi\n", + "│ │ │ │ │ └── setup.py\n", + "│ │ │ │ ├── _utils\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _convertions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _inspect.cpython-310.pyc\n", + "│ │ │ │ │ │ └── _pep440.cpython-310.pyc\n", + "│ │ │ │ │ ├── _convertions.py\n", + "│ │ │ │ │ ├── _inspect.py\n", + "│ │ │ │ │ └── _pep440.py\n", + "│ │ │ │ ├── array_api\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _array_object.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _constants.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _creation_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _data_type_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _dtypes.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _elementwise_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _indexing_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _manipulation_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _searching_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _set_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _sorting_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _statistical_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _typing.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _utility_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── linalg.cpython-310.pyc\n", + "│ │ │ │ │ │ └── setup.cpython-310.pyc\n", + "│ │ │ │ │ ├── _array_object.py\n", + "│ │ │ │ │ ├── _constants.py\n", + "│ │ │ │ │ ├── _creation_functions.py\n", + "│ │ │ │ │ ├── _data_type_functions.py\n", + "│ │ │ │ │ ├── _dtypes.py\n", + "│ │ │ │ │ ├── _elementwise_functions.py\n", + "│ │ │ │ │ ├── _indexing_functions.py\n", + "│ │ │ │ │ ├── _manipulation_functions.py\n", + "│ │ │ │ │ ├── _searching_functions.py\n", + "│ │ │ │ │ ├── _set_functions.py\n", + "│ │ │ │ │ ├── _sorting_functions.py\n", + "│ │ │ │ │ ├── _statistical_functions.py\n", + "│ │ │ │ │ ├── _typing.py\n", + "│ │ │ │ │ ├── _utility_functions.py\n", + "│ │ │ │ │ ├── linalg.py\n", + "│ │ │ │ │ ├── setup.py\n", + "│ │ │ │ │ └── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_array_object.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_creation_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_data_type_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_elementwise_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_indexing_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_manipulation_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_set_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_sorting_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_validation.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_array_object.py\n", + "│ │ │ │ │ ├── test_creation_functions.py\n", + "│ │ │ │ │ ├── test_data_type_functions.py\n", + "│ │ │ │ │ ├── test_elementwise_functions.py\n", + "│ │ │ │ │ ├── test_indexing_functions.py\n", + "│ │ │ │ │ ├── test_manipulation_functions.py\n", + "│ │ │ │ │ ├── test_set_functions.py\n", + "│ │ │ │ │ ├── test_sorting_functions.py\n", + "│ │ │ │ │ └── test_validation.py\n", + "│ │ │ │ ├── compat\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── py3k.cpython-310.pyc\n", + "│ │ │ │ │ │ └── setup.cpython-310.pyc\n", + "│ │ │ │ │ ├── py3k.py\n", + "│ │ │ │ │ ├── setup.py\n", + "│ │ │ │ │ └── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_compat.cpython-310.pyc\n", + "│ │ │ │ │ └── test_compat.py\n", + "│ │ │ │ ├── conftest.py\n", + "│ │ │ │ ├── core\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _add_newdocs.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _add_newdocs_scalars.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _asarray.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _dtype.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _dtype_ctypes.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _exceptions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _internal.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _machar.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _methods.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _string_helpers.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _type_aliases.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _ufunc_config.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── arrayprint.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cversions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── defchararray.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── einsumfunc.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── fromnumeric.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── function_base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── getlimits.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── memmap.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── multiarray.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── numeric.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── numerictypes.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── overrides.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── records.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── shape_base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── umath.cpython-310.pyc\n", + "│ │ │ │ │ │ └── umath_tests.cpython-310.pyc\n", + "│ │ │ │ │ ├── _add_newdocs.py\n", + "│ │ │ │ │ ├── _add_newdocs_scalars.py\n", + "│ │ │ │ │ ├── _asarray.py\n", + "│ │ │ │ │ ├── _asarray.pyi\n", + "│ │ │ │ │ ├── _dtype.py\n", + "│ │ │ │ │ ├── _dtype_ctypes.py\n", + "│ │ │ │ │ ├── _exceptions.py\n", + "│ │ │ │ │ ├── _internal.py\n", + "│ │ │ │ │ ├── _internal.pyi\n", + "│ │ │ │ │ ├── _machar.py\n", + "│ │ │ │ │ ├── _methods.py\n", + "│ │ │ │ │ ├── _multiarray_tests.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── _multiarray_umath.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── _operand_flag_tests.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── _rational_tests.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── _simd.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── _string_helpers.py\n", + "│ │ │ │ │ ├── _struct_ufunc_tests.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── _type_aliases.py\n", + "│ │ │ │ │ ├── _type_aliases.pyi\n", + "│ │ │ │ │ ├── _ufunc_config.py\n", + "│ │ │ │ │ ├── _ufunc_config.pyi\n", + "│ │ │ │ │ ├── _umath_tests.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── arrayprint.py\n", + "│ │ │ │ │ ├── arrayprint.pyi\n", + "│ │ │ │ │ ├── cversions.py\n", + "│ │ │ │ │ ├── defchararray.py\n", + "│ │ │ │ │ ├── defchararray.pyi\n", + "│ │ │ │ │ ├── einsumfunc.py\n", + "│ │ │ │ │ ├── einsumfunc.pyi\n", + "│ │ │ │ │ ├── fromnumeric.py\n", + "│ │ │ │ │ ├── fromnumeric.pyi\n", + "│ │ │ │ │ ├── function_base.py\n", + "│ │ │ │ │ ├── function_base.pyi\n", + "│ │ │ │ │ ├── getlimits.py\n", + "│ │ │ │ │ ├── getlimits.pyi\n", + "│ │ │ │ │ ├── include\n", + "│ │ │ │ │ │ └── numpy\n", + "│ │ │ │ │ │ ├── __multiarray_api.c\n", + "│ │ │ │ │ │ ├── __multiarray_api.h\n", + "│ │ │ │ │ │ ├── __ufunc_api.c\n", + "│ │ │ │ │ │ ├── __ufunc_api.h\n", + "│ │ │ │ │ │ ├── _dtype_api.h\n", + "│ │ │ │ │ │ ├── _neighborhood_iterator_imp.h\n", + "│ │ │ │ │ │ ├── _numpyconfig.h\n", + "│ │ │ │ │ │ ├── arrayobject.h\n", + "│ │ │ │ │ │ ├── arrayscalars.h\n", + "│ │ │ │ │ │ ├── experimental_dtype_api.h\n", + "│ │ │ │ │ │ ├── halffloat.h\n", + "│ │ │ │ │ │ ├── ndarrayobject.h\n", + "│ │ │ │ │ │ ├── ndarraytypes.h\n", + "│ │ │ │ │ │ ├── noprefix.h\n", + "│ │ │ │ │ │ ├── npy_1_7_deprecated_api.h\n", + "│ │ │ │ │ │ ├── npy_3kcompat.h\n", + "│ │ │ │ │ │ ├── npy_common.h\n", + "│ │ │ │ │ │ ├── npy_cpu.h\n", + "│ │ │ │ │ │ ├── npy_endian.h\n", + "│ │ │ │ │ │ ├── npy_interrupt.h\n", + "│ │ │ │ │ │ ├── npy_math.h\n", + "│ │ │ │ │ │ ├── npy_no_deprecated_api.h\n", + "│ │ │ │ │ │ ├── npy_os.h\n", + "│ │ │ │ │ │ ├── numpyconfig.h\n", + "│ │ │ │ │ │ ├── old_defines.h\n", + "│ │ │ │ │ │ ├── random\n", + "│ │ │ │ │ │ │ ├── LICENSE.txt\n", + "│ │ │ │ │ │ │ ├── bitgen.h\n", + "│ │ │ │ │ │ │ ├── distributions.h\n", + "│ │ │ │ │ │ │ └── libdivide.h\n", + "│ │ │ │ │ │ ├── ufuncobject.h\n", + "│ │ │ │ │ │ └── utils.h\n", + "│ │ │ │ │ ├── lib\n", + "│ │ │ │ │ │ ├── libnpymath.a\n", + "│ │ │ │ │ │ └── npy-pkg-config\n", + "│ │ │ │ │ │ ├── mlib.ini\n", + "│ │ │ │ │ │ └── npymath.ini\n", + "│ │ │ │ │ ├── memmap.py\n", + "│ │ │ │ │ ├── memmap.pyi\n", + "│ │ │ │ │ ├── multiarray.py\n", + "│ │ │ │ │ ├── multiarray.pyi\n", + "│ │ │ │ │ ├── numeric.py\n", + "│ │ │ │ │ ├── numeric.pyi\n", + "│ │ │ │ │ ├── numerictypes.py\n", + "│ │ │ │ │ ├── numerictypes.pyi\n", + "│ │ │ │ │ ├── overrides.py\n", + "│ │ │ │ │ ├── records.py\n", + "│ │ │ │ │ ├── records.pyi\n", + "│ │ │ │ │ ├── shape_base.py\n", + "│ │ │ │ │ ├── shape_base.pyi\n", + "│ │ │ │ │ ├── tests\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _locales.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test__exceptions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_abc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_argparse.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_array_coercion.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_array_interface.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arraymethod.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arrayprint.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_casting_floatingpoint_errors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_casting_unittests.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_conversion_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_cpu_dispatcher.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_cpu_features.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_custom_dtypes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_cython.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_datetime.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_defchararray.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_deprecations.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_dlpack.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_dtype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_einsum.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_errstate.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_extint128.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_function_base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_getlimits.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_half.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_hashtable.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_indexerrors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_item_selection.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_limited_api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_longdouble.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_machar.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_mem_overlap.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_mem_policy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_memmap.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_multiarray.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_nditer.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_nep50_promotions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_numeric.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_numerictypes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_numpy_2_0_compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_overrides.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_print.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_protocols.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_records.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_regression.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_scalar_ctors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_scalar_methods.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_scalarbuffer.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_scalarinherit.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_scalarmath.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_scalarprint.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_shape_base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_simd.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_simd_module.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_strings.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_ufunc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_umath.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_umath_accuracy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_umath_complex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_unicode.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _locales.py\n", + "│ │ │ │ │ │ ├── data\n", + "│ │ │ │ │ │ │ ├── astype_copy.pkl\n", + "│ │ │ │ │ │ │ ├── generate_umath_validation_data.cpp\n", + "│ │ │ │ │ │ │ ├── numpy_2_0_array.pkl\n", + "│ │ │ │ │ │ │ ├── recarray_from_file.fits\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-README.txt\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-arccos.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-arccosh.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-arcsin.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-arcsinh.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-arctan.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-arctanh.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-cbrt.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-cos.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-cosh.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-exp.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-exp2.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-expm1.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-log.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-log10.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-log1p.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-log2.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-sin.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-sinh.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-tan.csv\n", + "│ │ │ │ │ │ │ └── umath-validation-set-tanh.csv\n", + "│ │ │ │ │ │ ├── examples\n", + "│ │ │ │ │ │ │ ├── cython\n", + "│ │ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ │ └── setup.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── checks.pyx\n", + "│ │ │ │ │ │ │ │ ├── meson.build\n", + "│ │ │ │ │ │ │ │ └── setup.py\n", + "│ │ │ │ │ │ │ └── limited_api\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ └── setup.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── limited_api.c\n", + "│ │ │ │ │ │ │ └── setup.py\n", + "│ │ │ │ │ │ ├── test__exceptions.py\n", + "│ │ │ │ │ │ ├── test_abc.py\n", + "│ │ │ │ │ │ ├── test_api.py\n", + "│ │ │ │ │ │ ├── test_argparse.py\n", + "│ │ │ │ │ │ ├── test_array_coercion.py\n", + "│ │ │ │ │ │ ├── test_array_interface.py\n", + "│ │ │ │ │ │ ├── test_arraymethod.py\n", + "│ │ │ │ │ │ ├── test_arrayprint.py\n", + "│ │ │ │ │ │ ├── test_casting_floatingpoint_errors.py\n", + "│ │ │ │ │ │ ├── test_casting_unittests.py\n", + "│ │ │ │ │ │ ├── test_conversion_utils.py\n", + "│ │ │ │ │ │ ├── test_cpu_dispatcher.py\n", + "│ │ │ │ │ │ ├── test_cpu_features.py\n", + "│ │ │ │ │ │ ├── test_custom_dtypes.py\n", + "│ │ │ │ │ │ ├── test_cython.py\n", + "│ │ │ │ │ │ ├── test_datetime.py\n", + "│ │ │ │ │ │ ├── test_defchararray.py\n", + "│ │ │ │ │ │ ├── test_deprecations.py\n", + "│ │ │ │ │ │ ├── test_dlpack.py\n", + "│ │ │ │ │ │ ├── test_dtype.py\n", + "│ │ │ │ │ │ ├── test_einsum.py\n", + "│ │ │ │ │ │ ├── test_errstate.py\n", + "│ │ │ │ │ │ ├── test_extint128.py\n", + "│ │ │ │ │ │ ├── test_function_base.py\n", + "│ │ │ │ │ │ ├── test_getlimits.py\n", + "│ │ │ │ │ │ ├── test_half.py\n", + "│ │ │ │ │ │ ├── test_hashtable.py\n", + "│ │ │ │ │ │ ├── test_indexerrors.py\n", + "│ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ ├── test_item_selection.py\n", + "│ │ │ │ │ │ ├── test_limited_api.py\n", + "│ │ │ │ │ │ ├── test_longdouble.py\n", + "│ │ │ │ │ │ ├── test_machar.py\n", + "│ │ │ │ │ │ ├── test_mem_overlap.py\n", + "│ │ │ │ │ │ ├── test_mem_policy.py\n", + "│ │ │ │ │ │ ├── test_memmap.py\n", + "│ │ │ │ │ │ ├── test_multiarray.py\n", + "│ │ │ │ │ │ ├── test_nditer.py\n", + "│ │ │ │ │ │ ├── test_nep50_promotions.py\n", + "│ │ │ │ │ │ ├── test_numeric.py\n", + "│ │ │ │ │ │ ├── test_numerictypes.py\n", + "│ │ │ │ │ │ ├── test_numpy_2_0_compat.py\n", + "│ │ │ │ │ │ ├── test_overrides.py\n", + "│ │ │ │ │ │ ├── test_print.py\n", + "│ │ │ │ │ │ ├── test_protocols.py\n", + "│ │ │ │ │ │ ├── test_records.py\n", + "│ │ │ │ │ │ ├── test_regression.py\n", + "│ │ │ │ │ │ ├── test_scalar_ctors.py\n", + "│ │ │ │ │ │ ├── test_scalar_methods.py\n", + "│ │ │ │ │ │ ├── test_scalarbuffer.py\n", + "│ │ │ │ │ │ ├── test_scalarinherit.py\n", + "│ │ │ │ │ │ ├── test_scalarmath.py\n", + "│ │ │ │ │ │ ├── test_scalarprint.py\n", + "│ │ │ │ │ │ ├── test_shape_base.py\n", + "│ │ │ │ │ │ ├── test_simd.py\n", + "│ │ │ │ │ │ ├── test_simd_module.py\n", + "│ │ │ │ │ │ ├── test_strings.py\n", + "│ │ │ │ │ │ ├── test_ufunc.py\n", + "│ │ │ │ │ │ ├── test_umath.py\n", + "│ │ │ │ │ │ ├── test_umath_accuracy.py\n", + "│ │ │ │ │ │ ├── test_umath_complex.py\n", + "│ │ │ │ │ │ └── test_unicode.py\n", + "│ │ │ │ │ ├── umath.py\n", + "│ │ │ │ │ └── umath_tests.py\n", + "│ │ │ │ ├── ctypeslib.py\n", + "│ │ │ │ ├── ctypeslib.pyi\n", + "│ │ │ │ ├── distutils\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _shell_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── armccompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ccompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ccompiler_opt.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── conv_template.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── core.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cpuinfo.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── exec_command.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── extension.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── from_template.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── fujitsuccompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── intelccompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── lib2def.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── line_endings.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── log.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── mingw32ccompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── misc_util.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── msvc9compiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── msvccompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── npy_pkg_config.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── numpy_distribution.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pathccompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── setup.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── system_info.cpython-310.pyc\n", + "│ │ │ │ │ │ └── unixccompiler.cpython-310.pyc\n", + "│ │ │ │ │ ├── _shell_utils.py\n", + "│ │ │ │ │ ├── armccompiler.py\n", + "│ │ │ │ │ ├── ccompiler.py\n", + "│ │ │ │ │ ├── ccompiler_opt.py\n", + "│ │ │ │ │ ├── checks\n", + "│ │ │ │ │ │ ├── cpu_asimd.c\n", + "│ │ │ │ │ │ ├── cpu_asimddp.c\n", + "│ │ │ │ │ │ ├── cpu_asimdfhm.c\n", + "│ │ │ │ │ │ ├── cpu_asimdhp.c\n", + "│ │ │ │ │ │ ├── cpu_avx.c\n", + "│ │ │ │ │ │ ├── cpu_avx2.c\n", + "│ │ │ │ │ │ ├── cpu_avx512_clx.c\n", + "│ │ │ │ │ │ ├── cpu_avx512_cnl.c\n", + "│ │ │ │ │ │ ├── cpu_avx512_icl.c\n", + "│ │ │ │ │ │ ├── cpu_avx512_knl.c\n", + "│ │ │ │ │ │ ├── cpu_avx512_knm.c\n", + "│ │ │ │ │ │ ├── cpu_avx512_skx.c\n", + "│ │ │ │ │ │ ├── cpu_avx512_spr.c\n", + "│ │ │ │ │ │ ├── cpu_avx512cd.c\n", + "│ │ │ │ │ │ ├── cpu_avx512f.c\n", + "│ │ │ │ │ │ ├── cpu_f16c.c\n", + "│ │ │ │ │ │ ├── cpu_fma3.c\n", + "│ │ │ │ │ │ ├── cpu_fma4.c\n", + "│ │ │ │ │ │ ├── cpu_neon.c\n", + "│ │ │ │ │ │ ├── cpu_neon_fp16.c\n", + "│ │ │ │ │ │ ├── cpu_neon_vfpv4.c\n", + "│ │ │ │ │ │ ├── cpu_popcnt.c\n", + "│ │ │ │ │ │ ├── cpu_sse.c\n", + "│ │ │ │ │ │ ├── cpu_sse2.c\n", + "│ │ │ │ │ │ ├── cpu_sse3.c\n", + "│ │ │ │ │ │ ├── cpu_sse41.c\n", + "│ │ │ │ │ │ ├── cpu_sse42.c\n", + "│ │ │ │ │ │ ├── cpu_ssse3.c\n", + "│ │ │ │ │ │ ├── cpu_vsx.c\n", + "│ │ │ │ │ │ ├── cpu_vsx2.c\n", + "│ │ │ │ │ │ ├── cpu_vsx3.c\n", + "│ │ │ │ │ │ ├── cpu_vsx4.c\n", + "│ │ │ │ │ │ ├── cpu_vx.c\n", + "│ │ │ │ │ │ ├── cpu_vxe.c\n", + "│ │ │ │ │ │ ├── cpu_vxe2.c\n", + "│ │ │ │ │ │ ├── cpu_xop.c\n", + "│ │ │ │ │ │ ├── extra_avx512bw_mask.c\n", + "│ │ │ │ │ │ ├── extra_avx512dq_mask.c\n", + "│ │ │ │ │ │ ├── extra_avx512f_reduce.c\n", + "│ │ │ │ │ │ ├── extra_vsx3_half_double.c\n", + "│ │ │ │ │ │ ├── extra_vsx4_mma.c\n", + "│ │ │ │ │ │ ├── extra_vsx_asm.c\n", + "│ │ │ │ │ │ └── test_flags.c\n", + "│ │ │ │ │ ├── command\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── autodist.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── bdist_rpm.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── build.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── build_clib.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── build_ext.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── build_py.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── build_scripts.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── build_src.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── config.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── config_compiler.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── develop.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── egg_info.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── install.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── install_clib.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── install_data.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── install_headers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── sdist.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── autodist.py\n", + "│ │ │ │ │ │ ├── bdist_rpm.py\n", + "│ │ │ │ │ │ ├── build.py\n", + "│ │ │ │ │ │ ├── build_clib.py\n", + "│ │ │ │ │ │ ├── build_ext.py\n", + "│ │ │ │ │ │ ├── build_py.py\n", + "│ │ │ │ │ │ ├── build_scripts.py\n", + "│ │ │ │ │ │ ├── build_src.py\n", + "│ │ │ │ │ │ ├── config.py\n", + "│ │ │ │ │ │ ├── config_compiler.py\n", + "│ │ │ │ │ │ ├── develop.py\n", + "│ │ │ │ │ │ ├── egg_info.py\n", + "│ │ │ │ │ │ ├── install.py\n", + "│ │ │ │ │ │ ├── install_clib.py\n", + "│ │ │ │ │ │ ├── install_data.py\n", + "│ │ │ │ │ │ ├── install_headers.py\n", + "│ │ │ │ │ │ └── sdist.py\n", + "│ │ │ │ │ ├── conv_template.py\n", + "│ │ │ │ │ ├── core.py\n", + "│ │ │ │ │ ├── cpuinfo.py\n", + "│ │ │ │ │ ├── exec_command.py\n", + "│ │ │ │ │ ├── extension.py\n", + "│ │ │ │ │ ├── fcompiler\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── absoft.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── arm.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── compaq.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── environment.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── fujitsu.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── g95.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── gnu.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── hpux.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── ibm.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── intel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── lahey.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── mips.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── nag.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── none.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── nv.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pathf95.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pg.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── sun.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── vast.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── absoft.py\n", + "│ │ │ │ │ │ ├── arm.py\n", + "│ │ │ │ │ │ ├── compaq.py\n", + "│ │ │ │ │ │ ├── environment.py\n", + "│ │ │ │ │ │ ├── fujitsu.py\n", + "│ │ │ │ │ │ ├── g95.py\n", + "│ │ │ │ │ │ ├── gnu.py\n", + "│ │ │ │ │ │ ├── hpux.py\n", + "│ │ │ │ │ │ ├── ibm.py\n", + "│ │ │ │ │ │ ├── intel.py\n", + "│ │ │ │ │ │ ├── lahey.py\n", + "│ │ │ │ │ │ ├── mips.py\n", + "│ │ │ │ │ │ ├── nag.py\n", + "│ │ │ │ │ │ ├── none.py\n", + "│ │ │ │ │ │ ├── nv.py\n", + "│ │ │ │ │ │ ├── pathf95.py\n", + "│ │ │ │ │ │ ├── pg.py\n", + "│ │ │ │ │ │ ├── sun.py\n", + "│ │ │ │ │ │ └── vast.py\n", + "│ │ │ │ │ ├── from_template.py\n", + "│ │ │ │ │ ├── fujitsuccompiler.py\n", + "│ │ │ │ │ ├── intelccompiler.py\n", + "│ │ │ │ │ ├── lib2def.py\n", + "│ │ │ │ │ ├── line_endings.py\n", + "│ │ │ │ │ ├── log.py\n", + "│ │ │ │ │ ├── mingw\n", + "│ │ │ │ │ │ └── gfortran_vs2003_hack.c\n", + "│ │ │ │ │ ├── mingw32ccompiler.py\n", + "│ │ │ │ │ ├── misc_util.py\n", + "│ │ │ │ │ ├── msvc9compiler.py\n", + "│ │ │ │ │ ├── msvccompiler.py\n", + "│ │ │ │ │ ├── npy_pkg_config.py\n", + "│ │ │ │ │ ├── numpy_distribution.py\n", + "│ │ │ │ │ ├── pathccompiler.py\n", + "│ │ │ │ │ ├── setup.py\n", + "│ │ │ │ │ ├── system_info.py\n", + "│ │ │ │ │ ├── tests\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_build_ext.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_ccompiler_opt.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_ccompiler_opt_conf.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_exec_command.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_fcompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_fcompiler_gnu.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_fcompiler_intel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_fcompiler_nagfor.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_from_template.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_log.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_mingw32ccompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_misc_util.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_npy_pkg_config.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_shell_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_system_info.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_build_ext.py\n", + "│ │ │ │ │ │ ├── test_ccompiler_opt.py\n", + "│ │ │ │ │ │ ├── test_ccompiler_opt_conf.py\n", + "│ │ │ │ │ │ ├── test_exec_command.py\n", + "│ │ │ │ │ │ ├── test_fcompiler.py\n", + "│ │ │ │ │ │ ├── test_fcompiler_gnu.py\n", + "│ │ │ │ │ │ ├── test_fcompiler_intel.py\n", + "│ │ │ │ │ │ ├── test_fcompiler_nagfor.py\n", + "│ │ │ │ │ │ ├── test_from_template.py\n", + "│ │ │ │ │ │ ├── test_log.py\n", + "│ │ │ │ │ │ ├── test_mingw32ccompiler.py\n", + "│ │ │ │ │ │ ├── test_misc_util.py\n", + "│ │ │ │ │ │ ├── test_npy_pkg_config.py\n", + "│ │ │ │ │ │ ├── test_shell_utils.py\n", + "│ │ │ │ │ │ └── test_system_info.py\n", + "│ │ │ │ │ └── unixccompiler.py\n", + "│ │ │ │ ├── doc\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── constants.cpython-310.pyc\n", + "│ │ │ │ │ │ └── ufuncs.cpython-310.pyc\n", + "│ │ │ │ │ ├── constants.py\n", + "│ │ │ │ │ └── ufuncs.py\n", + "│ │ │ │ ├── dtypes.py\n", + "│ │ │ │ ├── dtypes.pyi\n", + "│ │ │ │ ├── exceptions.py\n", + "│ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ ├── f2py\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ ├── __main__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── __version__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _isocbind.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _src_pyf.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── auxfuncs.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── capi_maps.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cb_rules.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cfuncs.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── common_rules.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── crackfortran.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── diagnose.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── f2py2e.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── f90mod_rules.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── func2subr.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── rules.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── setup.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── symbolic.cpython-310.pyc\n", + "│ │ │ │ │ │ └── use_rules.cpython-310.pyc\n", + "│ │ │ │ │ ├── __version__.py\n", + "│ │ │ │ │ ├── _backends\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _backend.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _distutils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── _meson.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _backend.py\n", + "│ │ │ │ │ │ ├── _distutils.py\n", + "│ │ │ │ │ │ ├── _meson.py\n", + "│ │ │ │ │ │ └── meson.build.template\n", + "│ │ │ │ │ ├── _isocbind.py\n", + "│ │ │ │ │ ├── _src_pyf.py\n", + "│ │ │ │ │ ├── auxfuncs.py\n", + "│ │ │ │ │ ├── capi_maps.py\n", + "│ │ │ │ │ ├── cb_rules.py\n", + "│ │ │ │ │ ├── cfuncs.py\n", + "│ │ │ │ │ ├── common_rules.py\n", + "│ │ │ │ │ ├── crackfortran.py\n", + "│ │ │ │ │ ├── diagnose.py\n", + "│ │ │ │ │ ├── f2py2e.py\n", + "│ │ │ │ │ ├── f90mod_rules.py\n", + "│ │ │ │ │ ├── func2subr.py\n", + "│ │ │ │ │ ├── rules.py\n", + "│ │ │ │ │ ├── setup.cfg\n", + "│ │ │ │ │ ├── setup.py\n", + "│ │ │ │ │ ├── src\n", + "│ │ │ │ │ │ ├── fortranobject.c\n", + "│ │ │ │ │ │ └── fortranobject.h\n", + "│ │ │ │ │ ├── symbolic.py\n", + "│ │ │ │ │ ├── tests\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_abstract_interface.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_array_from_pyobj.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_assumed_shape.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_block_docstring.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_callback.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_character.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_compile_function.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_crackfortran.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_data.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_docs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_f2cmap.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_f2py2e.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_isoc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_kind.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_mixed.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_module_doc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_parameter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_pyf_src.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_quoted_character.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_regression.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_return_character.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_return_complex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_return_integer.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_return_logical.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_return_real.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_semicolon_split.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_size.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_string.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_symbolic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_value_attrspec.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── util.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── src\n", + "│ │ │ │ │ │ │ ├── abstract_interface\n", + "│ │ │ │ │ │ │ │ ├── foo.f90\n", + "│ │ │ │ │ │ │ │ └── gh18403_mod.f90\n", + "│ │ │ │ │ │ │ ├── array_from_pyobj\n", + "│ │ │ │ │ │ │ │ └── wrapmodule.c\n", + "│ │ │ │ │ │ │ ├── assumed_shape\n", + "│ │ │ │ │ │ │ │ ├── .f2py_f2cmap\n", + "│ │ │ │ │ │ │ │ ├── foo_free.f90\n", + "│ │ │ │ │ │ │ │ ├── foo_mod.f90\n", + "│ │ │ │ │ │ │ │ ├── foo_use.f90\n", + "│ │ │ │ │ │ │ │ └── precision.f90\n", + "│ │ │ │ │ │ │ ├── block_docstring\n", + "│ │ │ │ │ │ │ │ └── foo.f\n", + "│ │ │ │ │ │ │ ├── callback\n", + "│ │ │ │ │ │ │ │ ├── foo.f\n", + "│ │ │ │ │ │ │ │ ├── gh17797.f90\n", + "│ │ │ │ │ │ │ │ ├── gh18335.f90\n", + "│ │ │ │ │ │ │ │ ├── gh25211.f\n", + "│ │ │ │ │ │ │ │ └── gh25211.pyf\n", + "│ │ │ │ │ │ │ ├── cli\n", + "│ │ │ │ │ │ │ │ ├── gh_22819.pyf\n", + "│ │ │ │ │ │ │ │ ├── hi77.f\n", + "│ │ │ │ │ │ │ │ └── hiworld.f90\n", + "│ │ │ │ │ │ │ ├── common\n", + "│ │ │ │ │ │ │ │ ├── block.f\n", + "│ │ │ │ │ │ │ │ └── gh19161.f90\n", + "│ │ │ │ │ │ │ ├── crackfortran\n", + "│ │ │ │ │ │ │ │ ├── accesstype.f90\n", + "│ │ │ │ │ │ │ │ ├── data_common.f\n", + "│ │ │ │ │ │ │ │ ├── data_multiplier.f\n", + "│ │ │ │ │ │ │ │ ├── data_stmts.f90\n", + "│ │ │ │ │ │ │ │ ├── data_with_comments.f\n", + "│ │ │ │ │ │ │ │ ├── foo_deps.f90\n", + "│ │ │ │ │ │ │ │ ├── gh15035.f\n", + "│ │ │ │ │ │ │ │ ├── gh17859.f\n", + "│ │ │ │ │ │ │ │ ├── gh22648.pyf\n", + "│ │ │ │ │ │ │ │ ├── gh23533.f\n", + "│ │ │ │ │ │ │ │ ├── gh23598.f90\n", + "│ │ │ │ │ │ │ │ ├── gh23598Warn.f90\n", + "│ │ │ │ │ │ │ │ ├── gh23879.f90\n", + "│ │ │ │ │ │ │ │ ├── gh2848.f90\n", + "│ │ │ │ │ │ │ │ ├── operators.f90\n", + "│ │ │ │ │ │ │ │ ├── privatemod.f90\n", + "│ │ │ │ │ │ │ │ ├── publicmod.f90\n", + "│ │ │ │ │ │ │ │ ├── pubprivmod.f90\n", + "│ │ │ │ │ │ │ │ └── unicode_comment.f90\n", + "│ │ │ │ │ │ │ ├── f2cmap\n", + "│ │ │ │ │ │ │ │ ├── .f2py_f2cmap\n", + "│ │ │ │ │ │ │ │ └── isoFortranEnvMap.f90\n", + "│ │ │ │ │ │ │ ├── isocintrin\n", + "│ │ │ │ │ │ │ │ └── isoCtests.f90\n", + "│ │ │ │ │ │ │ ├── kind\n", + "│ │ │ │ │ │ │ │ └── foo.f90\n", + "│ │ │ │ │ │ │ ├── mixed\n", + "│ │ │ │ │ │ │ │ ├── foo.f\n", + "│ │ │ │ │ │ │ │ ├── foo_fixed.f90\n", + "│ │ │ │ │ │ │ │ └── foo_free.f90\n", + "│ │ │ │ │ │ │ ├── module_data\n", + "│ │ │ │ │ │ │ │ ├── mod.mod\n", + "│ │ │ │ │ │ │ │ └── module_data_docstring.f90\n", + "│ │ │ │ │ │ │ ├── negative_bounds\n", + "│ │ │ │ │ │ │ │ └── issue_20853.f90\n", + "│ │ │ │ │ │ │ ├── parameter\n", + "│ │ │ │ │ │ │ │ ├── constant_both.f90\n", + "│ │ │ │ │ │ │ │ ├── constant_compound.f90\n", + "│ │ │ │ │ │ │ │ ├── constant_integer.f90\n", + "│ │ │ │ │ │ │ │ ├── constant_non_compound.f90\n", + "│ │ │ │ │ │ │ │ └── constant_real.f90\n", + "│ │ │ │ │ │ │ ├── quoted_character\n", + "│ │ │ │ │ │ │ │ └── foo.f\n", + "│ │ │ │ │ │ │ ├── regression\n", + "│ │ │ │ │ │ │ │ ├── gh25337\n", + "│ │ │ │ │ │ │ │ │ ├── data.f90\n", + "│ │ │ │ │ │ │ │ │ └── use_data.f90\n", + "│ │ │ │ │ │ │ │ └── inout.f90\n", + "│ │ │ │ │ │ │ ├── return_character\n", + "│ │ │ │ │ │ │ │ ├── foo77.f\n", + "│ │ │ │ │ │ │ │ └── foo90.f90\n", + "│ │ │ │ │ │ │ ├── return_complex\n", + "│ │ │ │ │ │ │ │ ├── foo77.f\n", + "│ │ │ │ │ │ │ │ └── foo90.f90\n", + "│ │ │ │ │ │ │ ├── return_integer\n", + "│ │ │ │ │ │ │ │ ├── foo77.f\n", + "│ │ │ │ │ │ │ │ └── foo90.f90\n", + "│ │ │ │ │ │ │ ├── return_logical\n", + "│ │ │ │ │ │ │ │ ├── foo77.f\n", + "│ │ │ │ │ │ │ │ └── foo90.f90\n", + "│ │ │ │ │ │ │ ├── return_real\n", + "│ │ │ │ │ │ │ │ ├── foo77.f\n", + "│ │ │ │ │ │ │ │ └── foo90.f90\n", + "│ │ │ │ │ │ │ ├── size\n", + "│ │ │ │ │ │ │ │ └── foo.f90\n", + "│ │ │ │ │ │ │ ├── string\n", + "│ │ │ │ │ │ │ │ ├── char.f90\n", + "│ │ │ │ │ │ │ │ ├── fixed_string.f90\n", + "│ │ │ │ │ │ │ │ ├── gh24008.f\n", + "│ │ │ │ │ │ │ │ ├── gh24662.f90\n", + "│ │ │ │ │ │ │ │ ├── gh25286.f90\n", + "│ │ │ │ │ │ │ │ ├── gh25286.pyf\n", + "│ │ │ │ │ │ │ │ ├── gh25286_bc.pyf\n", + "│ │ │ │ │ │ │ │ ├── scalar_string.f90\n", + "│ │ │ │ │ │ │ │ └── string.f\n", + "│ │ │ │ │ │ │ └── value_attrspec\n", + "│ │ │ │ │ │ │ └── gh21665.f90\n", + "│ │ │ │ │ │ ├── test_abstract_interface.py\n", + "│ │ │ │ │ │ ├── test_array_from_pyobj.py\n", + "│ │ │ │ │ │ ├── test_assumed_shape.py\n", + "│ │ │ │ │ │ ├── test_block_docstring.py\n", + "│ │ │ │ │ │ ├── test_callback.py\n", + "│ │ │ │ │ │ ├── test_character.py\n", + "│ │ │ │ │ │ ├── test_common.py\n", + "│ │ │ │ │ │ ├── test_compile_function.py\n", + "│ │ │ │ │ │ ├── test_crackfortran.py\n", + "│ │ │ │ │ │ ├── test_data.py\n", + "│ │ │ │ │ │ ├── test_docs.py\n", + "│ │ │ │ │ │ ├── test_f2cmap.py\n", + "│ │ │ │ │ │ ├── test_f2py2e.py\n", + "│ │ │ │ │ │ ├── test_isoc.py\n", + "│ │ │ │ │ │ ├── test_kind.py\n", + "│ │ │ │ │ │ ├── test_mixed.py\n", + "│ │ │ │ │ │ ├── test_module_doc.py\n", + "│ │ │ │ │ │ ├── test_parameter.py\n", + "│ │ │ │ │ │ ├── test_pyf_src.py\n", + "│ │ │ │ │ │ ├── test_quoted_character.py\n", + "│ │ │ │ │ │ ├── test_regression.py\n", + "│ │ │ │ │ │ ├── test_return_character.py\n", + "│ │ │ │ │ │ ├── test_return_complex.py\n", + "│ │ │ │ │ │ ├── test_return_integer.py\n", + "│ │ │ │ │ │ ├── test_return_logical.py\n", + "│ │ │ │ │ │ ├── test_return_real.py\n", + "│ │ │ │ │ │ ├── test_semicolon_split.py\n", + "│ │ │ │ │ │ ├── test_size.py\n", + "│ │ │ │ │ │ ├── test_string.py\n", + "│ │ │ │ │ │ ├── test_symbolic.py\n", + "│ │ │ │ │ │ ├── test_value_attrspec.py\n", + "│ │ │ │ │ │ └── util.py\n", + "│ │ │ │ │ └── use_rules.py\n", + "│ │ │ │ ├── fft\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _pocketfft.cpython-310.pyc\n", + "│ │ │ │ │ │ └── helper.cpython-310.pyc\n", + "│ │ │ │ │ ├── _pocketfft.py\n", + "│ │ │ │ │ ├── _pocketfft.pyi\n", + "│ │ │ │ │ ├── _pocketfft_internal.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── helper.py\n", + "│ │ │ │ │ ├── helper.pyi\n", + "│ │ │ │ │ └── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_helper.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_pocketfft.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_helper.py\n", + "│ │ │ │ │ └── test_pocketfft.py\n", + "│ │ │ │ ├── lib\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _datasource.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _iotools.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _version.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── arraypad.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── arraysetops.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── arrayterator.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── format.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── function_base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── histograms.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── index_tricks.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── mixins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── nanfunctions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── npyio.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── polynomial.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── recfunctions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── scimath.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── setup.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── shape_base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── stride_tricks.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── twodim_base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── type_check.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ufunclike.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── user_array.cpython-310.pyc\n", + "│ │ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ │ ├── _datasource.py\n", + "│ │ │ │ │ ├── _iotools.py\n", + "│ │ │ │ │ ├── _version.py\n", + "│ │ │ │ │ ├── _version.pyi\n", + "│ │ │ │ │ ├── arraypad.py\n", + "│ │ │ │ │ ├── arraypad.pyi\n", + "│ │ │ │ │ ├── arraysetops.py\n", + "│ │ │ │ │ ├── arraysetops.pyi\n", + "│ │ │ │ │ ├── arrayterator.py\n", + "│ │ │ │ │ ├── arrayterator.pyi\n", + "│ │ │ │ │ ├── format.py\n", + "│ │ │ │ │ ├── format.pyi\n", + "│ │ │ │ │ ├── function_base.py\n", + "│ │ │ │ │ ├── function_base.pyi\n", + "│ │ │ │ │ ├── histograms.py\n", + "│ │ │ │ │ ├── histograms.pyi\n", + "│ │ │ │ │ ├── index_tricks.py\n", + "│ │ │ │ │ ├── index_tricks.pyi\n", + "│ │ │ │ │ ├── mixins.py\n", + "│ │ │ │ │ ├── mixins.pyi\n", + "│ │ │ │ │ ├── nanfunctions.py\n", + "│ │ │ │ │ ├── nanfunctions.pyi\n", + "│ │ │ │ │ ├── npyio.py\n", + "│ │ │ │ │ ├── npyio.pyi\n", + "│ │ │ │ │ ├── polynomial.py\n", + "│ │ │ │ │ ├── polynomial.pyi\n", + "│ │ │ │ │ ├── recfunctions.py\n", + "│ │ │ │ │ ├── scimath.py\n", + "│ │ │ │ │ ├── scimath.pyi\n", + "│ │ │ │ │ ├── setup.py\n", + "│ │ │ │ │ ├── shape_base.py\n", + "│ │ │ │ │ ├── shape_base.pyi\n", + "│ │ │ │ │ ├── stride_tricks.py\n", + "│ │ │ │ │ ├── stride_tricks.pyi\n", + "│ │ │ │ │ ├── tests\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test__datasource.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test__iotools.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test__version.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arraypad.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arraysetops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arrayterator.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_financial_expired.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_format.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_function_base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_histograms.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_index_tricks.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_io.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_loadtxt.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_mixins.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_nanfunctions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_packbits.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_polynomial.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_recfunctions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_regression.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_shape_base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_stride_tricks.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_twodim_base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_type_check.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_ufunclike.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── data\n", + "│ │ │ │ │ │ │ ├── py2-objarr.npy\n", + "│ │ │ │ │ │ │ ├── py2-objarr.npz\n", + "│ │ │ │ │ │ │ ├── py3-objarr.npy\n", + "│ │ │ │ │ │ │ ├── py3-objarr.npz\n", + "│ │ │ │ │ │ │ ├── python3.npy\n", + "│ │ │ │ │ │ │ └── win64python2.npy\n", + "│ │ │ │ │ │ ├── test__datasource.py\n", + "│ │ │ │ │ │ ├── test__iotools.py\n", + "│ │ │ │ │ │ ├── test__version.py\n", + "│ │ │ │ │ │ ├── test_arraypad.py\n", + "│ │ │ │ │ │ ├── test_arraysetops.py\n", + "│ │ │ │ │ │ ├── test_arrayterator.py\n", + "│ │ │ │ │ │ ├── test_financial_expired.py\n", + "│ │ │ │ │ │ ├── test_format.py\n", + "│ │ │ │ │ │ ├── test_function_base.py\n", + "│ │ │ │ │ │ ├── test_histograms.py\n", + "│ │ │ │ │ │ ├── test_index_tricks.py\n", + "│ │ │ │ │ │ ├── test_io.py\n", + "│ │ │ │ │ │ ├── test_loadtxt.py\n", + "│ │ │ │ │ │ ├── test_mixins.py\n", + "│ │ │ │ │ │ ├── test_nanfunctions.py\n", + "│ │ │ │ │ │ ├── test_packbits.py\n", + "│ │ │ │ │ │ ├── test_polynomial.py\n", + "│ │ │ │ │ │ ├── test_recfunctions.py\n", + "│ │ │ │ │ │ ├── test_regression.py\n", + "│ │ │ │ │ │ ├── test_shape_base.py\n", + "│ │ │ │ │ │ ├── test_stride_tricks.py\n", + "│ │ │ │ │ │ ├── test_twodim_base.py\n", + "│ │ │ │ │ │ ├── test_type_check.py\n", + "│ │ │ │ │ │ ├── test_ufunclike.py\n", + "│ │ │ │ │ │ └── test_utils.py\n", + "│ │ │ │ │ ├── twodim_base.py\n", + "│ │ │ │ │ ├── twodim_base.pyi\n", + "│ │ │ │ │ ├── type_check.py\n", + "│ │ │ │ │ ├── type_check.pyi\n", + "│ │ │ │ │ ├── ufunclike.py\n", + "│ │ │ │ │ ├── ufunclike.pyi\n", + "│ │ │ │ │ ├── user_array.py\n", + "│ │ │ │ │ ├── utils.py\n", + "│ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ ├── linalg\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ └── linalg.cpython-310.pyc\n", + "│ │ │ │ │ ├── _umath_linalg.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── lapack_lite.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── linalg.py\n", + "│ │ │ │ │ ├── linalg.pyi\n", + "│ │ │ │ │ └── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_deprecations.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_linalg.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_regression.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_deprecations.py\n", + "│ │ │ │ │ ├── test_linalg.py\n", + "│ │ │ │ │ └── test_regression.py\n", + "│ │ │ │ ├── ma\n", + "│ │ │ │ │ ├── API_CHANGES.txt\n", + "│ │ │ │ │ ├── LICENSE\n", + "│ │ │ │ │ ├── README.rst\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── core.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── extras.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── mrecords.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── setup.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── testutils.cpython-310.pyc\n", + "│ │ │ │ │ │ └── timer_comparison.cpython-310.pyc\n", + "│ │ │ │ │ ├── core.py\n", + "│ │ │ │ │ ├── core.pyi\n", + "│ │ │ │ │ ├── extras.py\n", + "│ │ │ │ │ ├── extras.pyi\n", + "│ │ │ │ │ ├── mrecords.py\n", + "│ │ │ │ │ ├── mrecords.pyi\n", + "│ │ │ │ │ ├── setup.py\n", + "│ │ │ │ │ ├── tests\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_core.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_deprecations.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_extras.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_mrecords.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_old_ma.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_regression.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_subclassing.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_core.py\n", + "│ │ │ │ │ │ ├── test_deprecations.py\n", + "│ │ │ │ │ │ ├── test_extras.py\n", + "│ │ │ │ │ │ ├── test_mrecords.py\n", + "│ │ │ │ │ │ ├── test_old_ma.py\n", + "│ │ │ │ │ │ ├── test_regression.py\n", + "│ │ │ │ │ │ └── test_subclassing.py\n", + "│ │ │ │ │ ├── testutils.py\n", + "│ │ │ │ │ └── timer_comparison.py\n", + "│ │ │ │ ├── matlib.py\n", + "│ │ │ │ ├── matrixlib\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── defmatrix.cpython-310.pyc\n", + "│ │ │ │ │ │ └── setup.cpython-310.pyc\n", + "│ │ │ │ │ ├── defmatrix.py\n", + "│ │ │ │ │ ├── defmatrix.pyi\n", + "│ │ │ │ │ ├── setup.py\n", + "│ │ │ │ │ └── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_defmatrix.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_interaction.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_masked_matrix.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_matrix_linalg.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_multiarray.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_numeric.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_regression.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_defmatrix.py\n", + "│ │ │ │ │ ├── test_interaction.py\n", + "│ │ │ │ │ ├── test_masked_matrix.py\n", + "│ │ │ │ │ ├── test_matrix_linalg.py\n", + "│ │ │ │ │ ├── test_multiarray.py\n", + "│ │ │ │ │ ├── test_numeric.py\n", + "│ │ │ │ │ └── test_regression.py\n", + "│ │ │ │ ├── polynomial\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _polybase.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── chebyshev.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── hermite.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── hermite_e.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── laguerre.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── legendre.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── polynomial.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── polyutils.cpython-310.pyc\n", + "│ │ │ │ │ │ └── setup.cpython-310.pyc\n", + "│ │ │ │ │ ├── _polybase.py\n", + "│ │ │ │ │ ├── _polybase.pyi\n", + "│ │ │ │ │ ├── chebyshev.py\n", + "│ │ │ │ │ ├── chebyshev.pyi\n", + "│ │ │ │ │ ├── hermite.py\n", + "│ │ │ │ │ ├── hermite.pyi\n", + "│ │ │ │ │ ├── hermite_e.py\n", + "│ │ │ │ │ ├── hermite_e.pyi\n", + "│ │ │ │ │ ├── laguerre.py\n", + "│ │ │ │ │ ├── laguerre.pyi\n", + "│ │ │ │ │ ├── legendre.py\n", + "│ │ │ │ │ ├── legendre.pyi\n", + "│ │ │ │ │ ├── polynomial.py\n", + "│ │ │ │ │ ├── polynomial.pyi\n", + "│ │ │ │ │ ├── polyutils.py\n", + "│ │ │ │ │ ├── polyutils.pyi\n", + "│ │ │ │ │ ├── setup.py\n", + "│ │ │ │ │ └── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_chebyshev.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_classes.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_hermite.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_hermite_e.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_laguerre.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_legendre.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_polynomial.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_polyutils.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_printing.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_symbol.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_chebyshev.py\n", + "│ │ │ │ │ ├── test_classes.py\n", + "│ │ │ │ │ ├── test_hermite.py\n", + "│ │ │ │ │ ├── test_hermite_e.py\n", + "│ │ │ │ │ ├── test_laguerre.py\n", + "│ │ │ │ │ ├── test_legendre.py\n", + "│ │ │ │ │ ├── test_polynomial.py\n", + "│ │ │ │ │ ├── test_polyutils.py\n", + "│ │ │ │ │ ├── test_printing.py\n", + "│ │ │ │ │ └── test_symbol.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ ├── random\n", + "│ │ │ │ │ ├── LICENSE.md\n", + "│ │ │ │ │ ├── __init__.pxd\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ └── _pickle.cpython-310.pyc\n", + "│ │ │ │ │ ├── _bounded_integers.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── _bounded_integers.pxd\n", + "│ │ │ │ │ ├── _common.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── _common.pxd\n", + "│ │ │ │ │ ├── _examples\n", + "│ │ │ │ │ │ ├── cffi\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── extending.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── parse.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── extending.py\n", + "│ │ │ │ │ │ │ └── parse.py\n", + "│ │ │ │ │ │ ├── cython\n", + "│ │ │ │ │ │ │ ├── extending.pyx\n", + "│ │ │ │ │ │ │ ├── extending_distributions.pyx\n", + "│ │ │ │ │ │ │ └── meson.build\n", + "│ │ │ │ │ │ └── numba\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── extending.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── extending_distributions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── extending.py\n", + "│ │ │ │ │ │ └── extending_distributions.py\n", + "│ │ │ │ │ ├── _generator.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── _generator.pyi\n", + "│ │ │ │ │ ├── _mt19937.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── _mt19937.pyi\n", + "│ │ │ │ │ ├── _pcg64.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── _pcg64.pyi\n", + "│ │ │ │ │ ├── _philox.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── _philox.pyi\n", + "│ │ │ │ │ ├── _pickle.py\n", + "│ │ │ │ │ ├── _sfc64.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── _sfc64.pyi\n", + "│ │ │ │ │ ├── bit_generator.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── bit_generator.pxd\n", + "│ │ │ │ │ ├── bit_generator.pyi\n", + "│ │ │ │ │ ├── c_distributions.pxd\n", + "│ │ │ │ │ ├── lib\n", + "│ │ │ │ │ │ └── libnpyrandom.a\n", + "│ │ │ │ │ ├── mtrand.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── mtrand.pyi\n", + "│ │ │ │ │ └── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_direct.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_extending.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_generator_mt19937.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_generator_mt19937_regressions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_random.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_randomstate.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_randomstate_regression.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_regression.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_seed_sequence.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_smoke.cpython-310.pyc\n", + "│ │ │ │ │ ├── data\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── mt19937-testset-1.csv\n", + "│ │ │ │ │ │ ├── mt19937-testset-2.csv\n", + "│ │ │ │ │ │ ├── pcg64-testset-1.csv\n", + "│ │ │ │ │ │ ├── pcg64-testset-2.csv\n", + "│ │ │ │ │ │ ├── pcg64dxsm-testset-1.csv\n", + "│ │ │ │ │ │ ├── pcg64dxsm-testset-2.csv\n", + "│ │ │ │ │ │ ├── philox-testset-1.csv\n", + "│ │ │ │ │ │ ├── philox-testset-2.csv\n", + "│ │ │ │ │ │ ├── sfc64-testset-1.csv\n", + "│ │ │ │ │ │ └── sfc64-testset-2.csv\n", + "│ │ │ │ │ ├── test_direct.py\n", + "│ │ │ │ │ ├── test_extending.py\n", + "│ │ │ │ │ ├── test_generator_mt19937.py\n", + "│ │ │ │ │ ├── test_generator_mt19937_regressions.py\n", + "│ │ │ │ │ ├── test_random.py\n", + "│ │ │ │ │ ├── test_randomstate.py\n", + "│ │ │ │ │ ├── test_randomstate_regression.py\n", + "│ │ │ │ │ ├── test_regression.py\n", + "│ │ │ │ │ ├── test_seed_sequence.py\n", + "│ │ │ │ │ └── test_smoke.py\n", + "│ │ │ │ ├── testing\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── overrides.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── print_coercion_tables.cpython-310.pyc\n", + "│ │ │ │ │ │ └── setup.cpython-310.pyc\n", + "│ │ │ │ │ ├── _private\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── extbuild.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── extbuild.py\n", + "│ │ │ │ │ │ ├── utils.py\n", + "│ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ ├── overrides.py\n", + "│ │ │ │ │ ├── print_coercion_tables.py\n", + "│ │ │ │ │ ├── setup.py\n", + "│ │ │ │ │ └── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_utils.cpython-310.pyc\n", + "│ │ │ │ │ └── test_utils.py\n", + "│ │ │ │ ├── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test__all__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_ctypeslib.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_lazyloading.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_matlib.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_numpy_config.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_numpy_version.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_public_api.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_reloading.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_scripts.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_warnings.cpython-310.pyc\n", + "│ │ │ │ │ ├── test__all__.py\n", + "│ │ │ │ │ ├── test_ctypeslib.py\n", + "│ │ │ │ │ ├── test_lazyloading.py\n", + "│ │ │ │ │ ├── test_matlib.py\n", + "│ │ │ │ │ ├── test_numpy_config.py\n", + "│ │ │ │ │ ├── test_numpy_version.py\n", + "│ │ │ │ │ ├── test_public_api.py\n", + "│ │ │ │ │ ├── test_reloading.py\n", + "│ │ │ │ │ ├── test_scripts.py\n", + "│ │ │ │ │ └── test_warnings.py\n", + "│ │ │ │ ├── typing\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── mypy_plugin.cpython-310.pyc\n", + "│ │ │ │ │ │ └── setup.cpython-310.pyc\n", + "│ │ │ │ │ ├── mypy_plugin.py\n", + "│ │ │ │ │ ├── setup.py\n", + "│ │ │ │ │ └── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_isfile.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_runtime.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_typing.cpython-310.pyc\n", + "│ │ │ │ │ ├── data\n", + "│ │ │ │ │ │ ├── fail\n", + "│ │ │ │ │ │ │ ├── arithmetic.pyi\n", + "│ │ │ │ │ │ │ ├── array_constructors.pyi\n", + "│ │ │ │ │ │ │ ├── array_like.pyi\n", + "│ │ │ │ │ │ │ ├── array_pad.pyi\n", + "│ │ │ │ │ │ │ ├── arrayprint.pyi\n", + "│ │ │ │ │ │ │ ├── arrayterator.pyi\n", + "│ │ │ │ │ │ │ ├── bitwise_ops.pyi\n", + "│ │ │ │ │ │ │ ├── char.pyi\n", + "│ │ │ │ │ │ │ ├── chararray.pyi\n", + "│ │ │ │ │ │ │ ├── comparisons.pyi\n", + "│ │ │ │ │ │ │ ├── constants.pyi\n", + "│ │ │ │ │ │ │ ├── datasource.pyi\n", + "│ │ │ │ │ │ │ ├── dtype.pyi\n", + "│ │ │ │ │ │ │ ├── einsumfunc.pyi\n", + "│ │ │ │ │ │ │ ├── false_positives.pyi\n", + "│ │ │ │ │ │ │ ├── flatiter.pyi\n", + "│ │ │ │ │ │ │ ├── fromnumeric.pyi\n", + "│ │ │ │ │ │ │ ├── histograms.pyi\n", + "│ │ │ │ │ │ │ ├── index_tricks.pyi\n", + "│ │ │ │ │ │ │ ├── lib_function_base.pyi\n", + "│ │ │ │ │ │ │ ├── lib_polynomial.pyi\n", + "│ │ │ │ │ │ │ ├── lib_utils.pyi\n", + "│ │ │ │ │ │ │ ├── lib_version.pyi\n", + "│ │ │ │ │ │ │ ├── linalg.pyi\n", + "│ │ │ │ │ │ │ ├── memmap.pyi\n", + "│ │ │ │ │ │ │ ├── modules.pyi\n", + "│ │ │ │ │ │ │ ├── multiarray.pyi\n", + "│ │ │ │ │ │ │ ├── ndarray.pyi\n", + "│ │ │ │ │ │ │ ├── ndarray_misc.pyi\n", + "│ │ │ │ │ │ │ ├── nditer.pyi\n", + "│ │ │ │ │ │ │ ├── nested_sequence.pyi\n", + "│ │ │ │ │ │ │ ├── npyio.pyi\n", + "│ │ │ │ │ │ │ ├── numerictypes.pyi\n", + "│ │ │ │ │ │ │ ├── random.pyi\n", + "│ │ │ │ │ │ │ ├── rec.pyi\n", + "│ │ │ │ │ │ │ ├── scalars.pyi\n", + "│ │ │ │ │ │ │ ├── shape_base.pyi\n", + "│ │ │ │ │ │ │ ├── stride_tricks.pyi\n", + "│ │ │ │ │ │ │ ├── testing.pyi\n", + "│ │ │ │ │ │ │ ├── twodim_base.pyi\n", + "│ │ │ │ │ │ │ ├── type_check.pyi\n", + "│ │ │ │ │ │ │ ├── ufunc_config.pyi\n", + "│ │ │ │ │ │ │ ├── ufunclike.pyi\n", + "│ │ │ │ │ │ │ ├── ufuncs.pyi\n", + "│ │ │ │ │ │ │ └── warnings_and_errors.pyi\n", + "│ │ │ │ │ │ ├── misc\n", + "│ │ │ │ │ │ │ └── extended_precision.pyi\n", + "│ │ │ │ │ │ ├── mypy.ini\n", + "│ │ │ │ │ │ ├── pass\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── arithmetic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── array_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── array_like.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── arrayprint.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── arrayterator.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── bitwise_ops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── comparisons.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── dtype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── einsumfunc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── flatiter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── fromnumeric.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── index_tricks.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── lib_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── lib_version.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── literal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── mod.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── modules.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── multiarray.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── ndarray_conversion.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── ndarray_misc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── ndarray_shape_manipulation.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── numeric.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── numerictypes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── random.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── scalars.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── simple.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── simple_py3.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── ufunc_config.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── ufunclike.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── ufuncs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── warnings_and_errors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── arithmetic.py\n", + "│ │ │ │ │ │ │ ├── array_constructors.py\n", + "│ │ │ │ │ │ │ ├── array_like.py\n", + "│ │ │ │ │ │ │ ├── arrayprint.py\n", + "│ │ │ │ │ │ │ ├── arrayterator.py\n", + "│ │ │ │ │ │ │ ├── bitwise_ops.py\n", + "│ │ │ │ │ │ │ ├── comparisons.py\n", + "│ │ │ │ │ │ │ ├── dtype.py\n", + "│ │ │ │ │ │ │ ├── einsumfunc.py\n", + "│ │ │ │ │ │ │ ├── flatiter.py\n", + "│ │ │ │ │ │ │ ├── fromnumeric.py\n", + "│ │ │ │ │ │ │ ├── index_tricks.py\n", + "│ │ │ │ │ │ │ ├── lib_utils.py\n", + "│ │ │ │ │ │ │ ├── lib_version.py\n", + "│ │ │ │ │ │ │ ├── literal.py\n", + "│ │ │ │ │ │ │ ├── mod.py\n", + "│ │ │ │ │ │ │ ├── modules.py\n", + "│ │ │ │ │ │ │ ├── multiarray.py\n", + "│ │ │ │ │ │ │ ├── ndarray_conversion.py\n", + "│ │ │ │ │ │ │ ├── ndarray_misc.py\n", + "│ │ │ │ │ │ │ ├── ndarray_shape_manipulation.py\n", + "│ │ │ │ │ │ │ ├── numeric.py\n", + "│ │ │ │ │ │ │ ├── numerictypes.py\n", + "│ │ │ │ │ │ │ ├── random.py\n", + "│ │ │ │ │ │ │ ├── scalars.py\n", + "│ │ │ │ │ │ │ ├── simple.py\n", + "│ │ │ │ │ │ │ ├── simple_py3.py\n", + "│ │ │ │ │ │ │ ├── ufunc_config.py\n", + "│ │ │ │ │ │ │ ├── ufunclike.py\n", + "│ │ │ │ │ │ │ ├── ufuncs.py\n", + "│ │ │ │ │ │ │ └── warnings_and_errors.py\n", + "│ │ │ │ │ │ └── reveal\n", + "│ │ │ │ │ │ ├── arithmetic.pyi\n", + "│ │ │ │ │ │ ├── array_constructors.pyi\n", + "│ │ │ │ │ │ ├── arraypad.pyi\n", + "│ │ │ │ │ │ ├── arrayprint.pyi\n", + "│ │ │ │ │ │ ├── arraysetops.pyi\n", + "│ │ │ │ │ │ ├── arrayterator.pyi\n", + "│ │ │ │ │ │ ├── bitwise_ops.pyi\n", + "│ │ │ │ │ │ ├── char.pyi\n", + "│ │ │ │ │ │ ├── chararray.pyi\n", + "│ │ │ │ │ │ ├── comparisons.pyi\n", + "│ │ │ │ │ │ ├── constants.pyi\n", + "│ │ │ │ │ │ ├── ctypeslib.pyi\n", + "│ │ │ │ │ │ ├── datasource.pyi\n", + "│ │ │ │ │ │ ├── dtype.pyi\n", + "│ │ │ │ │ │ ├── einsumfunc.pyi\n", + "│ │ │ │ │ │ ├── emath.pyi\n", + "│ │ │ │ │ │ ├── false_positives.pyi\n", + "│ │ │ │ │ │ ├── fft.pyi\n", + "│ │ │ │ │ │ ├── flatiter.pyi\n", + "│ │ │ │ │ │ ├── fromnumeric.pyi\n", + "│ │ │ │ │ │ ├── getlimits.pyi\n", + "│ │ │ │ │ │ ├── histograms.pyi\n", + "│ │ │ │ │ │ ├── index_tricks.pyi\n", + "│ │ │ │ │ │ ├── lib_function_base.pyi\n", + "│ │ │ │ │ │ ├── lib_polynomial.pyi\n", + "│ │ │ │ │ │ ├── lib_utils.pyi\n", + "│ │ │ │ │ │ ├── lib_version.pyi\n", + "│ │ │ │ │ │ ├── linalg.pyi\n", + "│ │ │ │ │ │ ├── matrix.pyi\n", + "│ │ │ │ │ │ ├── memmap.pyi\n", + "│ │ │ │ │ │ ├── mod.pyi\n", + "│ │ │ │ │ │ ├── modules.pyi\n", + "│ │ │ │ │ │ ├── multiarray.pyi\n", + "│ │ │ │ │ │ ├── nbit_base_example.pyi\n", + "│ │ │ │ │ │ ├── ndarray_conversion.pyi\n", + "│ │ │ │ │ │ ├── ndarray_misc.pyi\n", + "│ │ │ │ │ │ ├── ndarray_shape_manipulation.pyi\n", + "│ │ │ │ │ │ ├── nditer.pyi\n", + "│ │ │ │ │ │ ├── nested_sequence.pyi\n", + "│ │ │ │ │ │ ├── npyio.pyi\n", + "│ │ │ │ │ │ ├── numeric.pyi\n", + "│ │ │ │ │ │ ├── numerictypes.pyi\n", + "│ │ │ │ │ │ ├── random.pyi\n", + "│ │ │ │ │ │ ├── rec.pyi\n", + "│ │ │ │ │ │ ├── scalars.pyi\n", + "│ │ │ │ │ │ ├── shape_base.pyi\n", + "│ │ │ │ │ │ ├── stride_tricks.pyi\n", + "│ │ │ │ │ │ ├── testing.pyi\n", + "│ │ │ │ │ │ ├── twodim_base.pyi\n", + "│ │ │ │ │ │ ├── type_check.pyi\n", + "│ │ │ │ │ │ ├── ufunc_config.pyi\n", + "│ │ │ │ │ │ ├── ufunclike.pyi\n", + "│ │ │ │ │ │ ├── ufuncs.pyi\n", + "│ │ │ │ │ │ └── warnings_and_errors.pyi\n", + "│ │ │ │ │ ├── test_isfile.py\n", + "│ │ │ │ │ ├── test_runtime.py\n", + "│ │ │ │ │ └── test_typing.py\n", + "│ │ │ │ └── version.py\n", + "│ │ │ ├── numpy-1.26.4.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE.txt\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── entry_points.txt\n", + "│ │ │ ├── numpy.libs\n", + "│ │ │ │ ├── libgfortran-040039e1.so.5.0.0\n", + "│ │ │ │ ├── libopenblas64_p-r0-0cf96a72.3.23.dev.so\n", + "│ │ │ │ └── libquadmath-96973f99.so.0.0.0\n", + "│ │ │ ├── packaging\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _elffile.cpython-310.pyc\n", + "│ │ │ │ │ ├── _manylinux.cpython-310.pyc\n", + "│ │ │ │ │ ├── _musllinux.cpython-310.pyc\n", + "│ │ │ │ │ ├── _parser.cpython-310.pyc\n", + "│ │ │ │ │ ├── _structures.cpython-310.pyc\n", + "│ │ │ │ │ ├── _tokenizer.cpython-310.pyc\n", + "│ │ │ │ │ ├── markers.cpython-310.pyc\n", + "│ │ │ │ │ ├── metadata.cpython-310.pyc\n", + "│ │ │ │ │ ├── requirements.cpython-310.pyc\n", + "│ │ │ │ │ ├── specifiers.cpython-310.pyc\n", + "│ │ │ │ │ ├── tags.cpython-310.pyc\n", + "│ │ │ │ │ ├── utils.cpython-310.pyc\n", + "│ │ │ │ │ └── version.cpython-310.pyc\n", + "│ │ │ │ ├── _elffile.py\n", + "│ │ │ │ ├── _manylinux.py\n", + "│ │ │ │ ├── _musllinux.py\n", + "│ │ │ │ ├── _parser.py\n", + "│ │ │ │ ├── _structures.py\n", + "│ │ │ │ ├── _tokenizer.py\n", + "│ │ │ │ ├── markers.py\n", + "│ │ │ │ ├── metadata.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ ├── requirements.py\n", + "│ │ │ │ ├── specifiers.py\n", + "│ │ │ │ ├── tags.py\n", + "│ │ │ │ ├── utils.py\n", + "│ │ │ │ └── version.py\n", + "│ │ │ ├── packaging-24.0.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── LICENSE.APACHE\n", + "│ │ │ │ ├── LICENSE.BSD\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ └── WHEEL\n", + "│ │ │ ├── pandas\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _typing.cpython-310.pyc\n", + "│ │ │ │ │ ├── _version.cpython-310.pyc\n", + "│ │ │ │ │ ├── _version_meson.cpython-310.pyc\n", + "│ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ └── testing.cpython-310.pyc\n", + "│ │ │ │ ├── _config\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── config.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dates.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── display.cpython-310.pyc\n", + "│ │ │ │ │ │ └── localization.cpython-310.pyc\n", + "│ │ │ │ │ ├── config.py\n", + "│ │ │ │ │ ├── dates.py\n", + "│ │ │ │ │ ├── display.py\n", + "│ │ │ │ │ └── localization.py\n", + "│ │ │ │ ├── _libs\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── algos.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── algos.pyi\n", + "│ │ │ │ │ ├── arrays.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── arrays.pyi\n", + "│ │ │ │ │ ├── byteswap.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── byteswap.pyi\n", + "│ │ │ │ │ ├── groupby.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── groupby.pyi\n", + "│ │ │ │ │ ├── hashing.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── hashing.pyi\n", + "│ │ │ │ │ ├── hashtable.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── hashtable.pyi\n", + "│ │ │ │ │ ├── index.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── index.pyi\n", + "│ │ │ │ │ ├── indexing.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── indexing.pyi\n", + "│ │ │ │ │ ├── internals.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── internals.pyi\n", + "│ │ │ │ │ ├── interval.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── interval.pyi\n", + "│ │ │ │ │ ├── join.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── join.pyi\n", + "│ │ │ │ │ ├── json.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── json.pyi\n", + "│ │ │ │ │ ├── lib.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── lib.pyi\n", + "│ │ │ │ │ ├── missing.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── missing.pyi\n", + "│ │ │ │ │ ├── ops.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── ops.pyi\n", + "│ │ │ │ │ ├── ops_dispatch.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── ops_dispatch.pyi\n", + "│ │ │ │ │ ├── pandas_datetime.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── pandas_parser.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── parsers.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── parsers.pyi\n", + "│ │ │ │ │ ├── properties.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── properties.pyi\n", + "│ │ │ │ │ ├── reshape.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── reshape.pyi\n", + "│ │ │ │ │ ├── sas.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── sas.pyi\n", + "│ │ │ │ │ ├── sparse.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── sparse.pyi\n", + "│ │ │ │ │ ├── testing.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── testing.pyi\n", + "│ │ │ │ │ ├── tslib.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── tslib.pyi\n", + "│ │ │ │ │ ├── tslibs\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── ccalendar.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── ccalendar.pyi\n", + "│ │ │ │ │ │ ├── conversion.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── conversion.pyi\n", + "│ │ │ │ │ │ ├── dtypes.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── dtypes.pyi\n", + "│ │ │ │ │ │ ├── fields.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── fields.pyi\n", + "│ │ │ │ │ │ ├── nattype.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── nattype.pyi\n", + "│ │ │ │ │ │ ├── np_datetime.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── np_datetime.pyi\n", + "│ │ │ │ │ │ ├── offsets.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── offsets.pyi\n", + "│ │ │ │ │ │ ├── parsing.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── parsing.pyi\n", + "│ │ │ │ │ │ ├── period.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── period.pyi\n", + "│ │ │ │ │ │ ├── strptime.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── strptime.pyi\n", + "│ │ │ │ │ │ ├── timedeltas.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── timedeltas.pyi\n", + "│ │ │ │ │ │ ├── timestamps.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── timestamps.pyi\n", + "│ │ │ │ │ │ ├── timezones.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── timezones.pyi\n", + "│ │ │ │ │ │ ├── tzconversion.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── tzconversion.pyi\n", + "│ │ │ │ │ │ ├── vectorized.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ └── vectorized.pyi\n", + "│ │ │ │ │ ├── window\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── aggregations.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── aggregations.pyi\n", + "│ │ │ │ │ │ ├── indexers.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ └── indexers.pyi\n", + "│ │ │ │ │ ├── writers.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ └── writers.pyi\n", + "│ │ │ │ ├── _testing\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _hypothesis.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _io.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _warnings.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── asserters.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── compat.cpython-310.pyc\n", + "│ │ │ │ │ │ └── contexts.cpython-310.pyc\n", + "│ │ │ │ │ ├── _hypothesis.py\n", + "│ │ │ │ │ ├── _io.py\n", + "│ │ │ │ │ ├── _warnings.py\n", + "│ │ │ │ │ ├── asserters.py\n", + "│ │ │ │ │ ├── compat.py\n", + "│ │ │ │ │ └── contexts.py\n", + "│ │ │ │ ├── _typing.py\n", + "│ │ │ │ ├── _version.py\n", + "│ │ │ │ ├── _version_meson.py\n", + "│ │ │ │ ├── api\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── extensions\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── indexers\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── interchange\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── types\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ └── typing\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ ├── arrays\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ ├── compat\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _constants.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _optional.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── compressors.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pickle_compat.cpython-310.pyc\n", + "│ │ │ │ │ │ └── pyarrow.cpython-310.pyc\n", + "│ │ │ │ │ ├── _constants.py\n", + "│ │ │ │ │ ├── _optional.py\n", + "│ │ │ │ │ ├── compressors.py\n", + "│ │ │ │ │ ├── numpy\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── function.cpython-310.pyc\n", + "│ │ │ │ │ │ └── function.py\n", + "│ │ │ │ │ ├── pickle_compat.py\n", + "│ │ │ │ │ └── pyarrow.py\n", + "│ │ │ │ ├── conftest.py\n", + "│ │ │ │ ├── core\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── accessor.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── algorithms.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── api.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── apply.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── arraylike.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── config_init.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── construction.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── flags.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── frame.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── generic.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── missing.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── nanops.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── resample.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── roperator.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── sample.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── series.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── shared_docs.cpython-310.pyc\n", + "│ │ │ │ │ │ └── sorting.cpython-310.pyc\n", + "│ │ │ │ │ ├── _numba\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── executor.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── extensions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── executor.py\n", + "│ │ │ │ │ │ ├── extensions.py\n", + "│ │ │ │ │ │ └── kernels\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── mean_.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── min_max_.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── shared.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── sum_.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── var_.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── mean_.py\n", + "│ │ │ │ │ │ ├── min_max_.py\n", + "│ │ │ │ │ │ ├── shared.py\n", + "│ │ │ │ │ │ ├── sum_.py\n", + "│ │ │ │ │ │ └── var_.py\n", + "│ │ │ │ │ ├── accessor.py\n", + "│ │ │ │ │ ├── algorithms.py\n", + "│ │ │ │ │ ├── api.py\n", + "│ │ │ │ │ ├── apply.py\n", + "│ │ │ │ │ ├── array_algos\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── datetimelike_accumulations.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── masked_accumulations.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── masked_reductions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── putmask.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── quantile.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── replace.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── take.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── transforms.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── datetimelike_accumulations.py\n", + "│ │ │ │ │ │ ├── masked_accumulations.py\n", + "│ │ │ │ │ │ ├── masked_reductions.py\n", + "│ │ │ │ │ │ ├── putmask.py\n", + "│ │ │ │ │ │ ├── quantile.py\n", + "│ │ │ │ │ │ ├── replace.py\n", + "│ │ │ │ │ │ ├── take.py\n", + "│ │ │ │ │ │ └── transforms.py\n", + "│ │ │ │ │ ├── arraylike.py\n", + "│ │ │ │ │ ├── arrays\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _arrow_string_mixins.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _mixins.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _ranges.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── boolean.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── categorical.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── datetimelike.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── datetimes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── floating.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── integer.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── interval.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── masked.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── numeric.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── numpy_.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── period.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── string_.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── string_arrow.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── timedeltas.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _arrow_string_mixins.py\n", + "│ │ │ │ │ │ ├── _mixins.py\n", + "│ │ │ │ │ │ ├── _ranges.py\n", + "│ │ │ │ │ │ ├── _utils.py\n", + "│ │ │ │ │ │ ├── arrow\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── _arrow_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── accessors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── array.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── extension_types.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _arrow_utils.py\n", + "│ │ │ │ │ │ │ ├── accessors.py\n", + "│ │ │ │ │ │ │ ├── array.py\n", + "│ │ │ │ │ │ │ └── extension_types.py\n", + "│ │ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ │ ├── boolean.py\n", + "│ │ │ │ │ │ ├── categorical.py\n", + "│ │ │ │ │ │ ├── datetimelike.py\n", + "│ │ │ │ │ │ ├── datetimes.py\n", + "│ │ │ │ │ │ ├── floating.py\n", + "│ │ │ │ │ │ ├── integer.py\n", + "│ │ │ │ │ │ ├── interval.py\n", + "│ │ │ │ │ │ ├── masked.py\n", + "│ │ │ │ │ │ ├── numeric.py\n", + "│ │ │ │ │ │ ├── numpy_.py\n", + "│ │ │ │ │ │ ├── period.py\n", + "│ │ │ │ │ │ ├── sparse\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── accessor.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── array.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── scipy_sparse.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── accessor.py\n", + "│ │ │ │ │ │ │ ├── array.py\n", + "│ │ │ │ │ │ │ └── scipy_sparse.py\n", + "│ │ │ │ │ │ ├── string_.py\n", + "│ │ │ │ │ │ ├── string_arrow.py\n", + "│ │ │ │ │ │ └── timedeltas.py\n", + "│ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ ├── computation\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── align.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── check.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── engines.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── eval.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── expr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── expressions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── ops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── parsing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pytables.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── scope.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── align.py\n", + "│ │ │ │ │ │ ├── api.py\n", + "│ │ │ │ │ │ ├── check.py\n", + "│ │ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ │ ├── engines.py\n", + "│ │ │ │ │ │ ├── eval.py\n", + "│ │ │ │ │ │ ├── expr.py\n", + "│ │ │ │ │ │ ├── expressions.py\n", + "│ │ │ │ │ │ ├── ops.py\n", + "│ │ │ │ │ │ ├── parsing.py\n", + "│ │ │ │ │ │ ├── pytables.py\n", + "│ │ │ │ │ │ └── scope.py\n", + "│ │ │ │ │ ├── config_init.py\n", + "│ │ │ │ │ ├── construction.py\n", + "│ │ │ │ │ ├── dtypes\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── cast.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── concat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── dtypes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── generic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── inference.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── missing.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── api.py\n", + "│ │ │ │ │ │ ├── astype.py\n", + "│ │ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ │ ├── cast.py\n", + "│ │ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ │ ├── concat.py\n", + "│ │ │ │ │ │ ├── dtypes.py\n", + "│ │ │ │ │ │ ├── generic.py\n", + "│ │ │ │ │ │ ├── inference.py\n", + "│ │ │ │ │ │ └── missing.py\n", + "│ │ │ │ │ ├── flags.py\n", + "│ │ │ │ │ ├── frame.py\n", + "│ │ │ │ │ ├── generic.py\n", + "│ │ │ │ │ ├── groupby\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── categorical.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── generic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── groupby.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── grouper.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── numba_.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── ops.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ │ ├── categorical.py\n", + "│ │ │ │ │ │ ├── generic.py\n", + "│ │ │ │ │ │ ├── groupby.py\n", + "│ │ │ │ │ │ ├── grouper.py\n", + "│ │ │ │ │ │ ├── indexing.py\n", + "│ │ │ │ │ │ ├── numba_.py\n", + "│ │ │ │ │ │ └── ops.py\n", + "│ │ │ │ │ ├── indexers\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── objects.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── objects.py\n", + "│ │ │ │ │ │ └── utils.py\n", + "│ │ │ │ │ ├── indexes\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── accessors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── category.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── datetimelike.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── datetimes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── extension.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── frozen.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── interval.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── multi.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── period.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── range.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── timedeltas.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── accessors.py\n", + "│ │ │ │ │ │ ├── api.py\n", + "│ │ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ │ ├── category.py\n", + "│ │ │ │ │ │ ├── datetimelike.py\n", + "│ │ │ │ │ │ ├── datetimes.py\n", + "│ │ │ │ │ │ ├── extension.py\n", + "│ │ │ │ │ │ ├── frozen.py\n", + "│ │ │ │ │ │ ├── interval.py\n", + "│ │ │ │ │ │ ├── multi.py\n", + "│ │ │ │ │ │ ├── period.py\n", + "│ │ │ │ │ │ ├── range.py\n", + "│ │ │ │ │ │ └── timedeltas.py\n", + "│ │ │ │ │ ├── indexing.py\n", + "│ │ │ │ │ ├── interchange\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── buffer.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── column.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── dataframe.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── dataframe_protocol.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── from_dataframe.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── buffer.py\n", + "│ │ │ │ │ │ ├── column.py\n", + "│ │ │ │ │ │ ├── dataframe.py\n", + "│ │ │ │ │ │ ├── dataframe_protocol.py\n", + "│ │ │ │ │ │ ├── from_dataframe.py\n", + "│ │ │ │ │ │ └── utils.py\n", + "│ │ │ │ │ ├── internals\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── array_manager.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── blocks.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── concat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── construction.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── managers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── ops.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── api.py\n", + "│ │ │ │ │ │ ├── array_manager.py\n", + "│ │ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ │ ├── blocks.py\n", + "│ │ │ │ │ │ ├── concat.py\n", + "│ │ │ │ │ │ ├── construction.py\n", + "│ │ │ │ │ │ ├── managers.py\n", + "│ │ │ │ │ │ └── ops.py\n", + "│ │ │ │ │ ├── methods\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── describe.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── selectn.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── to_dict.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── describe.py\n", + "│ │ │ │ │ │ ├── selectn.py\n", + "│ │ │ │ │ │ └── to_dict.py\n", + "│ │ │ │ │ ├── missing.py\n", + "│ │ │ │ │ ├── nanops.py\n", + "│ │ │ │ │ ├── ops\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── array_ops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── dispatch.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── docstrings.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── invalid.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── mask_ops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── missing.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── array_ops.py\n", + "│ │ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ │ ├── dispatch.py\n", + "│ │ │ │ │ │ ├── docstrings.py\n", + "│ │ │ │ │ │ ├── invalid.py\n", + "│ │ │ │ │ │ ├── mask_ops.py\n", + "│ │ │ │ │ │ └── missing.py\n", + "│ │ │ │ │ ├── resample.py\n", + "│ │ │ │ │ ├── reshape\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── concat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── encoding.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── melt.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── merge.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pivot.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── reshape.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── tile.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── util.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── api.py\n", + "│ │ │ │ │ │ ├── concat.py\n", + "│ │ │ │ │ │ ├── encoding.py\n", + "│ │ │ │ │ │ ├── melt.py\n", + "│ │ │ │ │ │ ├── merge.py\n", + "│ │ │ │ │ │ ├── pivot.py\n", + "│ │ │ │ │ │ ├── reshape.py\n", + "│ │ │ │ │ │ ├── tile.py\n", + "│ │ │ │ │ │ └── util.py\n", + "│ │ │ │ │ ├── roperator.py\n", + "│ │ │ │ │ ├── sample.py\n", + "│ │ │ │ │ ├── series.py\n", + "│ │ │ │ │ ├── shared_docs.py\n", + "│ │ │ │ │ ├── sorting.py\n", + "│ │ │ │ │ ├── sparse\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── api.cpython-310.pyc\n", + "│ │ │ │ │ │ └── api.py\n", + "│ │ │ │ │ ├── strings\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── accessor.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── object_array.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── accessor.py\n", + "│ │ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ │ └── object_array.py\n", + "│ │ │ │ │ ├── tools\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── datetimes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── numeric.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── timedeltas.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── times.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── datetimes.py\n", + "│ │ │ │ │ │ ├── numeric.py\n", + "│ │ │ │ │ │ ├── timedeltas.py\n", + "│ │ │ │ │ │ └── times.py\n", + "│ │ │ │ │ ├── util\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── hashing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── numba_.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── hashing.py\n", + "│ │ │ │ │ │ └── numba_.py\n", + "│ │ │ │ │ └── window\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── doc.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ewm.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── expanding.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── numba_.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── online.cpython-310.pyc\n", + "│ │ │ │ │ │ └── rolling.cpython-310.pyc\n", + "│ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ ├── doc.py\n", + "│ │ │ │ │ ├── ewm.py\n", + "│ │ │ │ │ ├── expanding.py\n", + "│ │ │ │ │ ├── numba_.py\n", + "│ │ │ │ │ ├── online.py\n", + "│ │ │ │ │ └── rolling.py\n", + "│ │ │ │ ├── errors\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ ├── io\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _util.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── api.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── clipboards.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── feather_format.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── gbq.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── html.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── orc.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── parquet.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pickle.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pytables.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── spss.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── sql.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── stata.cpython-310.pyc\n", + "│ │ │ │ │ │ └── xml.cpython-310.pyc\n", + "│ │ │ │ │ ├── _util.py\n", + "│ │ │ │ │ ├── api.py\n", + "│ │ │ │ │ ├── clipboard\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── clipboards.py\n", + "│ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ ├── excel\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _calamine.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _odfreader.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _odswriter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _openpyxl.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _pyxlsb.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _util.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _xlrd.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── _xlsxwriter.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _base.py\n", + "│ │ │ │ │ │ ├── _calamine.py\n", + "│ │ │ │ │ │ ├── _odfreader.py\n", + "│ │ │ │ │ │ ├── _odswriter.py\n", + "│ │ │ │ │ │ ├── _openpyxl.py\n", + "│ │ │ │ │ │ ├── _pyxlsb.py\n", + "│ │ │ │ │ │ ├── _util.py\n", + "│ │ │ │ │ │ ├── _xlrd.py\n", + "│ │ │ │ │ │ └── _xlsxwriter.py\n", + "│ │ │ │ │ ├── feather_format.py\n", + "│ │ │ │ │ ├── formats\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _color_data.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── console.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── css.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── csvs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── excel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── format.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── html.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── info.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── printing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── string.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── style.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── style_render.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── xml.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _color_data.py\n", + "│ │ │ │ │ │ ├── console.py\n", + "│ │ │ │ │ │ ├── css.py\n", + "│ │ │ │ │ │ ├── csvs.py\n", + "│ │ │ │ │ │ ├── excel.py\n", + "│ │ │ │ │ │ ├── format.py\n", + "│ │ │ │ │ │ ├── html.py\n", + "│ │ │ │ │ │ ├── info.py\n", + "│ │ │ │ │ │ ├── printing.py\n", + "│ │ │ │ │ │ ├── string.py\n", + "│ │ │ │ │ │ ├── style.py\n", + "│ │ │ │ │ │ ├── style_render.py\n", + "│ │ │ │ │ │ ├── templates\n", + "│ │ │ │ │ │ │ ├── html.tpl\n", + "│ │ │ │ │ │ │ ├── html_style.tpl\n", + "│ │ │ │ │ │ │ ├── html_table.tpl\n", + "│ │ │ │ │ │ │ ├── latex.tpl\n", + "│ │ │ │ │ │ │ ├── latex_longtable.tpl\n", + "│ │ │ │ │ │ │ ├── latex_table.tpl\n", + "│ │ │ │ │ │ │ └── string.tpl\n", + "│ │ │ │ │ │ └── xml.py\n", + "│ │ │ │ │ ├── gbq.py\n", + "│ │ │ │ │ ├── html.py\n", + "│ │ │ │ │ ├── json\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _json.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _normalize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── _table_schema.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _json.py\n", + "│ │ │ │ │ │ ├── _normalize.py\n", + "│ │ │ │ │ │ └── _table_schema.py\n", + "│ │ │ │ │ ├── orc.py\n", + "│ │ │ │ │ ├── parquet.py\n", + "│ │ │ │ │ ├── parsers\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── arrow_parser_wrapper.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── base_parser.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── c_parser_wrapper.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── python_parser.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── readers.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── arrow_parser_wrapper.py\n", + "│ │ │ │ │ │ ├── base_parser.py\n", + "│ │ │ │ │ │ ├── c_parser_wrapper.py\n", + "│ │ │ │ │ │ ├── python_parser.py\n", + "│ │ │ │ │ │ └── readers.py\n", + "│ │ │ │ │ ├── pickle.py\n", + "│ │ │ │ │ ├── pytables.py\n", + "│ │ │ │ │ ├── sas\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── sas7bdat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── sas_constants.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── sas_xport.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── sasreader.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── sas7bdat.py\n", + "│ │ │ │ │ │ ├── sas_constants.py\n", + "│ │ │ │ │ │ ├── sas_xport.py\n", + "│ │ │ │ │ │ └── sasreader.py\n", + "│ │ │ │ │ ├── spss.py\n", + "│ │ │ │ │ ├── sql.py\n", + "│ │ │ │ │ ├── stata.py\n", + "│ │ │ │ │ └── xml.py\n", + "│ │ │ │ ├── plotting\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _core.cpython-310.pyc\n", + "│ │ │ │ │ │ └── _misc.cpython-310.pyc\n", + "│ │ │ │ │ ├── _core.py\n", + "│ │ │ │ │ ├── _matplotlib\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── boxplot.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── converter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── core.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── groupby.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── hist.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── misc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── style.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── timeseries.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── tools.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── boxplot.py\n", + "│ │ │ │ │ │ ├── converter.py\n", + "│ │ │ │ │ │ ├── core.py\n", + "│ │ │ │ │ │ ├── groupby.py\n", + "│ │ │ │ │ │ ├── hist.py\n", + "│ │ │ │ │ │ ├── misc.py\n", + "│ │ │ │ │ │ ├── style.py\n", + "│ │ │ │ │ │ ├── timeseries.py\n", + "│ │ │ │ │ │ └── tools.py\n", + "│ │ │ │ │ └── _misc.py\n", + "│ │ │ │ ├── pyproject.toml\n", + "│ │ │ │ ├── testing.py\n", + "│ │ │ │ ├── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_aggregation.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_algos.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_common.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_downstream.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_errors.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_expressions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_flags.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_multilevel.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_nanops.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_optional_dependency.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_register_accessor.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_sorting.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_take.cpython-310.pyc\n", + "│ │ │ │ │ ├── api\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_types.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_api.py\n", + "│ │ │ │ │ │ └── test_types.py\n", + "│ │ │ │ │ ├── apply\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_frame_apply.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_frame_apply_relabeling.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_frame_transform.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_invalid_arg.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_numba.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_series_apply.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_series_apply_relabeling.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_series_transform.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_str.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ │ ├── test_frame_apply.py\n", + "│ │ │ │ │ │ ├── test_frame_apply_relabeling.py\n", + "│ │ │ │ │ │ ├── test_frame_transform.py\n", + "│ │ │ │ │ │ ├── test_invalid_arg.py\n", + "│ │ │ │ │ │ ├── test_numba.py\n", + "│ │ │ │ │ │ ├── test_series_apply.py\n", + "│ │ │ │ │ │ ├── test_series_apply_relabeling.py\n", + "│ │ │ │ │ │ ├── test_series_transform.py\n", + "│ │ │ │ │ │ └── test_str.py\n", + "│ │ │ │ │ ├── arithmetic\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_array_ops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_categorical.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_datetime64.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_interval.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_numeric.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_object.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_period.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_timedelta64.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ ├── test_array_ops.py\n", + "│ │ │ │ │ │ ├── test_categorical.py\n", + "│ │ │ │ │ │ ├── test_datetime64.py\n", + "│ │ │ │ │ │ ├── test_interval.py\n", + "│ │ │ │ │ │ ├── test_numeric.py\n", + "│ │ │ │ │ │ ├── test_object.py\n", + "│ │ │ │ │ │ ├── test_period.py\n", + "│ │ │ │ │ │ └── test_timedelta64.py\n", + "│ │ │ │ │ ├── arrays\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── masked_shared.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_array.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_datetimelike.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_datetimes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_ndarray_backed.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_period.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_timedeltas.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── boolean\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_arithmetic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_comparison.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_construction.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_function.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_logical.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_ops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reduction.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_repr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arithmetic.py\n", + "│ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ ├── test_comparison.py\n", + "│ │ │ │ │ │ │ ├── test_construction.py\n", + "│ │ │ │ │ │ │ ├── test_function.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_logical.py\n", + "│ │ │ │ │ │ │ ├── test_ops.py\n", + "│ │ │ │ │ │ │ ├── test_reduction.py\n", + "│ │ │ │ │ │ │ └── test_repr.py\n", + "│ │ │ │ │ │ ├── categorical\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_algos.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_analytics.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_dtypes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_map.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_missing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_operators.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_replace.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_repr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_sorting.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_subclass.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_take.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_warnings.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_algos.py\n", + "│ │ │ │ │ │ │ ├── test_analytics.py\n", + "│ │ │ │ │ │ │ ├── test_api.py\n", + "│ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ │ ├── test_dtypes.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_map.py\n", + "│ │ │ │ │ │ │ ├── test_missing.py\n", + "│ │ │ │ │ │ │ ├── test_operators.py\n", + "│ │ │ │ │ │ │ ├── test_replace.py\n", + "│ │ │ │ │ │ │ ├── test_repr.py\n", + "│ │ │ │ │ │ │ ├── test_sorting.py\n", + "│ │ │ │ │ │ │ ├── test_subclass.py\n", + "│ │ │ │ │ │ │ ├── test_take.py\n", + "│ │ │ │ │ │ │ └── test_warnings.py\n", + "│ │ │ │ │ │ ├── datetimes\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_cumulative.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_reductions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ │ ├── test_cumulative.py\n", + "│ │ │ │ │ │ │ └── test_reductions.py\n", + "│ │ │ │ │ │ ├── floating\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_arithmetic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_comparison.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_concat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_construction.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_contains.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_function.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_repr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_to_numpy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ │ ├── test_arithmetic.py\n", + "│ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ ├── test_comparison.py\n", + "│ │ │ │ │ │ │ ├── test_concat.py\n", + "│ │ │ │ │ │ │ ├── test_construction.py\n", + "│ │ │ │ │ │ │ ├── test_contains.py\n", + "│ │ │ │ │ │ │ ├── test_function.py\n", + "│ │ │ │ │ │ │ ├── test_repr.py\n", + "│ │ │ │ │ │ │ └── test_to_numpy.py\n", + "│ │ │ │ │ │ ├── integer\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_arithmetic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_comparison.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_concat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_construction.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_dtypes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_function.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reduction.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_repr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ │ ├── test_arithmetic.py\n", + "│ │ │ │ │ │ │ ├── test_comparison.py\n", + "│ │ │ │ │ │ │ ├── test_concat.py\n", + "│ │ │ │ │ │ │ ├── test_construction.py\n", + "│ │ │ │ │ │ │ ├── test_dtypes.py\n", + "│ │ │ │ │ │ │ ├── test_function.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_reduction.py\n", + "│ │ │ │ │ │ │ └── test_repr.py\n", + "│ │ │ │ │ │ ├── interval\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_formats.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_interval.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_interval_pyarrow.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_overlaps.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ ├── test_formats.py\n", + "│ │ │ │ │ │ │ ├── test_interval.py\n", + "│ │ │ │ │ │ │ ├── test_interval_pyarrow.py\n", + "│ │ │ │ │ │ │ └── test_overlaps.py\n", + "│ │ │ │ │ │ ├── masked\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_arithmetic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_arrow_compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_function.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arithmetic.py\n", + "│ │ │ │ │ │ │ ├── test_arrow_compat.py\n", + "│ │ │ │ │ │ │ ├── test_function.py\n", + "│ │ │ │ │ │ │ └── test_indexing.py\n", + "│ │ │ │ │ │ ├── masked_shared.py\n", + "│ │ │ │ │ │ ├── numpy_\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_numpy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ └── test_numpy.py\n", + "│ │ │ │ │ │ ├── period\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_arrow_compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_reductions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arrow_compat.py\n", + "│ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ │ └── test_reductions.py\n", + "│ │ │ │ │ │ ├── sparse\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_accessor.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_arithmetics.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_array.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_combine_concat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_dtype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_libsparse.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reductions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_unary.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_accessor.py\n", + "│ │ │ │ │ │ │ ├── test_arithmetics.py\n", + "│ │ │ │ │ │ │ ├── test_array.py\n", + "│ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ ├── test_combine_concat.py\n", + "│ │ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ │ ├── test_dtype.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_libsparse.py\n", + "│ │ │ │ │ │ │ ├── test_reductions.py\n", + "│ │ │ │ │ │ │ └── test_unary.py\n", + "│ │ │ │ │ │ ├── string_\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_string.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_string_arrow.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_string.py\n", + "│ │ │ │ │ │ │ └── test_string_arrow.py\n", + "│ │ │ │ │ │ ├── test_array.py\n", + "│ │ │ │ │ │ ├── test_datetimelike.py\n", + "│ │ │ │ │ │ ├── test_datetimes.py\n", + "│ │ │ │ │ │ ├── test_ndarray_backed.py\n", + "│ │ │ │ │ │ ├── test_period.py\n", + "│ │ │ │ │ │ ├── test_timedeltas.py\n", + "│ │ │ │ │ │ └── timedeltas\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_cumulative.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_reductions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ ├── test_cumulative.py\n", + "│ │ │ │ │ │ └── test_reductions.py\n", + "│ │ │ │ │ ├── base\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_conversion.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_fillna.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_misc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_transpose.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_unique.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_value_counts.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ ├── test_conversion.py\n", + "│ │ │ │ │ │ ├── test_fillna.py\n", + "│ │ │ │ │ │ ├── test_misc.py\n", + "│ │ │ │ │ │ ├── test_transpose.py\n", + "│ │ │ │ │ │ ├── test_unique.py\n", + "│ │ │ │ │ │ └── test_value_counts.py\n", + "│ │ │ │ │ ├── computation\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_eval.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_compat.py\n", + "│ │ │ │ │ │ └── test_eval.py\n", + "│ │ │ │ │ ├── config\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_config.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_localization.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_config.py\n", + "│ │ │ │ │ │ └── test_localization.py\n", + "│ │ │ │ │ ├── construction\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_extract_array.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_extract_array.py\n", + "│ │ │ │ │ ├── copy_view\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_array.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_chained_assignment_deprecation.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_clip.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_core_functionalities.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_internals.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_interp_fillna.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_methods.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_replace.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_setitem.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_util.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── util.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── index\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_datetimeindex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_periodindex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_timedeltaindex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_datetimeindex.py\n", + "│ │ │ │ │ │ │ ├── test_index.py\n", + "│ │ │ │ │ │ │ ├── test_periodindex.py\n", + "│ │ │ │ │ │ │ └── test_timedeltaindex.py\n", + "│ │ │ │ │ │ ├── test_array.py\n", + "│ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ ├── test_chained_assignment_deprecation.py\n", + "│ │ │ │ │ │ ├── test_clip.py\n", + "│ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ ├── test_core_functionalities.py\n", + "│ │ │ │ │ │ ├── test_functions.py\n", + "│ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ ├── test_internals.py\n", + "│ │ │ │ │ │ ├── test_interp_fillna.py\n", + "│ │ │ │ │ │ ├── test_methods.py\n", + "│ │ │ │ │ │ ├── test_replace.py\n", + "│ │ │ │ │ │ ├── test_setitem.py\n", + "│ │ │ │ │ │ ├── test_util.py\n", + "│ │ │ │ │ │ └── util.py\n", + "│ │ │ │ │ ├── dtypes\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_concat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_dtypes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_generic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_inference.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_missing.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cast\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_can_hold_element.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_construct_from_scalar.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_construct_ndarray.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_construct_object_arr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_dict_compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_downcast.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_find_common_type.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_infer_datetimelike.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_infer_dtype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_maybe_box_native.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_promote.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_can_hold_element.py\n", + "│ │ │ │ │ │ │ ├── test_construct_from_scalar.py\n", + "│ │ │ │ │ │ │ ├── test_construct_ndarray.py\n", + "│ │ │ │ │ │ │ ├── test_construct_object_arr.py\n", + "│ │ │ │ │ │ │ ├── test_dict_compat.py\n", + "│ │ │ │ │ │ │ ├── test_downcast.py\n", + "│ │ │ │ │ │ │ ├── test_find_common_type.py\n", + "│ │ │ │ │ │ │ ├── test_infer_datetimelike.py\n", + "│ │ │ │ │ │ │ ├── test_infer_dtype.py\n", + "│ │ │ │ │ │ │ ├── test_maybe_box_native.py\n", + "│ │ │ │ │ │ │ └── test_promote.py\n", + "│ │ │ │ │ │ ├── test_common.py\n", + "│ │ │ │ │ │ ├── test_concat.py\n", + "│ │ │ │ │ │ ├── test_dtypes.py\n", + "│ │ │ │ │ │ ├── test_generic.py\n", + "│ │ │ │ │ │ ├── test_inference.py\n", + "│ │ │ │ │ │ └── test_missing.py\n", + "│ │ │ │ │ ├── extension\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arrow.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_categorical.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_datetime.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_extension.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_interval.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_masked.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_numpy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_period.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_sparse.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_string.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── array_with_attr\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── array.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_array_with_attr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── array.py\n", + "│ │ │ │ │ │ │ └── test_array_with_attr.py\n", + "│ │ │ │ │ │ ├── base\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── accumulate.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── casting.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── dim2.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── dtype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── getitem.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── groupby.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── interface.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── io.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── methods.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── missing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── ops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── printing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── reduce.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── reshaping.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── setitem.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── accumulate.py\n", + "│ │ │ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ │ │ ├── casting.py\n", + "│ │ │ │ │ │ │ ├── constructors.py\n", + "│ │ │ │ │ │ │ ├── dim2.py\n", + "│ │ │ │ │ │ │ ├── dtype.py\n", + "│ │ │ │ │ │ │ ├── getitem.py\n", + "│ │ │ │ │ │ │ ├── groupby.py\n", + "│ │ │ │ │ │ │ ├── index.py\n", + "│ │ │ │ │ │ │ ├── interface.py\n", + "│ │ │ │ │ │ │ ├── io.py\n", + "│ │ │ │ │ │ │ ├── methods.py\n", + "│ │ │ │ │ │ │ ├── missing.py\n", + "│ │ │ │ │ │ │ ├── ops.py\n", + "│ │ │ │ │ │ │ ├── printing.py\n", + "│ │ │ │ │ │ │ ├── reduce.py\n", + "│ │ │ │ │ │ │ ├── reshaping.py\n", + "│ │ │ │ │ │ │ └── setitem.py\n", + "│ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ ├── date\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── array.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── array.py\n", + "│ │ │ │ │ │ ├── decimal\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── array.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_decimal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── array.py\n", + "│ │ │ │ │ │ │ └── test_decimal.py\n", + "│ │ │ │ │ │ ├── json\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── array.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_json.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── array.py\n", + "│ │ │ │ │ │ │ └── test_json.py\n", + "│ │ │ │ │ │ ├── list\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── array.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_list.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── array.py\n", + "│ │ │ │ │ │ │ └── test_list.py\n", + "│ │ │ │ │ │ ├── test_arrow.py\n", + "│ │ │ │ │ │ ├── test_categorical.py\n", + "│ │ │ │ │ │ ├── test_common.py\n", + "│ │ │ │ │ │ ├── test_datetime.py\n", + "│ │ │ │ │ │ ├── test_extension.py\n", + "│ │ │ │ │ │ ├── test_interval.py\n", + "│ │ │ │ │ │ ├── test_masked.py\n", + "│ │ │ │ │ │ ├── test_numpy.py\n", + "│ │ │ │ │ │ ├── test_period.py\n", + "│ │ │ │ │ │ ├── test_sparse.py\n", + "│ │ │ │ │ │ └── test_string.py\n", + "│ │ │ │ │ ├── frame\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_alter_axes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arithmetic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arrow_interface.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_block_internals.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_cumulative.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_iteration.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_logical_ops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_nonunique_indexes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_npfuncs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_query_eval.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_reductions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_repr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_stack_unstack.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_subclass.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_ufunc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_unary.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_validate.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ ├── constructors\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_from_dict.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_from_records.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_from_dict.py\n", + "│ │ │ │ │ │ │ └── test_from_records.py\n", + "│ │ │ │ │ │ ├── indexing\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_coercion.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_delitem.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_get.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_get_value.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_getitem.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_insert.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_mask.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_set_value.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_setitem.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_take.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_where.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_xs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_coercion.py\n", + "│ │ │ │ │ │ │ ├── test_delitem.py\n", + "│ │ │ │ │ │ │ ├── test_get.py\n", + "│ │ │ │ │ │ │ ├── test_get_value.py\n", + "│ │ │ │ │ │ │ ├── test_getitem.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_insert.py\n", + "│ │ │ │ │ │ │ ├── test_mask.py\n", + "│ │ │ │ │ │ │ ├── test_set_value.py\n", + "│ │ │ │ │ │ │ ├── test_setitem.py\n", + "│ │ │ │ │ │ │ ├── test_take.py\n", + "│ │ │ │ │ │ │ ├── test_where.py\n", + "│ │ │ │ │ │ │ └── test_xs.py\n", + "│ │ │ │ │ │ ├── methods\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_add_prefix_suffix.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_align.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_asfreq.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_asof.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_assign.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_at_time.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_between_time.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_clip.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_combine.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_combine_first.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_compare.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_convert_dtypes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_copy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_count.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_cov_corr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_describe.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_diff.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_dot.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_drop.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_drop_duplicates.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_droplevel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_dropna.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_dtypes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_duplicated.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_equals.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_explode.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_fillna.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_filter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_first_and_last.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_first_valid_index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_get_numeric_data.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_head_tail.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_infer_objects.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_info.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_interpolate.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_is_homogeneous_dtype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_isetitem.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_isin.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_iterrows.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_join.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_map.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_matmul.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_nlargest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_pct_change.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_pipe.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_pop.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_quantile.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_rank.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reindex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reindex_like.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_rename.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_rename_axis.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reorder_levels.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_replace.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reset_index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_round.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_sample.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_select_dtypes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_set_axis.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_set_index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_shift.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_size.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_sort_index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_sort_values.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_swapaxes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_swaplevel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_csv.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_dict.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_dict_of_blocks.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_numpy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_period.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_records.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_timestamp.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_transpose.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_truncate.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_tz_convert.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_tz_localize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_update.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_value_counts.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_values.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_add_prefix_suffix.py\n", + "│ │ │ │ │ │ │ ├── test_align.py\n", + "│ │ │ │ │ │ │ ├── test_asfreq.py\n", + "│ │ │ │ │ │ │ ├── test_asof.py\n", + "│ │ │ │ │ │ │ ├── test_assign.py\n", + "│ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ ├── test_at_time.py\n", + "│ │ │ │ │ │ │ ├── test_between_time.py\n", + "│ │ │ │ │ │ │ ├── test_clip.py\n", + "│ │ │ │ │ │ │ ├── test_combine.py\n", + "│ │ │ │ │ │ │ ├── test_combine_first.py\n", + "│ │ │ │ │ │ │ ├── test_compare.py\n", + "│ │ │ │ │ │ │ ├── test_convert_dtypes.py\n", + "│ │ │ │ │ │ │ ├── test_copy.py\n", + "│ │ │ │ │ │ │ ├── test_count.py\n", + "│ │ │ │ │ │ │ ├── test_cov_corr.py\n", + "│ │ │ │ │ │ │ ├── test_describe.py\n", + "│ │ │ │ │ │ │ ├── test_diff.py\n", + "│ │ │ │ │ │ │ ├── test_dot.py\n", + "│ │ │ │ │ │ │ ├── test_drop.py\n", + "│ │ │ │ │ │ │ ├── test_drop_duplicates.py\n", + "│ │ │ │ │ │ │ ├── test_droplevel.py\n", + "│ │ │ │ │ │ │ ├── test_dropna.py\n", + "│ │ │ │ │ │ │ ├── test_dtypes.py\n", + "│ │ │ │ │ │ │ ├── test_duplicated.py\n", + "│ │ │ │ │ │ │ ├── test_equals.py\n", + "│ │ │ │ │ │ │ ├── test_explode.py\n", + "│ │ │ │ │ │ │ ├── test_fillna.py\n", + "│ │ │ │ │ │ │ ├── test_filter.py\n", + "│ │ │ │ │ │ │ ├── test_first_and_last.py\n", + "│ │ │ │ │ │ │ ├── test_first_valid_index.py\n", + "│ │ │ │ │ │ │ ├── test_get_numeric_data.py\n", + "│ │ │ │ │ │ │ ├── test_head_tail.py\n", + "│ │ │ │ │ │ │ ├── test_infer_objects.py\n", + "│ │ │ │ │ │ │ ├── test_info.py\n", + "│ │ │ │ │ │ │ ├── test_interpolate.py\n", + "│ │ │ │ │ │ │ ├── test_is_homogeneous_dtype.py\n", + "│ │ │ │ │ │ │ ├── test_isetitem.py\n", + "│ │ │ │ │ │ │ ├── test_isin.py\n", + "│ │ │ │ │ │ │ ├── test_iterrows.py\n", + "│ │ │ │ │ │ │ ├── test_join.py\n", + "│ │ │ │ │ │ │ ├── test_map.py\n", + "│ │ │ │ │ │ │ ├── test_matmul.py\n", + "│ │ │ │ │ │ │ ├── test_nlargest.py\n", + "│ │ │ │ │ │ │ ├── test_pct_change.py\n", + "│ │ │ │ │ │ │ ├── test_pipe.py\n", + "│ │ │ │ │ │ │ ├── test_pop.py\n", + "│ │ │ │ │ │ │ ├── test_quantile.py\n", + "│ │ │ │ │ │ │ ├── test_rank.py\n", + "│ │ │ │ │ │ │ ├── test_reindex.py\n", + "│ │ │ │ │ │ │ ├── test_reindex_like.py\n", + "│ │ │ │ │ │ │ ├── test_rename.py\n", + "│ │ │ │ │ │ │ ├── test_rename_axis.py\n", + "│ │ │ │ │ │ │ ├── test_reorder_levels.py\n", + "│ │ │ │ │ │ │ ├── test_replace.py\n", + "│ │ │ │ │ │ │ ├── test_reset_index.py\n", + "│ │ │ │ │ │ │ ├── test_round.py\n", + "│ │ │ │ │ │ │ ├── test_sample.py\n", + "│ │ │ │ │ │ │ ├── test_select_dtypes.py\n", + "│ │ │ │ │ │ │ ├── test_set_axis.py\n", + "│ │ │ │ │ │ │ ├── test_set_index.py\n", + "│ │ │ │ │ │ │ ├── test_shift.py\n", + "│ │ │ │ │ │ │ ├── test_size.py\n", + "│ │ │ │ │ │ │ ├── test_sort_index.py\n", + "│ │ │ │ │ │ │ ├── test_sort_values.py\n", + "│ │ │ │ │ │ │ ├── test_swapaxes.py\n", + "│ │ │ │ │ │ │ ├── test_swaplevel.py\n", + "│ │ │ │ │ │ │ ├── test_to_csv.py\n", + "│ │ │ │ │ │ │ ├── test_to_dict.py\n", + "│ │ │ │ │ │ │ ├── test_to_dict_of_blocks.py\n", + "│ │ │ │ │ │ │ ├── test_to_numpy.py\n", + "│ │ │ │ │ │ │ ├── test_to_period.py\n", + "│ │ │ │ │ │ │ ├── test_to_records.py\n", + "│ │ │ │ │ │ │ ├── test_to_timestamp.py\n", + "│ │ │ │ │ │ │ ├── test_transpose.py\n", + "│ │ │ │ │ │ │ ├── test_truncate.py\n", + "│ │ │ │ │ │ │ ├── test_tz_convert.py\n", + "│ │ │ │ │ │ │ ├── test_tz_localize.py\n", + "│ │ │ │ │ │ │ ├── test_update.py\n", + "│ │ │ │ │ │ │ ├── test_value_counts.py\n", + "│ │ │ │ │ │ │ └── test_values.py\n", + "│ │ │ │ │ │ ├── test_alter_axes.py\n", + "│ │ │ │ │ │ ├── test_api.py\n", + "│ │ │ │ │ │ ├── test_arithmetic.py\n", + "│ │ │ │ │ │ ├── test_arrow_interface.py\n", + "│ │ │ │ │ │ ├── test_block_internals.py\n", + "│ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ ├── test_cumulative.py\n", + "│ │ │ │ │ │ ├── test_iteration.py\n", + "│ │ │ │ │ │ ├── test_logical_ops.py\n", + "│ │ │ │ │ │ ├── test_nonunique_indexes.py\n", + "│ │ │ │ │ │ ├── test_npfuncs.py\n", + "│ │ │ │ │ │ ├── test_query_eval.py\n", + "│ │ │ │ │ │ ├── test_reductions.py\n", + "│ │ │ │ │ │ ├── test_repr.py\n", + "│ │ │ │ │ │ ├── test_stack_unstack.py\n", + "│ │ │ │ │ │ ├── test_subclass.py\n", + "│ │ │ │ │ │ ├── test_ufunc.py\n", + "│ │ │ │ │ │ ├── test_unary.py\n", + "│ │ │ │ │ │ └── test_validate.py\n", + "│ │ │ │ │ ├── generic\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_duplicate_labels.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_finalize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_frame.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_generic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_label_or_level_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_series.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_to_xarray.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_duplicate_labels.py\n", + "│ │ │ │ │ │ ├── test_finalize.py\n", + "│ │ │ │ │ │ ├── test_frame.py\n", + "│ │ │ │ │ │ ├── test_generic.py\n", + "│ │ │ │ │ │ ├── test_label_or_level_utils.py\n", + "│ │ │ │ │ │ ├── test_series.py\n", + "│ │ │ │ │ │ └── test_to_xarray.py\n", + "│ │ │ │ │ ├── groupby\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_all_methods.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_apply.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_apply_mutate.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_bin_groupby.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_categorical.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_counting.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_cumulative.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_filters.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_groupby.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_groupby_dropna.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_groupby_subclass.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_grouping.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_index_as_string.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_libgroupby.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_missing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_numba.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_numeric_only.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_pipe.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_raises.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_reductions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_timegrouper.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── aggregate\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_aggregate.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_cython.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_numba.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_other.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_aggregate.py\n", + "│ │ │ │ │ │ │ ├── test_cython.py\n", + "│ │ │ │ │ │ │ ├── test_numba.py\n", + "│ │ │ │ │ │ │ └── test_other.py\n", + "│ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ ├── methods\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_corrwith.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_describe.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_groupby_shift_diff.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_is_monotonic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_nlargest_nsmallest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_nth.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_quantile.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_rank.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_sample.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_size.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_skew.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_value_counts.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_corrwith.py\n", + "│ │ │ │ │ │ │ ├── test_describe.py\n", + "│ │ │ │ │ │ │ ├── test_groupby_shift_diff.py\n", + "│ │ │ │ │ │ │ ├── test_is_monotonic.py\n", + "│ │ │ │ │ │ │ ├── test_nlargest_nsmallest.py\n", + "│ │ │ │ │ │ │ ├── test_nth.py\n", + "│ │ │ │ │ │ │ ├── test_quantile.py\n", + "│ │ │ │ │ │ │ ├── test_rank.py\n", + "│ │ │ │ │ │ │ ├── test_sample.py\n", + "│ │ │ │ │ │ │ ├── test_size.py\n", + "│ │ │ │ │ │ │ ├── test_skew.py\n", + "│ │ │ │ │ │ │ └── test_value_counts.py\n", + "│ │ │ │ │ │ ├── test_all_methods.py\n", + "│ │ │ │ │ │ ├── test_api.py\n", + "│ │ │ │ │ │ ├── test_apply.py\n", + "│ │ │ │ │ │ ├── test_apply_mutate.py\n", + "│ │ │ │ │ │ ├── test_bin_groupby.py\n", + "│ │ │ │ │ │ ├── test_categorical.py\n", + "│ │ │ │ │ │ ├── test_counting.py\n", + "│ │ │ │ │ │ ├── test_cumulative.py\n", + "│ │ │ │ │ │ ├── test_filters.py\n", + "│ │ │ │ │ │ ├── test_groupby.py\n", + "│ │ │ │ │ │ ├── test_groupby_dropna.py\n", + "│ │ │ │ │ │ ├── test_groupby_subclass.py\n", + "│ │ │ │ │ │ ├── test_grouping.py\n", + "│ │ │ │ │ │ ├── test_index_as_string.py\n", + "│ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ ├── test_libgroupby.py\n", + "│ │ │ │ │ │ ├── test_missing.py\n", + "│ │ │ │ │ │ ├── test_numba.py\n", + "│ │ │ │ │ │ ├── test_numeric_only.py\n", + "│ │ │ │ │ │ ├── test_pipe.py\n", + "│ │ │ │ │ │ ├── test_raises.py\n", + "│ │ │ │ │ │ ├── test_reductions.py\n", + "│ │ │ │ │ │ ├── test_timegrouper.py\n", + "│ │ │ │ │ │ └── transform\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_numba.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_transform.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_numba.py\n", + "│ │ │ │ │ │ └── test_transform.py\n", + "│ │ │ │ │ ├── indexes\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_any_index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_datetimelike.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_engines.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_frozen.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_index_new.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_numpy_compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_old_base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_setops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_subclass.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base_class\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_formats.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_pickle.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reshape.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_setops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_where.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ │ ├── test_formats.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_pickle.py\n", + "│ │ │ │ │ │ │ ├── test_reshape.py\n", + "│ │ │ │ │ │ │ ├── test_setops.py\n", + "│ │ │ │ │ │ │ └── test_where.py\n", + "│ │ │ │ │ │ ├── categorical\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_append.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_category.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_equals.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_fillna.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_formats.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_map.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reindex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_setops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_append.py\n", + "│ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ ├── test_category.py\n", + "│ │ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ │ ├── test_equals.py\n", + "│ │ │ │ │ │ │ ├── test_fillna.py\n", + "│ │ │ │ │ │ │ ├── test_formats.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_map.py\n", + "│ │ │ │ │ │ │ ├── test_reindex.py\n", + "│ │ │ │ │ │ │ └── test_setops.py\n", + "│ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ ├── datetimelike_\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_drop_duplicates.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_equals.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_is_monotonic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_nat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_sort_values.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_value_counts.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_drop_duplicates.py\n", + "│ │ │ │ │ │ │ ├── test_equals.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_is_monotonic.py\n", + "│ │ │ │ │ │ │ ├── test_nat.py\n", + "│ │ │ │ │ │ │ ├── test_sort_values.py\n", + "│ │ │ │ │ │ │ └── test_value_counts.py\n", + "│ │ │ │ │ │ ├── datetimes\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_arithmetic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_date_range.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_datetime.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_formats.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_freq_attr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_iter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_join.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_npfuncs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_ops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_partial_slicing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_pickle.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reindex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_scalar_compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_setops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_timezones.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── methods\n", + "│ │ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_asof.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_delete.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_factorize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_fillna.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_insert.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_isocalendar.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_map.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_normalize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_repeat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_resolution.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_round.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_shift.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_snap.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_to_frame.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_to_julian_date.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_to_period.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_to_pydatetime.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_to_series.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_tz_convert.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_tz_localize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ └── test_unique.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_asof.py\n", + "│ │ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ │ ├── test_delete.py\n", + "│ │ │ │ │ │ │ │ ├── test_factorize.py\n", + "│ │ │ │ │ │ │ │ ├── test_fillna.py\n", + "│ │ │ │ │ │ │ │ ├── test_insert.py\n", + "│ │ │ │ │ │ │ │ ├── test_isocalendar.py\n", + "│ │ │ │ │ │ │ │ ├── test_map.py\n", + "│ │ │ │ │ │ │ │ ├── test_normalize.py\n", + "│ │ │ │ │ │ │ │ ├── test_repeat.py\n", + "│ │ │ │ │ │ │ │ ├── test_resolution.py\n", + "│ │ │ │ │ │ │ │ ├── test_round.py\n", + "│ │ │ │ │ │ │ │ ├── test_shift.py\n", + "│ │ │ │ │ │ │ │ ├── test_snap.py\n", + "│ │ │ │ │ │ │ │ ├── test_to_frame.py\n", + "│ │ │ │ │ │ │ │ ├── test_to_julian_date.py\n", + "│ │ │ │ │ │ │ │ ├── test_to_period.py\n", + "│ │ │ │ │ │ │ │ ├── test_to_pydatetime.py\n", + "│ │ │ │ │ │ │ │ ├── test_to_series.py\n", + "│ │ │ │ │ │ │ │ ├── test_tz_convert.py\n", + "│ │ │ │ │ │ │ │ ├── test_tz_localize.py\n", + "│ │ │ │ │ │ │ │ └── test_unique.py\n", + "│ │ │ │ │ │ │ ├── test_arithmetic.py\n", + "│ │ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ │ ├── test_date_range.py\n", + "│ │ │ │ │ │ │ ├── test_datetime.py\n", + "│ │ │ │ │ │ │ ├── test_formats.py\n", + "│ │ │ │ │ │ │ ├── test_freq_attr.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_iter.py\n", + "│ │ │ │ │ │ │ ├── test_join.py\n", + "│ │ │ │ │ │ │ ├── test_npfuncs.py\n", + "│ │ │ │ │ │ │ ├── test_ops.py\n", + "│ │ │ │ │ │ │ ├── test_partial_slicing.py\n", + "│ │ │ │ │ │ │ ├── test_pickle.py\n", + "│ │ │ │ │ │ │ ├── test_reindex.py\n", + "│ │ │ │ │ │ │ ├── test_scalar_compat.py\n", + "│ │ │ │ │ │ │ ├── test_setops.py\n", + "│ │ │ │ │ │ │ └── test_timezones.py\n", + "│ │ │ │ │ │ ├── interval\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_equals.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_formats.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_interval.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_interval_range.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_interval_tree.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_join.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_pickle.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_setops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ │ ├── test_equals.py\n", + "│ │ │ │ │ │ │ ├── test_formats.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_interval.py\n", + "│ │ │ │ │ │ │ ├── test_interval_range.py\n", + "│ │ │ │ │ │ │ ├── test_interval_tree.py\n", + "│ │ │ │ │ │ │ ├── test_join.py\n", + "│ │ │ │ │ │ │ ├── test_pickle.py\n", + "│ │ │ │ │ │ │ └── test_setops.py\n", + "│ │ │ │ │ │ ├── multi\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_analytics.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_conversion.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_copy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_drop.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_duplicates.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_equivalence.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_formats.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_get_level_values.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_get_set.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_integrity.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_isin.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_join.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_lexsort.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_missing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_monotonic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_names.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_partial_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_pickle.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reindex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reshape.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_setops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_sorting.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_take.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ │ ├── test_analytics.py\n", + "│ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ ├── test_compat.py\n", + "│ │ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ │ ├── test_conversion.py\n", + "│ │ │ │ │ │ │ ├── test_copy.py\n", + "│ │ │ │ │ │ │ ├── test_drop.py\n", + "│ │ │ │ │ │ │ ├── test_duplicates.py\n", + "│ │ │ │ │ │ │ ├── test_equivalence.py\n", + "│ │ │ │ │ │ │ ├── test_formats.py\n", + "│ │ │ │ │ │ │ ├── test_get_level_values.py\n", + "│ │ │ │ │ │ │ ├── test_get_set.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_integrity.py\n", + "│ │ │ │ │ │ │ ├── test_isin.py\n", + "│ │ │ │ │ │ │ ├── test_join.py\n", + "│ │ │ │ │ │ │ ├── test_lexsort.py\n", + "│ │ │ │ │ │ │ ├── test_missing.py\n", + "│ │ │ │ │ │ │ ├── test_monotonic.py\n", + "│ │ │ │ │ │ │ ├── test_names.py\n", + "│ │ │ │ │ │ │ ├── test_partial_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_pickle.py\n", + "│ │ │ │ │ │ │ ├── test_reindex.py\n", + "│ │ │ │ │ │ │ ├── test_reshape.py\n", + "│ │ │ │ │ │ │ ├── test_setops.py\n", + "│ │ │ │ │ │ │ ├── test_sorting.py\n", + "│ │ │ │ │ │ │ └── test_take.py\n", + "│ │ │ │ │ │ ├── numeric\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_join.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_numeric.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_setops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_join.py\n", + "│ │ │ │ │ │ │ ├── test_numeric.py\n", + "│ │ │ │ │ │ │ └── test_setops.py\n", + "│ │ │ │ │ │ ├── object\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ └── test_indexing.py\n", + "│ │ │ │ │ │ ├── period\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_formats.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_freq_attr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_join.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_monotonic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_partial_slicing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_period.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_period_range.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_pickle.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_resolution.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_scalar_compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_searchsorted.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_setops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_tools.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── methods\n", + "│ │ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_asfreq.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_factorize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_fillna.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_insert.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_is_full.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_repeat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_shift.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ └── test_to_timestamp.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_asfreq.py\n", + "│ │ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ │ ├── test_factorize.py\n", + "│ │ │ │ │ │ │ │ ├── test_fillna.py\n", + "│ │ │ │ │ │ │ │ ├── test_insert.py\n", + "│ │ │ │ │ │ │ │ ├── test_is_full.py\n", + "│ │ │ │ │ │ │ │ ├── test_repeat.py\n", + "│ │ │ │ │ │ │ │ ├── test_shift.py\n", + "│ │ │ │ │ │ │ │ └── test_to_timestamp.py\n", + "│ │ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ │ ├── test_formats.py\n", + "│ │ │ │ │ │ │ ├── test_freq_attr.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_join.py\n", + "│ │ │ │ │ │ │ ├── test_monotonic.py\n", + "│ │ │ │ │ │ │ ├── test_partial_slicing.py\n", + "│ │ │ │ │ │ │ ├── test_period.py\n", + "│ │ │ │ │ │ │ ├── test_period_range.py\n", + "│ │ │ │ │ │ │ ├── test_pickle.py\n", + "│ │ │ │ │ │ │ ├── test_resolution.py\n", + "│ │ │ │ │ │ │ ├── test_scalar_compat.py\n", + "│ │ │ │ │ │ │ ├── test_searchsorted.py\n", + "│ │ │ │ │ │ │ ├── test_setops.py\n", + "│ │ │ │ │ │ │ └── test_tools.py\n", + "│ │ │ │ │ │ ├── ranges\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_join.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_range.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_setops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_join.py\n", + "│ │ │ │ │ │ │ ├── test_range.py\n", + "│ │ │ │ │ │ │ └── test_setops.py\n", + "│ │ │ │ │ │ ├── test_any_index.py\n", + "│ │ │ │ │ │ ├── test_base.py\n", + "│ │ │ │ │ │ ├── test_common.py\n", + "│ │ │ │ │ │ ├── test_datetimelike.py\n", + "│ │ │ │ │ │ ├── test_engines.py\n", + "│ │ │ │ │ │ ├── test_frozen.py\n", + "│ │ │ │ │ │ ├── test_index_new.py\n", + "│ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ ├── test_numpy_compat.py\n", + "│ │ │ │ │ │ ├── test_old_base.py\n", + "│ │ │ │ │ │ ├── test_setops.py\n", + "│ │ │ │ │ │ ├── test_subclass.py\n", + "│ │ │ │ │ │ └── timedeltas\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arithmetic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_delete.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_formats.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_freq_attr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_join.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_ops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_pickle.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_scalar_compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_searchsorted.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_setops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_timedelta.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_timedelta_range.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── methods\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_factorize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_fillna.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_insert.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_repeat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_shift.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ ├── test_factorize.py\n", + "│ │ │ │ │ │ │ ├── test_fillna.py\n", + "│ │ │ │ │ │ │ ├── test_insert.py\n", + "│ │ │ │ │ │ │ ├── test_repeat.py\n", + "│ │ │ │ │ │ │ └── test_shift.py\n", + "│ │ │ │ │ │ ├── test_arithmetic.py\n", + "│ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ ├── test_delete.py\n", + "│ │ │ │ │ │ ├── test_formats.py\n", + "│ │ │ │ │ │ ├── test_freq_attr.py\n", + "│ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ ├── test_join.py\n", + "│ │ │ │ │ │ ├── test_ops.py\n", + "│ │ │ │ │ │ ├── test_pickle.py\n", + "│ │ │ │ │ │ ├── test_scalar_compat.py\n", + "│ │ │ │ │ │ ├── test_searchsorted.py\n", + "│ │ │ │ │ │ ├── test_setops.py\n", + "│ │ │ │ │ │ ├── test_timedelta.py\n", + "│ │ │ │ │ │ └── test_timedelta_range.py\n", + "│ │ │ │ │ ├── indexing\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_at.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_categorical.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_chaining_and_caching.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_check_indexer.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_coercion.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_datetime.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_floats.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_iat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_iloc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_indexers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_loc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_na_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_partial.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_scalar.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ ├── interval\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_interval.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_interval_new.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_interval.py\n", + "│ │ │ │ │ │ │ └── test_interval_new.py\n", + "│ │ │ │ │ │ ├── multiindex\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_chaining_and_caching.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_datetime.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_getitem.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_iloc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing_slow.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_loc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_multiindex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_partial.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_setitem.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_slice.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_sorted.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_chaining_and_caching.py\n", + "│ │ │ │ │ │ │ ├── test_datetime.py\n", + "│ │ │ │ │ │ │ ├── test_getitem.py\n", + "│ │ │ │ │ │ │ ├── test_iloc.py\n", + "│ │ │ │ │ │ │ ├── test_indexing_slow.py\n", + "│ │ │ │ │ │ │ ├── test_loc.py\n", + "│ │ │ │ │ │ │ ├── test_multiindex.py\n", + "│ │ │ │ │ │ │ ├── test_partial.py\n", + "│ │ │ │ │ │ │ ├── test_setitem.py\n", + "│ │ │ │ │ │ │ ├── test_slice.py\n", + "│ │ │ │ │ │ │ └── test_sorted.py\n", + "│ │ │ │ │ │ ├── test_at.py\n", + "│ │ │ │ │ │ ├── test_categorical.py\n", + "│ │ │ │ │ │ ├── test_chaining_and_caching.py\n", + "│ │ │ │ │ │ ├── test_check_indexer.py\n", + "│ │ │ │ │ │ ├── test_coercion.py\n", + "│ │ │ │ │ │ ├── test_datetime.py\n", + "│ │ │ │ │ │ ├── test_floats.py\n", + "│ │ │ │ │ │ ├── test_iat.py\n", + "│ │ │ │ │ │ ├── test_iloc.py\n", + "│ │ │ │ │ │ ├── test_indexers.py\n", + "│ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ ├── test_loc.py\n", + "│ │ │ │ │ │ ├── test_na_indexing.py\n", + "│ │ │ │ │ │ ├── test_partial.py\n", + "│ │ │ │ │ │ └── test_scalar.py\n", + "│ │ │ │ │ ├── interchange\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_impl.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_spec_conformance.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_impl.py\n", + "│ │ │ │ │ │ ├── test_spec_conformance.py\n", + "│ │ │ │ │ │ └── test_utils.py\n", + "│ │ │ │ │ ├── internals\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_internals.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_managers.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_api.py\n", + "│ │ │ │ │ │ ├── test_internals.py\n", + "│ │ │ │ │ │ └── test_managers.py\n", + "│ │ │ │ │ ├── io\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── generate_legacy_storage_files.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_clipboard.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_compression.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_feather.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_fsspec.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_gbq.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_gcs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_html.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_http_headers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_orc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_parquet.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_pickle.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_s3.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_spss.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_sql.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_stata.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ ├── excel\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_odf.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_odswriter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_openpyxl.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_readers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_style.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_writers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_xlrd.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_xlsxwriter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_odf.py\n", + "│ │ │ │ │ │ │ ├── test_odswriter.py\n", + "│ │ │ │ │ │ │ ├── test_openpyxl.py\n", + "│ │ │ │ │ │ │ ├── test_readers.py\n", + "│ │ │ │ │ │ │ ├── test_style.py\n", + "│ │ │ │ │ │ │ ├── test_writers.py\n", + "│ │ │ │ │ │ │ ├── test_xlrd.py\n", + "│ │ │ │ │ │ │ └── test_xlsxwriter.py\n", + "│ │ │ │ │ │ ├── formats\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_console.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_css.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_eng_formatting.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_format.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_ipython_compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_printing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_csv.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_excel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_html.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_latex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_markdown.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_to_string.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── style\n", + "│ │ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_bar.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_exceptions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_format.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_highlight.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_html.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_matplotlib.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_non_unique.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_style.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_to_latex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_to_string.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ └── test_tooltip.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_bar.py\n", + "│ │ │ │ │ │ │ │ ├── test_exceptions.py\n", + "│ │ │ │ │ │ │ │ ├── test_format.py\n", + "│ │ │ │ │ │ │ │ ├── test_highlight.py\n", + "│ │ │ │ │ │ │ │ ├── test_html.py\n", + "│ │ │ │ │ │ │ │ ├── test_matplotlib.py\n", + "│ │ │ │ │ │ │ │ ├── test_non_unique.py\n", + "│ │ │ │ │ │ │ │ ├── test_style.py\n", + "│ │ │ │ │ │ │ │ ├── test_to_latex.py\n", + "│ │ │ │ │ │ │ │ ├── test_to_string.py\n", + "│ │ │ │ │ │ │ │ └── test_tooltip.py\n", + "│ │ │ │ │ │ │ ├── test_console.py\n", + "│ │ │ │ │ │ │ ├── test_css.py\n", + "│ │ │ │ │ │ │ ├── test_eng_formatting.py\n", + "│ │ │ │ │ │ │ ├── test_format.py\n", + "│ │ │ │ │ │ │ ├── test_ipython_compat.py\n", + "│ │ │ │ │ │ │ ├── test_printing.py\n", + "│ │ │ │ │ │ │ ├── test_to_csv.py\n", + "│ │ │ │ │ │ │ ├── test_to_excel.py\n", + "│ │ │ │ │ │ │ ├── test_to_html.py\n", + "│ │ │ │ │ │ │ ├── test_to_latex.py\n", + "│ │ │ │ │ │ │ ├── test_to_markdown.py\n", + "│ │ │ │ │ │ │ └── test_to_string.py\n", + "│ │ │ │ │ │ ├── generate_legacy_storage_files.py\n", + "│ │ │ │ │ │ ├── json\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_compression.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_deprecated_kwargs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_json_table_schema.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_json_table_schema_ext_dtype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_normalize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_pandas.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_readlines.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_ujson.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ │ ├── test_compression.py\n", + "│ │ │ │ │ │ │ ├── test_deprecated_kwargs.py\n", + "│ │ │ │ │ │ │ ├── test_json_table_schema.py\n", + "│ │ │ │ │ │ │ ├── test_json_table_schema_ext_dtype.py\n", + "│ │ │ │ │ │ │ ├── test_normalize.py\n", + "│ │ │ │ │ │ │ ├── test_pandas.py\n", + "│ │ │ │ │ │ │ ├── test_readlines.py\n", + "│ │ │ │ │ │ │ └── test_ujson.py\n", + "│ │ │ │ │ │ ├── parser\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_c_parser_only.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_comment.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_compression.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_concatenate_chunks.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_converters.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_dialect.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_encoding.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_header.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_index_col.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_mangle_dupes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_multi_thread.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_na_values.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_network.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_parse_dates.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_python_parser_only.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_quoting.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_read_fwf.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_skiprows.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_textreader.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_unsupported.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_upcast.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── common\n", + "│ │ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_chunksize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_common_basic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_data_list.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_decimal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_file_buffer_url.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_float.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_inf.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_ints.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_iterator.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_read_errors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ └── test_verbose.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_chunksize.py\n", + "│ │ │ │ │ │ │ │ ├── test_common_basic.py\n", + "│ │ │ │ │ │ │ │ ├── test_data_list.py\n", + "│ │ │ │ │ │ │ │ ├── test_decimal.py\n", + "│ │ │ │ │ │ │ │ ├── test_file_buffer_url.py\n", + "│ │ │ │ │ │ │ │ ├── test_float.py\n", + "│ │ │ │ │ │ │ │ ├── test_index.py\n", + "│ │ │ │ │ │ │ │ ├── test_inf.py\n", + "│ │ │ │ │ │ │ │ ├── test_ints.py\n", + "│ │ │ │ │ │ │ │ ├── test_iterator.py\n", + "│ │ │ │ │ │ │ │ ├── test_read_errors.py\n", + "│ │ │ │ │ │ │ │ └── test_verbose.py\n", + "│ │ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ │ ├── dtypes\n", + "│ │ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_categorical.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_dtypes_basic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ └── test_empty.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_categorical.py\n", + "│ │ │ │ │ │ │ │ ├── test_dtypes_basic.py\n", + "│ │ │ │ │ │ │ │ └── test_empty.py\n", + "│ │ │ │ │ │ │ ├── test_c_parser_only.py\n", + "│ │ │ │ │ │ │ ├── test_comment.py\n", + "│ │ │ │ │ │ │ ├── test_compression.py\n", + "│ │ │ │ │ │ │ ├── test_concatenate_chunks.py\n", + "│ │ │ │ │ │ │ ├── test_converters.py\n", + "│ │ │ │ │ │ │ ├── test_dialect.py\n", + "│ │ │ │ │ │ │ ├── test_encoding.py\n", + "│ │ │ │ │ │ │ ├── test_header.py\n", + "│ │ │ │ │ │ │ ├── test_index_col.py\n", + "│ │ │ │ │ │ │ ├── test_mangle_dupes.py\n", + "│ │ │ │ │ │ │ ├── test_multi_thread.py\n", + "│ │ │ │ │ │ │ ├── test_na_values.py\n", + "│ │ │ │ │ │ │ ├── test_network.py\n", + "│ │ │ │ │ │ │ ├── test_parse_dates.py\n", + "│ │ │ │ │ │ │ ├── test_python_parser_only.py\n", + "│ │ │ │ │ │ │ ├── test_quoting.py\n", + "│ │ │ │ │ │ │ ├── test_read_fwf.py\n", + "│ │ │ │ │ │ │ ├── test_skiprows.py\n", + "│ │ │ │ │ │ │ ├── test_textreader.py\n", + "│ │ │ │ │ │ │ ├── test_unsupported.py\n", + "│ │ │ │ │ │ │ ├── test_upcast.py\n", + "│ │ │ │ │ │ │ └── usecols\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_parse_dates.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_strings.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_usecols_basic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_parse_dates.py\n", + "│ │ │ │ │ │ │ ├── test_strings.py\n", + "│ │ │ │ │ │ │ └── test_usecols_basic.py\n", + "│ │ │ │ │ │ ├── pytables\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_append.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_categorical.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_complex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_errors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_file_handling.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_keys.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_put.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_pytables_missing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_read.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_retain_attributes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_round_trip.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_select.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_store.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_subclass.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_time_series.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_timezones.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ │ ├── test_append.py\n", + "│ │ │ │ │ │ │ ├── test_categorical.py\n", + "│ │ │ │ │ │ │ ├── test_compat.py\n", + "│ │ │ │ │ │ │ ├── test_complex.py\n", + "│ │ │ │ │ │ │ ├── test_errors.py\n", + "│ │ │ │ │ │ │ ├── test_file_handling.py\n", + "│ │ │ │ │ │ │ ├── test_keys.py\n", + "│ │ │ │ │ │ │ ├── test_put.py\n", + "│ │ │ │ │ │ │ ├── test_pytables_missing.py\n", + "│ │ │ │ │ │ │ ├── test_read.py\n", + "│ │ │ │ │ │ │ ├── test_retain_attributes.py\n", + "│ │ │ │ │ │ │ ├── test_round_trip.py\n", + "│ │ │ │ │ │ │ ├── test_select.py\n", + "│ │ │ │ │ │ │ ├── test_store.py\n", + "│ │ │ │ │ │ │ ├── test_subclass.py\n", + "│ │ │ │ │ │ │ ├── test_time_series.py\n", + "│ │ │ │ │ │ │ └── test_timezones.py\n", + "│ │ │ │ │ │ ├── sas\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_byteswap.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_sas.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_sas7bdat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_xport.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_byteswap.py\n", + "│ │ │ │ │ │ │ ├── test_sas.py\n", + "│ │ │ │ │ │ │ ├── test_sas7bdat.py\n", + "│ │ │ │ │ │ │ └── test_xport.py\n", + "│ │ │ │ │ │ ├── test_clipboard.py\n", + "│ │ │ │ │ │ ├── test_common.py\n", + "│ │ │ │ │ │ ├── test_compression.py\n", + "│ │ │ │ │ │ ├── test_feather.py\n", + "│ │ │ │ │ │ ├── test_fsspec.py\n", + "│ │ │ │ │ │ ├── test_gbq.py\n", + "│ │ │ │ │ │ ├── test_gcs.py\n", + "│ │ │ │ │ │ ├── test_html.py\n", + "│ │ │ │ │ │ ├── test_http_headers.py\n", + "│ │ │ │ │ │ ├── test_orc.py\n", + "│ │ │ │ │ │ ├── test_parquet.py\n", + "│ │ │ │ │ │ ├── test_pickle.py\n", + "│ │ │ │ │ │ ├── test_s3.py\n", + "│ │ │ │ │ │ ├── test_spss.py\n", + "│ │ │ │ │ │ ├── test_sql.py\n", + "│ │ │ │ │ │ ├── test_stata.py\n", + "│ │ │ │ │ │ └── xml\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_to_xml.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_xml.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_xml_dtypes.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ ├── test_to_xml.py\n", + "│ │ │ │ │ │ ├── test_xml.py\n", + "│ │ │ │ │ │ └── test_xml_dtypes.py\n", + "│ │ │ │ │ ├── libs\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_hashtable.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_join.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_lib.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_libalgos.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_hashtable.py\n", + "│ │ │ │ │ │ ├── test_join.py\n", + "│ │ │ │ │ │ ├── test_lib.py\n", + "│ │ │ │ │ │ └── test_libalgos.py\n", + "│ │ │ │ │ ├── plotting\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_backend.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_boxplot_method.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_converter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_datetimelike.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_groupby.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_hist_method.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_misc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_series.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_style.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ ├── frame\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_frame.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_frame_color.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_frame_groupby.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_frame_legend.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_frame_subplots.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_hist_box_by.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_frame.py\n", + "│ │ │ │ │ │ │ ├── test_frame_color.py\n", + "│ │ │ │ │ │ │ ├── test_frame_groupby.py\n", + "│ │ │ │ │ │ │ ├── test_frame_legend.py\n", + "│ │ │ │ │ │ │ ├── test_frame_subplots.py\n", + "│ │ │ │ │ │ │ └── test_hist_box_by.py\n", + "│ │ │ │ │ │ ├── test_backend.py\n", + "│ │ │ │ │ │ ├── test_boxplot_method.py\n", + "│ │ │ │ │ │ ├── test_common.py\n", + "│ │ │ │ │ │ ├── test_converter.py\n", + "│ │ │ │ │ │ ├── test_datetimelike.py\n", + "│ │ │ │ │ │ ├── test_groupby.py\n", + "│ │ │ │ │ │ ├── test_hist_method.py\n", + "│ │ │ │ │ │ ├── test_misc.py\n", + "│ │ │ │ │ │ ├── test_series.py\n", + "│ │ │ │ │ │ └── test_style.py\n", + "│ │ │ │ │ ├── reductions\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_reductions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_stat_reductions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_reductions.py\n", + "│ │ │ │ │ │ └── test_stat_reductions.py\n", + "│ │ │ │ │ ├── resample\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_datetime_index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_period_index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_resample_api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_resampler_grouper.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_time_grouper.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_timedelta.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ ├── test_base.py\n", + "│ │ │ │ │ │ ├── test_datetime_index.py\n", + "│ │ │ │ │ │ ├── test_period_index.py\n", + "│ │ │ │ │ │ ├── test_resample_api.py\n", + "│ │ │ │ │ │ ├── test_resampler_grouper.py\n", + "│ │ │ │ │ │ ├── test_time_grouper.py\n", + "│ │ │ │ │ │ └── test_timedelta.py\n", + "│ │ │ │ │ ├── reshape\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_crosstab.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_cut.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_from_dummies.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_get_dummies.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_melt.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_pivot.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_pivot_multilevel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_qcut.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_union_categoricals.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_util.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── concat\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_append.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_append_common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_categorical.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_concat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_dataframe.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_datetimes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_empty.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_invalid.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_series.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_sort.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ │ ├── test_append.py\n", + "│ │ │ │ │ │ │ ├── test_append_common.py\n", + "│ │ │ │ │ │ │ ├── test_categorical.py\n", + "│ │ │ │ │ │ │ ├── test_concat.py\n", + "│ │ │ │ │ │ │ ├── test_dataframe.py\n", + "│ │ │ │ │ │ │ ├── test_datetimes.py\n", + "│ │ │ │ │ │ │ ├── test_empty.py\n", + "│ │ │ │ │ │ │ ├── test_index.py\n", + "│ │ │ │ │ │ │ ├── test_invalid.py\n", + "│ │ │ │ │ │ │ ├── test_series.py\n", + "│ │ │ │ │ │ │ └── test_sort.py\n", + "│ │ │ │ │ │ ├── merge\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_join.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_merge.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_merge_asof.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_merge_cross.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_merge_index_as_string.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_merge_ordered.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_multi.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_join.py\n", + "│ │ │ │ │ │ │ ├── test_merge.py\n", + "│ │ │ │ │ │ │ ├── test_merge_asof.py\n", + "│ │ │ │ │ │ │ ├── test_merge_cross.py\n", + "│ │ │ │ │ │ │ ├── test_merge_index_as_string.py\n", + "│ │ │ │ │ │ │ ├── test_merge_ordered.py\n", + "│ │ │ │ │ │ │ └── test_multi.py\n", + "│ │ │ │ │ │ ├── test_crosstab.py\n", + "│ │ │ │ │ │ ├── test_cut.py\n", + "│ │ │ │ │ │ ├── test_from_dummies.py\n", + "│ │ │ │ │ │ ├── test_get_dummies.py\n", + "│ │ │ │ │ │ ├── test_melt.py\n", + "│ │ │ │ │ │ ├── test_pivot.py\n", + "│ │ │ │ │ │ ├── test_pivot_multilevel.py\n", + "│ │ │ │ │ │ ├── test_qcut.py\n", + "│ │ │ │ │ │ ├── test_union_categoricals.py\n", + "│ │ │ │ │ │ └── test_util.py\n", + "│ │ │ │ │ ├── scalar\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_na_scalar.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_nat.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── interval\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_arithmetic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_contains.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_formats.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_interval.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_overlaps.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arithmetic.py\n", + "│ │ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ │ ├── test_contains.py\n", + "│ │ │ │ │ │ │ ├── test_formats.py\n", + "│ │ │ │ │ │ │ ├── test_interval.py\n", + "│ │ │ │ │ │ │ └── test_overlaps.py\n", + "│ │ │ │ │ │ ├── period\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_arithmetic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_asfreq.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_period.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arithmetic.py\n", + "│ │ │ │ │ │ │ ├── test_asfreq.py\n", + "│ │ │ │ │ │ │ └── test_period.py\n", + "│ │ │ │ │ │ ├── test_na_scalar.py\n", + "│ │ │ │ │ │ ├── test_nat.py\n", + "│ │ │ │ │ │ ├── timedelta\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_arithmetic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_formats.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_timedelta.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── methods\n", + "│ │ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_as_unit.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ └── test_round.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_as_unit.py\n", + "│ │ │ │ │ │ │ │ └── test_round.py\n", + "│ │ │ │ │ │ │ ├── test_arithmetic.py\n", + "│ │ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ │ ├── test_formats.py\n", + "│ │ │ │ │ │ │ └── test_timedelta.py\n", + "│ │ │ │ │ │ └── timestamp\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arithmetic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_comparisons.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_formats.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_timestamp.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_timezones.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── methods\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_as_unit.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_normalize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_replace.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_round.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_timestamp_method.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_julian_date.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_pydatetime.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_tz_convert.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_tz_localize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_as_unit.py\n", + "│ │ │ │ │ │ │ ├── test_normalize.py\n", + "│ │ │ │ │ │ │ ├── test_replace.py\n", + "│ │ │ │ │ │ │ ├── test_round.py\n", + "│ │ │ │ │ │ │ ├── test_timestamp_method.py\n", + "│ │ │ │ │ │ │ ├── test_to_julian_date.py\n", + "│ │ │ │ │ │ │ ├── test_to_pydatetime.py\n", + "│ │ │ │ │ │ │ ├── test_tz_convert.py\n", + "│ │ │ │ │ │ │ └── test_tz_localize.py\n", + "│ │ │ │ │ │ ├── test_arithmetic.py\n", + "│ │ │ │ │ │ ├── test_comparisons.py\n", + "│ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ ├── test_formats.py\n", + "│ │ │ │ │ │ ├── test_timestamp.py\n", + "│ │ │ │ │ │ └── test_timezones.py\n", + "│ │ │ │ │ ├── series\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arithmetic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_cumulative.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_formats.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_iteration.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_logical_ops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_missing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_npfuncs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_reductions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_subclass.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_ufunc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_unary.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_validate.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── accessors\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_cat_accessor.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_dt_accessor.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_list_accessor.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_sparse_accessor.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_str_accessor.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_struct_accessor.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_cat_accessor.py\n", + "│ │ │ │ │ │ │ ├── test_dt_accessor.py\n", + "│ │ │ │ │ │ │ ├── test_list_accessor.py\n", + "│ │ │ │ │ │ │ ├── test_sparse_accessor.py\n", + "│ │ │ │ │ │ │ ├── test_str_accessor.py\n", + "│ │ │ │ │ │ │ └── test_struct_accessor.py\n", + "│ │ │ │ │ │ ├── indexing\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_datetime.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_delitem.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_get.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_getitem.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_mask.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_set_value.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_setitem.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_take.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_where.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_xs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_datetime.py\n", + "│ │ │ │ │ │ │ ├── test_delitem.py\n", + "│ │ │ │ │ │ │ ├── test_get.py\n", + "│ │ │ │ │ │ │ ├── test_getitem.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_mask.py\n", + "│ │ │ │ │ │ │ ├── test_set_value.py\n", + "│ │ │ │ │ │ │ ├── test_setitem.py\n", + "│ │ │ │ │ │ │ ├── test_take.py\n", + "│ │ │ │ │ │ │ ├── test_where.py\n", + "│ │ │ │ │ │ │ └── test_xs.py\n", + "│ │ │ │ │ │ ├── methods\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_add_prefix_suffix.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_align.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_argsort.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_asof.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_autocorr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_between.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_case_when.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_clip.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_combine.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_combine_first.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_compare.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_convert_dtypes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_copy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_count.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_cov_corr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_describe.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_diff.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_drop.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_drop_duplicates.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_dropna.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_dtypes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_duplicated.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_equals.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_explode.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_fillna.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_get_numeric_data.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_head_tail.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_infer_objects.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_info.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_interpolate.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_is_monotonic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_is_unique.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_isin.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_isna.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_item.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_map.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_matmul.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_nlargest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_nunique.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_pct_change.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_pop.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_quantile.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_rank.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reindex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reindex_like.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_rename.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_rename_axis.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_repeat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_replace.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reset_index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_round.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_searchsorted.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_set_name.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_size.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_sort_index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_sort_values.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_csv.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_dict.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_frame.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_numpy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_tolist.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_truncate.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_tz_localize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_unique.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_unstack.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_update.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_value_counts.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_values.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_view.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_add_prefix_suffix.py\n", + "│ │ │ │ │ │ │ ├── test_align.py\n", + "│ │ │ │ │ │ │ ├── test_argsort.py\n", + "│ │ │ │ │ │ │ ├── test_asof.py\n", + "│ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ ├── test_autocorr.py\n", + "│ │ │ │ │ │ │ ├── test_between.py\n", + "│ │ │ │ │ │ │ ├── test_case_when.py\n", + "│ │ │ │ │ │ │ ├── test_clip.py\n", + "│ │ │ │ │ │ │ ├── test_combine.py\n", + "│ │ │ │ │ │ │ ├── test_combine_first.py\n", + "│ │ │ │ │ │ │ ├── test_compare.py\n", + "│ │ │ │ │ │ │ ├── test_convert_dtypes.py\n", + "│ │ │ │ │ │ │ ├── test_copy.py\n", + "│ │ │ │ │ │ │ ├── test_count.py\n", + "│ │ │ │ │ │ │ ├── test_cov_corr.py\n", + "│ │ │ │ │ │ │ ├── test_describe.py\n", + "│ │ │ │ │ │ │ ├── test_diff.py\n", + "│ │ │ │ │ │ │ ├── test_drop.py\n", + "│ │ │ │ │ │ │ ├── test_drop_duplicates.py\n", + "│ │ │ │ │ │ │ ├── test_dropna.py\n", + "│ │ │ │ │ │ │ ├── test_dtypes.py\n", + "│ │ │ │ │ │ │ ├── test_duplicated.py\n", + "│ │ │ │ │ │ │ ├── test_equals.py\n", + "│ │ │ │ │ │ │ ├── test_explode.py\n", + "│ │ │ │ │ │ │ ├── test_fillna.py\n", + "│ │ │ │ │ │ │ ├── test_get_numeric_data.py\n", + "│ │ │ │ │ │ │ ├── test_head_tail.py\n", + "│ │ │ │ │ │ │ ├── test_infer_objects.py\n", + "│ │ │ │ │ │ │ ├── test_info.py\n", + "│ │ │ │ │ │ │ ├── test_interpolate.py\n", + "│ │ │ │ │ │ │ ├── test_is_monotonic.py\n", + "│ │ │ │ │ │ │ ├── test_is_unique.py\n", + "│ │ │ │ │ │ │ ├── test_isin.py\n", + "│ │ │ │ │ │ │ ├── test_isna.py\n", + "│ │ │ │ │ │ │ ├── test_item.py\n", + "│ │ │ │ │ │ │ ├── test_map.py\n", + "│ │ │ │ │ │ │ ├── test_matmul.py\n", + "│ │ │ │ │ │ │ ├── test_nlargest.py\n", + "│ │ │ │ │ │ │ ├── test_nunique.py\n", + "│ │ │ │ │ │ │ ├── test_pct_change.py\n", + "│ │ │ │ │ │ │ ├── test_pop.py\n", + "│ │ │ │ │ │ │ ├── test_quantile.py\n", + "│ │ │ │ │ │ │ ├── test_rank.py\n", + "│ │ │ │ │ │ │ ├── test_reindex.py\n", + "│ │ │ │ │ │ │ ├── test_reindex_like.py\n", + "│ │ │ │ │ │ │ ├── test_rename.py\n", + "│ │ │ │ │ │ │ ├── test_rename_axis.py\n", + "│ │ │ │ │ │ │ ├── test_repeat.py\n", + "│ │ │ │ │ │ │ ├── test_replace.py\n", + "│ │ │ │ │ │ │ ├── test_reset_index.py\n", + "│ │ │ │ │ │ │ ├── test_round.py\n", + "│ │ │ │ │ │ │ ├── test_searchsorted.py\n", + "│ │ │ │ │ │ │ ├── test_set_name.py\n", + "│ │ │ │ │ │ │ ├── test_size.py\n", + "│ │ │ │ │ │ │ ├── test_sort_index.py\n", + "│ │ │ │ │ │ │ ├── test_sort_values.py\n", + "│ │ │ │ │ │ │ ├── test_to_csv.py\n", + "│ │ │ │ │ │ │ ├── test_to_dict.py\n", + "│ │ │ │ │ │ │ ├── test_to_frame.py\n", + "│ │ │ │ │ │ │ ├── test_to_numpy.py\n", + "│ │ │ │ │ │ │ ├── test_tolist.py\n", + "│ │ │ │ │ │ │ ├── test_truncate.py\n", + "│ │ │ │ │ │ │ ├── test_tz_localize.py\n", + "│ │ │ │ │ │ │ ├── test_unique.py\n", + "│ │ │ │ │ │ │ ├── test_unstack.py\n", + "│ │ │ │ │ │ │ ├── test_update.py\n", + "│ │ │ │ │ │ │ ├── test_value_counts.py\n", + "│ │ │ │ │ │ │ ├── test_values.py\n", + "│ │ │ │ │ │ │ └── test_view.py\n", + "│ │ │ │ │ │ ├── test_api.py\n", + "│ │ │ │ │ │ ├── test_arithmetic.py\n", + "│ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ ├── test_cumulative.py\n", + "│ │ │ │ │ │ ├── test_formats.py\n", + "│ │ │ │ │ │ ├── test_iteration.py\n", + "│ │ │ │ │ │ ├── test_logical_ops.py\n", + "│ │ │ │ │ │ ├── test_missing.py\n", + "│ │ │ │ │ │ ├── test_npfuncs.py\n", + "│ │ │ │ │ │ ├── test_reductions.py\n", + "│ │ │ │ │ │ ├── test_subclass.py\n", + "│ │ │ │ │ │ ├── test_ufunc.py\n", + "│ │ │ │ │ │ ├── test_unary.py\n", + "│ │ │ │ │ │ └── test_validate.py\n", + "│ │ │ │ │ ├── strings\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_case_justify.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_cat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_extract.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_find_replace.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_get_dummies.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_split_partition.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_string_array.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_strings.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ ├── test_api.py\n", + "│ │ │ │ │ │ ├── test_case_justify.py\n", + "│ │ │ │ │ │ ├── test_cat.py\n", + "│ │ │ │ │ │ ├── test_extract.py\n", + "│ │ │ │ │ │ ├── test_find_replace.py\n", + "│ │ │ │ │ │ ├── test_get_dummies.py\n", + "│ │ │ │ │ │ ├── test_split_partition.py\n", + "│ │ │ │ │ │ ├── test_string_array.py\n", + "│ │ │ │ │ │ └── test_strings.py\n", + "│ │ │ │ │ ├── test_aggregation.py\n", + "│ │ │ │ │ ├── test_algos.py\n", + "│ │ │ │ │ ├── test_common.py\n", + "│ │ │ │ │ ├── test_downstream.py\n", + "│ │ │ │ │ ├── test_errors.py\n", + "│ │ │ │ │ ├── test_expressions.py\n", + "│ │ │ │ │ ├── test_flags.py\n", + "│ │ │ │ │ ├── test_multilevel.py\n", + "│ │ │ │ │ ├── test_nanops.py\n", + "│ │ │ │ │ ├── test_optional_dependency.py\n", + "│ │ │ │ │ ├── test_register_accessor.py\n", + "│ │ │ │ │ ├── test_sorting.py\n", + "│ │ │ │ │ ├── test_take.py\n", + "│ │ │ │ │ ├── tools\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_to_datetime.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_to_numeric.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_to_time.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_to_timedelta.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_to_datetime.py\n", + "│ │ │ │ │ │ ├── test_to_numeric.py\n", + "│ │ │ │ │ │ ├── test_to_time.py\n", + "│ │ │ │ │ │ └── test_to_timedelta.py\n", + "│ │ │ │ │ ├── tseries\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── frequencies\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_freq_code.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_frequencies.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_inference.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_freq_code.py\n", + "│ │ │ │ │ │ │ ├── test_frequencies.py\n", + "│ │ │ │ │ │ │ └── test_inference.py\n", + "│ │ │ │ │ │ ├── holiday\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_calendar.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_federal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_holiday.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_observance.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_calendar.py\n", + "│ │ │ │ │ │ │ ├── test_federal.py\n", + "│ │ │ │ │ │ │ ├── test_holiday.py\n", + "│ │ │ │ │ │ │ └── test_observance.py\n", + "│ │ │ │ │ │ └── offsets\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_business_day.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_business_hour.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_business_month.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_business_quarter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_business_year.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_custom_business_day.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_custom_business_hour.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_custom_business_month.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_dst.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_easter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_fiscal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_month.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_offsets.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_offsets_properties.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_quarter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_ticks.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_week.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_year.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ │ ├── test_business_day.py\n", + "│ │ │ │ │ │ ├── test_business_hour.py\n", + "│ │ │ │ │ │ ├── test_business_month.py\n", + "│ │ │ │ │ │ ├── test_business_quarter.py\n", + "│ │ │ │ │ │ ├── test_business_year.py\n", + "│ │ │ │ │ │ ├── test_common.py\n", + "│ │ │ │ │ │ ├── test_custom_business_day.py\n", + "│ │ │ │ │ │ ├── test_custom_business_hour.py\n", + "│ │ │ │ │ │ ├── test_custom_business_month.py\n", + "│ │ │ │ │ │ ├── test_dst.py\n", + "│ │ │ │ │ │ ├── test_easter.py\n", + "│ │ │ │ │ │ ├── test_fiscal.py\n", + "│ │ │ │ │ │ ├── test_index.py\n", + "│ │ │ │ │ │ ├── test_month.py\n", + "│ │ │ │ │ │ ├── test_offsets.py\n", + "│ │ │ │ │ │ ├── test_offsets_properties.py\n", + "│ │ │ │ │ │ ├── test_quarter.py\n", + "│ │ │ │ │ │ ├── test_ticks.py\n", + "│ │ │ │ │ │ ├── test_week.py\n", + "│ │ │ │ │ │ └── test_year.py\n", + "│ │ │ │ │ ├── tslibs\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_array_to_datetime.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_ccalendar.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_conversion.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_fields.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_libfrequencies.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_liboffsets.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_np_datetime.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_npy_units.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_parse_iso8601.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_parsing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_period.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_resolution.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_strptime.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_timedeltas.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_timezones.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_to_offset.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_tzconversion.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_api.py\n", + "│ │ │ │ │ │ ├── test_array_to_datetime.py\n", + "│ │ │ │ │ │ ├── test_ccalendar.py\n", + "│ │ │ │ │ │ ├── test_conversion.py\n", + "│ │ │ │ │ │ ├── test_fields.py\n", + "│ │ │ │ │ │ ├── test_libfrequencies.py\n", + "│ │ │ │ │ │ ├── test_liboffsets.py\n", + "│ │ │ │ │ │ ├── test_np_datetime.py\n", + "│ │ │ │ │ │ ├── test_npy_units.py\n", + "│ │ │ │ │ │ ├── test_parse_iso8601.py\n", + "│ │ │ │ │ │ ├── test_parsing.py\n", + "│ │ │ │ │ │ ├── test_period.py\n", + "│ │ │ │ │ │ ├── test_resolution.py\n", + "│ │ │ │ │ │ ├── test_strptime.py\n", + "│ │ │ │ │ │ ├── test_timedeltas.py\n", + "│ │ │ │ │ │ ├── test_timezones.py\n", + "│ │ │ │ │ │ ├── test_to_offset.py\n", + "│ │ │ │ │ │ └── test_tzconversion.py\n", + "│ │ │ │ │ ├── util\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_assert_almost_equal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_assert_attr_equal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_assert_categorical_equal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_assert_extension_array_equal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_assert_frame_equal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_assert_index_equal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_assert_interval_array_equal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_assert_numpy_array_equal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_assert_produces_warning.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_assert_series_equal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_deprecate.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_deprecate_kwarg.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_deprecate_nonkeyword_arguments.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_doc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_hashing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_numba.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_rewrite_warning.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_shares_memory.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_show_versions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_util.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_validate_args.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_validate_args_and_kwargs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_validate_inclusive.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_validate_kwargs.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ ├── test_assert_almost_equal.py\n", + "│ │ │ │ │ │ ├── test_assert_attr_equal.py\n", + "│ │ │ │ │ │ ├── test_assert_categorical_equal.py\n", + "│ │ │ │ │ │ ├── test_assert_extension_array_equal.py\n", + "│ │ │ │ │ │ ├── test_assert_frame_equal.py\n", + "│ │ │ │ │ │ ├── test_assert_index_equal.py\n", + "│ │ │ │ │ │ ├── test_assert_interval_array_equal.py\n", + "│ │ │ │ │ │ ├── test_assert_numpy_array_equal.py\n", + "│ │ │ │ │ │ ├── test_assert_produces_warning.py\n", + "│ │ │ │ │ │ ├── test_assert_series_equal.py\n", + "│ │ │ │ │ │ ├── test_deprecate.py\n", + "│ │ │ │ │ │ ├── test_deprecate_kwarg.py\n", + "│ │ │ │ │ │ ├── test_deprecate_nonkeyword_arguments.py\n", + "│ │ │ │ │ │ ├── test_doc.py\n", + "│ │ │ │ │ │ ├── test_hashing.py\n", + "│ │ │ │ │ │ ├── test_numba.py\n", + "│ │ │ │ │ │ ├── test_rewrite_warning.py\n", + "│ │ │ │ │ │ ├── test_shares_memory.py\n", + "│ │ │ │ │ │ ├── test_show_versions.py\n", + "│ │ │ │ │ │ ├── test_util.py\n", + "│ │ │ │ │ │ ├── test_validate_args.py\n", + "│ │ │ │ │ │ ├── test_validate_args_and_kwargs.py\n", + "│ │ │ │ │ │ ├── test_validate_inclusive.py\n", + "│ │ │ │ │ │ └── test_validate_kwargs.py\n", + "│ │ │ │ │ └── window\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_api.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_apply.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_base_indexer.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_cython_aggregations.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_dtypes.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_ewm.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_expanding.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_groupby.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_numba.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_online.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_pairwise.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_rolling.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_rolling_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_rolling_quantile.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_rolling_skew_kurt.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_timeseries_window.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_win_type.cpython-310.pyc\n", + "│ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ ├── moments\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_moments_consistency_ewm.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_moments_consistency_expanding.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_moments_consistency_rolling.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ ├── test_moments_consistency_ewm.py\n", + "│ │ │ │ │ │ ├── test_moments_consistency_expanding.py\n", + "│ │ │ │ │ │ └── test_moments_consistency_rolling.py\n", + "│ │ │ │ │ ├── test_api.py\n", + "│ │ │ │ │ ├── test_apply.py\n", + "│ │ │ │ │ ├── test_base_indexer.py\n", + "│ │ │ │ │ ├── test_cython_aggregations.py\n", + "│ │ │ │ │ ├── test_dtypes.py\n", + "│ │ │ │ │ ├── test_ewm.py\n", + "│ │ │ │ │ ├── test_expanding.py\n", + "│ │ │ │ │ ├── test_groupby.py\n", + "│ │ │ │ │ ├── test_numba.py\n", + "│ │ │ │ │ ├── test_online.py\n", + "│ │ │ │ │ ├── test_pairwise.py\n", + "│ │ │ │ │ ├── test_rolling.py\n", + "│ │ │ │ │ ├── test_rolling_functions.py\n", + "│ │ │ │ │ ├── test_rolling_quantile.py\n", + "│ │ │ │ │ ├── test_rolling_skew_kurt.py\n", + "│ │ │ │ │ ├── test_timeseries_window.py\n", + "│ │ │ │ │ └── test_win_type.py\n", + "│ │ │ │ ├── tseries\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── api.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── frequencies.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── holiday.cpython-310.pyc\n", + "│ │ │ │ │ │ └── offsets.cpython-310.pyc\n", + "│ │ │ │ │ ├── api.py\n", + "│ │ │ │ │ ├── frequencies.py\n", + "│ │ │ │ │ ├── holiday.py\n", + "│ │ │ │ │ └── offsets.py\n", + "│ │ │ │ └── util\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _decorators.cpython-310.pyc\n", + "│ │ │ │ │ ├── _doctools.cpython-310.pyc\n", + "│ │ │ │ │ ├── _exceptions.cpython-310.pyc\n", + "│ │ │ │ │ ├── _print_versions.cpython-310.pyc\n", + "│ │ │ │ │ ├── _test_decorators.cpython-310.pyc\n", + "│ │ │ │ │ ├── _tester.cpython-310.pyc\n", + "│ │ │ │ │ └── _validators.cpython-310.pyc\n", + "│ │ │ │ ├── _decorators.py\n", + "│ │ │ │ ├── _doctools.py\n", + "│ │ │ │ ├── _exceptions.py\n", + "│ │ │ │ ├── _print_versions.py\n", + "│ │ │ │ ├── _test_decorators.py\n", + "│ │ │ │ ├── _tester.py\n", + "│ │ │ │ ├── _validators.py\n", + "│ │ │ │ └── version\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ └── __pycache__\n", + "│ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ ├── pandas-2.2.2.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── REQUESTED\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── entry_points.txt\n", + "│ │ │ ├── parso\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _compatibility.cpython-310.pyc\n", + "│ │ │ │ │ ├── cache.cpython-310.pyc\n", + "│ │ │ │ │ ├── file_io.cpython-310.pyc\n", + "│ │ │ │ │ ├── grammar.cpython-310.pyc\n", + "│ │ │ │ │ ├── normalizer.cpython-310.pyc\n", + "│ │ │ │ │ ├── parser.cpython-310.pyc\n", + "│ │ │ │ │ ├── tree.cpython-310.pyc\n", + "│ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ ├── _compatibility.py\n", + "│ │ │ │ ├── cache.py\n", + "│ │ │ │ ├── file_io.py\n", + "│ │ │ │ ├── grammar.py\n", + "│ │ │ │ ├── normalizer.py\n", + "│ │ │ │ ├── parser.py\n", + "│ │ │ │ ├── pgen2\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── generator.cpython-310.pyc\n", + "│ │ │ │ │ │ └── grammar_parser.cpython-310.pyc\n", + "│ │ │ │ │ ├── generator.py\n", + "│ │ │ │ │ └── grammar_parser.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ ├── python\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── diff.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── errors.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── parser.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pep8.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── prefix.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── token.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── tokenize.cpython-310.pyc\n", + "│ │ │ │ │ │ └── tree.cpython-310.pyc\n", + "│ │ │ │ │ ├── diff.py\n", + "│ │ │ │ │ ├── errors.py\n", + "│ │ │ │ │ ├── grammar310.txt\n", + "│ │ │ │ │ ├── grammar311.txt\n", + "│ │ │ │ │ ├── grammar312.txt\n", + "│ │ │ │ │ ├── grammar313.txt\n", + "│ │ │ │ │ ├── grammar36.txt\n", + "│ │ │ │ │ ├── grammar37.txt\n", + "│ │ │ │ │ ├── grammar38.txt\n", + "│ │ │ │ │ ├── grammar39.txt\n", + "│ │ │ │ │ ├── parser.py\n", + "│ │ │ │ │ ├── pep8.py\n", + "│ │ │ │ │ ├── prefix.py\n", + "│ │ │ │ │ ├── token.py\n", + "│ │ │ │ │ ├── tokenize.py\n", + "│ │ │ │ │ └── tree.py\n", + "│ │ │ │ ├── tree.py\n", + "│ │ │ │ └── utils.py\n", + "│ │ │ ├── parso-0.8.4.dist-info\n", + "│ │ │ │ ├── AUTHORS.txt\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE.txt\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── pexpect\n", + "│ │ │ │ ├── ANSI.py\n", + "│ │ │ │ ├── FSM.py\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── ANSI.cpython-310.pyc\n", + "│ │ │ │ │ ├── FSM.cpython-310.pyc\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _async.cpython-310.pyc\n", + "│ │ │ │ │ ├── _async_pre_await.cpython-310.pyc\n", + "│ │ │ │ │ ├── _async_w_await.cpython-310.pyc\n", + "│ │ │ │ │ ├── exceptions.cpython-310.pyc\n", + "│ │ │ │ │ ├── expect.cpython-310.pyc\n", + "│ │ │ │ │ ├── fdpexpect.cpython-310.pyc\n", + "│ │ │ │ │ ├── popen_spawn.cpython-310.pyc\n", + "│ │ │ │ │ ├── pty_spawn.cpython-310.pyc\n", + "│ │ │ │ │ ├── pxssh.cpython-310.pyc\n", + "│ │ │ │ │ ├── replwrap.cpython-310.pyc\n", + "│ │ │ │ │ ├── run.cpython-310.pyc\n", + "│ │ │ │ │ ├── screen.cpython-310.pyc\n", + "│ │ │ │ │ ├── socket_pexpect.cpython-310.pyc\n", + "│ │ │ │ │ ├── spawnbase.cpython-310.pyc\n", + "│ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ ├── _async.py\n", + "│ │ │ │ ├── _async_pre_await.py\n", + "│ │ │ │ ├── _async_w_await.py\n", + "│ │ │ │ ├── bashrc.sh\n", + "│ │ │ │ ├── exceptions.py\n", + "│ │ │ │ ├── expect.py\n", + "│ │ │ │ ├── fdpexpect.py\n", + "│ │ │ │ ├── popen_spawn.py\n", + "│ │ │ │ ├── pty_spawn.py\n", + "│ │ │ │ ├── pxssh.py\n", + "│ │ │ │ ├── replwrap.py\n", + "│ │ │ │ ├── run.py\n", + "│ │ │ │ ├── screen.py\n", + "│ │ │ │ ├── socket_pexpect.py\n", + "│ │ │ │ ├── spawnbase.py\n", + "│ │ │ │ └── utils.py\n", + "│ │ │ ├── pexpect-4.9.0.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── pip\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __main__.py\n", + "│ │ │ │ ├── __pip-runner__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ └── __pip-runner__.cpython-310.pyc\n", + "│ │ │ │ ├── _internal\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── build_env.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cache.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── configuration.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── exceptions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── main.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pyproject.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── self_outdated_check.cpython-310.pyc\n", + "│ │ │ │ │ │ └── wheel_builder.cpython-310.pyc\n", + "│ │ │ │ │ ├── build_env.py\n", + "│ │ │ │ │ ├── cache.py\n", + "│ │ │ │ │ ├── cli\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── autocompletion.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── base_command.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── cmdoptions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── command_context.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── main.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── main_parser.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── parser.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── progress_bars.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── req_command.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── spinners.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── status_codes.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── autocompletion.py\n", + "│ │ │ │ │ │ ├── base_command.py\n", + "│ │ │ │ │ │ ├── cmdoptions.py\n", + "│ │ │ │ │ │ ├── command_context.py\n", + "│ │ │ │ │ │ ├── main.py\n", + "│ │ │ │ │ │ ├── main_parser.py\n", + "│ │ │ │ │ │ ├── parser.py\n", + "│ │ │ │ │ │ ├── progress_bars.py\n", + "│ │ │ │ │ │ ├── req_command.py\n", + "│ │ │ │ │ │ ├── spinners.py\n", + "│ │ │ │ │ │ └── status_codes.py\n", + "│ │ │ │ │ ├── commands\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── cache.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── check.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── completion.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── configuration.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── debug.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── download.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── freeze.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── hash.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── help.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── inspect.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── install.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── list.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── search.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── show.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── uninstall.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── wheel.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cache.py\n", + "│ │ │ │ │ │ ├── check.py\n", + "│ │ │ │ │ │ ├── completion.py\n", + "│ │ │ │ │ │ ├── configuration.py\n", + "│ │ │ │ │ │ ├── debug.py\n", + "│ │ │ │ │ │ ├── download.py\n", + "│ │ │ │ │ │ ├── freeze.py\n", + "│ │ │ │ │ │ ├── hash.py\n", + "│ │ │ │ │ │ ├── help.py\n", + "│ │ │ │ │ │ ├── index.py\n", + "│ │ │ │ │ │ ├── inspect.py\n", + "│ │ │ │ │ │ ├── install.py\n", + "│ │ │ │ │ │ ├── list.py\n", + "│ │ │ │ │ │ ├── search.py\n", + "│ │ │ │ │ │ ├── show.py\n", + "│ │ │ │ │ │ ├── uninstall.py\n", + "│ │ │ │ │ │ └── wheel.py\n", + "│ │ │ │ │ ├── configuration.py\n", + "│ │ │ │ │ ├── distributions\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── installed.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── sdist.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── wheel.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ │ ├── installed.py\n", + "│ │ │ │ │ │ ├── sdist.py\n", + "│ │ │ │ │ │ └── wheel.py\n", + "│ │ │ │ │ ├── exceptions.py\n", + "│ │ │ │ │ ├── index\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── collector.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── package_finder.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── sources.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── collector.py\n", + "│ │ │ │ │ │ ├── package_finder.py\n", + "│ │ │ │ │ │ └── sources.py\n", + "│ │ │ │ │ ├── locations\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _distutils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _sysconfig.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _distutils.py\n", + "│ │ │ │ │ │ ├── _sysconfig.py\n", + "│ │ │ │ │ │ └── base.py\n", + "│ │ │ │ │ ├── main.py\n", + "│ │ │ │ │ ├── metadata\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _json.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── pkg_resources.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _json.py\n", + "│ │ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ │ ├── importlib\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── _compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── _dists.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── _envs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _compat.py\n", + "│ │ │ │ │ │ │ ├── _dists.py\n", + "│ │ │ │ │ │ │ └── _envs.py\n", + "│ │ │ │ │ │ └── pkg_resources.py\n", + "│ │ │ │ │ ├── models\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── candidate.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── direct_url.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── format_control.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── installation_report.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── link.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── scheme.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── search_scope.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── selection_prefs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── target_python.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── wheel.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── candidate.py\n", + "│ │ │ │ │ │ ├── direct_url.py\n", + "│ │ │ │ │ │ ├── format_control.py\n", + "│ │ │ │ │ │ ├── index.py\n", + "│ │ │ │ │ │ ├── installation_report.py\n", + "│ │ │ │ │ │ ├── link.py\n", + "│ │ │ │ │ │ ├── scheme.py\n", + "│ │ │ │ │ │ ├── search_scope.py\n", + "│ │ │ │ │ │ ├── selection_prefs.py\n", + "│ │ │ │ │ │ ├── target_python.py\n", + "│ │ │ │ │ │ └── wheel.py\n", + "│ │ │ │ │ ├── network\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── auth.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── cache.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── download.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── lazy_wheel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── session.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── xmlrpc.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── auth.py\n", + "│ │ │ │ │ │ ├── cache.py\n", + "│ │ │ │ │ │ ├── download.py\n", + "│ │ │ │ │ │ ├── lazy_wheel.py\n", + "│ │ │ │ │ │ ├── session.py\n", + "│ │ │ │ │ │ ├── utils.py\n", + "│ │ │ │ │ │ └── xmlrpc.py\n", + "│ │ │ │ │ ├── operations\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── check.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── freeze.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── prepare.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── build\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── build_tracker.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── metadata.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── metadata_editable.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── metadata_legacy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── wheel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── wheel_editable.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── wheel_legacy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── build_tracker.py\n", + "│ │ │ │ │ │ │ ├── metadata.py\n", + "│ │ │ │ │ │ │ ├── metadata_editable.py\n", + "│ │ │ │ │ │ │ ├── metadata_legacy.py\n", + "│ │ │ │ │ │ │ ├── wheel.py\n", + "│ │ │ │ │ │ │ ├── wheel_editable.py\n", + "│ │ │ │ │ │ │ └── wheel_legacy.py\n", + "│ │ │ │ │ │ ├── check.py\n", + "│ │ │ │ │ │ ├── freeze.py\n", + "│ │ │ │ │ │ ├── install\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── editable_legacy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── wheel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── editable_legacy.py\n", + "│ │ │ │ │ │ │ └── wheel.py\n", + "│ │ │ │ │ │ └── prepare.py\n", + "│ │ │ │ │ ├── pyproject.py\n", + "│ │ │ │ │ ├── req\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── req_file.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── req_install.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── req_set.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── req_uninstall.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── constructors.py\n", + "│ │ │ │ │ │ ├── req_file.py\n", + "│ │ │ │ │ │ ├── req_install.py\n", + "│ │ │ │ │ │ ├── req_set.py\n", + "│ │ │ │ │ │ └── req_uninstall.py\n", + "│ │ │ │ │ ├── resolution\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ │ ├── legacy\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── resolver.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── resolver.py\n", + "│ │ │ │ │ │ └── resolvelib\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── candidates.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── factory.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── found_candidates.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── provider.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── reporter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── requirements.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── resolver.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ │ ├── candidates.py\n", + "│ │ │ │ │ │ ├── factory.py\n", + "│ │ │ │ │ │ ├── found_candidates.py\n", + "│ │ │ │ │ │ ├── provider.py\n", + "│ │ │ │ │ │ ├── reporter.py\n", + "│ │ │ │ │ │ ├── requirements.py\n", + "│ │ │ │ │ │ └── resolver.py\n", + "│ │ │ │ │ ├── self_outdated_check.py\n", + "│ │ │ │ │ ├── utils\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _jaraco_text.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _log.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── appdirs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── compatibility_tags.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── datetime.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── deprecation.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── direct_url_helpers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── egg_link.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── encoding.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── entrypoints.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── filesystem.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── filetypes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── glibc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── hashes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── logging.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── misc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── models.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── packaging.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── setuptools_build.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── subprocess.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── temp_dir.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── unpacking.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── urls.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── virtualenv.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── wheel.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _jaraco_text.py\n", + "│ │ │ │ │ │ ├── _log.py\n", + "│ │ │ │ │ │ ├── appdirs.py\n", + "│ │ │ │ │ │ ├── compat.py\n", + "│ │ │ │ │ │ ├── compatibility_tags.py\n", + "│ │ │ │ │ │ ├── datetime.py\n", + "│ │ │ │ │ │ ├── deprecation.py\n", + "│ │ │ │ │ │ ├── direct_url_helpers.py\n", + "│ │ │ │ │ │ ├── egg_link.py\n", + "│ │ │ │ │ │ ├── encoding.py\n", + "│ │ │ │ │ │ ├── entrypoints.py\n", + "│ │ │ │ │ │ ├── filesystem.py\n", + "│ │ │ │ │ │ ├── filetypes.py\n", + "│ │ │ │ │ │ ├── glibc.py\n", + "│ │ │ │ │ │ ├── hashes.py\n", + "│ │ │ │ │ │ ├── logging.py\n", + "│ │ │ │ │ │ ├── misc.py\n", + "│ │ │ │ │ │ ├── models.py\n", + "│ │ │ │ │ │ ├── packaging.py\n", + "│ │ │ │ │ │ ├── setuptools_build.py\n", + "│ │ │ │ │ │ ├── subprocess.py\n", + "│ │ │ │ │ │ ├── temp_dir.py\n", + "│ │ │ │ │ │ ├── unpacking.py\n", + "│ │ │ │ │ │ ├── urls.py\n", + "│ │ │ │ │ │ ├── virtualenv.py\n", + "│ │ │ │ │ │ └── wheel.py\n", + "│ │ │ │ │ ├── vcs\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── bazaar.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── git.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── mercurial.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── subversion.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── versioncontrol.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── bazaar.py\n", + "│ │ │ │ │ │ ├── git.py\n", + "│ │ │ │ │ │ ├── mercurial.py\n", + "│ │ │ │ │ │ ├── subversion.py\n", + "│ │ │ │ │ │ └── versioncontrol.py\n", + "│ │ │ │ │ └── wheel_builder.py\n", + "│ │ │ │ ├── _vendor\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── six.cpython-310.pyc\n", + "│ │ │ │ │ │ └── typing_extensions.cpython-310.pyc\n", + "│ │ │ │ │ ├── cachecontrol\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _cmd.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── adapter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── cache.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── controller.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── filewrapper.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── heuristics.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── serialize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── wrapper.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _cmd.py\n", + "│ │ │ │ │ │ ├── adapter.py\n", + "│ │ │ │ │ │ ├── cache.py\n", + "│ │ │ │ │ │ ├── caches\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── file_cache.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── redis_cache.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── file_cache.py\n", + "│ │ │ │ │ │ │ └── redis_cache.py\n", + "│ │ │ │ │ │ ├── controller.py\n", + "│ │ │ │ │ │ ├── filewrapper.py\n", + "│ │ │ │ │ │ ├── heuristics.py\n", + "│ │ │ │ │ │ ├── py.typed\n", + "│ │ │ │ │ │ ├── serialize.py\n", + "│ │ │ │ │ │ └── wrapper.py\n", + "│ │ │ │ │ ├── certifi\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __main__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── core.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cacert.pem\n", + "│ │ │ │ │ │ ├── core.py\n", + "│ │ │ │ │ │ └── py.typed\n", + "│ │ │ │ │ ├── chardet\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── big5freq.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── big5prober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── chardistribution.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── charsetgroupprober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── charsetprober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── codingstatemachine.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── codingstatemachinedict.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── cp949prober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── enums.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── escprober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── escsm.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── eucjpprober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── euckrfreq.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── euckrprober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── euctwfreq.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── euctwprober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── gb2312freq.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── gb2312prober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── hebrewprober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── jisfreq.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── johabfreq.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── johabprober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── jpcntx.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── langbulgarianmodel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── langgreekmodel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── langhebrewmodel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── langhungarianmodel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── langrussianmodel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── langthaimodel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── langturkishmodel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── latin1prober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── macromanprober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── mbcharsetprober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── mbcsgroupprober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── mbcssm.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── resultdict.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── sbcharsetprober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── sbcsgroupprober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── sjisprober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── universaldetector.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── utf1632prober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── utf8prober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── version.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── big5freq.py\n", + "│ │ │ │ │ │ ├── big5prober.py\n", + "│ │ │ │ │ │ ├── chardistribution.py\n", + "│ │ │ │ │ │ ├── charsetgroupprober.py\n", + "│ │ │ │ │ │ ├── charsetprober.py\n", + "│ │ │ │ │ │ ├── cli\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── chardetect.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── chardetect.py\n", + "│ │ │ │ │ │ ├── codingstatemachine.py\n", + "│ │ │ │ │ │ ├── codingstatemachinedict.py\n", + "│ │ │ │ │ │ ├── cp949prober.py\n", + "│ │ │ │ │ │ ├── enums.py\n", + "│ │ │ │ │ │ ├── escprober.py\n", + "│ │ │ │ │ │ ├── escsm.py\n", + "│ │ │ │ │ │ ├── eucjpprober.py\n", + "│ │ │ │ │ │ ├── euckrfreq.py\n", + "│ │ │ │ │ │ ├── euckrprober.py\n", + "│ │ │ │ │ │ ├── euctwfreq.py\n", + "│ │ │ │ │ │ ├── euctwprober.py\n", + "│ │ │ │ │ │ ├── gb2312freq.py\n", + "│ │ │ │ │ │ ├── gb2312prober.py\n", + "│ │ │ │ │ │ ├── hebrewprober.py\n", + "│ │ │ │ │ │ ├── jisfreq.py\n", + "│ │ │ │ │ │ ├── johabfreq.py\n", + "│ │ │ │ │ │ ├── johabprober.py\n", + "│ │ │ │ │ │ ├── jpcntx.py\n", + "│ │ │ │ │ │ ├── langbulgarianmodel.py\n", + "│ │ │ │ │ │ ├── langgreekmodel.py\n", + "│ │ │ │ │ │ ├── langhebrewmodel.py\n", + "│ │ │ │ │ │ ├── langhungarianmodel.py\n", + "│ │ │ │ │ │ ├── langrussianmodel.py\n", + "│ │ │ │ │ │ ├── langthaimodel.py\n", + "│ │ │ │ │ │ ├── langturkishmodel.py\n", + "│ │ │ │ │ │ ├── latin1prober.py\n", + "│ │ │ │ │ │ ├── macromanprober.py\n", + "│ │ │ │ │ │ ├── mbcharsetprober.py\n", + "│ │ │ │ │ │ ├── mbcsgroupprober.py\n", + "│ │ │ │ │ │ ├── mbcssm.py\n", + "│ │ │ │ │ │ ├── metadata\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── languages.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── languages.py\n", + "│ │ │ │ │ │ ├── py.typed\n", + "│ │ │ │ │ │ ├── resultdict.py\n", + "│ │ │ │ │ │ ├── sbcharsetprober.py\n", + "│ │ │ │ │ │ ├── sbcsgroupprober.py\n", + "│ │ │ │ │ │ ├── sjisprober.py\n", + "│ │ │ │ │ │ ├── universaldetector.py\n", + "│ │ │ │ │ │ ├── utf1632prober.py\n", + "│ │ │ │ │ │ ├── utf8prober.py\n", + "│ │ │ │ │ │ └── version.py\n", + "│ │ │ │ │ ├── colorama\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── ansi.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── ansitowin32.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── initialise.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── win32.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── winterm.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ansi.py\n", + "│ │ │ │ │ │ ├── ansitowin32.py\n", + "│ │ │ │ │ │ ├── initialise.py\n", + "│ │ │ │ │ │ ├── tests\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── ansi_test.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── ansitowin32_test.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── initialise_test.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── isatty_test.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── winterm_test.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── ansi_test.py\n", + "│ │ │ │ │ │ │ ├── ansitowin32_test.py\n", + "│ │ │ │ │ │ │ ├── initialise_test.py\n", + "│ │ │ │ │ │ │ ├── isatty_test.py\n", + "│ │ │ │ │ │ │ ├── utils.py\n", + "│ │ │ │ │ │ │ └── winterm_test.py\n", + "│ │ │ │ │ │ ├── win32.py\n", + "│ │ │ │ │ │ └── winterm.py\n", + "│ │ │ │ │ ├── distlib\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── database.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── locators.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── manifest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── markers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── metadata.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── resources.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── scripts.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── util.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── version.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── wheel.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── compat.py\n", + "│ │ │ │ │ │ ├── database.py\n", + "│ │ │ │ │ │ ├── index.py\n", + "│ │ │ │ │ │ ├── locators.py\n", + "│ │ │ │ │ │ ├── manifest.py\n", + "│ │ │ │ │ │ ├── markers.py\n", + "│ │ │ │ │ │ ├── metadata.py\n", + "│ │ │ │ │ │ ├── resources.py\n", + "│ │ │ │ │ │ ├── scripts.py\n", + "│ │ │ │ │ │ ├── t32.exe\n", + "│ │ │ │ │ │ ├── t64-arm.exe\n", + "│ │ │ │ │ │ ├── t64.exe\n", + "│ │ │ │ │ │ ├── util.py\n", + "│ │ │ │ │ │ ├── version.py\n", + "│ │ │ │ │ │ ├── w32.exe\n", + "│ │ │ │ │ │ ├── w64-arm.exe\n", + "│ │ │ │ │ │ ├── w64.exe\n", + "│ │ │ │ │ │ └── wheel.py\n", + "│ │ │ │ │ ├── distro\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __main__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── distro.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── distro.py\n", + "│ │ │ │ │ │ └── py.typed\n", + "│ │ │ │ │ ├── idna\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── codec.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── core.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── idnadata.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── intranges.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── package_data.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── uts46data.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── codec.py\n", + "│ │ │ │ │ │ ├── compat.py\n", + "│ │ │ │ │ │ ├── core.py\n", + "│ │ │ │ │ │ ├── idnadata.py\n", + "│ │ │ │ │ │ ├── intranges.py\n", + "│ │ │ │ │ │ ├── package_data.py\n", + "│ │ │ │ │ │ ├── py.typed\n", + "│ │ │ │ │ │ └── uts46data.py\n", + "│ │ │ │ │ ├── msgpack\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── exceptions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── ext.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── fallback.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── exceptions.py\n", + "│ │ │ │ │ │ ├── ext.py\n", + "│ │ │ │ │ │ └── fallback.py\n", + "│ │ │ │ │ ├── packaging\n", + "│ │ │ │ │ │ ├── __about__.py\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __about__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _manylinux.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _musllinux.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _structures.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── markers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── requirements.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── specifiers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── tags.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── version.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _manylinux.py\n", + "│ │ │ │ │ │ ├── _musllinux.py\n", + "│ │ │ │ │ │ ├── _structures.py\n", + "│ │ │ │ │ │ ├── markers.py\n", + "│ │ │ │ │ │ ├── py.typed\n", + "│ │ │ │ │ │ ├── requirements.py\n", + "│ │ │ │ │ │ ├── specifiers.py\n", + "│ │ │ │ │ │ ├── tags.py\n", + "│ │ │ │ │ │ ├── utils.py\n", + "│ │ │ │ │ │ └── version.py\n", + "│ │ │ │ │ ├── pkg_resources\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── platformdirs\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __main__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── android.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── macos.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── unix.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── version.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── windows.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── android.py\n", + "│ │ │ │ │ │ ├── api.py\n", + "│ │ │ │ │ │ ├── macos.py\n", + "│ │ │ │ │ │ ├── py.typed\n", + "│ │ │ │ │ │ ├── unix.py\n", + "│ │ │ │ │ │ ├── version.py\n", + "│ │ │ │ │ │ └── windows.py\n", + "│ │ │ │ │ ├── pygments\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __main__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── cmdline.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── console.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── filter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── formatter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── lexer.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── modeline.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── plugin.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── regexopt.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── scanner.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── sphinxext.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── style.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── token.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── unistring.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── util.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cmdline.py\n", + "│ │ │ │ │ │ ├── console.py\n", + "│ │ │ │ │ │ ├── filter.py\n", + "│ │ │ │ │ │ ├── filters\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── formatter.py\n", + "│ │ │ │ │ │ ├── formatters\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── _mapping.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── bbcode.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── groff.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── html.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── img.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── irc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── latex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── other.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── pangomarkup.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── rtf.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── svg.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── terminal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── terminal256.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _mapping.py\n", + "│ │ │ │ │ │ │ ├── bbcode.py\n", + "│ │ │ │ │ │ │ ├── groff.py\n", + "│ │ │ │ │ │ │ ├── html.py\n", + "│ │ │ │ │ │ │ ├── img.py\n", + "│ │ │ │ │ │ │ ├── irc.py\n", + "│ │ │ │ │ │ │ ├── latex.py\n", + "│ │ │ │ │ │ │ ├── other.py\n", + "│ │ │ │ │ │ │ ├── pangomarkup.py\n", + "│ │ │ │ │ │ │ ├── rtf.py\n", + "│ │ │ │ │ │ │ ├── svg.py\n", + "│ │ │ │ │ │ │ ├── terminal.py\n", + "│ │ │ │ │ │ │ └── terminal256.py\n", + "│ │ │ │ │ │ ├── lexer.py\n", + "│ │ │ │ │ │ ├── lexers\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── _mapping.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── python.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _mapping.py\n", + "│ │ │ │ │ │ │ └── python.py\n", + "│ │ │ │ │ │ ├── modeline.py\n", + "│ │ │ │ │ │ ├── plugin.py\n", + "│ │ │ │ │ │ ├── regexopt.py\n", + "│ │ │ │ │ │ ├── scanner.py\n", + "│ │ │ │ │ │ ├── sphinxext.py\n", + "│ │ │ │ │ │ ├── style.py\n", + "│ │ │ │ │ │ ├── styles\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── token.py\n", + "│ │ │ │ │ │ ├── unistring.py\n", + "│ │ │ │ │ │ └── util.py\n", + "│ │ │ │ │ ├── pyparsing\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── actions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── core.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── exceptions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── helpers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── results.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── testing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── unicode.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── util.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── actions.py\n", + "│ │ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ │ ├── core.py\n", + "│ │ │ │ │ │ ├── diagram\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── exceptions.py\n", + "│ │ │ │ │ │ ├── helpers.py\n", + "│ │ │ │ │ │ ├── py.typed\n", + "│ │ │ │ │ │ ├── results.py\n", + "│ │ │ │ │ │ ├── testing.py\n", + "│ │ │ │ │ │ ├── unicode.py\n", + "│ │ │ │ │ │ └── util.py\n", + "│ │ │ │ │ ├── pyproject_hooks\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── _impl.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _compat.py\n", + "│ │ │ │ │ │ ├── _impl.py\n", + "│ │ │ │ │ │ └── _in_process\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── _in_process.cpython-310.pyc\n", + "│ │ │ │ │ │ └── _in_process.py\n", + "│ │ │ │ │ ├── requests\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── __version__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _internal_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── adapters.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── auth.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── certs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── cookies.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── exceptions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── help.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── hooks.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── models.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── packages.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── sessions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── status_codes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── structures.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── __version__.py\n", + "│ │ │ │ │ │ ├── _internal_utils.py\n", + "│ │ │ │ │ │ ├── adapters.py\n", + "│ │ │ │ │ │ ├── api.py\n", + "│ │ │ │ │ │ ├── auth.py\n", + "│ │ │ │ │ │ ├── certs.py\n", + "│ │ │ │ │ │ ├── compat.py\n", + "│ │ │ │ │ │ ├── cookies.py\n", + "│ │ │ │ │ │ ├── exceptions.py\n", + "│ │ │ │ │ │ ├── help.py\n", + "│ │ │ │ │ │ ├── hooks.py\n", + "│ │ │ │ │ │ ├── models.py\n", + "│ │ │ │ │ │ ├── packages.py\n", + "│ │ │ │ │ │ ├── sessions.py\n", + "│ │ │ │ │ │ ├── status_codes.py\n", + "│ │ │ │ │ │ ├── structures.py\n", + "│ │ │ │ │ │ └── utils.py\n", + "│ │ │ │ │ ├── resolvelib\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── providers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── reporters.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── resolvers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── structs.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── compat\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── collections_abc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── collections_abc.py\n", + "│ │ │ │ │ │ ├── providers.py\n", + "│ │ │ │ │ │ ├── py.typed\n", + "│ │ │ │ │ │ ├── reporters.py\n", + "│ │ │ │ │ │ ├── resolvers.py\n", + "│ │ │ │ │ │ └── structs.py\n", + "│ │ │ │ │ ├── rich\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __main__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _cell_widths.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _emoji_codes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _emoji_replace.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _export_format.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _extension.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _fileno.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _inspect.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _log_render.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _loop.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _null_file.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _palettes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _pick.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _ratio.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _spinners.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _stack.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _timer.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _win32_console.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _windows.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _windows_renderer.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _wrap.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── abc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── align.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── ansi.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── bar.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── box.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── cells.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── color.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── color_triplet.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── columns.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── console.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── constrain.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── containers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── control.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── default_styles.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── diagnose.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── emoji.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── errors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── file_proxy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── filesize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── highlighter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── json.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── jupyter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── layout.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── live.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── live_render.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── logging.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── markup.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── measure.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── padding.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pager.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── palette.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── panel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pretty.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── progress.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── progress_bar.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── prompt.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── protocol.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── region.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── repr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── rule.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── scope.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── screen.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── segment.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── spinner.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── status.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── style.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── styled.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── syntax.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── table.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── terminal_theme.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── text.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── theme.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── themes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── traceback.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── tree.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _cell_widths.py\n", + "│ │ │ │ │ │ ├── _emoji_codes.py\n", + "│ │ │ │ │ │ ├── _emoji_replace.py\n", + "│ │ │ │ │ │ ├── _export_format.py\n", + "│ │ │ │ │ │ ├── _extension.py\n", + "│ │ │ │ │ │ ├── _fileno.py\n", + "│ │ │ │ │ │ ├── _inspect.py\n", + "│ │ │ │ │ │ ├── _log_render.py\n", + "│ │ │ │ │ │ ├── _loop.py\n", + "│ │ │ │ │ │ ├── _null_file.py\n", + "│ │ │ │ │ │ ├── _palettes.py\n", + "│ │ │ │ │ │ ├── _pick.py\n", + "│ │ │ │ │ │ ├── _ratio.py\n", + "│ │ │ │ │ │ ├── _spinners.py\n", + "│ │ │ │ │ │ ├── _stack.py\n", + "│ │ │ │ │ │ ├── _timer.py\n", + "│ │ │ │ │ │ ├── _win32_console.py\n", + "│ │ │ │ │ │ ├── _windows.py\n", + "│ │ │ │ │ │ ├── _windows_renderer.py\n", + "│ │ │ │ │ │ ├── _wrap.py\n", + "│ │ │ │ │ │ ├── abc.py\n", + "│ │ │ │ │ │ ├── align.py\n", + "│ │ │ │ │ │ ├── ansi.py\n", + "│ │ │ │ │ │ ├── bar.py\n", + "│ │ │ │ │ │ ├── box.py\n", + "│ │ │ │ │ │ ├── cells.py\n", + "│ │ │ │ │ │ ├── color.py\n", + "│ │ │ │ │ │ ├── color_triplet.py\n", + "│ │ │ │ │ │ ├── columns.py\n", + "│ │ │ │ │ │ ├── console.py\n", + "│ │ │ │ │ │ ├── constrain.py\n", + "│ │ │ │ │ │ ├── containers.py\n", + "│ │ │ │ │ │ ├── control.py\n", + "│ │ │ │ │ │ ├── default_styles.py\n", + "│ │ │ │ │ │ ├── diagnose.py\n", + "│ │ │ │ │ │ ├── emoji.py\n", + "│ │ │ │ │ │ ├── errors.py\n", + "│ │ │ │ │ │ ├── file_proxy.py\n", + "│ │ │ │ │ │ ├── filesize.py\n", + "│ │ │ │ │ │ ├── highlighter.py\n", + "│ │ │ │ │ │ ├── json.py\n", + "│ │ │ │ │ │ ├── jupyter.py\n", + "│ │ │ │ │ │ ├── layout.py\n", + "│ │ │ │ │ │ ├── live.py\n", + "│ │ │ │ │ │ ├── live_render.py\n", + "│ │ │ │ │ │ ├── logging.py\n", + "│ │ │ │ │ │ ├── markup.py\n", + "│ │ │ │ │ │ ├── measure.py\n", + "│ │ │ │ │ │ ├── padding.py\n", + "│ │ │ │ │ │ ├── pager.py\n", + "│ │ │ │ │ │ ├── palette.py\n", + "│ │ │ │ │ │ ├── panel.py\n", + "│ │ │ │ │ │ ├── pretty.py\n", + "│ │ │ │ │ │ ├── progress.py\n", + "│ │ │ │ │ │ ├── progress_bar.py\n", + "│ │ │ │ │ │ ├── prompt.py\n", + "│ │ │ │ │ │ ├── protocol.py\n", + "│ │ │ │ │ │ ├── py.typed\n", + "│ │ │ │ │ │ ├── region.py\n", + "│ │ │ │ │ │ ├── repr.py\n", + "│ │ │ │ │ │ ├── rule.py\n", + "│ │ │ │ │ │ ├── scope.py\n", + "│ │ │ │ │ │ ├── screen.py\n", + "│ │ │ │ │ │ ├── segment.py\n", + "│ │ │ │ │ │ ├── spinner.py\n", + "│ │ │ │ │ │ ├── status.py\n", + "│ │ │ │ │ │ ├── style.py\n", + "│ │ │ │ │ │ ├── styled.py\n", + "│ │ │ │ │ │ ├── syntax.py\n", + "│ │ │ │ │ │ ├── table.py\n", + "│ │ │ │ │ │ ├── terminal_theme.py\n", + "│ │ │ │ │ │ ├── text.py\n", + "│ │ │ │ │ │ ├── theme.py\n", + "│ │ │ │ │ │ ├── themes.py\n", + "│ │ │ │ │ │ ├── traceback.py\n", + "│ │ │ │ │ │ └── tree.py\n", + "│ │ │ │ │ ├── six.py\n", + "│ │ │ │ │ ├── tenacity\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _asyncio.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── after.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── before.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── before_sleep.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── nap.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── retry.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── stop.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── tornadoweb.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── wait.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _asyncio.py\n", + "│ │ │ │ │ │ ├── _utils.py\n", + "│ │ │ │ │ │ ├── after.py\n", + "│ │ │ │ │ │ ├── before.py\n", + "│ │ │ │ │ │ ├── before_sleep.py\n", + "│ │ │ │ │ │ ├── nap.py\n", + "│ │ │ │ │ │ ├── py.typed\n", + "│ │ │ │ │ │ ├── retry.py\n", + "│ │ │ │ │ │ ├── stop.py\n", + "│ │ │ │ │ │ ├── tornadoweb.py\n", + "│ │ │ │ │ │ └── wait.py\n", + "│ │ │ │ │ ├── tomli\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _parser.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _re.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── _types.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _parser.py\n", + "│ │ │ │ │ │ ├── _re.py\n", + "│ │ │ │ │ │ ├── _types.py\n", + "│ │ │ │ │ │ └── py.typed\n", + "│ │ │ │ │ ├── truststore\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _macos.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _openssl.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _ssl_constants.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── _windows.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _api.py\n", + "│ │ │ │ │ │ ├── _macos.py\n", + "│ │ │ │ │ │ ├── _openssl.py\n", + "│ │ │ │ │ │ ├── _ssl_constants.py\n", + "│ │ │ │ │ │ ├── _windows.py\n", + "│ │ │ │ │ │ └── py.typed\n", + "│ │ │ │ │ ├── typing_extensions.py\n", + "│ │ │ │ │ ├── urllib3\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _collections.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _version.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── connection.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── connectionpool.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── exceptions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── fields.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── filepost.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── poolmanager.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── request.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── response.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _collections.py\n", + "│ │ │ │ │ │ ├── _version.py\n", + "│ │ │ │ │ │ ├── connection.py\n", + "│ │ │ │ │ │ ├── connectionpool.py\n", + "│ │ │ │ │ │ ├── contrib\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── _appengine_environ.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── appengine.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── ntlmpool.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── pyopenssl.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── securetransport.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── socks.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _appengine_environ.py\n", + "│ │ │ │ │ │ │ ├── _securetransport\n", + "│ │ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── bindings.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ └── low_level.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── bindings.py\n", + "│ │ │ │ │ │ │ │ └── low_level.py\n", + "│ │ │ │ │ │ │ ├── appengine.py\n", + "│ │ │ │ │ │ │ ├── ntlmpool.py\n", + "│ │ │ │ │ │ │ ├── pyopenssl.py\n", + "│ │ │ │ │ │ │ ├── securetransport.py\n", + "│ │ │ │ │ │ │ └── socks.py\n", + "│ │ │ │ │ │ ├── exceptions.py\n", + "│ │ │ │ │ │ ├── fields.py\n", + "│ │ │ │ │ │ ├── filepost.py\n", + "│ │ │ │ │ │ ├── packages\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── six.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── backports\n", + "│ │ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── makefile.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ └── weakref_finalize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── makefile.py\n", + "│ │ │ │ │ │ │ │ └── weakref_finalize.py\n", + "│ │ │ │ │ │ │ └── six.py\n", + "│ │ │ │ │ │ ├── poolmanager.py\n", + "│ │ │ │ │ │ ├── request.py\n", + "│ │ │ │ │ │ ├── response.py\n", + "│ │ │ │ │ │ └── util\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── connection.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── proxy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── queue.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── request.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── response.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── retry.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── ssl_.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── ssl_match_hostname.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── ssltransport.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── timeout.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── url.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── wait.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── connection.py\n", + "│ │ │ │ │ │ ├── proxy.py\n", + "│ │ │ │ │ │ ├── queue.py\n", + "│ │ │ │ │ │ ├── request.py\n", + "│ │ │ │ │ │ ├── response.py\n", + "│ │ │ │ │ │ ├── retry.py\n", + "│ │ │ │ │ │ ├── ssl_.py\n", + "│ │ │ │ │ │ ├── ssl_match_hostname.py\n", + "│ │ │ │ │ │ ├── ssltransport.py\n", + "│ │ │ │ │ │ ├── timeout.py\n", + "│ │ │ │ │ │ ├── url.py\n", + "│ │ │ │ │ │ └── wait.py\n", + "│ │ │ │ │ ├── vendor.txt\n", + "│ │ │ │ │ └── webencodings\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── labels.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── mklabels.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── tests.cpython-310.pyc\n", + "│ │ │ │ │ │ └── x_user_defined.cpython-310.pyc\n", + "│ │ │ │ │ ├── labels.py\n", + "│ │ │ │ │ ├── mklabels.py\n", + "│ │ │ │ │ ├── tests.py\n", + "│ │ │ │ │ └── x_user_defined.py\n", + "│ │ │ │ └── py.typed\n", + "│ │ │ ├── pip-24.0.dist-info\n", + "│ │ │ │ ├── AUTHORS.txt\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE.txt\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── REQUESTED\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ ├── entry_points.txt\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── pkg_resources\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ ├── _vendor\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── appdirs.cpython-310.pyc\n", + "│ │ │ │ │ │ └── pyparsing.cpython-310.pyc\n", + "│ │ │ │ │ ├── appdirs.py\n", + "│ │ │ │ │ ├── packaging\n", + "│ │ │ │ │ │ ├── __about__.py\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __about__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _manylinux.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _musllinux.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _structures.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── markers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── requirements.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── specifiers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── tags.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── version.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _manylinux.py\n", + "│ │ │ │ │ │ ├── _musllinux.py\n", + "│ │ │ │ │ │ ├── _structures.py\n", + "│ │ │ │ │ │ ├── markers.py\n", + "│ │ │ │ │ │ ├── requirements.py\n", + "│ │ │ │ │ │ ├── specifiers.py\n", + "│ │ │ │ │ │ ├── tags.py\n", + "│ │ │ │ │ │ ├── utils.py\n", + "│ │ │ │ │ │ └── version.py\n", + "│ │ │ │ │ └── pyparsing.py\n", + "│ │ │ │ ├── extern\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ └── tests\n", + "│ │ │ │ └── data\n", + "│ │ │ │ └── my-test-package-source\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ └── setup.cpython-310.pyc\n", + "│ │ │ │ └── setup.py\n", + "│ │ │ ├── platformdirs\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __main__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ ├── android.cpython-310.pyc\n", + "│ │ │ │ │ ├── api.cpython-310.pyc\n", + "│ │ │ │ │ ├── macos.cpython-310.pyc\n", + "│ │ │ │ │ ├── unix.cpython-310.pyc\n", + "│ │ │ │ │ ├── version.cpython-310.pyc\n", + "│ │ │ │ │ └── windows.cpython-310.pyc\n", + "│ │ │ │ ├── android.py\n", + "│ │ │ │ ├── api.py\n", + "│ │ │ │ ├── macos.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ ├── unix.py\n", + "│ │ │ │ ├── version.py\n", + "│ │ │ │ └── windows.py\n", + "│ │ │ ├── platformdirs-4.2.1.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── licenses\n", + "│ │ │ │ └── LICENSE\n", + "│ │ │ ├── prompt_toolkit\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── auto_suggest.cpython-310.pyc\n", + "│ │ │ │ │ ├── buffer.cpython-310.pyc\n", + "│ │ │ │ │ ├── cache.cpython-310.pyc\n", + "│ │ │ │ │ ├── cursor_shapes.cpython-310.pyc\n", + "│ │ │ │ │ ├── data_structures.cpython-310.pyc\n", + "│ │ │ │ │ ├── document.cpython-310.pyc\n", + "│ │ │ │ │ ├── enums.cpython-310.pyc\n", + "│ │ │ │ │ ├── history.cpython-310.pyc\n", + "│ │ │ │ │ ├── keys.cpython-310.pyc\n", + "│ │ │ │ │ ├── log.cpython-310.pyc\n", + "│ │ │ │ │ ├── mouse_events.cpython-310.pyc\n", + "│ │ │ │ │ ├── patch_stdout.cpython-310.pyc\n", + "│ │ │ │ │ ├── renderer.cpython-310.pyc\n", + "│ │ │ │ │ ├── search.cpython-310.pyc\n", + "│ │ │ │ │ ├── selection.cpython-310.pyc\n", + "│ │ │ │ │ ├── token.cpython-310.pyc\n", + "│ │ │ │ │ ├── utils.cpython-310.pyc\n", + "│ │ │ │ │ ├── validation.cpython-310.pyc\n", + "│ │ │ │ │ └── win32_types.cpython-310.pyc\n", + "│ │ │ │ ├── application\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── application.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── current.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dummy.cpython-310.pyc\n", + "│ │ │ │ │ │ └── run_in_terminal.cpython-310.pyc\n", + "│ │ │ │ │ ├── application.py\n", + "│ │ │ │ │ ├── current.py\n", + "│ │ │ │ │ ├── dummy.py\n", + "│ │ │ │ │ └── run_in_terminal.py\n", + "│ │ │ │ ├── auto_suggest.py\n", + "│ │ │ │ ├── buffer.py\n", + "│ │ │ │ ├── cache.py\n", + "│ │ │ │ ├── clipboard\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── in_memory.cpython-310.pyc\n", + "│ │ │ │ │ │ └── pyperclip.cpython-310.pyc\n", + "│ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ ├── in_memory.py\n", + "│ │ │ │ │ └── pyperclip.py\n", + "│ │ │ │ ├── completion\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── deduplicate.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── filesystem.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── fuzzy_completer.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── nested.cpython-310.pyc\n", + "│ │ │ │ │ │ └── word_completer.cpython-310.pyc\n", + "│ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ ├── deduplicate.py\n", + "│ │ │ │ │ ├── filesystem.py\n", + "│ │ │ │ │ ├── fuzzy_completer.py\n", + "│ │ │ │ │ ├── nested.py\n", + "│ │ │ │ │ └── word_completer.py\n", + "│ │ │ │ ├── contrib\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── completers\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── system.cpython-310.pyc\n", + "│ │ │ │ │ │ └── system.py\n", + "│ │ │ │ │ ├── regular_languages\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── compiler.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── completion.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── lexer.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── regex_parser.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── validation.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── compiler.py\n", + "│ │ │ │ │ │ ├── completion.py\n", + "│ │ │ │ │ │ ├── lexer.py\n", + "│ │ │ │ │ │ ├── regex_parser.py\n", + "│ │ │ │ │ │ └── validation.py\n", + "│ │ │ │ │ ├── ssh\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── server.cpython-310.pyc\n", + "│ │ │ │ │ │ └── server.py\n", + "│ │ │ │ │ └── telnet\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── log.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── protocol.cpython-310.pyc\n", + "│ │ │ │ │ │ └── server.cpython-310.pyc\n", + "│ │ │ │ │ ├── log.py\n", + "│ │ │ │ │ ├── protocol.py\n", + "│ │ │ │ │ └── server.py\n", + "│ │ │ │ ├── cursor_shapes.py\n", + "│ │ │ │ ├── data_structures.py\n", + "│ │ │ │ ├── document.py\n", + "│ │ │ │ ├── enums.py\n", + "│ │ │ │ ├── eventloop\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── async_generator.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── inputhook.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── utils.cpython-310.pyc\n", + "│ │ │ │ │ │ └── win32.cpython-310.pyc\n", + "│ │ │ │ │ ├── async_generator.py\n", + "│ │ │ │ │ ├── inputhook.py\n", + "│ │ │ │ │ ├── utils.py\n", + "│ │ │ │ │ └── win32.py\n", + "│ │ │ │ ├── filters\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── app.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cli.cpython-310.pyc\n", + "│ │ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ │ ├── app.py\n", + "│ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ ├── cli.py\n", + "│ │ │ │ │ └── utils.py\n", + "│ │ │ │ ├── formatted_text\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ansi.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── html.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pygments.cpython-310.pyc\n", + "│ │ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ │ ├── ansi.py\n", + "│ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ ├── html.py\n", + "│ │ │ │ │ ├── pygments.py\n", + "│ │ │ │ │ └── utils.py\n", + "│ │ │ │ ├── history.py\n", + "│ │ │ │ ├── input\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ansi_escape_sequences.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── defaults.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── posix_pipe.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── posix_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── typeahead.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── vt100.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── vt100_parser.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── win32.cpython-310.pyc\n", + "│ │ │ │ │ │ └── win32_pipe.cpython-310.pyc\n", + "│ │ │ │ │ ├── ansi_escape_sequences.py\n", + "│ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ ├── defaults.py\n", + "│ │ │ │ │ ├── posix_pipe.py\n", + "│ │ │ │ │ ├── posix_utils.py\n", + "│ │ │ │ │ ├── typeahead.py\n", + "│ │ │ │ │ ├── vt100.py\n", + "│ │ │ │ │ ├── vt100_parser.py\n", + "│ │ │ │ │ ├── win32.py\n", + "│ │ │ │ │ └── win32_pipe.py\n", + "│ │ │ │ ├── key_binding\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── defaults.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── digraphs.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── emacs_state.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── key_bindings.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── key_processor.cpython-310.pyc\n", + "│ │ │ │ │ │ └── vi_state.cpython-310.pyc\n", + "│ │ │ │ │ ├── bindings\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── auto_suggest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── basic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── completion.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── cpr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── emacs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── focus.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── mouse.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── named_commands.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── open_in_editor.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── page_navigation.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── scroll.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── search.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── vi.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── auto_suggest.py\n", + "│ │ │ │ │ │ ├── basic.py\n", + "│ │ │ │ │ │ ├── completion.py\n", + "│ │ │ │ │ │ ├── cpr.py\n", + "│ │ │ │ │ │ ├── emacs.py\n", + "│ │ │ │ │ │ ├── focus.py\n", + "│ │ │ │ │ │ ├── mouse.py\n", + "│ │ │ │ │ │ ├── named_commands.py\n", + "│ │ │ │ │ │ ├── open_in_editor.py\n", + "│ │ │ │ │ │ ├── page_navigation.py\n", + "│ │ │ │ │ │ ├── scroll.py\n", + "│ │ │ │ │ │ ├── search.py\n", + "│ │ │ │ │ │ └── vi.py\n", + "│ │ │ │ │ ├── defaults.py\n", + "│ │ │ │ │ ├── digraphs.py\n", + "│ │ │ │ │ ├── emacs_state.py\n", + "│ │ │ │ │ ├── key_bindings.py\n", + "│ │ │ │ │ ├── key_processor.py\n", + "│ │ │ │ │ └── vi_state.py\n", + "│ │ │ │ ├── keys.py\n", + "│ │ │ │ ├── layout\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── containers.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── controls.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dimension.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dummy.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── layout.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── margins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── menus.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── mouse_handlers.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── processors.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── screen.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── scrollable_pane.cpython-310.pyc\n", + "│ │ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ │ ├── containers.py\n", + "│ │ │ │ │ ├── controls.py\n", + "│ │ │ │ │ ├── dimension.py\n", + "│ │ │ │ │ ├── dummy.py\n", + "│ │ │ │ │ ├── layout.py\n", + "│ │ │ │ │ ├── margins.py\n", + "│ │ │ │ │ ├── menus.py\n", + "│ │ │ │ │ ├── mouse_handlers.py\n", + "│ │ │ │ │ ├── processors.py\n", + "│ │ │ │ │ ├── screen.py\n", + "│ │ │ │ │ ├── scrollable_pane.py\n", + "│ │ │ │ │ └── utils.py\n", + "│ │ │ │ ├── lexers\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ └── pygments.cpython-310.pyc\n", + "│ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ └── pygments.py\n", + "│ │ │ │ ├── log.py\n", + "│ │ │ │ ├── mouse_events.py\n", + "│ │ │ │ ├── output\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── color_depth.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── conemu.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── defaults.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── flush_stdout.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── plain_text.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── vt100.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── win32.cpython-310.pyc\n", + "│ │ │ │ │ │ └── windows10.cpython-310.pyc\n", + "│ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ ├── color_depth.py\n", + "│ │ │ │ │ ├── conemu.py\n", + "│ │ │ │ │ ├── defaults.py\n", + "│ │ │ │ │ ├── flush_stdout.py\n", + "│ │ │ │ │ ├── plain_text.py\n", + "│ │ │ │ │ ├── vt100.py\n", + "│ │ │ │ │ ├── win32.py\n", + "│ │ │ │ │ └── windows10.py\n", + "│ │ │ │ ├── patch_stdout.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ ├── renderer.py\n", + "│ │ │ │ ├── search.py\n", + "│ │ │ │ ├── selection.py\n", + "│ │ │ │ ├── shortcuts\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dialogs.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── prompt.cpython-310.pyc\n", + "│ │ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ │ ├── dialogs.py\n", + "│ │ │ │ │ ├── progress_bar\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── formatters.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ │ └── formatters.py\n", + "│ │ │ │ │ ├── prompt.py\n", + "│ │ │ │ │ └── utils.py\n", + "│ │ │ │ ├── styles\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── defaults.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── named_colors.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pygments.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── style.cpython-310.pyc\n", + "│ │ │ │ │ │ └── style_transformation.cpython-310.pyc\n", + "│ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ ├── defaults.py\n", + "│ │ │ │ │ ├── named_colors.py\n", + "│ │ │ │ │ ├── pygments.py\n", + "│ │ │ │ │ ├── style.py\n", + "│ │ │ │ │ └── style_transformation.py\n", + "│ │ │ │ ├── token.py\n", + "│ │ │ │ ├── utils.py\n", + "│ │ │ │ ├── validation.py\n", + "│ │ │ │ ├── widgets\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dialogs.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── menus.cpython-310.pyc\n", + "│ │ │ │ │ │ └── toolbars.cpython-310.pyc\n", + "│ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ ├── dialogs.py\n", + "│ │ │ │ │ ├── menus.py\n", + "│ │ │ │ │ └── toolbars.py\n", + "│ │ │ │ └── win32_types.py\n", + "│ │ │ ├── prompt_toolkit-3.0.43.dist-info\n", + "│ │ │ │ ├── AUTHORS.rst\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── psutil\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _common.cpython-310.pyc\n", + "│ │ │ │ │ ├── _compat.cpython-310.pyc\n", + "│ │ │ │ │ ├── _psaix.cpython-310.pyc\n", + "│ │ │ │ │ ├── _psbsd.cpython-310.pyc\n", + "│ │ │ │ │ ├── _pslinux.cpython-310.pyc\n", + "│ │ │ │ │ ├── _psosx.cpython-310.pyc\n", + "│ │ │ │ │ ├── _psposix.cpython-310.pyc\n", + "│ │ │ │ │ ├── _pssunos.cpython-310.pyc\n", + "│ │ │ │ │ └── _pswindows.cpython-310.pyc\n", + "│ │ │ │ ├── _common.py\n", + "│ │ │ │ ├── _compat.py\n", + "│ │ │ │ ├── _psaix.py\n", + "│ │ │ │ ├── _psbsd.py\n", + "│ │ │ │ ├── _pslinux.py\n", + "│ │ │ │ ├── _psosx.py\n", + "│ │ │ │ ├── _psposix.py\n", + "│ │ │ │ ├── _pssunos.py\n", + "│ │ │ │ ├── _psutil_linux.abi3.so\n", + "│ │ │ │ ├── _psutil_posix.abi3.so\n", + "│ │ │ │ ├── _pswindows.py\n", + "│ │ │ │ └── tests\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __main__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ ├── runner.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_aix.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_bsd.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_connections.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_contracts.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_linux.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_memleaks.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_misc.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_osx.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_posix.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_process.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_process_all.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_sunos.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_system.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_testutils.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_unicode.cpython-310.pyc\n", + "│ │ │ │ │ └── test_windows.cpython-310.pyc\n", + "│ │ │ │ ├── runner.py\n", + "│ │ │ │ ├── test_aix.py\n", + "│ │ │ │ ├── test_bsd.py\n", + "│ │ │ │ ├── test_connections.py\n", + "│ │ │ │ ├── test_contracts.py\n", + "│ │ │ │ ├── test_linux.py\n", + "│ │ │ │ ├── test_memleaks.py\n", + "│ │ │ │ ├── test_misc.py\n", + "│ │ │ │ ├── test_osx.py\n", + "│ │ │ │ ├── test_posix.py\n", + "│ │ │ │ ├── test_process.py\n", + "│ │ │ │ ├── test_process_all.py\n", + "│ │ │ │ ├── test_sunos.py\n", + "│ │ │ │ ├── test_system.py\n", + "│ │ │ │ ├── test_testutils.py\n", + "│ │ │ │ ├── test_unicode.py\n", + "│ │ │ │ └── test_windows.py\n", + "│ │ │ ├── psutil-5.9.8.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── ptyprocess\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _fork_pty.cpython-310.pyc\n", + "│ │ │ │ │ ├── ptyprocess.cpython-310.pyc\n", + "│ │ │ │ │ └── util.cpython-310.pyc\n", + "│ │ │ │ ├── _fork_pty.py\n", + "│ │ │ │ ├── ptyprocess.py\n", + "│ │ │ │ └── util.py\n", + "│ │ │ ├── ptyprocess-0.7.0.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ └── WHEEL\n", + "│ │ │ ├── pure_eval\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── core.cpython-310.pyc\n", + "│ │ │ │ │ ├── my_getattr_static.cpython-310.pyc\n", + "│ │ │ │ │ ├── utils.cpython-310.pyc\n", + "│ │ │ │ │ └── version.cpython-310.pyc\n", + "│ │ │ │ ├── core.py\n", + "│ │ │ │ ├── my_getattr_static.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ ├── utils.py\n", + "│ │ │ │ └── version.py\n", + "│ │ │ ├── pure_eval-0.2.2.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE.txt\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── pygments\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __main__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ ├── cmdline.cpython-310.pyc\n", + "│ │ │ │ │ ├── console.cpython-310.pyc\n", + "│ │ │ │ │ ├── filter.cpython-310.pyc\n", + "│ │ │ │ │ ├── formatter.cpython-310.pyc\n", + "│ │ │ │ │ ├── lexer.cpython-310.pyc\n", + "│ │ │ │ │ ├── modeline.cpython-310.pyc\n", + "│ │ │ │ │ ├── plugin.cpython-310.pyc\n", + "│ │ │ │ │ ├── regexopt.cpython-310.pyc\n", + "│ │ │ │ │ ├── scanner.cpython-310.pyc\n", + "│ │ │ │ │ ├── sphinxext.cpython-310.pyc\n", + "│ │ │ │ │ ├── style.cpython-310.pyc\n", + "│ │ │ │ │ ├── token.cpython-310.pyc\n", + "│ │ │ │ │ ├── unistring.cpython-310.pyc\n", + "│ │ │ │ │ └── util.cpython-310.pyc\n", + "│ │ │ │ ├── cmdline.py\n", + "│ │ │ │ ├── console.py\n", + "│ │ │ │ ├── filter.py\n", + "│ │ │ │ ├── filters\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ ├── formatter.py\n", + "│ │ │ │ ├── formatters\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _mapping.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── bbcode.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── groff.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── html.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── img.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── irc.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── latex.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── other.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pangomarkup.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── rtf.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── svg.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── terminal.cpython-310.pyc\n", + "│ │ │ │ │ │ └── terminal256.cpython-310.pyc\n", + "│ │ │ │ │ ├── _mapping.py\n", + "│ │ │ │ │ ├── bbcode.py\n", + "│ │ │ │ │ ├── groff.py\n", + "│ │ │ │ │ ├── html.py\n", + "│ │ │ │ │ ├── img.py\n", + "│ │ │ │ │ ├── irc.py\n", + "│ │ │ │ │ ├── latex.py\n", + "│ │ │ │ │ ├── other.py\n", + "│ │ │ │ │ ├── pangomarkup.py\n", + "│ │ │ │ │ ├── rtf.py\n", + "│ │ │ │ │ ├── svg.py\n", + "│ │ │ │ │ ├── terminal.py\n", + "│ │ │ │ │ └── terminal256.py\n", + "│ │ │ │ ├── lexer.py\n", + "│ │ │ │ ├── lexers\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _ada_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _asy_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _cl_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _cocoa_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _csound_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _css_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _julia_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _lasso_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _lilypond_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _lua_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _mapping.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _mql_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _mysql_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _openedge_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _php_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _postgres_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _qlik_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _scheme_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _scilab_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _sourcemod_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _stan_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _stata_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _tsql_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _usd_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _vbscript_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _vim_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── actionscript.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ada.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── agile.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── algebra.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ambient.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── amdgpu.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ampl.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── apdlexer.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── apl.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── archetype.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── arrow.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── arturo.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── asc.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── asm.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── asn1.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── automation.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── bare.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── basic.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── bdd.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── berry.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── bibtex.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── blueprint.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── boa.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── bqn.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── business.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── c_cpp.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── c_like.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── capnproto.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── carbon.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cddl.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── chapel.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── clean.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── comal.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── compiled.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── configs.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── console.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cplint.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── crystal.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── csound.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── css.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── d.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dalvik.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── data.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dax.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── devicetree.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── diff.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dns.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dotnet.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dsls.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dylan.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ecl.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── eiffel.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── elm.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── elpi.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── email.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── erlang.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── esoteric.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ezhil.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── factor.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── fantom.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── felix.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── fift.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── floscript.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── forth.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── fortran.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── foxpro.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── freefem.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── func.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── functional.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── futhark.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── gcodelexer.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── gdscript.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── go.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── grammar_notation.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── graph.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── graphics.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── graphql.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── graphviz.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── gsql.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── haskell.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── haxe.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── hdl.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── hexdump.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── html.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── idl.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── igor.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── inferno.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── installers.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── int_fiction.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── iolang.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── j.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── javascript.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── jmespath.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── jslt.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── jsonnet.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── jsx.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── julia.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── jvm.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── kuin.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── kusto.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ldap.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── lean.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── lilypond.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── lisp.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── macaulay2.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── make.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── markup.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── math.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── matlab.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── maxima.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── meson.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── mime.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── minecraft.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── mips.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ml.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── modeling.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── modula2.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── monte.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── mosel.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ncl.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── nimrod.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── nit.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── nix.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── oberon.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── objective.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ooc.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── openscad.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── other.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── parasail.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── parsers.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pascal.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pawn.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── perl.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── phix.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── php.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pointless.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pony.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── praat.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── procfile.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── prolog.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── promql.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── prql.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ptx.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── python.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── q.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── qlik.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── qvt.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── r.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── rdf.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── rebol.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── resource.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ride.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── rita.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── rnc.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── roboconf.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── robotframework.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ruby.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── rust.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── sas.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── savi.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── scdoc.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── scripting.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── sgf.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── shell.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── sieve.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── slash.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── smalltalk.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── smithy.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── smv.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── snobol.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── solidity.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── sophia.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── special.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── spice.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── sql.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── srcinfo.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── stata.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── supercollider.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── tal.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── tcl.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── teal.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── templates.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── teraterm.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── testing.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── text.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── textedit.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── textfmts.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── theorem.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── thingsdb.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── tlb.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── tls.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── tnt.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── trafficscript.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── typoscript.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ul4.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── unicon.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── urbi.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── usd.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── varnish.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── verification.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── verifpal.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── vip.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── vyper.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── web.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── webassembly.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── webidl.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── webmisc.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── wgsl.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── whiley.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── wowtoc.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── wren.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── x10.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── xorg.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── yang.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── yara.cpython-310.pyc\n", + "│ │ │ │ │ │ └── zig.cpython-310.pyc\n", + "│ │ │ │ │ ├── _ada_builtins.py\n", + "│ │ │ │ │ ├── _asy_builtins.py\n", + "│ │ │ │ │ ├── _cl_builtins.py\n", + "│ │ │ │ │ ├── _cocoa_builtins.py\n", + "│ │ │ │ │ ├── _csound_builtins.py\n", + "│ │ │ │ │ ├── _css_builtins.py\n", + "│ │ │ │ │ ├── _julia_builtins.py\n", + "│ │ │ │ │ ├── _lasso_builtins.py\n", + "│ │ │ │ │ ├── _lilypond_builtins.py\n", + "│ │ │ │ │ ├── _lua_builtins.py\n", + "│ │ │ │ │ ├── _mapping.py\n", + "│ │ │ │ │ ├── _mql_builtins.py\n", + "│ │ │ │ │ ├── _mysql_builtins.py\n", + "│ │ │ │ │ ├── _openedge_builtins.py\n", + "│ │ │ │ │ ├── _php_builtins.py\n", + "│ │ │ │ │ ├── _postgres_builtins.py\n", + "│ │ │ │ │ ├── _qlik_builtins.py\n", + "│ │ │ │ │ ├── _scheme_builtins.py\n", + "│ │ │ │ │ ├── _scilab_builtins.py\n", + "│ │ │ │ │ ├── _sourcemod_builtins.py\n", + "│ │ │ │ │ ├── _stan_builtins.py\n", + "│ │ │ │ │ ├── _stata_builtins.py\n", + "│ │ │ │ │ ├── _tsql_builtins.py\n", + "│ │ │ │ │ ├── _usd_builtins.py\n", + "│ │ │ │ │ ├── _vbscript_builtins.py\n", + "│ │ │ │ │ ├── _vim_builtins.py\n", + "│ │ │ │ │ ├── actionscript.py\n", + "│ │ │ │ │ ├── ada.py\n", + "│ │ │ │ │ ├── agile.py\n", + "│ │ │ │ │ ├── algebra.py\n", + "│ │ │ │ │ ├── ambient.py\n", + "│ │ │ │ │ ├── amdgpu.py\n", + "│ │ │ │ │ ├── ampl.py\n", + "│ │ │ │ │ ├── apdlexer.py\n", + "│ │ │ │ │ ├── apl.py\n", + "│ │ │ │ │ ├── archetype.py\n", + "│ │ │ │ │ ├── arrow.py\n", + "│ │ │ │ │ ├── arturo.py\n", + "│ │ │ │ │ ├── asc.py\n", + "│ │ │ │ │ ├── asm.py\n", + "│ │ │ │ │ ├── asn1.py\n", + "│ │ │ │ │ ├── automation.py\n", + "│ │ │ │ │ ├── bare.py\n", + "│ │ │ │ │ ├── basic.py\n", + "│ │ │ │ │ ├── bdd.py\n", + "│ │ │ │ │ ├── berry.py\n", + "│ │ │ │ │ ├── bibtex.py\n", + "│ │ │ │ │ ├── blueprint.py\n", + "│ │ │ │ │ ├── boa.py\n", + "│ │ │ │ │ ├── bqn.py\n", + "│ │ │ │ │ ├── business.py\n", + "│ │ │ │ │ ├── c_cpp.py\n", + "│ │ │ │ │ ├── c_like.py\n", + "│ │ │ │ │ ├── capnproto.py\n", + "│ │ │ │ │ ├── carbon.py\n", + "│ │ │ │ │ ├── cddl.py\n", + "│ │ │ │ │ ├── chapel.py\n", + "│ │ │ │ │ ├── clean.py\n", + "│ │ │ │ │ ├── comal.py\n", + "│ │ │ │ │ ├── compiled.py\n", + "│ │ │ │ │ ├── configs.py\n", + "│ │ │ │ │ ├── console.py\n", + "│ │ │ │ │ ├── cplint.py\n", + "│ │ │ │ │ ├── crystal.py\n", + "│ │ │ │ │ ├── csound.py\n", + "│ │ │ │ │ ├── css.py\n", + "│ │ │ │ │ ├── d.py\n", + "│ │ │ │ │ ├── dalvik.py\n", + "│ │ │ │ │ ├── data.py\n", + "│ │ │ │ │ ├── dax.py\n", + "│ │ │ │ │ ├── devicetree.py\n", + "│ │ │ │ │ ├── diff.py\n", + "│ │ │ │ │ ├── dns.py\n", + "│ │ │ │ │ ├── dotnet.py\n", + "│ │ │ │ │ ├── dsls.py\n", + "│ │ │ │ │ ├── dylan.py\n", + "│ │ │ │ │ ├── ecl.py\n", + "│ │ │ │ │ ├── eiffel.py\n", + "│ │ │ │ │ ├── elm.py\n", + "│ │ │ │ │ ├── elpi.py\n", + "│ │ │ │ │ ├── email.py\n", + "│ │ │ │ │ ├── erlang.py\n", + "│ │ │ │ │ ├── esoteric.py\n", + "│ │ │ │ │ ├── ezhil.py\n", + "│ │ │ │ │ ├── factor.py\n", + "│ │ │ │ │ ├── fantom.py\n", + "│ │ │ │ │ ├── felix.py\n", + "│ │ │ │ │ ├── fift.py\n", + "│ │ │ │ │ ├── floscript.py\n", + "│ │ │ │ │ ├── forth.py\n", + "│ │ │ │ │ ├── fortran.py\n", + "│ │ │ │ │ ├── foxpro.py\n", + "│ │ │ │ │ ├── freefem.py\n", + "│ │ │ │ │ ├── func.py\n", + "│ │ │ │ │ ├── functional.py\n", + "│ │ │ │ │ ├── futhark.py\n", + "│ │ │ │ │ ├── gcodelexer.py\n", + "│ │ │ │ │ ├── gdscript.py\n", + "│ │ │ │ │ ├── go.py\n", + "│ │ │ │ │ ├── grammar_notation.py\n", + "│ │ │ │ │ ├── graph.py\n", + "│ │ │ │ │ ├── graphics.py\n", + "│ │ │ │ │ ├── graphql.py\n", + "│ │ │ │ │ ├── graphviz.py\n", + "│ │ │ │ │ ├── gsql.py\n", + "│ │ │ │ │ ├── haskell.py\n", + "│ │ │ │ │ ├── haxe.py\n", + "│ │ │ │ │ ├── hdl.py\n", + "│ │ │ │ │ ├── hexdump.py\n", + "│ │ │ │ │ ├── html.py\n", + "│ │ │ │ │ ├── idl.py\n", + "│ │ │ │ │ ├── igor.py\n", + "│ │ │ │ │ ├── inferno.py\n", + "│ │ │ │ │ ├── installers.py\n", + "│ │ │ │ │ ├── int_fiction.py\n", + "│ │ │ │ │ ├── iolang.py\n", + "│ │ │ │ │ ├── j.py\n", + "│ │ │ │ │ ├── javascript.py\n", + "│ │ │ │ │ ├── jmespath.py\n", + "│ │ │ │ │ ├── jslt.py\n", + "│ │ │ │ │ ├── jsonnet.py\n", + "│ │ │ │ │ ├── jsx.py\n", + "│ │ │ │ │ ├── julia.py\n", + "│ │ │ │ │ ├── jvm.py\n", + "│ │ │ │ │ ├── kuin.py\n", + "│ │ │ │ │ ├── kusto.py\n", + "│ │ │ │ │ ├── ldap.py\n", + "│ │ │ │ │ ├── lean.py\n", + "│ │ │ │ │ ├── lilypond.py\n", + "│ │ │ │ │ ├── lisp.py\n", + "│ │ │ │ │ ├── macaulay2.py\n", + "│ │ │ │ │ ├── make.py\n", + "│ │ │ │ │ ├── markup.py\n", + "│ │ │ │ │ ├── math.py\n", + "│ │ │ │ │ ├── matlab.py\n", + "│ │ │ │ │ ├── maxima.py\n", + "│ │ │ │ │ ├── meson.py\n", + "│ │ │ │ │ ├── mime.py\n", + "│ │ │ │ │ ├── minecraft.py\n", + "│ │ │ │ │ ├── mips.py\n", + "│ │ │ │ │ ├── ml.py\n", + "│ │ │ │ │ ├── modeling.py\n", + "│ │ │ │ │ ├── modula2.py\n", + "│ │ │ │ │ ├── monte.py\n", + "│ │ │ │ │ ├── mosel.py\n", + "│ │ │ │ │ ├── ncl.py\n", + "│ │ │ │ │ ├── nimrod.py\n", + "│ │ │ │ │ ├── nit.py\n", + "│ │ │ │ │ ├── nix.py\n", + "│ │ │ │ │ ├── oberon.py\n", + "│ │ │ │ │ ├── objective.py\n", + "│ │ │ │ │ ├── ooc.py\n", + "│ │ │ │ │ ├── openscad.py\n", + "│ │ │ │ │ ├── other.py\n", + "│ │ │ │ │ ├── parasail.py\n", + "│ │ │ │ │ ├── parsers.py\n", + "│ │ │ │ │ ├── pascal.py\n", + "│ │ │ │ │ ├── pawn.py\n", + "│ │ │ │ │ ├── perl.py\n", + "│ │ │ │ │ ├── phix.py\n", + "│ │ │ │ │ ├── php.py\n", + "│ │ │ │ │ ├── pointless.py\n", + "│ │ │ │ │ ├── pony.py\n", + "│ │ │ │ │ ├── praat.py\n", + "│ │ │ │ │ ├── procfile.py\n", + "│ │ │ │ │ ├── prolog.py\n", + "│ │ │ │ │ ├── promql.py\n", + "│ │ │ │ │ ├── prql.py\n", + "│ │ │ │ │ ├── ptx.py\n", + "│ │ │ │ │ ├── python.py\n", + "│ │ │ │ │ ├── q.py\n", + "│ │ │ │ │ ├── qlik.py\n", + "│ │ │ │ │ ├── qvt.py\n", + "│ │ │ │ │ ├── r.py\n", + "│ │ │ │ │ ├── rdf.py\n", + "│ │ │ │ │ ├── rebol.py\n", + "│ │ │ │ │ ├── resource.py\n", + "│ │ │ │ │ ├── ride.py\n", + "│ │ │ │ │ ├── rita.py\n", + "│ │ │ │ │ ├── rnc.py\n", + "│ │ │ │ │ ├── roboconf.py\n", + "│ │ │ │ │ ├── robotframework.py\n", + "│ │ │ │ │ ├── ruby.py\n", + "│ │ │ │ │ ├── rust.py\n", + "│ │ │ │ │ ├── sas.py\n", + "│ │ │ │ │ ├── savi.py\n", + "│ │ │ │ │ ├── scdoc.py\n", + "│ │ │ │ │ ├── scripting.py\n", + "│ │ │ │ │ ├── sgf.py\n", + "│ │ │ │ │ ├── shell.py\n", + "│ │ │ │ │ ├── sieve.py\n", + "│ │ │ │ │ ├── slash.py\n", + "│ │ │ │ │ ├── smalltalk.py\n", + "│ │ │ │ │ ├── smithy.py\n", + "│ │ │ │ │ ├── smv.py\n", + "│ │ │ │ │ ├── snobol.py\n", + "│ │ │ │ │ ├── solidity.py\n", + "│ │ │ │ │ ├── sophia.py\n", + "│ │ │ │ │ ├── special.py\n", + "│ │ │ │ │ ├── spice.py\n", + "│ │ │ │ │ ├── sql.py\n", + "│ │ │ │ │ ├── srcinfo.py\n", + "│ │ │ │ │ ├── stata.py\n", + "│ │ │ │ │ ├── supercollider.py\n", + "│ │ │ │ │ ├── tal.py\n", + "│ │ │ │ │ ├── tcl.py\n", + "│ │ │ │ │ ├── teal.py\n", + "│ │ │ │ │ ├── templates.py\n", + "│ │ │ │ │ ├── teraterm.py\n", + "│ │ │ │ │ ├── testing.py\n", + "│ │ │ │ │ ├── text.py\n", + "│ │ │ │ │ ├── textedit.py\n", + "│ │ │ │ │ ├── textfmts.py\n", + "│ │ │ │ │ ├── theorem.py\n", + "│ │ │ │ │ ├── thingsdb.py\n", + "│ │ │ │ │ ├── tlb.py\n", + "│ │ │ │ │ ├── tls.py\n", + "│ │ │ │ │ ├── tnt.py\n", + "│ │ │ │ │ ├── trafficscript.py\n", + "│ │ │ │ │ ├── typoscript.py\n", + "│ │ │ │ │ ├── ul4.py\n", + "│ │ │ │ │ ├── unicon.py\n", + "│ │ │ │ │ ├── urbi.py\n", + "│ │ │ │ │ ├── usd.py\n", + "│ │ │ │ │ ├── varnish.py\n", + "│ │ │ │ │ ├── verification.py\n", + "│ │ │ │ │ ├── verifpal.py\n", + "│ │ │ │ │ ├── vip.py\n", + "│ │ │ │ │ ├── vyper.py\n", + "│ │ │ │ │ ├── web.py\n", + "│ │ │ │ │ ├── webassembly.py\n", + "│ │ │ │ │ ├── webidl.py\n", + "│ │ │ │ │ ├── webmisc.py\n", + "│ │ │ │ │ ├── wgsl.py\n", + "│ │ │ │ │ ├── whiley.py\n", + "│ │ │ │ │ ├── wowtoc.py\n", + "│ │ │ │ │ ├── wren.py\n", + "│ │ │ │ │ ├── x10.py\n", + "│ │ │ │ │ ├── xorg.py\n", + "│ │ │ │ │ ├── yang.py\n", + "│ │ │ │ │ ├── yara.py\n", + "│ │ │ │ │ └── zig.py\n", + "│ │ │ │ ├── modeline.py\n", + "│ │ │ │ ├── plugin.py\n", + "│ │ │ │ ├── regexopt.py\n", + "│ │ │ │ ├── scanner.py\n", + "│ │ │ │ ├── sphinxext.py\n", + "│ │ │ │ ├── style.py\n", + "│ │ │ │ ├── styles\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _mapping.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── abap.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── algol.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── algol_nu.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── arduino.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── autumn.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── borland.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── bw.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── colorful.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── default.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dracula.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── emacs.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── friendly.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── friendly_grayscale.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── fruity.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── gh_dark.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── gruvbox.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── igor.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── inkpot.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── lightbulb.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── lilypond.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── lovelace.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── manni.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── material.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── monokai.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── murphy.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── native.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── nord.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── onedark.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── paraiso_dark.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── paraiso_light.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pastie.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── perldoc.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── rainbow_dash.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── rrt.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── sas.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── solarized.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── staroffice.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── stata_dark.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── stata_light.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── tango.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── trac.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── vim.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── vs.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── xcode.cpython-310.pyc\n", + "│ │ │ │ │ │ └── zenburn.cpython-310.pyc\n", + "│ │ │ │ │ ├── _mapping.py\n", + "│ │ │ │ │ ├── abap.py\n", + "│ │ │ │ │ ├── algol.py\n", + "│ │ │ │ │ ├── algol_nu.py\n", + "│ │ │ │ │ ├── arduino.py\n", + "│ │ │ │ │ ├── autumn.py\n", + "│ │ │ │ │ ├── borland.py\n", + "│ │ │ │ │ ├── bw.py\n", + "│ │ │ │ │ ├── colorful.py\n", + "│ │ │ │ │ ├── default.py\n", + "│ │ │ │ │ ├── dracula.py\n", + "│ │ │ │ │ ├── emacs.py\n", + "│ │ │ │ │ ├── friendly.py\n", + "│ │ │ │ │ ├── friendly_grayscale.py\n", + "│ │ │ │ │ ├── fruity.py\n", + "│ │ │ │ │ ├── gh_dark.py\n", + "│ │ │ │ │ ├── gruvbox.py\n", + "│ │ │ │ │ ├── igor.py\n", + "│ │ │ │ │ ├── inkpot.py\n", + "│ │ │ │ │ ├── lightbulb.py\n", + "│ │ │ │ │ ├── lilypond.py\n", + "│ │ │ │ │ ├── lovelace.py\n", + "│ │ │ │ │ ├── manni.py\n", + "│ │ │ │ │ ├── material.py\n", + "│ │ │ │ │ ├── monokai.py\n", + "│ │ │ │ │ ├── murphy.py\n", + "│ │ │ │ │ ├── native.py\n", + "│ │ │ │ │ ├── nord.py\n", + "│ │ │ │ │ ├── onedark.py\n", + "│ │ │ │ │ ├── paraiso_dark.py\n", + "│ │ │ │ │ ├── paraiso_light.py\n", + "│ │ │ │ │ ├── pastie.py\n", + "│ │ │ │ │ ├── perldoc.py\n", + "│ │ │ │ │ ├── rainbow_dash.py\n", + "│ │ │ │ │ ├── rrt.py\n", + "│ │ │ │ │ ├── sas.py\n", + "│ │ │ │ │ ├── solarized.py\n", + "│ │ │ │ │ ├── staroffice.py\n", + "│ │ │ │ │ ├── stata_dark.py\n", + "│ │ │ │ │ ├── stata_light.py\n", + "│ │ │ │ │ ├── tango.py\n", + "│ │ │ │ │ ├── trac.py\n", + "│ │ │ │ │ ├── vim.py\n", + "│ │ │ │ │ ├── vs.py\n", + "│ │ │ │ │ ├── xcode.py\n", + "│ │ │ │ │ └── zenburn.py\n", + "│ │ │ │ ├── token.py\n", + "│ │ │ │ ├── unistring.py\n", + "│ │ │ │ └── util.py\n", + "│ │ │ ├── pygments-2.17.2.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ ├── entry_points.txt\n", + "│ │ │ │ └── licenses\n", + "│ │ │ │ ├── AUTHORS\n", + "│ │ │ │ └── LICENSE\n", + "│ │ │ ├── python_dateutil-2.9.0.post0.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ ├── top_level.txt\n", + "│ │ │ │ └── zip-safe\n", + "│ │ │ ├── pytz\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── exceptions.cpython-310.pyc\n", + "│ │ │ │ │ ├── lazy.cpython-310.pyc\n", + "│ │ │ │ │ ├── reference.cpython-310.pyc\n", + "│ │ │ │ │ ├── tzfile.cpython-310.pyc\n", + "│ │ │ │ │ └── tzinfo.cpython-310.pyc\n", + "│ │ │ │ ├── exceptions.py\n", + "│ │ │ │ ├── lazy.py\n", + "│ │ │ │ ├── reference.py\n", + "│ │ │ │ ├── tzfile.py\n", + "│ │ │ │ ├── tzinfo.py\n", + "│ │ │ │ └── zoneinfo\n", + "│ │ │ │ ├── Africa\n", + "│ │ │ │ │ ├── Abidjan\n", + "│ │ │ │ │ ├── Accra\n", + "│ │ │ │ │ ├── Addis_Ababa\n", + "│ │ │ │ │ ├── Algiers\n", + "│ │ │ │ │ ├── Asmara\n", + "│ │ │ │ │ ├── Asmera\n", + "│ │ │ │ │ ├── Bamako\n", + "│ │ │ │ │ ├── Bangui\n", + "│ │ │ │ │ ├── Banjul\n", + "│ │ │ │ │ ├── Bissau\n", + "│ │ │ │ │ ├── Blantyre\n", + "│ │ │ │ │ ├── Brazzaville\n", + "│ │ │ │ │ ├── Bujumbura\n", + "│ │ │ │ │ ├── Cairo\n", + "│ │ │ │ │ ├── Casablanca\n", + "│ │ │ │ │ ├── Ceuta\n", + "│ │ │ │ │ ├── Conakry\n", + "│ │ │ │ │ ├── Dakar\n", + "│ │ │ │ │ ├── Dar_es_Salaam\n", + "│ │ │ │ │ ├── Djibouti\n", + "│ │ │ │ │ ├── Douala\n", + "│ │ │ │ │ ├── El_Aaiun\n", + "│ │ │ │ │ ├── Freetown\n", + "│ │ │ │ │ ├── Gaborone\n", + "│ │ │ │ │ ├── Harare\n", + "│ │ │ │ │ ├── Johannesburg\n", + "│ │ │ │ │ ├── Juba\n", + "│ │ │ │ │ ├── Kampala\n", + "│ │ │ │ │ ├── Khartoum\n", + "│ │ │ │ │ ├── Kigali\n", + "│ │ │ │ │ ├── Kinshasa\n", + "│ │ │ │ │ ├── Lagos\n", + "│ │ │ │ │ ├── Libreville\n", + "│ │ │ │ │ ├── Lome\n", + "│ │ │ │ │ ├── Luanda\n", + "│ │ │ │ │ ├── Lubumbashi\n", + "│ │ │ │ │ ├── Lusaka\n", + "│ │ │ │ │ ├── Malabo\n", + "│ │ │ │ │ ├── Maputo\n", + "│ │ │ │ │ ├── Maseru\n", + "│ │ │ │ │ ├── Mbabane\n", + "│ │ │ │ │ ├── Mogadishu\n", + "│ │ │ │ │ ├── Monrovia\n", + "│ │ │ │ │ ├── Nairobi\n", + "│ │ │ │ │ ├── Ndjamena\n", + "│ │ │ │ │ ├── Niamey\n", + "│ │ │ │ │ ├── Nouakchott\n", + "│ │ │ │ │ ├── Ouagadougou\n", + "│ │ │ │ │ ├── Porto-Novo\n", + "│ │ │ │ │ ├── Sao_Tome\n", + "│ │ │ │ │ ├── Timbuktu\n", + "│ │ │ │ │ ├── Tripoli\n", + "│ │ │ │ │ ├── Tunis\n", + "│ │ │ │ │ └── Windhoek\n", + "│ │ │ │ ├── America\n", + "│ │ │ │ │ ├── Adak\n", + "│ │ │ │ │ ├── Anchorage\n", + "│ │ │ │ │ ├── Anguilla\n", + "│ │ │ │ │ ├── Antigua\n", + "│ │ │ │ │ ├── Araguaina\n", + "│ │ │ │ │ ├── Argentina\n", + "│ │ │ │ │ │ ├── Buenos_Aires\n", + "│ │ │ │ │ │ ├── Catamarca\n", + "│ │ │ │ │ │ ├── ComodRivadavia\n", + "│ │ │ │ │ │ ├── Cordoba\n", + "│ │ │ │ │ │ ├── Jujuy\n", + "│ │ │ │ │ │ ├── La_Rioja\n", + "│ │ │ │ │ │ ├── Mendoza\n", + "│ │ │ │ │ │ ├── Rio_Gallegos\n", + "│ │ │ │ │ │ ├── Salta\n", + "│ │ │ │ │ │ ├── San_Juan\n", + "│ │ │ │ │ │ ├── San_Luis\n", + "│ │ │ │ │ │ ├── Tucuman\n", + "│ │ │ │ │ │ └── Ushuaia\n", + "│ │ │ │ │ ├── Aruba\n", + "│ │ │ │ │ ├── Asuncion\n", + "│ │ │ │ │ ├── Atikokan\n", + "│ │ │ │ │ ├── Atka\n", + "│ │ │ │ │ ├── Bahia\n", + "│ │ │ │ │ ├── Bahia_Banderas\n", + "│ │ │ │ │ ├── Barbados\n", + "│ │ │ │ │ ├── Belem\n", + "│ │ │ │ │ ├── Belize\n", + "│ │ │ │ │ ├── Blanc-Sablon\n", + "│ │ │ │ │ ├── Boa_Vista\n", + "│ │ │ │ │ ├── Bogota\n", + "│ │ │ │ │ ├── Boise\n", + "│ │ │ │ │ ├── Buenos_Aires\n", + "│ │ │ │ │ ├── Cambridge_Bay\n", + "│ │ │ │ │ ├── Campo_Grande\n", + "│ │ │ │ │ ├── Cancun\n", + "│ │ │ │ │ ├── Caracas\n", + "│ │ │ │ │ ├── Catamarca\n", + "│ │ │ │ │ ├── Cayenne\n", + "│ │ │ │ │ ├── Cayman\n", + "│ │ │ │ │ ├── Chicago\n", + "│ │ │ │ │ ├── Chihuahua\n", + "│ │ │ │ │ ├── Ciudad_Juarez\n", + "│ │ │ │ │ ├── Coral_Harbour\n", + "│ │ │ │ │ ├── Cordoba\n", + "│ │ │ │ │ ├── Costa_Rica\n", + "│ │ │ │ │ ├── Creston\n", + "│ │ │ │ │ ├── Cuiaba\n", + "│ │ │ │ │ ├── Curacao\n", + "│ │ │ │ │ ├── Danmarkshavn\n", + "│ │ │ │ │ ├── Dawson\n", + "│ │ │ │ │ ├── Dawson_Creek\n", + "│ │ │ │ │ ├── Denver\n", + "│ │ │ │ │ ├── Detroit\n", + "│ │ │ │ │ ├── Dominica\n", + "│ │ │ │ │ ├── Edmonton\n", + "│ │ │ │ │ ├── Eirunepe\n", + "│ │ │ │ │ ├── El_Salvador\n", + "│ │ │ │ │ ├── Ensenada\n", + "│ │ │ │ │ ├── Fort_Nelson\n", + "│ │ │ │ │ ├── Fort_Wayne\n", + "│ │ │ │ │ ├── Fortaleza\n", + "│ │ │ │ │ ├── Glace_Bay\n", + "│ │ │ │ │ ├── Godthab\n", + "│ │ │ │ │ ├── Goose_Bay\n", + "│ │ │ │ │ ├── Grand_Turk\n", + "│ │ │ │ │ ├── Grenada\n", + "│ │ │ │ │ ├── Guadeloupe\n", + "│ │ │ │ │ ├── Guatemala\n", + "│ │ │ │ │ ├── Guayaquil\n", + "│ │ │ │ │ ├── Guyana\n", + "│ │ │ │ │ ├── Halifax\n", + "│ │ │ │ │ ├── Havana\n", + "│ │ │ │ │ ├── Hermosillo\n", + "│ │ │ │ │ ├── Indiana\n", + "│ │ │ │ │ │ ├── Indianapolis\n", + "│ │ │ │ │ │ ├── Knox\n", + "│ │ │ │ │ │ ├── Marengo\n", + "│ │ │ │ │ │ ├── Petersburg\n", + "│ │ │ │ │ │ ├── Tell_City\n", + "│ │ │ │ │ │ ├── Vevay\n", + "│ │ │ │ │ │ ├── Vincennes\n", + "│ │ │ │ │ │ └── Winamac\n", + "│ │ │ │ │ ├── Indianapolis\n", + "│ │ │ │ │ ├── Inuvik\n", + "│ │ │ │ │ ├── Iqaluit\n", + "│ │ │ │ │ ├── Jamaica\n", + "│ │ │ │ │ ├── Jujuy\n", + "│ │ │ │ │ ├── Juneau\n", + "│ │ │ │ │ ├── Kentucky\n", + "│ │ │ │ │ │ ├── Louisville\n", + "│ │ │ │ │ │ └── Monticello\n", + "│ │ │ │ │ ├── Knox_IN\n", + "│ │ │ │ │ ├── Kralendijk\n", + "│ │ │ │ │ ├── La_Paz\n", + "│ │ │ │ │ ├── Lima\n", + "│ │ │ │ │ ├── Los_Angeles\n", + "│ │ │ │ │ ├── Louisville\n", + "│ │ │ │ │ ├── Lower_Princes\n", + "│ │ │ │ │ ├── Maceio\n", + "│ │ │ │ │ ├── Managua\n", + "│ │ │ │ │ ├── Manaus\n", + "│ │ │ │ │ ├── Marigot\n", + "│ │ │ │ │ ├── Martinique\n", + "│ │ │ │ │ ├── Matamoros\n", + "│ │ │ │ │ ├── Mazatlan\n", + "│ │ │ │ │ ├── Mendoza\n", + "│ │ │ │ │ ├── Menominee\n", + "│ │ │ │ │ ├── Merida\n", + "│ │ │ │ │ ├── Metlakatla\n", + "│ │ │ │ │ ├── Mexico_City\n", + "│ │ │ │ │ ├── Miquelon\n", + "│ │ │ │ │ ├── Moncton\n", + "│ │ │ │ │ ├── Monterrey\n", + "│ │ │ │ │ ├── Montevideo\n", + "│ │ │ │ │ ├── Montreal\n", + "│ │ │ │ │ ├── Montserrat\n", + "│ │ │ │ │ ├── Nassau\n", + "│ │ │ │ │ ├── New_York\n", + "│ │ │ │ │ ├── Nipigon\n", + "│ │ │ │ │ ├── Nome\n", + "│ │ │ │ │ ├── Noronha\n", + "│ │ │ │ │ ├── North_Dakota\n", + "│ │ │ │ │ │ ├── Beulah\n", + "│ │ │ │ │ │ ├── Center\n", + "│ │ │ │ │ │ └── New_Salem\n", + "│ │ │ │ │ ├── Nuuk\n", + "│ │ │ │ │ ├── Ojinaga\n", + "│ │ │ │ │ ├── Panama\n", + "│ │ │ │ │ ├── Pangnirtung\n", + "│ │ │ │ │ ├── Paramaribo\n", + "│ │ │ │ │ ├── Phoenix\n", + "│ │ │ │ │ ├── Port-au-Prince\n", + "│ │ │ │ │ ├── Port_of_Spain\n", + "│ │ │ │ │ ├── Porto_Acre\n", + "│ │ │ │ │ ├── Porto_Velho\n", + "│ │ │ │ │ ├── Puerto_Rico\n", + "│ │ │ │ │ ├── Punta_Arenas\n", + "│ │ │ │ │ ├── Rainy_River\n", + "│ │ │ │ │ ├── Rankin_Inlet\n", + "│ │ │ │ │ ├── Recife\n", + "│ │ │ │ │ ├── Regina\n", + "│ │ │ │ │ ├── Resolute\n", + "│ │ │ │ │ ├── Rio_Branco\n", + "│ │ │ │ │ ├── Rosario\n", + "│ │ │ │ │ ├── Santa_Isabel\n", + "│ │ │ │ │ ├── Santarem\n", + "│ │ │ │ │ ├── Santiago\n", + "│ │ │ │ │ ├── Santo_Domingo\n", + "│ │ │ │ │ ├── Sao_Paulo\n", + "│ │ │ │ │ ├── Scoresbysund\n", + "│ │ │ │ │ ├── Shiprock\n", + "│ │ │ │ │ ├── Sitka\n", + "│ │ │ │ │ ├── St_Barthelemy\n", + "│ │ │ │ │ ├── St_Johns\n", + "│ │ │ │ │ ├── St_Kitts\n", + "│ │ │ │ │ ├── St_Lucia\n", + "│ │ │ │ │ ├── St_Thomas\n", + "│ │ │ │ │ ├── St_Vincent\n", + "│ │ │ │ │ ├── Swift_Current\n", + "│ │ │ │ │ ├── Tegucigalpa\n", + "│ │ │ │ │ ├── Thule\n", + "│ │ │ │ │ ├── Thunder_Bay\n", + "│ │ │ │ │ ├── Tijuana\n", + "│ │ │ │ │ ├── Toronto\n", + "│ │ │ │ │ ├── Tortola\n", + "│ │ │ │ │ ├── Vancouver\n", + "│ │ │ │ │ ├── Virgin\n", + "│ │ │ │ │ ├── Whitehorse\n", + "│ │ │ │ │ ├── Winnipeg\n", + "│ │ │ │ │ ├── Yakutat\n", + "│ │ │ │ │ └── Yellowknife\n", + "│ │ │ │ ├── Antarctica\n", + "│ │ │ │ │ ├── Casey\n", + "│ │ │ │ │ ├── Davis\n", + "│ │ │ │ │ ├── DumontDUrville\n", + "│ │ │ │ │ ├── Macquarie\n", + "│ │ │ │ │ ├── Mawson\n", + "│ │ │ │ │ ├── McMurdo\n", + "│ │ │ │ │ ├── Palmer\n", + "│ │ │ │ │ ├── Rothera\n", + "│ │ │ │ │ ├── South_Pole\n", + "│ │ │ │ │ ├── Syowa\n", + "│ │ │ │ │ ├── Troll\n", + "│ │ │ │ │ └── Vostok\n", + "│ │ │ │ ├── Arctic\n", + "│ │ │ │ │ └── Longyearbyen\n", + "│ │ │ │ ├── Asia\n", + "│ │ │ │ │ ├── Aden\n", + "│ │ │ │ │ ├── Almaty\n", + "│ │ │ │ │ ├── Amman\n", + "│ │ │ │ │ ├── Anadyr\n", + "│ │ │ │ │ ├── Aqtau\n", + "│ │ │ │ │ ├── Aqtobe\n", + "│ │ │ │ │ ├── Ashgabat\n", + "│ │ │ │ │ ├── Ashkhabad\n", + "│ │ │ │ │ ├── Atyrau\n", + "│ │ │ │ │ ├── Baghdad\n", + "│ │ │ │ │ ├── Bahrain\n", + "│ │ │ │ │ ├── Baku\n", + "│ │ │ │ │ ├── Bangkok\n", + "│ │ │ │ │ ├── Barnaul\n", + "│ │ │ │ │ ├── Beirut\n", + "│ │ │ │ │ ├── Bishkek\n", + "│ │ │ │ │ ├── Brunei\n", + "│ │ │ │ │ ├── Calcutta\n", + "│ │ │ │ │ ├── Chita\n", + "│ │ │ │ │ ├── Choibalsan\n", + "│ │ │ │ │ ├── Chongqing\n", + "│ │ │ │ │ ├── Chungking\n", + "│ │ │ │ │ ├── Colombo\n", + "│ │ │ │ │ ├── Dacca\n", + "│ │ │ │ │ ├── Damascus\n", + "│ │ │ │ │ ├── Dhaka\n", + "│ │ │ │ │ ├── Dili\n", + "│ │ │ │ │ ├── Dubai\n", + "│ │ │ │ │ ├── Dushanbe\n", + "│ │ │ │ │ ├── Famagusta\n", + "│ │ │ │ │ ├── Gaza\n", + "│ │ │ │ │ ├── Harbin\n", + "│ │ │ │ │ ├── Hebron\n", + "│ │ │ │ │ ├── Ho_Chi_Minh\n", + "│ │ │ │ │ ├── Hong_Kong\n", + "│ │ │ │ │ ├── Hovd\n", + "│ │ │ │ │ ├── Irkutsk\n", + "│ │ │ │ │ ├── Istanbul\n", + "│ │ │ │ │ ├── Jakarta\n", + "│ │ │ │ │ ├── Jayapura\n", + "│ │ │ │ │ ├── Jerusalem\n", + "│ │ │ │ │ ├── Kabul\n", + "│ │ │ │ │ ├── Kamchatka\n", + "│ │ │ │ │ ├── Karachi\n", + "│ │ │ │ │ ├── Kashgar\n", + "│ │ │ │ │ ├── Kathmandu\n", + "│ │ │ │ │ ├── Katmandu\n", + "│ │ │ │ │ ├── Khandyga\n", + "│ │ │ │ │ ├── Kolkata\n", + "│ │ │ │ │ ├── Krasnoyarsk\n", + "│ │ │ │ │ ├── Kuala_Lumpur\n", + "│ │ │ │ │ ├── Kuching\n", + "│ │ │ │ │ ├── Kuwait\n", + "│ │ │ │ │ ├── Macao\n", + "│ │ │ │ │ ├── Macau\n", + "│ │ │ │ │ ├── Magadan\n", + "│ │ │ │ │ ├── Makassar\n", + "│ │ │ │ │ ├── Manila\n", + "│ │ │ │ │ ├── Muscat\n", + "│ │ │ │ │ ├── Nicosia\n", + "│ │ │ │ │ ├── Novokuznetsk\n", + "│ │ │ │ │ ├── Novosibirsk\n", + "│ │ │ │ │ ├── Omsk\n", + "│ │ │ │ │ ├── Oral\n", + "│ │ │ │ │ ├── Phnom_Penh\n", + "│ │ │ │ │ ├── Pontianak\n", + "│ │ │ │ │ ├── Pyongyang\n", + "│ │ │ │ │ ├── Qatar\n", + "│ │ │ │ │ ├── Qostanay\n", + "│ │ │ │ │ ├── Qyzylorda\n", + "│ │ │ │ │ ├── Rangoon\n", + "│ │ │ │ │ ├── Riyadh\n", + "│ │ │ │ │ ├── Saigon\n", + "│ │ │ │ │ ├── Sakhalin\n", + "│ │ │ │ │ ├── Samarkand\n", + "│ │ │ │ │ ├── Seoul\n", + "│ │ │ │ │ ├── Shanghai\n", + "│ │ │ │ │ ├── Singapore\n", + "│ │ │ │ │ ├── Srednekolymsk\n", + "│ │ │ │ │ ├── Taipei\n", + "│ │ │ │ │ ├── Tashkent\n", + "│ │ │ │ │ ├── Tbilisi\n", + "│ │ │ │ │ ├── Tehran\n", + "│ │ │ │ │ ├── Tel_Aviv\n", + "│ │ │ │ │ ├── Thimbu\n", + "│ │ │ │ │ ├── Thimphu\n", + "│ │ │ │ │ ├── Tokyo\n", + "│ │ │ │ │ ├── Tomsk\n", + "│ │ │ │ │ ├── Ujung_Pandang\n", + "│ │ │ │ │ ├── Ulaanbaatar\n", + "│ │ │ │ │ ├── Ulan_Bator\n", + "│ │ │ │ │ ├── Urumqi\n", + "│ │ │ │ │ ├── Ust-Nera\n", + "│ │ │ │ │ ├── Vientiane\n", + "│ │ │ │ │ ├── Vladivostok\n", + "│ │ │ │ │ ├── Yakutsk\n", + "│ │ │ │ │ ├── Yangon\n", + "│ │ │ │ │ ├── Yekaterinburg\n", + "│ │ │ │ │ └── Yerevan\n", + "│ │ │ │ ├── Atlantic\n", + "│ │ │ │ │ ├── Azores\n", + "│ │ │ │ │ ├── Bermuda\n", + "│ │ │ │ │ ├── Canary\n", + "│ │ │ │ │ ├── Cape_Verde\n", + "│ │ │ │ │ ├── Faeroe\n", + "│ │ │ │ │ ├── Faroe\n", + "│ │ │ │ │ ├── Jan_Mayen\n", + "│ │ │ │ │ ├── Madeira\n", + "│ │ │ │ │ ├── Reykjavik\n", + "│ │ │ │ │ ├── South_Georgia\n", + "│ │ │ │ │ ├── St_Helena\n", + "│ │ │ │ │ └── Stanley\n", + "│ │ │ │ ├── Australia\n", + "│ │ │ │ │ ├── ACT\n", + "│ │ │ │ │ ├── Adelaide\n", + "│ │ │ │ │ ├── Brisbane\n", + "│ │ │ │ │ ├── Broken_Hill\n", + "│ │ │ │ │ ├── Canberra\n", + "│ │ │ │ │ ├── Currie\n", + "│ │ │ │ │ ├── Darwin\n", + "│ │ │ │ │ ├── Eucla\n", + "│ │ │ │ │ ├── Hobart\n", + "│ │ │ │ │ ├── LHI\n", + "│ │ │ │ │ ├── Lindeman\n", + "│ │ │ │ │ ├── Lord_Howe\n", + "│ │ │ │ │ ├── Melbourne\n", + "│ │ │ │ │ ├── NSW\n", + "│ │ │ │ │ ├── North\n", + "│ │ │ │ │ ├── Perth\n", + "│ │ │ │ │ ├── Queensland\n", + "│ │ │ │ │ ├── South\n", + "│ │ │ │ │ ├── Sydney\n", + "│ │ │ │ │ ├── Tasmania\n", + "│ │ │ │ │ ├── Victoria\n", + "│ │ │ │ │ ├── West\n", + "│ │ │ │ │ └── Yancowinna\n", + "│ │ │ │ ├── Brazil\n", + "│ │ │ │ │ ├── Acre\n", + "│ │ │ │ │ ├── DeNoronha\n", + "│ │ │ │ │ ├── East\n", + "│ │ │ │ │ └── West\n", + "│ │ │ │ ├── CET\n", + "│ │ │ │ ├── CST6CDT\n", + "│ │ │ │ ├── Canada\n", + "│ │ │ │ │ ├── Atlantic\n", + "│ │ │ │ │ ├── Central\n", + "│ │ │ │ │ ├── Eastern\n", + "│ │ │ │ │ ├── Mountain\n", + "│ │ │ │ │ ├── Newfoundland\n", + "│ │ │ │ │ ├── Pacific\n", + "│ │ │ │ │ ├── Saskatchewan\n", + "│ │ │ │ │ └── Yukon\n", + "│ │ │ │ ├── Chile\n", + "│ │ │ │ │ ├── Continental\n", + "│ │ │ │ │ └── EasterIsland\n", + "│ │ │ │ ├── Cuba\n", + "│ │ │ │ ├── EET\n", + "│ │ │ │ ├── EST\n", + "│ │ │ │ ├── EST5EDT\n", + "│ │ │ │ ├── Egypt\n", + "│ │ │ │ ├── Eire\n", + "│ │ │ │ ├── Etc\n", + "│ │ │ │ │ ├── GMT\n", + "│ │ │ │ │ ├── GMT+0\n", + "│ │ │ │ │ ├── GMT+1\n", + "│ │ │ │ │ ├── GMT+10\n", + "│ │ │ │ │ ├── GMT+11\n", + "│ │ │ │ │ ├── GMT+12\n", + "│ │ │ │ │ ├── GMT+2\n", + "│ │ │ │ │ ├── GMT+3\n", + "│ │ │ │ │ ├── GMT+4\n", + "│ │ │ │ │ ├── GMT+5\n", + "│ │ │ │ │ ├── GMT+6\n", + "│ │ │ │ │ ├── GMT+7\n", + "│ │ │ │ │ ├── GMT+8\n", + "│ │ │ │ │ ├── GMT+9\n", + "│ │ │ │ │ ├── GMT-0\n", + "│ │ │ │ │ ├── GMT-1\n", + "│ │ │ │ │ ├── GMT-10\n", + "│ │ │ │ │ ├── GMT-11\n", + "│ │ │ │ │ ├── GMT-12\n", + "│ │ │ │ │ ├── GMT-13\n", + "│ │ │ │ │ ├── GMT-14\n", + "│ │ │ │ │ ├── GMT-2\n", + "│ │ │ │ │ ├── GMT-3\n", + "│ │ │ │ │ ├── GMT-4\n", + "│ │ │ │ │ ├── GMT-5\n", + "│ │ │ │ │ ├── GMT-6\n", + "│ │ │ │ │ ├── GMT-7\n", + "│ │ │ │ │ ├── GMT-8\n", + "│ │ │ │ │ ├── GMT-9\n", + "│ │ │ │ │ ├── GMT0\n", + "│ │ │ │ │ ├── Greenwich\n", + "│ │ │ │ │ ├── UCT\n", + "│ │ │ │ │ ├── UTC\n", + "│ │ │ │ │ ├── Universal\n", + "│ │ │ │ │ └── Zulu\n", + "│ │ │ │ ├── Europe\n", + "│ │ │ │ │ ├── Amsterdam\n", + "│ │ │ │ │ ├── Andorra\n", + "│ │ │ │ │ ├── Astrakhan\n", + "│ │ │ │ │ ├── Athens\n", + "│ │ │ │ │ ├── Belfast\n", + "│ │ │ │ │ ├── Belgrade\n", + "│ │ │ │ │ ├── Berlin\n", + "│ │ │ │ │ ├── Bratislava\n", + "│ │ │ │ │ ├── Brussels\n", + "│ │ │ │ │ ├── Bucharest\n", + "│ │ │ │ │ ├── Budapest\n", + "│ │ │ │ │ ├── Busingen\n", + "│ │ │ │ │ ├── Chisinau\n", + "│ │ │ │ │ ├── Copenhagen\n", + "│ │ │ │ │ ├── Dublin\n", + "│ │ │ │ │ ├── Gibraltar\n", + "│ │ │ │ │ ├── Guernsey\n", + "│ │ │ │ │ ├── Helsinki\n", + "│ │ │ │ │ ├── Isle_of_Man\n", + "│ │ │ │ │ ├── Istanbul\n", + "│ │ │ │ │ ├── Jersey\n", + "│ │ │ │ │ ├── Kaliningrad\n", + "│ │ │ │ │ ├── Kiev\n", + "│ │ │ │ │ ├── Kirov\n", + "│ │ │ │ │ ├── Kyiv\n", + "│ │ │ │ │ ├── Lisbon\n", + "│ │ │ │ │ ├── Ljubljana\n", + "│ │ │ │ │ ├── London\n", + "│ │ │ │ │ ├── Luxembourg\n", + "│ │ │ │ │ ├── Madrid\n", + "│ │ │ │ │ ├── Malta\n", + "│ │ │ │ │ ├── Mariehamn\n", + "│ │ │ │ │ ├── Minsk\n", + "│ │ │ │ │ ├── Monaco\n", + "│ │ │ │ │ ├── Moscow\n", + "│ │ │ │ │ ├── Nicosia\n", + "│ │ │ │ │ ├── Oslo\n", + "│ │ │ │ │ ├── Paris\n", + "│ │ │ │ │ ├── Podgorica\n", + "│ │ │ │ │ ├── Prague\n", + "│ │ │ │ │ ├── Riga\n", + "│ │ │ │ │ ├── Rome\n", + "│ │ │ │ │ ├── Samara\n", + "│ │ │ │ │ ├── San_Marino\n", + "│ │ │ │ │ ├── Sarajevo\n", + "│ │ │ │ │ ├── Saratov\n", + "│ │ │ │ │ ├── Simferopol\n", + "│ │ │ │ │ ├── Skopje\n", + "│ │ │ │ │ ├── Sofia\n", + "│ │ │ │ │ ├── Stockholm\n", + "│ │ │ │ │ ├── Tallinn\n", + "│ │ │ │ │ ├── Tirane\n", + "│ │ │ │ │ ├── Tiraspol\n", + "│ │ │ │ │ ├── Ulyanovsk\n", + "│ │ │ │ │ ├── Uzhgorod\n", + "│ │ │ │ │ ├── Vaduz\n", + "│ │ │ │ │ ├── Vatican\n", + "│ │ │ │ │ ├── Vienna\n", + "│ │ │ │ │ ├── Vilnius\n", + "│ │ │ │ │ ├── Volgograd\n", + "│ │ │ │ │ ├── Warsaw\n", + "│ │ │ │ │ ├── Zagreb\n", + "│ │ │ │ │ ├── Zaporozhye\n", + "│ │ │ │ │ └── Zurich\n", + "│ │ │ │ ├── Factory\n", + "│ │ │ │ ├── GB\n", + "│ │ │ │ ├── GB-Eire\n", + "│ │ │ │ ├── GMT\n", + "│ │ │ │ ├── GMT+0\n", + "│ │ │ │ ├── GMT-0\n", + "│ │ │ │ ├── GMT0\n", + "│ │ │ │ ├── Greenwich\n", + "│ │ │ │ ├── HST\n", + "│ │ │ │ ├── Hongkong\n", + "│ │ │ │ ├── Iceland\n", + "│ │ │ │ ├── Indian\n", + "│ │ │ │ │ ├── Antananarivo\n", + "│ │ │ │ │ ├── Chagos\n", + "│ │ │ │ │ ├── Christmas\n", + "│ │ │ │ │ ├── Cocos\n", + "│ │ │ │ │ ├── Comoro\n", + "│ │ │ │ │ ├── Kerguelen\n", + "│ │ │ │ │ ├── Mahe\n", + "│ │ │ │ │ ├── Maldives\n", + "│ │ │ │ │ ├── Mauritius\n", + "│ │ │ │ │ ├── Mayotte\n", + "│ │ │ │ │ └── Reunion\n", + "│ │ │ │ ├── Iran\n", + "│ │ │ │ ├── Israel\n", + "│ │ │ │ ├── Jamaica\n", + "│ │ │ │ ├── Japan\n", + "│ │ │ │ ├── Kwajalein\n", + "│ │ │ │ ├── Libya\n", + "│ │ │ │ ├── MET\n", + "│ │ │ │ ├── MST\n", + "│ │ │ │ ├── MST7MDT\n", + "│ │ │ │ ├── Mexico\n", + "│ │ │ │ │ ├── BajaNorte\n", + "│ │ │ │ │ ├── BajaSur\n", + "│ │ │ │ │ └── General\n", + "│ │ │ │ ├── NZ\n", + "│ │ │ │ ├── NZ-CHAT\n", + "│ │ │ │ ├── Navajo\n", + "│ │ │ │ ├── PRC\n", + "│ │ │ │ ├── PST8PDT\n", + "│ │ │ │ ├── Pacific\n", + "│ │ │ │ │ ├── Apia\n", + "│ │ │ │ │ ├── Auckland\n", + "│ │ │ │ │ ├── Bougainville\n", + "│ │ │ │ │ ├── Chatham\n", + "│ │ │ │ │ ├── Chuuk\n", + "│ │ │ │ │ ├── Easter\n", + "│ │ │ │ │ ├── Efate\n", + "│ │ │ │ │ ├── Enderbury\n", + "│ │ │ │ │ ├── Fakaofo\n", + "│ │ │ │ │ ├── Fiji\n", + "│ │ │ │ │ ├── Funafuti\n", + "│ │ │ │ │ ├── Galapagos\n", + "│ │ │ │ │ ├── Gambier\n", + "│ │ │ │ │ ├── Guadalcanal\n", + "│ │ │ │ │ ├── Guam\n", + "│ │ │ │ │ ├── Honolulu\n", + "│ │ │ │ │ ├── Johnston\n", + "│ │ │ │ │ ├── Kanton\n", + "│ │ │ │ │ ├── Kiritimati\n", + "│ │ │ │ │ ├── Kosrae\n", + "│ │ │ │ │ ├── Kwajalein\n", + "│ │ │ │ │ ├── Majuro\n", + "│ │ │ │ │ ├── Marquesas\n", + "│ │ │ │ │ ├── Midway\n", + "│ │ │ │ │ ├── Nauru\n", + "│ │ │ │ │ ├── Niue\n", + "│ │ │ │ │ ├── Norfolk\n", + "│ │ │ │ │ ├── Noumea\n", + "│ │ │ │ │ ├── Pago_Pago\n", + "│ │ │ │ │ ├── Palau\n", + "│ │ │ │ │ ├── Pitcairn\n", + "│ │ │ │ │ ├── Pohnpei\n", + "│ │ │ │ │ ├── Ponape\n", + "│ │ │ │ │ ├── Port_Moresby\n", + "│ │ │ │ │ ├── Rarotonga\n", + "│ │ │ │ │ ├── Saipan\n", + "│ │ │ │ │ ├── Samoa\n", + "│ │ │ │ │ ├── Tahiti\n", + "│ │ │ │ │ ├── Tarawa\n", + "│ │ │ │ │ ├── Tongatapu\n", + "│ │ │ │ │ ├── Truk\n", + "│ │ │ │ │ ├── Wake\n", + "│ │ │ │ │ ├── Wallis\n", + "│ │ │ │ │ └── Yap\n", + "│ │ │ │ ├── Poland\n", + "│ │ │ │ ├── Portugal\n", + "│ │ │ │ ├── ROC\n", + "│ │ │ │ ├── ROK\n", + "│ │ │ │ ├── Singapore\n", + "│ │ │ │ ├── Turkey\n", + "│ │ │ │ ├── UCT\n", + "│ │ │ │ ├── US\n", + "│ │ │ │ │ ├── Alaska\n", + "│ │ │ │ │ ├── Aleutian\n", + "│ │ │ │ │ ├── Arizona\n", + "│ │ │ │ │ ├── Central\n", + "│ │ │ │ │ ├── East-Indiana\n", + "│ │ │ │ │ ├── Eastern\n", + "│ │ │ │ │ ├── Hawaii\n", + "│ │ │ │ │ ├── Indiana-Starke\n", + "│ │ │ │ │ ├── Michigan\n", + "│ │ │ │ │ ├── Mountain\n", + "│ │ │ │ │ ├── Pacific\n", + "│ │ │ │ │ └── Samoa\n", + "│ │ │ │ ├── UTC\n", + "│ │ │ │ ├── Universal\n", + "│ │ │ │ ├── W-SU\n", + "│ │ │ │ ├── WET\n", + "│ │ │ │ ├── Zulu\n", + "│ │ │ │ ├── iso3166.tab\n", + "│ │ │ │ ├── leapseconds\n", + "│ │ │ │ ├── tzdata.zi\n", + "│ │ │ │ ├── zone.tab\n", + "│ │ │ │ ├── zone1970.tab\n", + "│ │ │ │ └── zonenow.tab\n", + "│ │ │ ├── pytz-2024.1.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE.txt\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ ├── top_level.txt\n", + "│ │ │ │ └── zip-safe\n", + "│ │ │ ├── pyzmq-26.0.2.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ ├── entry_points.txt\n", + "│ │ │ │ └── licenses\n", + "│ │ │ │ ├── LICENSE.md\n", + "│ │ │ │ └── licenses\n", + "│ │ │ │ ├── LICENSE.libsodium.txt\n", + "│ │ │ │ └── LICENSE.zeromq.txt\n", + "│ │ │ ├── pyzmq.libs\n", + "│ │ │ │ ├── libsodium-b135f62c.so.26.1.0\n", + "│ │ │ │ └── libzmq-5dd2f677.so.5.2.5\n", + "│ │ │ ├── setuptools\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _deprecation_warning.cpython-310.pyc\n", + "│ │ │ │ │ ├── _imp.cpython-310.pyc\n", + "│ │ │ │ │ ├── archive_util.cpython-310.pyc\n", + "│ │ │ │ │ ├── build_meta.cpython-310.pyc\n", + "│ │ │ │ │ ├── config.cpython-310.pyc\n", + "│ │ │ │ │ ├── dep_util.cpython-310.pyc\n", + "│ │ │ │ │ ├── depends.cpython-310.pyc\n", + "│ │ │ │ │ ├── dist.cpython-310.pyc\n", + "│ │ │ │ │ ├── errors.cpython-310.pyc\n", + "│ │ │ │ │ ├── extension.cpython-310.pyc\n", + "│ │ │ │ │ ├── glob.cpython-310.pyc\n", + "│ │ │ │ │ ├── installer.cpython-310.pyc\n", + "│ │ │ │ │ ├── launch.cpython-310.pyc\n", + "│ │ │ │ │ ├── monkey.cpython-310.pyc\n", + "│ │ │ │ │ ├── msvc.cpython-310.pyc\n", + "│ │ │ │ │ ├── namespaces.cpython-310.pyc\n", + "│ │ │ │ │ ├── package_index.cpython-310.pyc\n", + "│ │ │ │ │ ├── py34compat.cpython-310.pyc\n", + "│ │ │ │ │ ├── sandbox.cpython-310.pyc\n", + "│ │ │ │ │ ├── unicode_utils.cpython-310.pyc\n", + "│ │ │ │ │ ├── version.cpython-310.pyc\n", + "│ │ │ │ │ ├── wheel.cpython-310.pyc\n", + "│ │ │ │ │ └── windows_support.cpython-310.pyc\n", + "│ │ │ │ ├── _deprecation_warning.py\n", + "│ │ │ │ ├── _distutils\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _msvccompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── archive_util.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── bcppcompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ccompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cmd.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── config.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── core.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cygwinccompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── debug.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dep_util.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dir_util.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dist.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── errors.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── extension.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── fancy_getopt.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── file_util.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── filelist.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── log.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── msvc9compiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── msvccompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── py35compat.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── py38compat.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── spawn.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── sysconfig.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── text_file.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── unixccompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── util.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── version.cpython-310.pyc\n", + "│ │ │ │ │ │ └── versionpredicate.cpython-310.pyc\n", + "│ │ │ │ │ ├── _msvccompiler.py\n", + "│ │ │ │ │ ├── archive_util.py\n", + "│ │ │ │ │ ├── bcppcompiler.py\n", + "│ │ │ │ │ ├── ccompiler.py\n", + "│ │ │ │ │ ├── cmd.py\n", + "│ │ │ │ │ ├── command\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── bdist.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── bdist_dumb.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── bdist_msi.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── bdist_rpm.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── bdist_wininst.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── build.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── build_clib.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── build_ext.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── build_py.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── build_scripts.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── check.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── clean.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── config.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── install.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── install_data.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── install_egg_info.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── install_headers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── install_lib.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── install_scripts.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── py37compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── register.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── sdist.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── upload.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── bdist.py\n", + "│ │ │ │ │ │ ├── bdist_dumb.py\n", + "│ │ │ │ │ │ ├── bdist_msi.py\n", + "│ │ │ │ │ │ ├── bdist_rpm.py\n", + "│ │ │ │ │ │ ├── bdist_wininst.py\n", + "│ │ │ │ │ │ ├── build.py\n", + "│ │ │ │ │ │ ├── build_clib.py\n", + "│ │ │ │ │ │ ├── build_ext.py\n", + "│ │ │ │ │ │ ├── build_py.py\n", + "│ │ │ │ │ │ ├── build_scripts.py\n", + "│ │ │ │ │ │ ├── check.py\n", + "│ │ │ │ │ │ ├── clean.py\n", + "│ │ │ │ │ │ ├── config.py\n", + "│ │ │ │ │ │ ├── install.py\n", + "│ │ │ │ │ │ ├── install_data.py\n", + "│ │ │ │ │ │ ├── install_egg_info.py\n", + "│ │ │ │ │ │ ├── install_headers.py\n", + "│ │ │ │ │ │ ├── install_lib.py\n", + "│ │ │ │ │ │ ├── install_scripts.py\n", + "│ │ │ │ │ │ ├── py37compat.py\n", + "│ │ │ │ │ │ ├── register.py\n", + "│ │ │ │ │ │ ├── sdist.py\n", + "│ │ │ │ │ │ └── upload.py\n", + "│ │ │ │ │ ├── config.py\n", + "│ │ │ │ │ ├── core.py\n", + "│ │ │ │ │ ├── cygwinccompiler.py\n", + "│ │ │ │ │ ├── debug.py\n", + "│ │ │ │ │ ├── dep_util.py\n", + "│ │ │ │ │ ├── dir_util.py\n", + "│ │ │ │ │ ├── dist.py\n", + "│ │ │ │ │ ├── errors.py\n", + "│ │ │ │ │ ├── extension.py\n", + "│ │ │ │ │ ├── fancy_getopt.py\n", + "│ │ │ │ │ ├── file_util.py\n", + "│ │ │ │ │ ├── filelist.py\n", + "│ │ │ │ │ ├── log.py\n", + "│ │ │ │ │ ├── msvc9compiler.py\n", + "│ │ │ │ │ ├── msvccompiler.py\n", + "│ │ │ │ │ ├── py35compat.py\n", + "│ │ │ │ │ ├── py38compat.py\n", + "│ │ │ │ │ ├── spawn.py\n", + "│ │ │ │ │ ├── sysconfig.py\n", + "│ │ │ │ │ ├── text_file.py\n", + "│ │ │ │ │ ├── unixccompiler.py\n", + "│ │ │ │ │ ├── util.py\n", + "│ │ │ │ │ ├── version.py\n", + "│ │ │ │ │ └── versionpredicate.py\n", + "│ │ │ │ ├── _imp.py\n", + "│ │ │ │ ├── _vendor\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ordered_set.cpython-310.pyc\n", + "│ │ │ │ │ │ └── pyparsing.cpython-310.pyc\n", + "│ │ │ │ │ ├── more_itertools\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── more.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── recipes.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── more.py\n", + "│ │ │ │ │ │ └── recipes.py\n", + "│ │ │ │ │ ├── ordered_set.py\n", + "│ │ │ │ │ ├── packaging\n", + "│ │ │ │ │ │ ├── __about__.py\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __about__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _manylinux.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _musllinux.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _structures.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── markers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── requirements.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── specifiers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── tags.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── version.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _manylinux.py\n", + "│ │ │ │ │ │ ├── _musllinux.py\n", + "│ │ │ │ │ │ ├── _structures.py\n", + "│ │ │ │ │ │ ├── markers.py\n", + "│ │ │ │ │ │ ├── requirements.py\n", + "│ │ │ │ │ │ ├── specifiers.py\n", + "│ │ │ │ │ │ ├── tags.py\n", + "│ │ │ │ │ │ ├── utils.py\n", + "│ │ │ │ │ │ └── version.py\n", + "│ │ │ │ │ └── pyparsing.py\n", + "│ │ │ │ ├── archive_util.py\n", + "│ │ │ │ ├── build_meta.py\n", + "│ │ │ │ ├── cli-32.exe\n", + "│ │ │ │ ├── cli-64.exe\n", + "│ │ │ │ ├── cli-arm64.exe\n", + "│ │ │ │ ├── cli.exe\n", + "│ │ │ │ ├── command\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── alias.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── bdist_egg.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── bdist_rpm.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── build_clib.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── build_ext.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── build_py.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── develop.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dist_info.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── easy_install.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── egg_info.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── install.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── install_egg_info.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── install_lib.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── install_scripts.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── py36compat.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── register.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── rotate.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── saveopts.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── sdist.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── setopt.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── upload.cpython-310.pyc\n", + "│ │ │ │ │ │ └── upload_docs.cpython-310.pyc\n", + "│ │ │ │ │ ├── alias.py\n", + "│ │ │ │ │ ├── bdist_egg.py\n", + "│ │ │ │ │ ├── bdist_rpm.py\n", + "│ │ │ │ │ ├── build_clib.py\n", + "│ │ │ │ │ ├── build_ext.py\n", + "│ │ │ │ │ ├── build_py.py\n", + "│ │ │ │ │ ├── develop.py\n", + "│ │ │ │ │ ├── dist_info.py\n", + "│ │ │ │ │ ├── easy_install.py\n", + "│ │ │ │ │ ├── egg_info.py\n", + "│ │ │ │ │ ├── install.py\n", + "│ │ │ │ │ ├── install_egg_info.py\n", + "│ │ │ │ │ ├── install_lib.py\n", + "│ │ │ │ │ ├── install_scripts.py\n", + "│ │ │ │ │ ├── launcher manifest.xml\n", + "│ │ │ │ │ ├── py36compat.py\n", + "│ │ │ │ │ ├── register.py\n", + "│ │ │ │ │ ├── rotate.py\n", + "│ │ │ │ │ ├── saveopts.py\n", + "│ │ │ │ │ ├── sdist.py\n", + "│ │ │ │ │ ├── setopt.py\n", + "│ │ │ │ │ ├── test.py\n", + "│ │ │ │ │ ├── upload.py\n", + "│ │ │ │ │ └── upload_docs.py\n", + "│ │ │ │ ├── config.py\n", + "│ │ │ │ ├── dep_util.py\n", + "│ │ │ │ ├── depends.py\n", + "│ │ │ │ ├── dist.py\n", + "│ │ │ │ ├── errors.py\n", + "│ │ │ │ ├── extension.py\n", + "│ │ │ │ ├── extern\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ ├── glob.py\n", + "│ │ │ │ ├── gui-32.exe\n", + "│ │ │ │ ├── gui-64.exe\n", + "│ │ │ │ ├── gui-arm64.exe\n", + "│ │ │ │ ├── gui.exe\n", + "│ │ │ │ ├── installer.py\n", + "│ │ │ │ ├── launch.py\n", + "│ │ │ │ ├── monkey.py\n", + "│ │ │ │ ├── msvc.py\n", + "│ │ │ │ ├── namespaces.py\n", + "│ │ │ │ ├── package_index.py\n", + "│ │ │ │ ├── py34compat.py\n", + "│ │ │ │ ├── sandbox.py\n", + "│ │ │ │ ├── script (dev).tmpl\n", + "│ │ │ │ ├── script.tmpl\n", + "│ │ │ │ ├── unicode_utils.py\n", + "│ │ │ │ ├── version.py\n", + "│ │ │ │ ├── wheel.py\n", + "│ │ │ │ └── windows_support.py\n", + "│ │ │ ├── setuptools-59.6.0.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── REQUESTED\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ ├── entry_points.txt\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── six-1.16.0.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── six.py\n", + "│ │ │ ├── stack_data\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── core.cpython-310.pyc\n", + "│ │ │ │ │ ├── formatting.cpython-310.pyc\n", + "│ │ │ │ │ ├── serializing.cpython-310.pyc\n", + "│ │ │ │ │ ├── utils.cpython-310.pyc\n", + "│ │ │ │ │ └── version.cpython-310.pyc\n", + "│ │ │ │ ├── core.py\n", + "│ │ │ │ ├── formatting.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ ├── serializing.py\n", + "│ │ │ │ ├── utils.py\n", + "│ │ │ │ └── version.py\n", + "│ │ │ ├── stack_data-0.6.3.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE.txt\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── tornado\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _locale_data.cpython-310.pyc\n", + "│ │ │ │ │ ├── auth.cpython-310.pyc\n", + "│ │ │ │ │ ├── autoreload.cpython-310.pyc\n", + "│ │ │ │ │ ├── concurrent.cpython-310.pyc\n", + "│ │ │ │ │ ├── curl_httpclient.cpython-310.pyc\n", + "│ │ │ │ │ ├── escape.cpython-310.pyc\n", + "│ │ │ │ │ ├── gen.cpython-310.pyc\n", + "│ │ │ │ │ ├── http1connection.cpython-310.pyc\n", + "│ │ │ │ │ ├── httpclient.cpython-310.pyc\n", + "│ │ │ │ │ ├── httpserver.cpython-310.pyc\n", + "│ │ │ │ │ ├── httputil.cpython-310.pyc\n", + "│ │ │ │ │ ├── ioloop.cpython-310.pyc\n", + "│ │ │ │ │ ├── iostream.cpython-310.pyc\n", + "│ │ │ │ │ ├── locale.cpython-310.pyc\n", + "│ │ │ │ │ ├── locks.cpython-310.pyc\n", + "│ │ │ │ │ ├── log.cpython-310.pyc\n", + "│ │ │ │ │ ├── netutil.cpython-310.pyc\n", + "│ │ │ │ │ ├── options.cpython-310.pyc\n", + "│ │ │ │ │ ├── process.cpython-310.pyc\n", + "│ │ │ │ │ ├── queues.cpython-310.pyc\n", + "│ │ │ │ │ ├── routing.cpython-310.pyc\n", + "│ │ │ │ │ ├── simple_httpclient.cpython-310.pyc\n", + "│ │ │ │ │ ├── tcpclient.cpython-310.pyc\n", + "│ │ │ │ │ ├── tcpserver.cpython-310.pyc\n", + "│ │ │ │ │ ├── template.cpython-310.pyc\n", + "│ │ │ │ │ ├── testing.cpython-310.pyc\n", + "│ │ │ │ │ ├── util.cpython-310.pyc\n", + "│ │ │ │ │ ├── web.cpython-310.pyc\n", + "│ │ │ │ │ ├── websocket.cpython-310.pyc\n", + "│ │ │ │ │ └── wsgi.cpython-310.pyc\n", + "│ │ │ │ ├── _locale_data.py\n", + "│ │ │ │ ├── auth.py\n", + "│ │ │ │ ├── autoreload.py\n", + "│ │ │ │ ├── concurrent.py\n", + "│ │ │ │ ├── curl_httpclient.py\n", + "│ │ │ │ ├── escape.py\n", + "│ │ │ │ ├── gen.py\n", + "│ │ │ │ ├── http1connection.py\n", + "│ │ │ │ ├── httpclient.py\n", + "│ │ │ │ ├── httpserver.py\n", + "│ │ │ │ ├── httputil.py\n", + "│ │ │ │ ├── ioloop.py\n", + "│ │ │ │ ├── iostream.py\n", + "│ │ │ │ ├── locale.py\n", + "│ │ │ │ ├── locks.py\n", + "│ │ │ │ ├── log.py\n", + "│ │ │ │ ├── netutil.py\n", + "│ │ │ │ ├── options.py\n", + "│ │ │ │ ├── platform\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── asyncio.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── caresresolver.cpython-310.pyc\n", + "│ │ │ │ │ │ └── twisted.cpython-310.pyc\n", + "│ │ │ │ │ ├── asyncio.py\n", + "│ │ │ │ │ ├── caresresolver.py\n", + "│ │ │ │ │ └── twisted.py\n", + "│ │ │ │ ├── process.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ ├── queues.py\n", + "│ │ │ │ ├── routing.py\n", + "│ │ │ │ ├── simple_httpclient.py\n", + "│ │ │ │ ├── speedups.abi3.so\n", + "│ │ │ │ ├── speedups.pyi\n", + "│ │ │ │ ├── tcpclient.py\n", + "│ │ │ │ ├── tcpserver.py\n", + "│ │ │ │ ├── template.py\n", + "│ │ │ │ ├── test\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __main__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── asyncio_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── auth_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── autoreload_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── circlerefs_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── concurrent_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── curl_httpclient_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── escape_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── gen_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── http1connection_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── httpclient_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── httpserver_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── httputil_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── import_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ioloop_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── iostream_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── locale_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── locks_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── log_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── netutil_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── options_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── process_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── queues_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── resolve_test_helper.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── routing_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── runtests.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── simple_httpclient_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── tcpclient_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── tcpserver_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── template_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── testing_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── twisted_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── util.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── util_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── web_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── websocket_test.cpython-310.pyc\n", + "│ │ │ │ │ │ └── wsgi_test.cpython-310.pyc\n", + "│ │ │ │ │ ├── asyncio_test.py\n", + "│ │ │ │ │ ├── auth_test.py\n", + "│ │ │ │ │ ├── autoreload_test.py\n", + "│ │ │ │ │ ├── circlerefs_test.py\n", + "│ │ │ │ │ ├── concurrent_test.py\n", + "│ │ │ │ │ ├── csv_translations\n", + "│ │ │ │ │ │ └── fr_FR.csv\n", + "│ │ │ │ │ ├── curl_httpclient_test.py\n", + "│ │ │ │ │ ├── escape_test.py\n", + "│ │ │ │ │ ├── gen_test.py\n", + "│ │ │ │ │ ├── gettext_translations\n", + "│ │ │ │ │ │ └── fr_FR\n", + "│ │ │ │ │ │ └── LC_MESSAGES\n", + "│ │ │ │ │ │ ├── tornado_test.mo\n", + "│ │ │ │ │ │ └── tornado_test.po\n", + "│ │ │ │ │ ├── http1connection_test.py\n", + "│ │ │ │ │ ├── httpclient_test.py\n", + "│ │ │ │ │ ├── httpserver_test.py\n", + "│ │ │ │ │ ├── httputil_test.py\n", + "│ │ │ │ │ ├── import_test.py\n", + "│ │ │ │ │ ├── ioloop_test.py\n", + "│ │ │ │ │ ├── iostream_test.py\n", + "│ │ │ │ │ ├── locale_test.py\n", + "│ │ │ │ │ ├── locks_test.py\n", + "│ │ │ │ │ ├── log_test.py\n", + "│ │ │ │ │ ├── netutil_test.py\n", + "│ │ │ │ │ ├── options_test.cfg\n", + "│ │ │ │ │ ├── options_test.py\n", + "│ │ │ │ │ ├── options_test_types.cfg\n", + "│ │ │ │ │ ├── options_test_types_str.cfg\n", + "│ │ │ │ │ ├── process_test.py\n", + "│ │ │ │ │ ├── queues_test.py\n", + "│ │ │ │ │ ├── resolve_test_helper.py\n", + "│ │ │ │ │ ├── routing_test.py\n", + "│ │ │ │ │ ├── runtests.py\n", + "│ │ │ │ │ ├── simple_httpclient_test.py\n", + "│ │ │ │ │ ├── static\n", + "│ │ │ │ │ │ ├── dir\n", + "│ │ │ │ │ │ │ └── index.html\n", + "│ │ │ │ │ │ ├── robots.txt\n", + "│ │ │ │ │ │ ├── sample.xml\n", + "│ │ │ │ │ │ ├── sample.xml.bz2\n", + "│ │ │ │ │ │ └── sample.xml.gz\n", + "│ │ │ │ │ ├── static_foo.txt\n", + "│ │ │ │ │ ├── tcpclient_test.py\n", + "│ │ │ │ │ ├── tcpserver_test.py\n", + "│ │ │ │ │ ├── template_test.py\n", + "│ │ │ │ │ ├── templates\n", + "│ │ │ │ │ │ └── utf8.html\n", + "│ │ │ │ │ ├── test.crt\n", + "│ │ │ │ │ ├── test.key\n", + "│ │ │ │ │ ├── testing_test.py\n", + "│ │ │ │ │ ├── twisted_test.py\n", + "│ │ │ │ │ ├── util.py\n", + "│ │ │ │ │ ├── util_test.py\n", + "│ │ │ │ │ ├── web_test.py\n", + "│ │ │ │ │ ├── websocket_test.py\n", + "│ │ │ │ │ └── wsgi_test.py\n", + "│ │ │ │ ├── testing.py\n", + "│ │ │ │ ├── util.py\n", + "│ │ │ │ ├── web.py\n", + "│ │ │ │ ├── websocket.py\n", + "│ │ │ │ └── wsgi.py\n", + "│ │ │ ├── tornado-6.4.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── traitlets\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _version.cpython-310.pyc\n", + "│ │ │ │ │ ├── log.cpython-310.pyc\n", + "│ │ │ │ │ └── traitlets.cpython-310.pyc\n", + "│ │ │ │ ├── _version.py\n", + "│ │ │ │ ├── config\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── application.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── argcomplete_config.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── configurable.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── loader.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── manager.cpython-310.pyc\n", + "│ │ │ │ │ │ └── sphinxdoc.cpython-310.pyc\n", + "│ │ │ │ │ ├── application.py\n", + "│ │ │ │ │ ├── argcomplete_config.py\n", + "│ │ │ │ │ ├── configurable.py\n", + "│ │ │ │ │ ├── loader.py\n", + "│ │ │ │ │ ├── manager.py\n", + "│ │ │ │ │ └── sphinxdoc.py\n", + "│ │ │ │ ├── log.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ ├── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_traitlets.cpython-310.pyc\n", + "│ │ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_traitlets.py\n", + "│ │ │ │ │ └── utils.py\n", + "│ │ │ │ ├── traitlets.py\n", + "│ │ │ │ └── utils\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── bunch.cpython-310.pyc\n", + "│ │ │ │ │ ├── decorators.cpython-310.pyc\n", + "│ │ │ │ │ ├── descriptions.cpython-310.pyc\n", + "│ │ │ │ │ ├── getargspec.cpython-310.pyc\n", + "│ │ │ │ │ ├── importstring.cpython-310.pyc\n", + "│ │ │ │ │ ├── nested_update.cpython-310.pyc\n", + "│ │ │ │ │ ├── sentinel.cpython-310.pyc\n", + "│ │ │ │ │ ├── text.cpython-310.pyc\n", + "│ │ │ │ │ └── warnings.cpython-310.pyc\n", + "│ │ │ │ ├── bunch.py\n", + "│ │ │ │ ├── decorators.py\n", + "│ │ │ │ ├── descriptions.py\n", + "│ │ │ │ ├── getargspec.py\n", + "│ │ │ │ ├── importstring.py\n", + "│ │ │ │ ├── nested_update.py\n", + "│ │ │ │ ├── sentinel.py\n", + "│ │ │ │ ├── text.py\n", + "│ │ │ │ └── warnings.py\n", + "│ │ │ ├── traitlets-5.14.3.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── licenses\n", + "│ │ │ │ └── LICENSE\n", + "│ │ │ ├── typing_extensions-4.11.0.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ └── WHEEL\n", + "│ │ │ ├── typing_extensions.py\n", + "│ │ │ ├── tzdata\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ ├── zoneinfo\n", + "│ │ │ │ │ ├── Africa\n", + "│ │ │ │ │ │ ├── Abidjan\n", + "│ │ │ │ │ │ ├── Accra\n", + "│ │ │ │ │ │ ├── Addis_Ababa\n", + "│ │ │ │ │ │ ├── Algiers\n", + "│ │ │ │ │ │ ├── Asmara\n", + "│ │ │ │ │ │ ├── Asmera\n", + "│ │ │ │ │ │ ├── Bamako\n", + "│ │ │ │ │ │ ├── Bangui\n", + "│ │ │ │ │ │ ├── Banjul\n", + "│ │ │ │ │ │ ├── Bissau\n", + "│ │ │ │ │ │ ├── Blantyre\n", + "│ │ │ │ │ │ ├── Brazzaville\n", + "│ │ │ │ │ │ ├── Bujumbura\n", + "│ │ │ │ │ │ ├── Cairo\n", + "│ │ │ │ │ │ ├── Casablanca\n", + "│ │ │ │ │ │ ├── Ceuta\n", + "│ │ │ │ │ │ ├── Conakry\n", + "│ │ │ │ │ │ ├── Dakar\n", + "│ │ │ │ │ │ ├── Dar_es_Salaam\n", + "│ │ │ │ │ │ ├── Djibouti\n", + "│ │ │ │ │ │ ├── Douala\n", + "│ │ │ │ │ │ ├── El_Aaiun\n", + "│ │ │ │ │ │ ├── Freetown\n", + "│ │ │ │ │ │ ├── Gaborone\n", + "│ │ │ │ │ │ ├── Harare\n", + "│ │ │ │ │ │ ├── Johannesburg\n", + "│ │ │ │ │ │ ├── Juba\n", + "│ │ │ │ │ │ ├── Kampala\n", + "│ │ │ │ │ │ ├── Khartoum\n", + "│ │ │ │ │ │ ├── Kigali\n", + "│ │ │ │ │ │ ├── Kinshasa\n", + "│ │ │ │ │ │ ├── Lagos\n", + "│ │ │ │ │ │ ├── Libreville\n", + "│ │ │ │ │ │ ├── Lome\n", + "│ │ │ │ │ │ ├── Luanda\n", + "│ │ │ │ │ │ ├── Lubumbashi\n", + "│ │ │ │ │ │ ├── Lusaka\n", + "│ │ │ │ │ │ ├── Malabo\n", + "│ │ │ │ │ │ ├── Maputo\n", + "│ │ │ │ │ │ ├── Maseru\n", + "│ │ │ │ │ │ ├── Mbabane\n", + "│ │ │ │ │ │ ├── Mogadishu\n", + "│ │ │ │ │ │ ├── Monrovia\n", + "│ │ │ │ │ │ ├── Nairobi\n", + "│ │ │ │ │ │ ├── Ndjamena\n", + "│ │ │ │ │ │ ├── Niamey\n", + "│ │ │ │ │ │ ├── Nouakchott\n", + "│ │ │ │ │ │ ├── Ouagadougou\n", + "│ │ │ │ │ │ ├── Porto-Novo\n", + "│ │ │ │ │ │ ├── Sao_Tome\n", + "│ │ │ │ │ │ ├── Timbuktu\n", + "│ │ │ │ │ │ ├── Tripoli\n", + "│ │ │ │ │ │ ├── Tunis\n", + "│ │ │ │ │ │ ├── Windhoek\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── America\n", + "│ │ │ │ │ │ ├── Adak\n", + "│ │ │ │ │ │ ├── Anchorage\n", + "│ │ │ │ │ │ ├── Anguilla\n", + "│ │ │ │ │ │ ├── Antigua\n", + "│ │ │ │ │ │ ├── Araguaina\n", + "│ │ │ │ │ │ ├── Argentina\n", + "│ │ │ │ │ │ │ ├── Buenos_Aires\n", + "│ │ │ │ │ │ │ ├── Catamarca\n", + "│ │ │ │ │ │ │ ├── ComodRivadavia\n", + "│ │ │ │ │ │ │ ├── Cordoba\n", + "│ │ │ │ │ │ │ ├── Jujuy\n", + "│ │ │ │ │ │ │ ├── La_Rioja\n", + "│ │ │ │ │ │ │ ├── Mendoza\n", + "│ │ │ │ │ │ │ ├── Rio_Gallegos\n", + "│ │ │ │ │ │ │ ├── Salta\n", + "│ │ │ │ │ │ │ ├── San_Juan\n", + "│ │ │ │ │ │ │ ├── San_Luis\n", + "│ │ │ │ │ │ │ ├── Tucuman\n", + "│ │ │ │ │ │ │ ├── Ushuaia\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── Aruba\n", + "│ │ │ │ │ │ ├── Asuncion\n", + "│ │ │ │ │ │ ├── Atikokan\n", + "│ │ │ │ │ │ ├── Atka\n", + "│ │ │ │ │ │ ├── Bahia\n", + "│ │ │ │ │ │ ├── Bahia_Banderas\n", + "│ │ │ │ │ │ ├── Barbados\n", + "│ │ │ │ │ │ ├── Belem\n", + "│ │ │ │ │ │ ├── Belize\n", + "│ │ │ │ │ │ ├── Blanc-Sablon\n", + "│ │ │ │ │ │ ├── Boa_Vista\n", + "│ │ │ │ │ │ ├── Bogota\n", + "│ │ │ │ │ │ ├── Boise\n", + "│ │ │ │ │ │ ├── Buenos_Aires\n", + "│ │ │ │ │ │ ├── Cambridge_Bay\n", + "│ │ │ │ │ │ ├── Campo_Grande\n", + "│ │ │ │ │ │ ├── Cancun\n", + "│ │ │ │ │ │ ├── Caracas\n", + "│ │ │ │ │ │ ├── Catamarca\n", + "│ │ │ │ │ │ ├── Cayenne\n", + "│ │ │ │ │ │ ├── Cayman\n", + "│ │ │ │ │ │ ├── Chicago\n", + "│ │ │ │ │ │ ├── Chihuahua\n", + "│ │ │ │ │ │ ├── Ciudad_Juarez\n", + "│ │ │ │ │ │ ├── Coral_Harbour\n", + "│ │ │ │ │ │ ├── Cordoba\n", + "│ │ │ │ │ │ ├── Costa_Rica\n", + "│ │ │ │ │ │ ├── Creston\n", + "│ │ │ │ │ │ ├── Cuiaba\n", + "│ │ │ │ │ │ ├── Curacao\n", + "│ │ │ │ │ │ ├── Danmarkshavn\n", + "│ │ │ │ │ │ ├── Dawson\n", + "│ │ │ │ │ │ ├── Dawson_Creek\n", + "│ │ │ │ │ │ ├── Denver\n", + "│ │ │ │ │ │ ├── Detroit\n", + "│ │ │ │ │ │ ├── Dominica\n", + "│ │ │ │ │ │ ├── Edmonton\n", + "│ │ │ │ │ │ ├── Eirunepe\n", + "│ │ │ │ │ │ ├── El_Salvador\n", + "│ │ │ │ │ │ ├── Ensenada\n", + "│ │ │ │ │ │ ├── Fort_Nelson\n", + "│ │ │ │ │ │ ├── Fort_Wayne\n", + "│ │ │ │ │ │ ├── Fortaleza\n", + "│ │ │ │ │ │ ├── Glace_Bay\n", + "│ │ │ │ │ │ ├── Godthab\n", + "│ │ │ │ │ │ ├── Goose_Bay\n", + "│ │ │ │ │ │ ├── Grand_Turk\n", + "│ │ │ │ │ │ ├── Grenada\n", + "│ │ │ │ │ │ ├── Guadeloupe\n", + "│ │ │ │ │ │ ├── Guatemala\n", + "│ │ │ │ │ │ ├── Guayaquil\n", + "│ │ │ │ │ │ ├── Guyana\n", + "│ │ │ │ │ │ ├── Halifax\n", + "│ │ │ │ │ │ ├── Havana\n", + "│ │ │ │ │ │ ├── Hermosillo\n", + "│ │ │ │ │ │ ├── Indiana\n", + "│ │ │ │ │ │ │ ├── Indianapolis\n", + "│ │ │ │ │ │ │ ├── Knox\n", + "│ │ │ │ │ │ │ ├── Marengo\n", + "│ │ │ │ │ │ │ ├── Petersburg\n", + "│ │ │ │ │ │ │ ├── Tell_City\n", + "│ │ │ │ │ │ │ ├── Vevay\n", + "│ │ │ │ │ │ │ ├── Vincennes\n", + "│ │ │ │ │ │ │ ├── Winamac\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── Indianapolis\n", + "│ │ │ │ │ │ ├── Inuvik\n", + "│ │ │ │ │ │ ├── Iqaluit\n", + "│ │ │ │ │ │ ├── Jamaica\n", + "│ │ │ │ │ │ ├── Jujuy\n", + "│ │ │ │ │ │ ├── Juneau\n", + "│ │ │ │ │ │ ├── Kentucky\n", + "│ │ │ │ │ │ │ ├── Louisville\n", + "│ │ │ │ │ │ │ ├── Monticello\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── Knox_IN\n", + "│ │ │ │ │ │ ├── Kralendijk\n", + "│ │ │ │ │ │ ├── La_Paz\n", + "│ │ │ │ │ │ ├── Lima\n", + "│ │ │ │ │ │ ├── Los_Angeles\n", + "│ │ │ │ │ │ ├── Louisville\n", + "│ │ │ │ │ │ ├── Lower_Princes\n", + "│ │ │ │ │ │ ├── Maceio\n", + "│ │ │ │ │ │ ├── Managua\n", + "│ │ │ │ │ │ ├── Manaus\n", + "│ │ │ │ │ │ ├── Marigot\n", + "│ │ │ │ │ │ ├── Martinique\n", + "│ │ │ │ │ │ ├── Matamoros\n", + "│ │ │ │ │ │ ├── Mazatlan\n", + "│ │ │ │ │ │ ├── Mendoza\n", + "│ │ │ │ │ │ ├── Menominee\n", + "│ │ │ │ │ │ ├── Merida\n", + "│ │ │ │ │ │ ├── Metlakatla\n", + "│ │ │ │ │ │ ├── Mexico_City\n", + "│ │ │ │ │ │ ├── Miquelon\n", + "│ │ │ │ │ │ ├── Moncton\n", + "│ │ │ │ │ │ ├── Monterrey\n", + "│ │ │ │ │ │ ├── Montevideo\n", + "│ │ │ │ │ │ ├── Montreal\n", + "│ │ │ │ │ │ ├── Montserrat\n", + "│ │ │ │ │ │ ├── Nassau\n", + "│ │ │ │ │ │ ├── New_York\n", + "│ │ │ │ │ │ ├── Nipigon\n", + "│ │ │ │ │ │ ├── Nome\n", + "│ │ │ │ │ │ ├── Noronha\n", + "│ │ │ │ │ │ ├── North_Dakota\n", + "│ │ │ │ │ │ │ ├── Beulah\n", + "│ │ │ │ │ │ │ ├── Center\n", + "│ │ │ │ │ │ │ ├── New_Salem\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── Nuuk\n", + "│ │ │ │ │ │ ├── Ojinaga\n", + "│ │ │ │ │ │ ├── Panama\n", + "│ │ │ │ │ │ ├── Pangnirtung\n", + "│ │ │ │ │ │ ├── Paramaribo\n", + "│ │ │ │ │ │ ├── Phoenix\n", + "│ │ │ │ │ │ ├── Port-au-Prince\n", + "│ │ │ │ │ │ ├── Port_of_Spain\n", + "│ │ │ │ │ │ ├── Porto_Acre\n", + "│ │ │ │ │ │ ├── Porto_Velho\n", + "│ │ │ │ │ │ ├── Puerto_Rico\n", + "│ │ │ │ │ │ ├── Punta_Arenas\n", + "│ │ │ │ │ │ ├── Rainy_River\n", + "│ │ │ │ │ │ ├── Rankin_Inlet\n", + "│ │ │ │ │ │ ├── Recife\n", + "│ │ │ │ │ │ ├── Regina\n", + "│ │ │ │ │ │ ├── Resolute\n", + "│ │ │ │ │ │ ├── Rio_Branco\n", + "│ │ │ │ │ │ ├── Rosario\n", + "│ │ │ │ │ │ ├── Santa_Isabel\n", + "│ │ │ │ │ │ ├── Santarem\n", + "│ │ │ │ │ │ ├── Santiago\n", + "│ │ │ │ │ │ ├── Santo_Domingo\n", + "│ │ │ │ │ │ ├── Sao_Paulo\n", + "│ │ │ │ │ │ ├── Scoresbysund\n", + "│ │ │ │ │ │ ├── Shiprock\n", + "│ │ │ │ │ │ ├── Sitka\n", + "│ │ │ │ │ │ ├── St_Barthelemy\n", + "│ │ │ │ │ │ ├── St_Johns\n", + "│ │ │ │ │ │ ├── St_Kitts\n", + "│ │ │ │ │ │ ├── St_Lucia\n", + "│ │ │ │ │ │ ├── St_Thomas\n", + "│ │ │ │ │ │ ├── St_Vincent\n", + "│ │ │ │ │ │ ├── Swift_Current\n", + "│ │ │ │ │ │ ├── Tegucigalpa\n", + "│ │ │ │ │ │ ├── Thule\n", + "│ │ │ │ │ │ ├── Thunder_Bay\n", + "│ │ │ │ │ │ ├── Tijuana\n", + "│ │ │ │ │ │ ├── Toronto\n", + "│ │ │ │ │ │ ├── Tortola\n", + "│ │ │ │ │ │ ├── Vancouver\n", + "│ │ │ │ │ │ ├── Virgin\n", + "│ │ │ │ │ │ ├── Whitehorse\n", + "│ │ │ │ │ │ ├── Winnipeg\n", + "│ │ │ │ │ │ ├── Yakutat\n", + "│ │ │ │ │ │ ├── Yellowknife\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── Antarctica\n", + "│ │ │ │ │ │ ├── Casey\n", + "│ │ │ │ │ │ ├── Davis\n", + "│ │ │ │ │ │ ├── DumontDUrville\n", + "│ │ │ │ │ │ ├── Macquarie\n", + "│ │ │ │ │ │ ├── Mawson\n", + "│ │ │ │ │ │ ├── McMurdo\n", + "│ │ │ │ │ │ ├── Palmer\n", + "│ │ │ │ │ │ ├── Rothera\n", + "│ │ │ │ │ │ ├── South_Pole\n", + "│ │ │ │ │ │ ├── Syowa\n", + "│ │ │ │ │ │ ├── Troll\n", + "│ │ │ │ │ │ ├── Vostok\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── Arctic\n", + "│ │ │ │ │ │ ├── Longyearbyen\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── Asia\n", + "│ │ │ │ │ │ ├── Aden\n", + "│ │ │ │ │ │ ├── Almaty\n", + "│ │ │ │ │ │ ├── Amman\n", + "│ │ │ │ │ │ ├── Anadyr\n", + "│ │ │ │ │ │ ├── Aqtau\n", + "│ │ │ │ │ │ ├── Aqtobe\n", + "│ │ │ │ │ │ ├── Ashgabat\n", + "│ │ │ │ │ │ ├── Ashkhabad\n", + "│ │ │ │ │ │ ├── Atyrau\n", + "│ │ │ │ │ │ ├── Baghdad\n", + "│ │ │ │ │ │ ├── Bahrain\n", + "│ │ │ │ │ │ ├── Baku\n", + "│ │ │ │ │ │ ├── Bangkok\n", + "│ │ │ │ │ │ ├── Barnaul\n", + "│ │ │ │ │ │ ├── Beirut\n", + "│ │ │ │ │ │ ├── Bishkek\n", + "│ │ │ │ │ │ ├── Brunei\n", + "│ │ │ │ │ │ ├── Calcutta\n", + "│ │ │ │ │ │ ├── Chita\n", + "│ │ │ │ │ │ ├── Choibalsan\n", + "│ │ │ │ │ │ ├── Chongqing\n", + "│ │ │ │ │ │ ├── Chungking\n", + "│ │ │ │ │ │ ├── Colombo\n", + "│ │ │ │ │ │ ├── Dacca\n", + "│ │ │ │ │ │ ├── Damascus\n", + "│ │ │ │ │ │ ├── Dhaka\n", + "│ │ │ │ │ │ ├── Dili\n", + "│ │ │ │ │ │ ├── Dubai\n", + "│ │ │ │ │ │ ├── Dushanbe\n", + "│ │ │ │ │ │ ├── Famagusta\n", + "│ │ │ │ │ │ ├── Gaza\n", + "│ │ │ │ │ │ ├── Harbin\n", + "│ │ │ │ │ │ ├── Hebron\n", + "│ │ │ │ │ │ ├── Ho_Chi_Minh\n", + "│ │ │ │ │ │ ├── Hong_Kong\n", + "│ │ │ │ │ │ ├── Hovd\n", + "│ │ │ │ │ │ ├── Irkutsk\n", + "│ │ │ │ │ │ ├── Istanbul\n", + "│ │ │ │ │ │ ├── Jakarta\n", + "│ │ │ │ │ │ ├── Jayapura\n", + "│ │ │ │ │ │ ├── Jerusalem\n", + "│ │ │ │ │ │ ├── Kabul\n", + "│ │ │ │ │ │ ├── Kamchatka\n", + "│ │ │ │ │ │ ├── Karachi\n", + "│ │ │ │ │ │ ├── Kashgar\n", + "│ │ │ │ │ │ ├── Kathmandu\n", + "│ │ │ │ │ │ ├── Katmandu\n", + "│ │ │ │ │ │ ├── Khandyga\n", + "│ │ │ │ │ │ ├── Kolkata\n", + "│ │ │ │ │ │ ├── Krasnoyarsk\n", + "│ │ │ │ │ │ ├── Kuala_Lumpur\n", + "│ │ │ │ │ │ ├── Kuching\n", + "│ │ │ │ │ │ ├── Kuwait\n", + "│ │ │ │ │ │ ├── Macao\n", + "│ │ │ │ │ │ ├── Macau\n", + "│ │ │ │ │ │ ├── Magadan\n", + "│ │ │ │ │ │ ├── Makassar\n", + "│ │ │ │ │ │ ├── Manila\n", + "│ │ │ │ │ │ ├── Muscat\n", + "│ │ │ │ │ │ ├── Nicosia\n", + "│ │ │ │ │ │ ├── Novokuznetsk\n", + "│ │ │ │ │ │ ├── Novosibirsk\n", + "│ │ │ │ │ │ ├── Omsk\n", + "│ │ │ │ │ │ ├── Oral\n", + "│ │ │ │ │ │ ├── Phnom_Penh\n", + "│ │ │ │ │ │ ├── Pontianak\n", + "│ │ │ │ │ │ ├── Pyongyang\n", + "│ │ │ │ │ │ ├── Qatar\n", + "│ │ │ │ │ │ ├── Qostanay\n", + "│ │ │ │ │ │ ├── Qyzylorda\n", + "│ │ │ │ │ │ ├── Rangoon\n", + "│ │ │ │ │ │ ├── Riyadh\n", + "│ │ │ │ │ │ ├── Saigon\n", + "│ │ │ │ │ │ ├── Sakhalin\n", + "│ │ │ │ │ │ ├── Samarkand\n", + "│ │ │ │ │ │ ├── Seoul\n", + "│ │ │ │ │ │ ├── Shanghai\n", + "│ │ │ │ │ │ ├── Singapore\n", + "│ │ │ │ │ │ ├── Srednekolymsk\n", + "│ │ │ │ │ │ ├── Taipei\n", + "│ │ │ │ │ │ ├── Tashkent\n", + "│ │ │ │ │ │ ├── Tbilisi\n", + "│ │ │ │ │ │ ├── Tehran\n", + "│ │ │ │ │ │ ├── Tel_Aviv\n", + "│ │ │ │ │ │ ├── Thimbu\n", + "│ │ │ │ │ │ ├── Thimphu\n", + "│ │ │ │ │ │ ├── Tokyo\n", + "│ │ │ │ │ │ ├── Tomsk\n", + "│ │ │ │ │ │ ├── Ujung_Pandang\n", + "│ │ │ │ │ │ ├── Ulaanbaatar\n", + "│ │ │ │ │ │ ├── Ulan_Bator\n", + "│ │ │ │ │ │ ├── Urumqi\n", + "│ │ │ │ │ │ ├── Ust-Nera\n", + "│ │ │ │ │ │ ├── Vientiane\n", + "│ │ │ │ │ │ ├── Vladivostok\n", + "│ │ │ │ │ │ ├── Yakutsk\n", + "│ │ │ │ │ │ ├── Yangon\n", + "│ │ │ │ │ │ ├── Yekaterinburg\n", + "│ │ │ │ │ │ ├── Yerevan\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── Atlantic\n", + "│ │ │ │ │ │ ├── Azores\n", + "│ │ │ │ │ │ ├── Bermuda\n", + "│ │ │ │ │ │ ├── Canary\n", + "│ │ │ │ │ │ ├── Cape_Verde\n", + "│ │ │ │ │ │ ├── Faeroe\n", + "│ │ │ │ │ │ ├── Faroe\n", + "│ │ │ │ │ │ ├── Jan_Mayen\n", + "│ │ │ │ │ │ ├── Madeira\n", + "│ │ │ │ │ │ ├── Reykjavik\n", + "│ │ │ │ │ │ ├── South_Georgia\n", + "│ │ │ │ │ │ ├── St_Helena\n", + "│ │ │ │ │ │ ├── Stanley\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── Australia\n", + "│ │ │ │ │ │ ├── ACT\n", + "│ │ │ │ │ │ ├── Adelaide\n", + "│ │ │ │ │ │ ├── Brisbane\n", + "│ │ │ │ │ │ ├── Broken_Hill\n", + "│ │ │ │ │ │ ├── Canberra\n", + "│ │ │ │ │ │ ├── Currie\n", + "│ │ │ │ │ │ ├── Darwin\n", + "│ │ │ │ │ │ ├── Eucla\n", + "│ │ │ │ │ │ ├── Hobart\n", + "│ │ │ │ │ │ ├── LHI\n", + "│ │ │ │ │ │ ├── Lindeman\n", + "│ │ │ │ │ │ ├── Lord_Howe\n", + "│ │ │ │ │ │ ├── Melbourne\n", + "│ │ │ │ │ │ ├── NSW\n", + "│ │ │ │ │ │ ├── North\n", + "│ │ │ │ │ │ ├── Perth\n", + "│ │ │ │ │ │ ├── Queensland\n", + "│ │ │ │ │ │ ├── South\n", + "│ │ │ │ │ │ ├── Sydney\n", + "│ │ │ │ │ │ ├── Tasmania\n", + "│ │ │ │ │ │ ├── Victoria\n", + "│ │ │ │ │ │ ├── West\n", + "│ │ │ │ │ │ ├── Yancowinna\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── Brazil\n", + "│ │ │ │ │ │ ├── Acre\n", + "│ │ │ │ │ │ ├── DeNoronha\n", + "│ │ │ │ │ │ ├── East\n", + "│ │ │ │ │ │ ├── West\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── CET\n", + "│ │ │ │ │ ├── CST6CDT\n", + "│ │ │ │ │ ├── Canada\n", + "│ │ │ │ │ │ ├── Atlantic\n", + "│ │ │ │ │ │ ├── Central\n", + "│ │ │ │ │ │ ├── Eastern\n", + "│ │ │ │ │ │ ├── Mountain\n", + "│ │ │ │ │ │ ├── Newfoundland\n", + "│ │ │ │ │ │ ├── Pacific\n", + "│ │ │ │ │ │ ├── Saskatchewan\n", + "│ │ │ │ │ │ ├── Yukon\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── Chile\n", + "│ │ │ │ │ │ ├── Continental\n", + "│ │ │ │ │ │ ├── EasterIsland\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── Cuba\n", + "│ │ │ │ │ ├── EET\n", + "│ │ │ │ │ ├── EST\n", + "│ │ │ │ │ ├── EST5EDT\n", + "│ │ │ │ │ ├── Egypt\n", + "│ │ │ │ │ ├── Eire\n", + "│ │ │ │ │ ├── Etc\n", + "│ │ │ │ │ │ ├── GMT\n", + "│ │ │ │ │ │ ├── GMT+0\n", + "│ │ │ │ │ │ ├── GMT+1\n", + "│ │ │ │ │ │ ├── GMT+10\n", + "│ │ │ │ │ │ ├── GMT+11\n", + "│ │ │ │ │ │ ├── GMT+12\n", + "│ │ │ │ │ │ ├── GMT+2\n", + "│ │ │ │ │ │ ├── GMT+3\n", + "│ │ │ │ │ │ ├── GMT+4\n", + "│ │ │ │ │ │ ├── GMT+5\n", + "│ │ │ │ │ │ ├── GMT+6\n", + "│ │ │ │ │ │ ├── GMT+7\n", + "│ │ │ │ │ │ ├── GMT+8\n", + "│ │ │ │ │ │ ├── GMT+9\n", + "│ │ │ │ │ │ ├── GMT-0\n", + "│ │ │ │ │ │ ├── GMT-1\n", + "│ │ │ │ │ │ ├── GMT-10\n", + "│ │ │ │ │ │ ├── GMT-11\n", + "│ │ │ │ │ │ ├── GMT-12\n", + "│ │ │ │ │ │ ├── GMT-13\n", + "│ │ │ │ │ │ ├── GMT-14\n", + "│ │ │ │ │ │ ├── GMT-2\n", + "│ │ │ │ │ │ ├── GMT-3\n", + "│ │ │ │ │ │ ├── GMT-4\n", + "│ │ │ │ │ │ ├── GMT-5\n", + "│ │ │ │ │ │ ├── GMT-6\n", + "│ │ │ │ │ │ ├── GMT-7\n", + "│ │ │ │ │ │ ├── GMT-8\n", + "│ │ │ │ │ │ ├── GMT-9\n", + "│ │ │ │ │ │ ├── GMT0\n", + "│ │ │ │ │ │ ├── Greenwich\n", + "│ │ │ │ │ │ ├── UCT\n", + "│ │ │ │ │ │ ├── UTC\n", + "│ │ │ │ │ │ ├── Universal\n", + "│ │ │ │ │ │ ├── Zulu\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── Europe\n", + "│ │ │ │ │ │ ├── Amsterdam\n", + "│ │ │ │ │ │ ├── Andorra\n", + "│ │ │ │ │ │ ├── Astrakhan\n", + "│ │ │ │ │ │ ├── Athens\n", + "│ │ │ │ │ │ ├── Belfast\n", + "│ │ │ │ │ │ ├── Belgrade\n", + "│ │ │ │ │ │ ├── Berlin\n", + "│ │ │ │ │ │ ├── Bratislava\n", + "│ │ │ │ │ │ ├── Brussels\n", + "│ │ │ │ │ │ ├── Bucharest\n", + "│ │ │ │ │ │ ├── Budapest\n", + "│ │ │ │ │ │ ├── Busingen\n", + "│ │ │ │ │ │ ├── Chisinau\n", + "│ │ │ │ │ │ ├── Copenhagen\n", + "│ │ │ │ │ │ ├── Dublin\n", + "│ │ │ │ │ │ ├── Gibraltar\n", + "│ │ │ │ │ │ ├── Guernsey\n", + "│ │ │ │ │ │ ├── Helsinki\n", + "│ │ │ │ │ │ ├── Isle_of_Man\n", + "│ │ │ │ │ │ ├── Istanbul\n", + "│ │ │ │ │ │ ├── Jersey\n", + "│ │ │ │ │ │ ├── Kaliningrad\n", + "│ │ │ │ │ │ ├── Kiev\n", + "│ │ │ │ │ │ ├── Kirov\n", + "│ │ │ │ │ │ ├── Kyiv\n", + "│ │ │ │ │ │ ├── Lisbon\n", + "│ │ │ │ │ │ ├── Ljubljana\n", + "│ │ │ │ │ │ ├── London\n", + "│ │ │ │ │ │ ├── Luxembourg\n", + "│ │ │ │ │ │ ├── Madrid\n", + "│ │ │ │ │ │ ├── Malta\n", + "│ │ │ │ │ │ ├── Mariehamn\n", + "│ │ │ │ │ │ ├── Minsk\n", + "│ │ │ │ │ │ ├── Monaco\n", + "│ │ │ │ │ │ ├── Moscow\n", + "│ │ │ │ │ │ ├── Nicosia\n", + "│ │ │ │ │ │ ├── Oslo\n", + "│ │ │ │ │ │ ├── Paris\n", + "│ │ │ │ │ │ ├── Podgorica\n", + "│ │ │ │ │ │ ├── Prague\n", + "│ │ │ │ │ │ ├── Riga\n", + "│ │ │ │ │ │ ├── Rome\n", + "│ │ │ │ │ │ ├── Samara\n", + "│ │ │ │ │ │ ├── San_Marino\n", + "│ │ │ │ │ │ ├── Sarajevo\n", + "│ │ │ │ │ │ ├── Saratov\n", + "│ │ │ │ │ │ ├── Simferopol\n", + "│ │ │ │ │ │ ├── Skopje\n", + "│ │ │ │ │ │ ├── Sofia\n", + "│ │ │ │ │ │ ├── Stockholm\n", + "│ │ │ │ │ │ ├── Tallinn\n", + "│ │ │ │ │ │ ├── Tirane\n", + "│ │ │ │ │ │ ├── Tiraspol\n", + "│ │ │ │ │ │ ├── Ulyanovsk\n", + "│ │ │ │ │ │ ├── Uzhgorod\n", + "│ │ │ │ │ │ ├── Vaduz\n", + "│ │ │ │ │ │ ├── Vatican\n", + "│ │ │ │ │ │ ├── Vienna\n", + "│ │ │ │ │ │ ├── Vilnius\n", + "│ │ │ │ │ │ ├── Volgograd\n", + "│ │ │ │ │ │ ├── Warsaw\n", + "│ │ │ │ │ │ ├── Zagreb\n", + "│ │ │ │ │ │ ├── Zaporozhye\n", + "│ │ │ │ │ │ ├── Zurich\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── Factory\n", + "│ │ │ │ │ ├── GB\n", + "│ │ │ │ │ ├── GB-Eire\n", + "│ │ │ │ │ ├── GMT\n", + "│ │ │ │ │ ├── GMT+0\n", + "│ │ │ │ │ ├── GMT-0\n", + "│ │ │ │ │ ├── GMT0\n", + "│ │ │ │ │ ├── Greenwich\n", + "│ │ │ │ │ ├── HST\n", + "│ │ │ │ │ ├── Hongkong\n", + "│ │ │ │ │ ├── Iceland\n", + "│ │ │ │ │ ├── Indian\n", + "│ │ │ │ │ │ ├── Antananarivo\n", + "│ │ │ │ │ │ ├── Chagos\n", + "│ │ │ │ │ │ ├── Christmas\n", + "│ │ │ │ │ │ ├── Cocos\n", + "│ │ │ │ │ │ ├── Comoro\n", + "│ │ │ │ │ │ ├── Kerguelen\n", + "│ │ │ │ │ │ ├── Mahe\n", + "│ │ │ │ │ │ ├── Maldives\n", + "│ │ │ │ │ │ ├── Mauritius\n", + "│ │ │ │ │ │ ├── Mayotte\n", + "│ │ │ │ │ │ ├── Reunion\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── Iran\n", + "│ │ │ │ │ ├── Israel\n", + "│ │ │ │ │ ├── Jamaica\n", + "│ │ │ │ │ ├── Japan\n", + "│ │ │ │ │ ├── Kwajalein\n", + "│ │ │ │ │ ├── Libya\n", + "│ │ │ │ │ ├── MET\n", + "│ │ │ │ │ ├── MST\n", + "│ │ │ │ │ ├── MST7MDT\n", + "│ │ │ │ │ ├── Mexico\n", + "│ │ │ │ │ │ ├── BajaNorte\n", + "│ │ │ │ │ │ ├── BajaSur\n", + "│ │ │ │ │ │ ├── General\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── NZ\n", + "│ │ │ │ │ ├── NZ-CHAT\n", + "│ │ │ │ │ ├── Navajo\n", + "│ │ │ │ │ ├── PRC\n", + "│ │ │ │ │ ├── PST8PDT\n", + "│ │ │ │ │ ├── Pacific\n", + "│ │ │ │ │ │ ├── Apia\n", + "│ │ │ │ │ │ ├── Auckland\n", + "│ │ │ │ │ │ ├── Bougainville\n", + "│ │ │ │ │ │ ├── Chatham\n", + "│ │ │ │ │ │ ├── Chuuk\n", + "│ │ │ │ │ │ ├── Easter\n", + "│ │ │ │ │ │ ├── Efate\n", + "│ │ │ │ │ │ ├── Enderbury\n", + "│ │ │ │ │ │ ├── Fakaofo\n", + "│ │ │ │ │ │ ├── Fiji\n", + "│ │ │ │ │ │ ├── Funafuti\n", + "│ │ │ │ │ │ ├── Galapagos\n", + "│ │ │ │ │ │ ├── Gambier\n", + "│ │ │ │ │ │ ├── Guadalcanal\n", + "│ │ │ │ │ │ ├── Guam\n", + "│ │ │ │ │ │ ├── Honolulu\n", + "│ │ │ │ │ │ ├── Johnston\n", + "│ │ │ │ │ │ ├── Kanton\n", + "│ │ │ │ │ │ ├── Kiritimati\n", + "│ │ │ │ │ │ ├── Kosrae\n", + "│ │ │ │ │ │ ├── Kwajalein\n", + "│ │ │ │ │ │ ├── Majuro\n", + "│ │ │ │ │ │ ├── Marquesas\n", + "│ │ │ │ │ │ ├── Midway\n", + "│ │ │ │ │ │ ├── Nauru\n", + "│ │ │ │ │ │ ├── Niue\n", + "│ │ │ │ │ │ ├── Norfolk\n", + "│ │ │ │ │ │ ├── Noumea\n", + "│ │ │ │ │ │ ├── Pago_Pago\n", + "│ │ │ │ │ │ ├── Palau\n", + "│ │ │ │ │ │ ├── Pitcairn\n", + "│ │ │ │ │ │ ├── Pohnpei\n", + "│ │ │ │ │ │ ├── Ponape\n", + "│ │ │ │ │ │ ├── Port_Moresby\n", + "│ │ │ │ │ │ ├── Rarotonga\n", + "│ │ │ │ │ │ ├── Saipan\n", + "│ │ │ │ │ │ ├── Samoa\n", + "│ │ │ │ │ │ ├── Tahiti\n", + "│ │ │ │ │ │ ├── Tarawa\n", + "│ │ │ │ │ │ ├── Tongatapu\n", + "│ │ │ │ │ │ ├── Truk\n", + "│ │ │ │ │ │ ├── Wake\n", + "│ │ │ │ │ │ ├── Wallis\n", + "│ │ │ │ │ │ ├── Yap\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── Poland\n", + "│ │ │ │ │ ├── Portugal\n", + "│ │ │ │ │ ├── ROC\n", + "│ │ │ │ │ ├── ROK\n", + "│ │ │ │ │ ├── Singapore\n", + "│ │ │ │ │ ├── Turkey\n", + "│ │ │ │ │ ├── UCT\n", + "│ │ │ │ │ ├── US\n", + "│ │ │ │ │ │ ├── Alaska\n", + "│ │ │ │ │ │ ├── Aleutian\n", + "│ │ │ │ │ │ ├── Arizona\n", + "│ │ │ │ │ │ ├── Central\n", + "│ │ │ │ │ │ ├── East-Indiana\n", + "│ │ │ │ │ │ ├── Eastern\n", + "│ │ │ │ │ │ ├── Hawaii\n", + "│ │ │ │ │ │ ├── Indiana-Starke\n", + "│ │ │ │ │ │ ├── Michigan\n", + "│ │ │ │ │ │ ├── Mountain\n", + "│ │ │ │ │ │ ├── Pacific\n", + "│ │ │ │ │ │ ├── Samoa\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── UTC\n", + "│ │ │ │ │ ├── Universal\n", + "│ │ │ │ │ ├── W-SU\n", + "│ │ │ │ │ ├── WET\n", + "│ │ │ │ │ ├── Zulu\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── iso3166.tab\n", + "│ │ │ │ │ ├── leapseconds\n", + "│ │ │ │ │ ├── tzdata.zi\n", + "│ │ │ │ │ ├── zone.tab\n", + "│ │ │ │ │ ├── zone1970.tab\n", + "│ │ │ │ │ └── zonenow.tab\n", + "│ │ │ │ └── zones\n", + "│ │ │ ├── tzdata-2024.1.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── LICENSE_APACHE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── wcwidth\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── table_vs16.cpython-310.pyc\n", + "│ │ │ │ │ ├── table_wide.cpython-310.pyc\n", + "│ │ │ │ │ ├── table_zero.cpython-310.pyc\n", + "│ │ │ │ │ ├── unicode_versions.cpython-310.pyc\n", + "│ │ │ │ │ └── wcwidth.cpython-310.pyc\n", + "│ │ │ │ ├── table_vs16.py\n", + "│ │ │ │ ├── table_wide.py\n", + "│ │ │ │ ├── table_zero.py\n", + "│ │ │ │ ├── unicode_versions.py\n", + "│ │ │ │ └── wcwidth.py\n", + "│ │ │ ├── wcwidth-0.2.13.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ ├── top_level.txt\n", + "│ │ │ │ └── zip-safe\n", + "│ │ │ └── zmq\n", + "│ │ │ ├── __init__.pxd\n", + "│ │ │ ├── __init__.py\n", + "│ │ │ ├── __init__.pyi\n", + "│ │ │ ├── __pycache__\n", + "│ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ ├── _future.cpython-310.pyc\n", + "│ │ │ │ ├── _typing.cpython-310.pyc\n", + "│ │ │ │ ├── asyncio.cpython-310.pyc\n", + "│ │ │ │ ├── constants.cpython-310.pyc\n", + "│ │ │ │ ├── decorators.cpython-310.pyc\n", + "│ │ │ │ └── error.cpython-310.pyc\n", + "│ │ │ ├── _future.py\n", + "│ │ │ ├── _typing.py\n", + "│ │ │ ├── asyncio.py\n", + "│ │ │ ├── auth\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── asyncio.cpython-310.pyc\n", + "│ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ ├── certs.cpython-310.pyc\n", + "│ │ │ │ │ ├── ioloop.cpython-310.pyc\n", + "│ │ │ │ │ └── thread.cpython-310.pyc\n", + "│ │ │ │ ├── asyncio.py\n", + "│ │ │ │ ├── base.py\n", + "│ │ │ │ ├── certs.py\n", + "│ │ │ │ ├── ioloop.py\n", + "│ │ │ │ └── thread.py\n", + "│ │ │ ├── backend\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ └── select.cpython-310.pyc\n", + "│ │ │ │ ├── cffi\n", + "│ │ │ │ │ ├── README.md\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _poll.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── context.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── devices.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── error.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── message.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── socket.cpython-310.pyc\n", + "│ │ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ │ ├── _cdefs.h\n", + "│ │ │ │ │ ├── _cffi_src.c\n", + "│ │ │ │ │ ├── _poll.py\n", + "│ │ │ │ │ ├── context.py\n", + "│ │ │ │ │ ├── devices.py\n", + "│ │ │ │ │ ├── error.py\n", + "│ │ │ │ │ ├── message.py\n", + "│ │ │ │ │ ├── socket.py\n", + "│ │ │ │ │ └── utils.py\n", + "│ │ │ │ ├── cython\n", + "│ │ │ │ │ ├── __init__.pxd\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ └── _zmq.cpython-310.pyc\n", + "│ │ │ │ │ ├── _externs.pxd\n", + "│ │ │ │ │ ├── _zmq.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── _zmq.pxd\n", + "│ │ │ │ │ ├── _zmq.py\n", + "│ │ │ │ │ ├── constant_enums.pxi\n", + "│ │ │ │ │ └── libzmq.pxd\n", + "│ │ │ │ └── select.py\n", + "│ │ │ ├── constants.py\n", + "│ │ │ ├── decorators.py\n", + "│ │ │ ├── devices\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── basedevice.cpython-310.pyc\n", + "│ │ │ │ │ ├── monitoredqueue.cpython-310.pyc\n", + "│ │ │ │ │ ├── monitoredqueuedevice.cpython-310.pyc\n", + "│ │ │ │ │ ├── proxydevice.cpython-310.pyc\n", + "│ │ │ │ │ └── proxysteerabledevice.cpython-310.pyc\n", + "│ │ │ │ ├── basedevice.py\n", + "│ │ │ │ ├── monitoredqueue.py\n", + "│ │ │ │ ├── monitoredqueuedevice.py\n", + "│ │ │ │ ├── proxydevice.py\n", + "│ │ │ │ └── proxysteerabledevice.py\n", + "│ │ │ ├── error.py\n", + "│ │ │ ├── eventloop\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _deprecated.cpython-310.pyc\n", + "│ │ │ │ │ ├── future.cpython-310.pyc\n", + "│ │ │ │ │ ├── ioloop.cpython-310.pyc\n", + "│ │ │ │ │ └── zmqstream.cpython-310.pyc\n", + "│ │ │ │ ├── _deprecated.py\n", + "│ │ │ │ ├── future.py\n", + "│ │ │ │ ├── ioloop.py\n", + "│ │ │ │ └── zmqstream.py\n", + "│ │ │ ├── green\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── core.cpython-310.pyc\n", + "│ │ │ │ │ ├── device.cpython-310.pyc\n", + "│ │ │ │ │ └── poll.cpython-310.pyc\n", + "│ │ │ │ ├── core.py\n", + "│ │ │ │ ├── device.py\n", + "│ │ │ │ ├── eventloop\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ioloop.cpython-310.pyc\n", + "│ │ │ │ │ │ └── zmqstream.cpython-310.pyc\n", + "│ │ │ │ │ ├── ioloop.py\n", + "│ │ │ │ │ └── zmqstream.py\n", + "│ │ │ │ └── poll.py\n", + "│ │ │ ├── log\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __main__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ └── handlers.cpython-310.pyc\n", + "│ │ │ │ └── handlers.py\n", + "│ │ │ ├── py.typed\n", + "│ │ │ ├── ssh\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── forward.cpython-310.pyc\n", + "│ │ │ │ │ └── tunnel.cpython-310.pyc\n", + "│ │ │ │ ├── forward.py\n", + "│ │ │ │ └── tunnel.py\n", + "│ │ │ ├── sugar\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── attrsettr.cpython-310.pyc\n", + "│ │ │ │ │ ├── context.cpython-310.pyc\n", + "│ │ │ │ │ ├── frame.cpython-310.pyc\n", + "│ │ │ │ │ ├── poll.cpython-310.pyc\n", + "│ │ │ │ │ ├── socket.cpython-310.pyc\n", + "│ │ │ │ │ ├── stopwatch.cpython-310.pyc\n", + "│ │ │ │ │ ├── tracker.cpython-310.pyc\n", + "│ │ │ │ │ └── version.cpython-310.pyc\n", + "│ │ │ │ ├── attrsettr.py\n", + "│ │ │ │ ├── context.py\n", + "│ │ │ │ ├── frame.py\n", + "│ │ │ │ ├── poll.py\n", + "│ │ │ │ ├── socket.py\n", + "│ │ │ │ ├── stopwatch.py\n", + "│ │ │ │ ├── tracker.py\n", + "│ │ │ │ └── version.py\n", + "│ │ │ ├── tests\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_asyncio.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_auth.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_cffi_backend.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_constants.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_context.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_cython.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_decorators.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_device.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_draft.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_error.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_etc.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_ext.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_future.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_imports.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_includes.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_ioloop.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_log.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_message.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_monitor.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_monqueue.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_multipart.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_mypy.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_pair.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_poll.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_proxy_steerable.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_pubsub.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_reqrep.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_retry_eintr.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_security.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_socket.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_ssh.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_version.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_win32_shim.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_z85.cpython-310.pyc\n", + "│ │ │ │ │ └── test_zmqstream.cpython-310.pyc\n", + "│ │ │ │ ├── conftest.py\n", + "│ │ │ │ ├── cython_ext.pyx\n", + "│ │ │ │ ├── test_asyncio.py\n", + "│ │ │ │ ├── test_auth.py\n", + "│ │ │ │ ├── test_cffi_backend.py\n", + "│ │ │ │ ├── test_constants.py\n", + "│ │ │ │ ├── test_context.py\n", + "│ │ │ │ ├── test_cython.py\n", + "│ │ │ │ ├── test_decorators.py\n", + "│ │ │ │ ├── test_device.py\n", + "│ │ │ │ ├── test_draft.py\n", + "│ │ │ │ ├── test_error.py\n", + "│ │ │ │ ├── test_etc.py\n", + "│ │ │ │ ├── test_ext.py\n", + "│ │ │ │ ├── test_future.py\n", + "│ │ │ │ ├── test_imports.py\n", + "│ │ │ │ ├── test_includes.py\n", + "│ │ │ │ ├── test_ioloop.py\n", + "│ │ │ │ ├── test_log.py\n", + "│ │ │ │ ├── test_message.py\n", + "│ │ │ │ ├── test_monitor.py\n", + "│ │ │ │ ├── test_monqueue.py\n", + "│ │ │ │ ├── test_multipart.py\n", + "│ │ │ │ ├── test_mypy.py\n", + "│ │ │ │ ├── test_pair.py\n", + "│ │ │ │ ├── test_poll.py\n", + "│ │ │ │ ├── test_proxy_steerable.py\n", + "│ │ │ │ ├── test_pubsub.py\n", + "│ │ │ │ ├── test_reqrep.py\n", + "│ │ │ │ ├── test_retry_eintr.py\n", + "│ │ │ │ ├── test_security.py\n", + "│ │ │ │ ├── test_socket.py\n", + "│ │ │ │ ├── test_ssh.py\n", + "│ │ │ │ ├── test_version.py\n", + "│ │ │ │ ├── test_win32_shim.py\n", + "│ │ │ │ ├── test_z85.py\n", + "│ │ │ │ └── test_zmqstream.py\n", + "│ │ │ └── utils\n", + "│ │ │ ├── __init__.py\n", + "│ │ │ ├── __pycache__\n", + "│ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ ├── garbage.cpython-310.pyc\n", + "│ │ │ │ ├── interop.cpython-310.pyc\n", + "│ │ │ │ ├── jsonapi.cpython-310.pyc\n", + "│ │ │ │ ├── monitor.cpython-310.pyc\n", + "│ │ │ │ ├── strtypes.cpython-310.pyc\n", + "│ │ │ │ ├── win32.cpython-310.pyc\n", + "│ │ │ │ └── z85.cpython-310.pyc\n", + "│ │ │ ├── buffers.pxd\n", + "│ │ │ ├── garbage.py\n", + "│ │ │ ├── getpid_compat.h\n", + "│ │ │ ├── interop.py\n", + "│ │ │ ├── ipcmaxlen.h\n", + "│ │ │ ├── jsonapi.py\n", + "│ │ │ ├── monitor.py\n", + "│ │ │ ├── mutex.h\n", + "│ │ │ ├── pyversion_compat.h\n", + "│ │ │ ├── strtypes.py\n", + "│ │ │ ├── win32.py\n", + "│ │ │ ├── z85.py\n", + "│ │ │ └── zmq_compat.h\n", + "│ │ ├── lib64\n", + "│ │ │ └── python3.10\n", + "│ │ │ └── site-packages\n", + "│ │ │ ├── IPython\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __main__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ ├── consoleapp.cpython-310.pyc\n", + "│ │ │ │ │ ├── display.cpython-310.pyc\n", + "│ │ │ │ │ └── paths.cpython-310.pyc\n", + "│ │ │ │ ├── conftest.py\n", + "│ │ │ │ ├── consoleapp.py\n", + "│ │ │ │ ├── core\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── alias.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── application.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── async_helpers.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── autocall.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── builtin_trap.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── compilerop.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── completer.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── completerlib.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── crashhandler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── debugger.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── display.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── display_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── display_trap.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── displayhook.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── displaypub.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── error.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── events.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── excolors.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── extensions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── formatters.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── getipython.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── guarded_eval.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── history.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── historyapp.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── hooks.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── inputsplitter.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── inputtransformer.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── inputtransformer2.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── interactiveshell.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── latex_symbols.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── logger.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── macro.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── magic.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── magic_arguments.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── oinspect.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── page.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── payload.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── payloadpage.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── prefilter.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── profileapp.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── profiledir.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── prompts.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pylabtools.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── release.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── shellapp.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── splitinput.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ultratb.cpython-310.pyc\n", + "│ │ │ │ │ │ └── usage.cpython-310.pyc\n", + "│ │ │ │ │ ├── alias.py\n", + "│ │ │ │ │ ├── application.py\n", + "│ │ │ │ │ ├── async_helpers.py\n", + "│ │ │ │ │ ├── autocall.py\n", + "│ │ │ │ │ ├── builtin_trap.py\n", + "│ │ │ │ │ ├── compilerop.py\n", + "│ │ │ │ │ ├── completer.py\n", + "│ │ │ │ │ ├── completerlib.py\n", + "│ │ │ │ │ ├── crashhandler.py\n", + "│ │ │ │ │ ├── debugger.py\n", + "│ │ │ │ │ ├── display.py\n", + "│ │ │ │ │ ├── display_functions.py\n", + "│ │ │ │ │ ├── display_trap.py\n", + "│ │ │ │ │ ├── displayhook.py\n", + "│ │ │ │ │ ├── displaypub.py\n", + "│ │ │ │ │ ├── error.py\n", + "│ │ │ │ │ ├── events.py\n", + "│ │ │ │ │ ├── excolors.py\n", + "│ │ │ │ │ ├── extensions.py\n", + "│ │ │ │ │ ├── formatters.py\n", + "│ │ │ │ │ ├── getipython.py\n", + "│ │ │ │ │ ├── guarded_eval.py\n", + "│ │ │ │ │ ├── history.py\n", + "│ │ │ │ │ ├── historyapp.py\n", + "│ │ │ │ │ ├── hooks.py\n", + "│ │ │ │ │ ├── inputsplitter.py\n", + "│ │ │ │ │ ├── inputtransformer.py\n", + "│ │ │ │ │ ├── inputtransformer2.py\n", + "│ │ │ │ │ ├── interactiveshell.py\n", + "│ │ │ │ │ ├── latex_symbols.py\n", + "│ │ │ │ │ ├── logger.py\n", + "│ │ │ │ │ ├── macro.py\n", + "│ │ │ │ │ ├── magic.py\n", + "│ │ │ │ │ ├── magic_arguments.py\n", + "│ │ │ │ │ ├── magics\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── ast_mod.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── auto.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── basic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── code.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── config.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── display.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── execution.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── extension.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── history.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── logging.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── namespace.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── osm.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── packaging.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pylab.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── script.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ast_mod.py\n", + "│ │ │ │ │ │ ├── auto.py\n", + "│ │ │ │ │ │ ├── basic.py\n", + "│ │ │ │ │ │ ├── code.py\n", + "│ │ │ │ │ │ ├── config.py\n", + "│ │ │ │ │ │ ├── display.py\n", + "│ │ │ │ │ │ ├── execution.py\n", + "│ │ │ │ │ │ ├── extension.py\n", + "│ │ │ │ │ │ ├── history.py\n", + "│ │ │ │ │ │ ├── logging.py\n", + "│ │ │ │ │ │ ├── namespace.py\n", + "│ │ │ │ │ │ ├── osm.py\n", + "│ │ │ │ │ │ ├── packaging.py\n", + "│ │ │ │ │ │ ├── pylab.py\n", + "│ │ │ │ │ │ └── script.py\n", + "│ │ │ │ │ ├── oinspect.py\n", + "│ │ │ │ │ ├── page.py\n", + "│ │ │ │ │ ├── payload.py\n", + "│ │ │ │ │ ├── payloadpage.py\n", + "│ │ │ │ │ ├── prefilter.py\n", + "│ │ │ │ │ ├── profile\n", + "│ │ │ │ │ │ └── README_STARTUP\n", + "│ │ │ │ │ ├── profileapp.py\n", + "│ │ │ │ │ ├── profiledir.py\n", + "│ │ │ │ │ ├── prompts.py\n", + "│ │ │ │ │ ├── pylabtools.py\n", + "│ │ │ │ │ ├── release.py\n", + "│ │ │ │ │ ├── shellapp.py\n", + "│ │ │ │ │ ├── splitinput.py\n", + "│ │ │ │ │ ├── tests\n", + "│ │ │ │ │ │ ├── 2x2.jpg\n", + "│ │ │ │ │ │ ├── 2x2.png\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── bad_all.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── nonascii.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── nonascii2.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── print_argv.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── refbug.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── simpleerr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── tclass.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_alias.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_application.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_async_helpers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_autocall.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_compilerop.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_completer.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_completerlib.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_debugger.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_display.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_displayhook.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_events.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_exceptiongroup_tb.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_extension.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_formatters.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_guarded_eval.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_handlers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_history.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_hooks.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_imports.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_inputsplitter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_inputtransformer.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_inputtransformer2.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_inputtransformer2_line.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_interactiveshell.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_iplib.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_logger.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_magic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_magic_arguments.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_magic_terminal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_oinspect.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_page.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_paths.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_prefilter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_profile.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_prompts.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_pylabtools.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_run.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_shellapp.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_splitinput.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_ultratb.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── bad_all.py\n", + "│ │ │ │ │ │ ├── daft_extension\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ └── daft_extension.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── daft_extension.py\n", + "│ │ │ │ │ │ ├── nonascii.py\n", + "│ │ │ │ │ │ ├── nonascii2.py\n", + "│ │ │ │ │ │ ├── print_argv.py\n", + "│ │ │ │ │ │ ├── refbug.py\n", + "│ │ │ │ │ │ ├── simpleerr.py\n", + "│ │ │ │ │ │ ├── tclass.py\n", + "│ │ │ │ │ │ ├── test_alias.py\n", + "│ │ │ │ │ │ ├── test_application.py\n", + "│ │ │ │ │ │ ├── test_async_helpers.py\n", + "│ │ │ │ │ │ ├── test_autocall.py\n", + "│ │ │ │ │ │ ├── test_compilerop.py\n", + "│ │ │ │ │ │ ├── test_completer.py\n", + "│ │ │ │ │ │ ├── test_completerlib.py\n", + "│ │ │ │ │ │ ├── test_debugger.py\n", + "│ │ │ │ │ │ ├── test_display.py\n", + "│ │ │ │ │ │ ├── test_displayhook.py\n", + "│ │ │ │ │ │ ├── test_events.py\n", + "│ │ │ │ │ │ ├── test_exceptiongroup_tb.py\n", + "│ │ │ │ │ │ ├── test_extension.py\n", + "│ │ │ │ │ │ ├── test_formatters.py\n", + "│ │ │ │ │ │ ├── test_guarded_eval.py\n", + "│ │ │ │ │ │ ├── test_handlers.py\n", + "│ │ │ │ │ │ ├── test_history.py\n", + "│ │ │ │ │ │ ├── test_hooks.py\n", + "│ │ │ │ │ │ ├── test_imports.py\n", + "│ │ │ │ │ │ ├── test_inputsplitter.py\n", + "│ │ │ │ │ │ ├── test_inputtransformer.py\n", + "│ │ │ │ │ │ ├── test_inputtransformer2.py\n", + "│ │ │ │ │ │ ├── test_inputtransformer2_line.py\n", + "│ │ │ │ │ │ ├── test_interactiveshell.py\n", + "│ │ │ │ │ │ ├── test_iplib.py\n", + "│ │ │ │ │ │ ├── test_logger.py\n", + "│ │ │ │ │ │ ├── test_magic.py\n", + "│ │ │ │ │ │ ├── test_magic_arguments.py\n", + "│ │ │ │ │ │ ├── test_magic_terminal.py\n", + "│ │ │ │ │ │ ├── test_oinspect.py\n", + "│ │ │ │ │ │ ├── test_page.py\n", + "│ │ │ │ │ │ ├── test_paths.py\n", + "│ │ │ │ │ │ ├── test_prefilter.py\n", + "│ │ │ │ │ │ ├── test_profile.py\n", + "│ │ │ │ │ │ ├── test_prompts.py\n", + "│ │ │ │ │ │ ├── test_pylabtools.py\n", + "│ │ │ │ │ │ ├── test_run.py\n", + "│ │ │ │ │ │ ├── test_shellapp.py\n", + "│ │ │ │ │ │ ├── test_splitinput.py\n", + "│ │ │ │ │ │ └── test_ultratb.py\n", + "│ │ │ │ │ ├── ultratb.py\n", + "│ │ │ │ │ └── usage.py\n", + "│ │ │ │ ├── display.py\n", + "│ │ │ │ ├── extensions\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── autoreload.cpython-310.pyc\n", + "│ │ │ │ │ │ └── storemagic.cpython-310.pyc\n", + "│ │ │ │ │ ├── autoreload.py\n", + "│ │ │ │ │ ├── storemagic.py\n", + "│ │ │ │ │ └── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_autoreload.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_storemagic.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_autoreload.py\n", + "│ │ │ │ │ └── test_storemagic.py\n", + "│ │ │ │ ├── external\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── qt_for_kernel.cpython-310.pyc\n", + "│ │ │ │ │ │ └── qt_loaders.cpython-310.pyc\n", + "│ │ │ │ │ ├── qt_for_kernel.py\n", + "│ │ │ │ │ ├── qt_loaders.py\n", + "│ │ │ │ │ └── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_qt_loaders.cpython-310.pyc\n", + "│ │ │ │ │ └── test_qt_loaders.py\n", + "│ │ │ │ ├── lib\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── backgroundjobs.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── clipboard.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── deepreload.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── demo.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── display.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── editorhooks.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── guisupport.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── latextools.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── lexers.cpython-310.pyc\n", + "│ │ │ │ │ │ └── pretty.cpython-310.pyc\n", + "│ │ │ │ │ ├── backgroundjobs.py\n", + "│ │ │ │ │ ├── clipboard.py\n", + "│ │ │ │ │ ├── deepreload.py\n", + "│ │ │ │ │ ├── demo.py\n", + "│ │ │ │ │ ├── display.py\n", + "│ │ │ │ │ ├── editorhooks.py\n", + "│ │ │ │ │ ├── guisupport.py\n", + "│ │ │ │ │ ├── latextools.py\n", + "│ │ │ │ │ ├── lexers.py\n", + "│ │ │ │ │ ├── pretty.py\n", + "│ │ │ │ │ └── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_backgroundjobs.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_clipboard.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_deepreload.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_display.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_editorhooks.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_imports.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_latextools.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_lexers.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_pretty.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_pygments.cpython-310.pyc\n", + "│ │ │ │ │ ├── test.wav\n", + "│ │ │ │ │ ├── test_backgroundjobs.py\n", + "│ │ │ │ │ ├── test_clipboard.py\n", + "│ │ │ │ │ ├── test_deepreload.py\n", + "│ │ │ │ │ ├── test_display.py\n", + "│ │ │ │ │ ├── test_editorhooks.py\n", + "│ │ │ │ │ ├── test_imports.py\n", + "│ │ │ │ │ ├── test_latextools.py\n", + "│ │ │ │ │ ├── test_lexers.py\n", + "│ │ │ │ │ ├── test_pretty.py\n", + "│ │ │ │ │ └── test_pygments.py\n", + "│ │ │ │ ├── paths.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ ├── sphinxext\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── custom_doctests.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ipython_console_highlighting.cpython-310.pyc\n", + "│ │ │ │ │ │ └── ipython_directive.cpython-310.pyc\n", + "│ │ │ │ │ ├── custom_doctests.py\n", + "│ │ │ │ │ ├── ipython_console_highlighting.py\n", + "│ │ │ │ │ └── ipython_directive.py\n", + "│ │ │ │ ├── terminal\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── console.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── debugger.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── embed.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── interactiveshell.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ipapp.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── magics.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── prompts.cpython-310.pyc\n", + "│ │ │ │ │ │ └── ptutils.cpython-310.pyc\n", + "│ │ │ │ │ ├── console.py\n", + "│ │ │ │ │ ├── debugger.py\n", + "│ │ │ │ │ ├── embed.py\n", + "│ │ │ │ │ ├── interactiveshell.py\n", + "│ │ │ │ │ ├── ipapp.py\n", + "│ │ │ │ │ ├── magics.py\n", + "│ │ │ │ │ ├── prompts.py\n", + "│ │ │ │ │ ├── pt_inputhooks\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── asyncio.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── glut.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── gtk.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── gtk3.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── gtk4.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── osx.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pyglet.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── qt.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── tk.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── wx.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── asyncio.py\n", + "│ │ │ │ │ │ ├── glut.py\n", + "│ │ │ │ │ │ ├── gtk.py\n", + "│ │ │ │ │ │ ├── gtk3.py\n", + "│ │ │ │ │ │ ├── gtk4.py\n", + "│ │ │ │ │ │ ├── osx.py\n", + "│ │ │ │ │ │ ├── pyglet.py\n", + "│ │ │ │ │ │ ├── qt.py\n", + "│ │ │ │ │ │ ├── tk.py\n", + "│ │ │ │ │ │ └── wx.py\n", + "│ │ │ │ │ ├── ptutils.py\n", + "│ │ │ │ │ ├── shortcuts\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── auto_match.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── auto_suggest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── filters.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── auto_match.py\n", + "│ │ │ │ │ │ ├── auto_suggest.py\n", + "│ │ │ │ │ │ └── filters.py\n", + "│ │ │ │ │ └── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_debug_magic.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_embed.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_help.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_interactivshell.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_pt_inputhooks.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_shortcuts.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_debug_magic.py\n", + "│ │ │ │ │ ├── test_embed.py\n", + "│ │ │ │ │ ├── test_help.py\n", + "│ │ │ │ │ ├── test_interactivshell.py\n", + "│ │ │ │ │ ├── test_pt_inputhooks.py\n", + "│ │ │ │ │ └── test_shortcuts.py\n", + "│ │ │ │ ├── testing\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── decorators.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── globalipapp.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ipunittest.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── skipdoctest.cpython-310.pyc\n", + "│ │ │ │ │ │ └── tools.cpython-310.pyc\n", + "│ │ │ │ │ ├── decorators.py\n", + "│ │ │ │ │ ├── globalipapp.py\n", + "│ │ │ │ │ ├── ipunittest.py\n", + "│ │ │ │ │ ├── plugin\n", + "│ │ │ │ │ │ ├── README.txt\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── dtexample.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── ipdoctest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pytest_ipdoctest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── setup.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── simple.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── simplevars.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_ipdoctest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_refs.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dtexample.py\n", + "│ │ │ │ │ │ ├── ipdoctest.py\n", + "│ │ │ │ │ │ ├── pytest_ipdoctest.py\n", + "│ │ │ │ │ │ ├── setup.py\n", + "│ │ │ │ │ │ ├── simple.py\n", + "│ │ │ │ │ │ ├── simplevars.py\n", + "│ │ │ │ │ │ ├── test_combo.txt\n", + "│ │ │ │ │ │ ├── test_example.txt\n", + "│ │ │ │ │ │ ├── test_exampleip.txt\n", + "│ │ │ │ │ │ ├── test_ipdoctest.py\n", + "│ │ │ │ │ │ └── test_refs.py\n", + "│ │ │ │ │ ├── skipdoctest.py\n", + "│ │ │ │ │ ├── tests\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_decorators.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_ipunittest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_tools.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_decorators.py\n", + "│ │ │ │ │ │ ├── test_ipunittest.py\n", + "│ │ │ │ │ │ └── test_tools.py\n", + "│ │ │ │ │ └── tools.py\n", + "│ │ │ │ └── utils\n", + "│ │ │ │ ├── PyColorize.py\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── PyColorize.cpython-310.pyc\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _process_cli.cpython-310.pyc\n", + "│ │ │ │ │ ├── _process_common.cpython-310.pyc\n", + "│ │ │ │ │ ├── _process_emscripten.cpython-310.pyc\n", + "│ │ │ │ │ ├── _process_posix.cpython-310.pyc\n", + "│ │ │ │ │ ├── _process_win32.cpython-310.pyc\n", + "│ │ │ │ │ ├── _process_win32_controller.cpython-310.pyc\n", + "│ │ │ │ │ ├── _sysinfo.cpython-310.pyc\n", + "│ │ │ │ │ ├── capture.cpython-310.pyc\n", + "│ │ │ │ │ ├── colorable.cpython-310.pyc\n", + "│ │ │ │ │ ├── coloransi.cpython-310.pyc\n", + "│ │ │ │ │ ├── contexts.cpython-310.pyc\n", + "│ │ │ │ │ ├── daemonize.cpython-310.pyc\n", + "│ │ │ │ │ ├── data.cpython-310.pyc\n", + "│ │ │ │ │ ├── decorators.cpython-310.pyc\n", + "│ │ │ │ │ ├── dir2.cpython-310.pyc\n", + "│ │ │ │ │ ├── docs.cpython-310.pyc\n", + "│ │ │ │ │ ├── encoding.cpython-310.pyc\n", + "│ │ │ │ │ ├── eventful.cpython-310.pyc\n", + "│ │ │ │ │ ├── frame.cpython-310.pyc\n", + "│ │ │ │ │ ├── generics.cpython-310.pyc\n", + "│ │ │ │ │ ├── importstring.cpython-310.pyc\n", + "│ │ │ │ │ ├── io.cpython-310.pyc\n", + "│ │ │ │ │ ├── ipstruct.cpython-310.pyc\n", + "│ │ │ │ │ ├── jsonutil.cpython-310.pyc\n", + "│ │ │ │ │ ├── localinterfaces.cpython-310.pyc\n", + "│ │ │ │ │ ├── log.cpython-310.pyc\n", + "│ │ │ │ │ ├── module_paths.cpython-310.pyc\n", + "│ │ │ │ │ ├── openpy.cpython-310.pyc\n", + "│ │ │ │ │ ├── path.cpython-310.pyc\n", + "│ │ │ │ │ ├── process.cpython-310.pyc\n", + "│ │ │ │ │ ├── py3compat.cpython-310.pyc\n", + "│ │ │ │ │ ├── sentinel.cpython-310.pyc\n", + "│ │ │ │ │ ├── shimmodule.cpython-310.pyc\n", + "│ │ │ │ │ ├── signatures.cpython-310.pyc\n", + "│ │ │ │ │ ├── strdispatch.cpython-310.pyc\n", + "│ │ │ │ │ ├── sysinfo.cpython-310.pyc\n", + "│ │ │ │ │ ├── syspathcontext.cpython-310.pyc\n", + "│ │ │ │ │ ├── tempdir.cpython-310.pyc\n", + "│ │ │ │ │ ├── terminal.cpython-310.pyc\n", + "│ │ │ │ │ ├── text.cpython-310.pyc\n", + "│ │ │ │ │ ├── timing.cpython-310.pyc\n", + "│ │ │ │ │ ├── tokenutil.cpython-310.pyc\n", + "│ │ │ │ │ ├── traitlets.cpython-310.pyc\n", + "│ │ │ │ │ ├── tz.cpython-310.pyc\n", + "│ │ │ │ │ ├── ulinecache.cpython-310.pyc\n", + "│ │ │ │ │ ├── version.cpython-310.pyc\n", + "│ │ │ │ │ └── wildcard.cpython-310.pyc\n", + "│ │ │ │ ├── _process_cli.py\n", + "│ │ │ │ ├── _process_common.py\n", + "│ │ │ │ ├── _process_emscripten.py\n", + "│ │ │ │ ├── _process_posix.py\n", + "│ │ │ │ ├── _process_win32.py\n", + "│ │ │ │ ├── _process_win32_controller.py\n", + "│ │ │ │ ├── _sysinfo.py\n", + "│ │ │ │ ├── capture.py\n", + "│ │ │ │ ├── colorable.py\n", + "│ │ │ │ ├── coloransi.py\n", + "│ │ │ │ ├── contexts.py\n", + "│ │ │ │ ├── daemonize.py\n", + "│ │ │ │ ├── data.py\n", + "│ │ │ │ ├── decorators.py\n", + "│ │ │ │ ├── dir2.py\n", + "│ │ │ │ ├── docs.py\n", + "│ │ │ │ ├── encoding.py\n", + "│ │ │ │ ├── eventful.py\n", + "│ │ │ │ ├── frame.py\n", + "│ │ │ │ ├── generics.py\n", + "│ │ │ │ ├── importstring.py\n", + "│ │ │ │ ├── io.py\n", + "│ │ │ │ ├── ipstruct.py\n", + "│ │ │ │ ├── jsonutil.py\n", + "│ │ │ │ ├── localinterfaces.py\n", + "│ │ │ │ ├── log.py\n", + "│ │ │ │ ├── module_paths.py\n", + "│ │ │ │ ├── openpy.py\n", + "│ │ │ │ ├── path.py\n", + "│ │ │ │ ├── process.py\n", + "│ │ │ │ ├── py3compat.py\n", + "│ │ │ │ ├── sentinel.py\n", + "│ │ │ │ ├── shimmodule.py\n", + "│ │ │ │ ├── signatures.py\n", + "│ │ │ │ ├── strdispatch.py\n", + "│ │ │ │ ├── sysinfo.py\n", + "│ │ │ │ ├── syspathcontext.py\n", + "│ │ │ │ ├── tempdir.py\n", + "│ │ │ │ ├── terminal.py\n", + "│ │ │ │ ├── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_capture.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_decorators.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_deprecated.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_dir2.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_imports.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_importstring.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_io.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_module_paths.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_openpy.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_path.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_process.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_pycolorize.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_shimmodule.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_sysinfo.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_tempdir.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_text.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_tokenutil.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_wildcard.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_capture.py\n", + "│ │ │ │ │ ├── test_decorators.py\n", + "│ │ │ │ │ ├── test_deprecated.py\n", + "│ │ │ │ │ ├── test_dir2.py\n", + "│ │ │ │ │ ├── test_imports.py\n", + "│ │ │ │ │ ├── test_importstring.py\n", + "│ │ │ │ │ ├── test_io.py\n", + "│ │ │ │ │ ├── test_module_paths.py\n", + "│ │ │ │ │ ├── test_openpy.py\n", + "│ │ │ │ │ ├── test_path.py\n", + "│ │ │ │ │ ├── test_process.py\n", + "│ │ │ │ │ ├── test_pycolorize.py\n", + "│ │ │ │ │ ├── test_shimmodule.py\n", + "│ │ │ │ │ ├── test_sysinfo.py\n", + "│ │ │ │ │ ├── test_tempdir.py\n", + "│ │ │ │ │ ├── test_text.py\n", + "│ │ │ │ │ ├── test_tokenutil.py\n", + "│ │ │ │ │ └── test_wildcard.py\n", + "│ │ │ │ ├── text.py\n", + "│ │ │ │ ├── timing.py\n", + "│ │ │ │ ├── tokenutil.py\n", + "│ │ │ │ ├── traitlets.py\n", + "│ │ │ │ ├── tz.py\n", + "│ │ │ │ ├── ulinecache.py\n", + "│ │ │ │ ├── version.py\n", + "│ │ │ │ └── wildcard.py\n", + "│ │ │ ├── __pycache__\n", + "│ │ │ │ ├── decorator.cpython-310.pyc\n", + "│ │ │ │ ├── ipykernel_launcher.cpython-310.pyc\n", + "│ │ │ │ ├── jupyter.cpython-310.pyc\n", + "│ │ │ │ ├── nest_asyncio.cpython-310.pyc\n", + "│ │ │ │ ├── six.cpython-310.pyc\n", + "│ │ │ │ └── typing_extensions.cpython-310.pyc\n", + "│ │ │ ├── _distutils_hack\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ └── override.cpython-310.pyc\n", + "│ │ │ │ └── override.py\n", + "│ │ │ ├── asttokens\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── astroid_compat.cpython-310.pyc\n", + "│ │ │ │ │ ├── asttokens.cpython-310.pyc\n", + "│ │ │ │ │ ├── line_numbers.cpython-310.pyc\n", + "│ │ │ │ │ ├── mark_tokens.cpython-310.pyc\n", + "│ │ │ │ │ ├── util.cpython-310.pyc\n", + "│ │ │ │ │ └── version.cpython-310.pyc\n", + "│ │ │ │ ├── astroid_compat.py\n", + "│ │ │ │ ├── asttokens.py\n", + "│ │ │ │ ├── line_numbers.py\n", + "│ │ │ │ ├── mark_tokens.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ ├── util.py\n", + "│ │ │ │ └── version.py\n", + "│ │ │ ├── asttokens-2.4.1.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── comm\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ └── base_comm.cpython-310.pyc\n", + "│ │ │ │ ├── base_comm.py\n", + "│ │ │ │ └── py.typed\n", + "│ │ │ ├── comm-0.2.2.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── licenses\n", + "│ │ │ │ └── LICENSE\n", + "│ │ │ ├── dateutil\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _common.cpython-310.pyc\n", + "│ │ │ │ │ ├── _version.cpython-310.pyc\n", + "│ │ │ │ │ ├── easter.cpython-310.pyc\n", + "│ │ │ │ │ ├── relativedelta.cpython-310.pyc\n", + "│ │ │ │ │ ├── rrule.cpython-310.pyc\n", + "│ │ │ │ │ ├── tzwin.cpython-310.pyc\n", + "│ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ ├── _common.py\n", + "│ │ │ │ ├── _version.py\n", + "│ │ │ │ ├── easter.py\n", + "│ │ │ │ ├── parser\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _parser.cpython-310.pyc\n", + "│ │ │ │ │ │ └── isoparser.cpython-310.pyc\n", + "│ │ │ │ │ ├── _parser.py\n", + "│ │ │ │ │ └── isoparser.py\n", + "│ │ │ │ ├── relativedelta.py\n", + "│ │ │ │ ├── rrule.py\n", + "│ │ │ │ ├── tz\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _common.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _factories.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── tz.cpython-310.pyc\n", + "│ │ │ │ │ │ └── win.cpython-310.pyc\n", + "│ │ │ │ │ ├── _common.py\n", + "│ │ │ │ │ ├── _factories.py\n", + "│ │ │ │ │ ├── tz.py\n", + "│ │ │ │ │ └── win.py\n", + "│ │ │ │ ├── tzwin.py\n", + "│ │ │ │ ├── utils.py\n", + "│ │ │ │ └── zoneinfo\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ └── rebuild.cpython-310.pyc\n", + "│ │ │ │ ├── dateutil-zoneinfo.tar.gz\n", + "│ │ │ │ └── rebuild.py\n", + "│ │ │ ├── debugpy\n", + "│ │ │ │ ├── ThirdPartyNotices.txt\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __main__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _version.cpython-310.pyc\n", + "│ │ │ │ │ └── public_api.cpython-310.pyc\n", + "│ │ │ │ ├── _vendored\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _pydevd_packaging.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _util.cpython-310.pyc\n", + "│ │ │ │ │ │ └── force_pydevd.cpython-310.pyc\n", + "│ │ │ │ │ ├── _pydevd_packaging.py\n", + "│ │ │ │ │ ├── _util.py\n", + "│ │ │ │ │ ├── force_pydevd.py\n", + "│ │ │ │ │ └── pydevd\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── pydev_app_engine_debug_startup.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pydev_coverage.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pydev_pysrc.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pydev_run_in_console.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pydevconsole.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pydevd.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pydevd_file_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pydevd_tracing.cpython-310.pyc\n", + "│ │ │ │ │ │ └── setup_pydevd_cython.cpython-310.pyc\n", + "│ │ │ │ │ ├── _pydev_bundle\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _pydev_calltip_util.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _pydev_completer.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _pydev_execfile.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _pydev_filesystem_encoding.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _pydev_getopt.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _pydev_imports_tipper.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _pydev_jy_imports_tipper.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _pydev_log.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _pydev_saved_modules.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _pydev_sys_patch.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _pydev_tipper_common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_console_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_import_hook.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_imports.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_ipython_console.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_ipython_console_011.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_is_thread_alive.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_localhost.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_log.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_monkey.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_monkey_qt.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_override.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_umd.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── pydev_versioncheck.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _pydev_calltip_util.py\n", + "│ │ │ │ │ │ ├── _pydev_completer.py\n", + "│ │ │ │ │ │ ├── _pydev_execfile.py\n", + "│ │ │ │ │ │ ├── _pydev_filesystem_encoding.py\n", + "│ │ │ │ │ │ ├── _pydev_getopt.py\n", + "│ │ │ │ │ │ ├── _pydev_imports_tipper.py\n", + "│ │ │ │ │ │ ├── _pydev_jy_imports_tipper.py\n", + "│ │ │ │ │ │ ├── _pydev_log.py\n", + "│ │ │ │ │ │ ├── _pydev_saved_modules.py\n", + "│ │ │ │ │ │ ├── _pydev_sys_patch.py\n", + "│ │ │ │ │ │ ├── _pydev_tipper_common.py\n", + "│ │ │ │ │ │ ├── fsnotify\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pydev_console_utils.py\n", + "│ │ │ │ │ │ ├── pydev_import_hook.py\n", + "│ │ │ │ │ │ ├── pydev_imports.py\n", + "│ │ │ │ │ │ ├── pydev_ipython_console.py\n", + "│ │ │ │ │ │ ├── pydev_ipython_console_011.py\n", + "│ │ │ │ │ │ ├── pydev_is_thread_alive.py\n", + "│ │ │ │ │ │ ├── pydev_localhost.py\n", + "│ │ │ │ │ │ ├── pydev_log.py\n", + "│ │ │ │ │ │ ├── pydev_monkey.py\n", + "│ │ │ │ │ │ ├── pydev_monkey_qt.py\n", + "│ │ │ │ │ │ ├── pydev_override.py\n", + "│ │ │ │ │ │ ├── pydev_umd.py\n", + "│ │ │ │ │ │ └── pydev_versioncheck.py\n", + "│ │ │ │ │ ├── _pydev_runfiles\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_runfiles.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_runfiles_coverage.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_runfiles_nose.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_runfiles_parallel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_runfiles_parallel_client.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_runfiles_pytest2.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydev_runfiles_unittest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── pydev_runfiles_xml_rpc.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pydev_runfiles.py\n", + "│ │ │ │ │ │ ├── pydev_runfiles_coverage.py\n", + "│ │ │ │ │ │ ├── pydev_runfiles_nose.py\n", + "│ │ │ │ │ │ ├── pydev_runfiles_parallel.py\n", + "│ │ │ │ │ │ ├── pydev_runfiles_parallel_client.py\n", + "│ │ │ │ │ │ ├── pydev_runfiles_pytest2.py\n", + "│ │ │ │ │ │ ├── pydev_runfiles_unittest.py\n", + "│ │ │ │ │ │ └── pydev_runfiles_xml_rpc.py\n", + "│ │ │ │ │ ├── _pydevd_bundle\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevconsole_code.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_additional_thread_info.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_additional_thread_info_regular.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_breakpoints.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_bytecode_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_code_to_source.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_collect_bytecode_info.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_comm.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_comm_constants.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_command_line_handling.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_console.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_constants.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_custom_frames.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_cython_wrapper.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_daemon_thread.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_defaults.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_dont_trace.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_dont_trace_files.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_exec2.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_extension_api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_extension_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_filtering.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_frame.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_frame_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_gevent_integration.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_import_class.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_io.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_json_debug_options.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_net_command.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_net_command_factory_json.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_net_command_factory_xml.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_plugin_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_process_net_command.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_process_net_command_json.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_referrers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_reload.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_resolver.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_runpy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_safe_repr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_save_locals.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_signature.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_source_mapping.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_stackless.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_suspended_frames.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_thread_lifecycle.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_timeout.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_trace_api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_trace_dispatch.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_trace_dispatch_regular.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_traceproperty.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_vars.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_vm_type.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── pydevd_xml.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _debug_adapter\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __main__pydevd_gen_debug_adapter_protocol.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── __main__pydevd_gen_debug_adapter_protocol.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── pydevd_base_schema.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── pydevd_schema.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── pydevd_schema_log.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── debugProtocol.json\n", + "│ │ │ │ │ │ │ ├── debugProtocolCustom.json\n", + "│ │ │ │ │ │ │ ├── pydevd_base_schema.py\n", + "│ │ │ │ │ │ │ ├── pydevd_schema.py\n", + "│ │ │ │ │ │ │ └── pydevd_schema_log.py\n", + "│ │ │ │ │ │ ├── pydevconsole_code.py\n", + "│ │ │ │ │ │ ├── pydevd_additional_thread_info.py\n", + "│ │ │ │ │ │ ├── pydevd_additional_thread_info_regular.py\n", + "│ │ │ │ │ │ ├── pydevd_api.py\n", + "│ │ │ │ │ │ ├── pydevd_breakpoints.py\n", + "│ │ │ │ │ │ ├── pydevd_bytecode_utils.py\n", + "│ │ │ │ │ │ ├── pydevd_code_to_source.py\n", + "│ │ │ │ │ │ ├── pydevd_collect_bytecode_info.py\n", + "│ │ │ │ │ │ ├── pydevd_comm.py\n", + "│ │ │ │ │ │ ├── pydevd_comm_constants.py\n", + "│ │ │ │ │ │ ├── pydevd_command_line_handling.py\n", + "│ │ │ │ │ │ ├── pydevd_concurrency_analyser\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── pydevd_concurrency_logger.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── pydevd_thread_wrappers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_concurrency_logger.py\n", + "│ │ │ │ │ │ │ └── pydevd_thread_wrappers.py\n", + "│ │ │ │ │ │ ├── pydevd_console.py\n", + "│ │ │ │ │ │ ├── pydevd_constants.py\n", + "│ │ │ │ │ │ ├── pydevd_custom_frames.py\n", + "│ │ │ │ │ │ ├── pydevd_cython.c\n", + "│ │ │ │ │ │ ├── pydevd_cython.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── pydevd_cython.pxd\n", + "│ │ │ │ │ │ ├── pydevd_cython.pyx\n", + "│ │ │ │ │ │ ├── pydevd_cython_wrapper.py\n", + "│ │ │ │ │ │ ├── pydevd_daemon_thread.py\n", + "│ │ │ │ │ │ ├── pydevd_defaults.py\n", + "│ │ │ │ │ │ ├── pydevd_dont_trace.py\n", + "│ │ │ │ │ │ ├── pydevd_dont_trace_files.py\n", + "│ │ │ │ │ │ ├── pydevd_exec2.py\n", + "│ │ │ │ │ │ ├── pydevd_extension_api.py\n", + "│ │ │ │ │ │ ├── pydevd_extension_utils.py\n", + "│ │ │ │ │ │ ├── pydevd_filtering.py\n", + "│ │ │ │ │ │ ├── pydevd_frame.py\n", + "│ │ │ │ │ │ ├── pydevd_frame_utils.py\n", + "│ │ │ │ │ │ ├── pydevd_gevent_integration.py\n", + "│ │ │ │ │ │ ├── pydevd_import_class.py\n", + "│ │ │ │ │ │ ├── pydevd_io.py\n", + "│ │ │ │ │ │ ├── pydevd_json_debug_options.py\n", + "│ │ │ │ │ │ ├── pydevd_net_command.py\n", + "│ │ │ │ │ │ ├── pydevd_net_command_factory_json.py\n", + "│ │ │ │ │ │ ├── pydevd_net_command_factory_xml.py\n", + "│ │ │ │ │ │ ├── pydevd_plugin_utils.py\n", + "│ │ │ │ │ │ ├── pydevd_process_net_command.py\n", + "│ │ │ │ │ │ ├── pydevd_process_net_command_json.py\n", + "│ │ │ │ │ │ ├── pydevd_referrers.py\n", + "│ │ │ │ │ │ ├── pydevd_reload.py\n", + "│ │ │ │ │ │ ├── pydevd_resolver.py\n", + "│ │ │ │ │ │ ├── pydevd_runpy.py\n", + "│ │ │ │ │ │ ├── pydevd_safe_repr.py\n", + "│ │ │ │ │ │ ├── pydevd_save_locals.py\n", + "│ │ │ │ │ │ ├── pydevd_signature.py\n", + "│ │ │ │ │ │ ├── pydevd_source_mapping.py\n", + "│ │ │ │ │ │ ├── pydevd_stackless.py\n", + "│ │ │ │ │ │ ├── pydevd_suspended_frames.py\n", + "│ │ │ │ │ │ ├── pydevd_thread_lifecycle.py\n", + "│ │ │ │ │ │ ├── pydevd_timeout.py\n", + "│ │ │ │ │ │ ├── pydevd_trace_api.py\n", + "│ │ │ │ │ │ ├── pydevd_trace_dispatch.py\n", + "│ │ │ │ │ │ ├── pydevd_trace_dispatch_regular.py\n", + "│ │ │ │ │ │ ├── pydevd_traceproperty.py\n", + "│ │ │ │ │ │ ├── pydevd_utils.py\n", + "│ │ │ │ │ │ ├── pydevd_vars.py\n", + "│ │ │ │ │ │ ├── pydevd_vm_type.py\n", + "│ │ │ │ │ │ └── pydevd_xml.py\n", + "│ │ │ │ │ ├── _pydevd_frame_eval\n", + "│ │ │ │ │ │ ├── .gitignore\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_frame_eval_cython_wrapper.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_frame_eval_main.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_frame_tracing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── pydevd_modify_bytecode.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pydevd_frame_eval_cython_wrapper.py\n", + "│ │ │ │ │ │ ├── pydevd_frame_eval_main.py\n", + "│ │ │ │ │ │ ├── pydevd_frame_evaluator.c\n", + "│ │ │ │ │ │ ├── pydevd_frame_evaluator.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── pydevd_frame_evaluator.pxd\n", + "│ │ │ │ │ │ ├── pydevd_frame_evaluator.pyx\n", + "│ │ │ │ │ │ ├── pydevd_frame_evaluator.template.pyx\n", + "│ │ │ │ │ │ ├── pydevd_frame_tracing.py\n", + "│ │ │ │ │ │ ├── pydevd_modify_bytecode.py\n", + "│ │ │ │ │ │ ├── release_mem.h\n", + "│ │ │ │ │ │ └── vendored\n", + "│ │ │ │ │ │ ├── README.txt\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── pydevd_fix_code.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── bytecode\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── bytecode.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── cfg.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── concrete.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── flags.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── instr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── peephole_opt.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── bytecode.py\n", + "│ │ │ │ │ │ │ ├── cfg.py\n", + "│ │ │ │ │ │ │ ├── concrete.py\n", + "│ │ │ │ │ │ │ ├── flags.py\n", + "│ │ │ │ │ │ │ ├── instr.py\n", + "│ │ │ │ │ │ │ ├── peephole_opt.py\n", + "│ │ │ │ │ │ │ └── tests\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_bytecode.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_cfg.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_code.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_concrete.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_flags.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_instr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_misc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_peephole_opt.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── util_annotation.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_bytecode.py\n", + "│ │ │ │ │ │ │ ├── test_cfg.py\n", + "│ │ │ │ │ │ │ ├── test_code.py\n", + "│ │ │ │ │ │ │ ├── test_concrete.py\n", + "│ │ │ │ │ │ │ ├── test_flags.py\n", + "│ │ │ │ │ │ │ ├── test_instr.py\n", + "│ │ │ │ │ │ │ ├── test_misc.py\n", + "│ │ │ │ │ │ │ ├── test_peephole_opt.py\n", + "│ │ │ │ │ │ │ └── util_annotation.py\n", + "│ │ │ │ │ │ ├── bytecode-0.13.0.dev0.dist-info\n", + "│ │ │ │ │ │ │ ├── COPYING\n", + "│ │ │ │ │ │ │ ├── INSTALLER\n", + "│ │ │ │ │ │ │ ├── METADATA\n", + "│ │ │ │ │ │ │ ├── RECORD\n", + "│ │ │ │ │ │ │ ├── REQUESTED\n", + "│ │ │ │ │ │ │ ├── WHEEL\n", + "│ │ │ │ │ │ │ ├── direct_url.json\n", + "│ │ │ │ │ │ │ └── top_level.txt\n", + "│ │ │ │ │ │ └── pydevd_fix_code.py\n", + "│ │ │ │ │ ├── pydev_app_engine_debug_startup.py\n", + "│ │ │ │ │ ├── pydev_coverage.py\n", + "│ │ │ │ │ ├── pydev_ipython\n", + "│ │ │ │ │ │ ├── README\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── inputhook.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── inputhookglut.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── inputhookgtk.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── inputhookgtk3.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── inputhookpyglet.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── inputhookqt4.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── inputhookqt5.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── inputhooktk.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── inputhookwx.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── matplotlibtools.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── qt.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── qt_for_kernel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── qt_loaders.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── version.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── inputhook.py\n", + "│ │ │ │ │ │ ├── inputhookglut.py\n", + "│ │ │ │ │ │ ├── inputhookgtk.py\n", + "│ │ │ │ │ │ ├── inputhookgtk3.py\n", + "│ │ │ │ │ │ ├── inputhookpyglet.py\n", + "│ │ │ │ │ │ ├── inputhookqt4.py\n", + "│ │ │ │ │ │ ├── inputhookqt5.py\n", + "│ │ │ │ │ │ ├── inputhooktk.py\n", + "│ │ │ │ │ │ ├── inputhookwx.py\n", + "│ │ │ │ │ │ ├── matplotlibtools.py\n", + "│ │ │ │ │ │ ├── qt.py\n", + "│ │ │ │ │ │ ├── qt_for_kernel.py\n", + "│ │ │ │ │ │ ├── qt_loaders.py\n", + "│ │ │ │ │ │ └── version.py\n", + "│ │ │ │ │ ├── pydev_pysrc.py\n", + "│ │ │ │ │ ├── pydev_run_in_console.py\n", + "│ │ │ │ │ ├── pydev_sitecustomize\n", + "│ │ │ │ │ │ ├── __not_in_default_pythonpath.txt\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ └── sitecustomize.cpython-310.pyc\n", + "│ │ │ │ │ │ └── sitecustomize.py\n", + "│ │ │ │ │ ├── pydevconsole.py\n", + "│ │ │ │ │ ├── pydevd.py\n", + "│ │ │ │ │ ├── pydevd_attach_to_process\n", + "│ │ │ │ │ │ ├── README.txt\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── _always_live_program.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _check.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _test_attach_to_process.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _test_attach_to_process_linux.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── add_code_to_python_process.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── attach_pydevd.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── attach_script.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _always_live_program.py\n", + "│ │ │ │ │ │ ├── _check.py\n", + "│ │ │ │ │ │ ├── _test_attach_to_process.py\n", + "│ │ │ │ │ │ ├── _test_attach_to_process_linux.py\n", + "│ │ │ │ │ │ ├── add_code_to_python_process.py\n", + "│ │ │ │ │ │ ├── attach_linux_amd64.so\n", + "│ │ │ │ │ │ ├── attach_pydevd.py\n", + "│ │ │ │ │ │ ├── attach_script.py\n", + "│ │ │ │ │ │ ├── common\n", + "│ │ │ │ │ │ │ ├── py_custom_pyeval_settrace.hpp\n", + "│ │ │ │ │ │ │ ├── py_custom_pyeval_settrace_310.hpp\n", + "│ │ │ │ │ │ │ ├── py_custom_pyeval_settrace_311.hpp\n", + "│ │ │ │ │ │ │ ├── py_custom_pyeval_settrace_common.hpp\n", + "│ │ │ │ │ │ │ ├── py_settrace.hpp\n", + "│ │ │ │ │ │ │ ├── py_utils.hpp\n", + "│ │ │ │ │ │ │ ├── py_version.hpp\n", + "│ │ │ │ │ │ │ ├── python.h\n", + "│ │ │ │ │ │ │ └── ref_utils.hpp\n", + "│ │ │ │ │ │ ├── linux_and_mac\n", + "│ │ │ │ │ │ │ ├── .gitignore\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ └── lldb_prepare.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── attach.cpp\n", + "│ │ │ │ │ │ │ ├── compile_linux.sh\n", + "│ │ │ │ │ │ │ ├── compile_mac.sh\n", + "│ │ │ │ │ │ │ ├── compile_manylinux.cmd\n", + "│ │ │ │ │ │ │ └── lldb_prepare.py\n", + "│ │ │ │ │ │ ├── winappdbg\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── breakpoint.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── crash.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── debug.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── disasm.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── event.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── interactive.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── module.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── process.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── registry.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── search.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── sql.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── system.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── textio.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── thread.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── util.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── window.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── breakpoint.py\n", + "│ │ │ │ │ │ │ ├── compat.py\n", + "│ │ │ │ │ │ │ ├── crash.py\n", + "│ │ │ │ │ │ │ ├── debug.py\n", + "│ │ │ │ │ │ │ ├── disasm.py\n", + "│ │ │ │ │ │ │ ├── event.py\n", + "│ │ │ │ │ │ │ ├── interactive.py\n", + "│ │ │ │ │ │ │ ├── module.py\n", + "│ │ │ │ │ │ │ ├── process.py\n", + "│ │ │ │ │ │ │ ├── registry.py\n", + "│ │ │ │ │ │ │ ├── search.py\n", + "│ │ │ │ │ │ │ ├── sql.py\n", + "│ │ │ │ │ │ │ ├── system.py\n", + "│ │ │ │ │ │ │ ├── textio.py\n", + "│ │ │ │ │ │ │ ├── thread.py\n", + "│ │ │ │ │ │ │ ├── util.py\n", + "│ │ │ │ │ │ │ ├── win32\n", + "│ │ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── advapi32.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── context_amd64.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── context_i386.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── dbghelp.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── defines.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── gdi32.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── kernel32.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── ntdll.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── peb_teb.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── psapi.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── shell32.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── shlwapi.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── user32.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── version.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ └── wtsapi32.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── advapi32.py\n", + "│ │ │ │ │ │ │ │ ├── context_amd64.py\n", + "│ │ │ │ │ │ │ │ ├── context_i386.py\n", + "│ │ │ │ │ │ │ │ ├── dbghelp.py\n", + "│ │ │ │ │ │ │ │ ├── defines.py\n", + "│ │ │ │ │ │ │ │ ├── gdi32.py\n", + "│ │ │ │ │ │ │ │ ├── kernel32.py\n", + "│ │ │ │ │ │ │ │ ├── ntdll.py\n", + "│ │ │ │ │ │ │ │ ├── peb_teb.py\n", + "│ │ │ │ │ │ │ │ ├── psapi.py\n", + "│ │ │ │ │ │ │ │ ├── shell32.py\n", + "│ │ │ │ │ │ │ │ ├── shlwapi.py\n", + "│ │ │ │ │ │ │ │ ├── user32.py\n", + "│ │ │ │ │ │ │ │ ├── version.py\n", + "│ │ │ │ │ │ │ │ └── wtsapi32.py\n", + "│ │ │ │ │ │ │ └── window.py\n", + "│ │ │ │ │ │ └── windows\n", + "│ │ │ │ │ │ ├── attach.cpp\n", + "│ │ │ │ │ │ ├── attach.h\n", + "│ │ │ │ │ │ ├── compile_windows.bat\n", + "│ │ │ │ │ │ ├── inject_dll.cpp\n", + "│ │ │ │ │ │ ├── py_win_helpers.hpp\n", + "│ │ │ │ │ │ ├── run_code_in_memory.hpp\n", + "│ │ │ │ │ │ ├── run_code_on_dllmain.cpp\n", + "│ │ │ │ │ │ ├── stdafx.cpp\n", + "│ │ │ │ │ │ ├── stdafx.h\n", + "│ │ │ │ │ │ └── targetver.h\n", + "│ │ │ │ │ ├── pydevd_file_utils.py\n", + "│ │ │ │ │ ├── pydevd_plugins\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── django_debug.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── jinja2_debug.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── pydevd_line_validation.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── django_debug.py\n", + "│ │ │ │ │ │ ├── extensions\n", + "│ │ │ │ │ │ │ ├── README.md\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── types\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── pydevd_helpers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── pydevd_plugin_numpy_types.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── pydevd_plugin_pandas_types.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── pydevd_plugins_django_form_str.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pydevd_helpers.py\n", + "│ │ │ │ │ │ │ ├── pydevd_plugin_numpy_types.py\n", + "│ │ │ │ │ │ │ ├── pydevd_plugin_pandas_types.py\n", + "│ │ │ │ │ │ │ └── pydevd_plugins_django_form_str.py\n", + "│ │ │ │ │ │ ├── jinja2_debug.py\n", + "│ │ │ │ │ │ └── pydevd_line_validation.py\n", + "│ │ │ │ │ ├── pydevd_tracing.py\n", + "│ │ │ │ │ └── setup_pydevd_cython.py\n", + "│ │ │ │ ├── _version.py\n", + "│ │ │ │ ├── adapter\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __main__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── clients.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── components.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── launchers.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── servers.cpython-310.pyc\n", + "│ │ │ │ │ │ └── sessions.cpython-310.pyc\n", + "│ │ │ │ │ ├── clients.py\n", + "│ │ │ │ │ ├── components.py\n", + "│ │ │ │ │ ├── launchers.py\n", + "│ │ │ │ │ ├── servers.py\n", + "│ │ │ │ │ └── sessions.py\n", + "│ │ │ │ ├── common\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── json.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── log.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── messaging.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── singleton.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── sockets.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── stacks.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── timestamp.cpython-310.pyc\n", + "│ │ │ │ │ │ └── util.cpython-310.pyc\n", + "│ │ │ │ │ ├── json.py\n", + "│ │ │ │ │ ├── log.py\n", + "│ │ │ │ │ ├── messaging.py\n", + "│ │ │ │ │ ├── singleton.py\n", + "│ │ │ │ │ ├── sockets.py\n", + "│ │ │ │ │ ├── stacks.py\n", + "│ │ │ │ │ ├── timestamp.py\n", + "│ │ │ │ │ └── util.py\n", + "│ │ │ │ ├── launcher\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __main__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── debuggee.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── handlers.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── output.cpython-310.pyc\n", + "│ │ │ │ │ │ └── winapi.cpython-310.pyc\n", + "│ │ │ │ │ ├── debuggee.py\n", + "│ │ │ │ │ ├── handlers.py\n", + "│ │ │ │ │ ├── output.py\n", + "│ │ │ │ │ └── winapi.py\n", + "│ │ │ │ ├── public_api.py\n", + "│ │ │ │ └── server\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── api.cpython-310.pyc\n", + "│ │ │ │ │ ├── attach_pid_injected.cpython-310.pyc\n", + "│ │ │ │ │ └── cli.cpython-310.pyc\n", + "│ │ │ │ ├── api.py\n", + "│ │ │ │ ├── attach_pid_injected.py\n", + "│ │ │ │ └── cli.py\n", + "│ │ │ ├── debugpy-1.8.1.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── decorator-5.1.1.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE.txt\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ ├── pbr.json\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── decorator.py\n", + "│ │ │ ├── distutils-precedence.pth\n", + "│ │ │ ├── exceptiongroup\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _catch.cpython-310.pyc\n", + "│ │ │ │ │ ├── _exceptions.cpython-310.pyc\n", + "│ │ │ │ │ ├── _formatting.cpython-310.pyc\n", + "│ │ │ │ │ ├── _suppress.cpython-310.pyc\n", + "│ │ │ │ │ └── _version.cpython-310.pyc\n", + "│ │ │ │ ├── _catch.py\n", + "│ │ │ │ ├── _exceptions.py\n", + "│ │ │ │ ├── _formatting.py\n", + "│ │ │ │ ├── _suppress.py\n", + "│ │ │ │ ├── _version.py\n", + "│ │ │ │ └── py.typed\n", + "│ │ │ ├── exceptiongroup-1.2.1.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ └── WHEEL\n", + "│ │ │ ├── executing\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _exceptions.cpython-310.pyc\n", + "│ │ │ │ │ ├── _position_node_finder.cpython-310.pyc\n", + "│ │ │ │ │ ├── executing.cpython-310.pyc\n", + "│ │ │ │ │ └── version.cpython-310.pyc\n", + "│ │ │ │ ├── _exceptions.py\n", + "│ │ │ │ ├── _position_node_finder.py\n", + "│ │ │ │ ├── executing.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ └── version.py\n", + "│ │ │ ├── executing-2.0.1.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE.txt\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── ipykernel\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __main__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _eventloop_macos.cpython-310.pyc\n", + "│ │ │ │ │ ├── _version.cpython-310.pyc\n", + "│ │ │ │ │ ├── compiler.cpython-310.pyc\n", + "│ │ │ │ │ ├── connect.cpython-310.pyc\n", + "│ │ │ │ │ ├── control.cpython-310.pyc\n", + "│ │ │ │ │ ├── datapub.cpython-310.pyc\n", + "│ │ │ │ │ ├── debugger.cpython-310.pyc\n", + "│ │ │ │ │ ├── displayhook.cpython-310.pyc\n", + "│ │ │ │ │ ├── embed.cpython-310.pyc\n", + "│ │ │ │ │ ├── eventloops.cpython-310.pyc\n", + "│ │ │ │ │ ├── heartbeat.cpython-310.pyc\n", + "│ │ │ │ │ ├── iostream.cpython-310.pyc\n", + "│ │ │ │ │ ├── ipkernel.cpython-310.pyc\n", + "│ │ │ │ │ ├── jsonutil.cpython-310.pyc\n", + "│ │ │ │ │ ├── kernelapp.cpython-310.pyc\n", + "│ │ │ │ │ ├── kernelbase.cpython-310.pyc\n", + "│ │ │ │ │ ├── kernelspec.cpython-310.pyc\n", + "│ │ │ │ │ ├── log.cpython-310.pyc\n", + "│ │ │ │ │ ├── parentpoller.cpython-310.pyc\n", + "│ │ │ │ │ ├── pickleutil.cpython-310.pyc\n", + "│ │ │ │ │ ├── serialize.cpython-310.pyc\n", + "│ │ │ │ │ ├── trio_runner.cpython-310.pyc\n", + "│ │ │ │ │ └── zmqshell.cpython-310.pyc\n", + "│ │ │ │ ├── _eventloop_macos.py\n", + "│ │ │ │ ├── _version.py\n", + "│ │ │ │ ├── comm\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── comm.cpython-310.pyc\n", + "│ │ │ │ │ │ └── manager.cpython-310.pyc\n", + "│ │ │ │ │ ├── comm.py\n", + "│ │ │ │ │ └── manager.py\n", + "│ │ │ │ ├── compiler.py\n", + "│ │ │ │ ├── connect.py\n", + "│ │ │ │ ├── control.py\n", + "│ │ │ │ ├── datapub.py\n", + "│ │ │ │ ├── debugger.py\n", + "│ │ │ │ ├── displayhook.py\n", + "│ │ │ │ ├── embed.py\n", + "│ │ │ │ ├── eventloops.py\n", + "│ │ │ │ ├── gui\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── gtk3embed.cpython-310.pyc\n", + "│ │ │ │ │ │ └── gtkembed.cpython-310.pyc\n", + "│ │ │ │ │ ├── gtk3embed.py\n", + "│ │ │ │ │ └── gtkembed.py\n", + "│ │ │ │ ├── heartbeat.py\n", + "│ │ │ │ ├── inprocess\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── blocking.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── channels.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── client.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── constants.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ipkernel.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── manager.cpython-310.pyc\n", + "│ │ │ │ │ │ └── socket.cpython-310.pyc\n", + "│ │ │ │ │ ├── blocking.py\n", + "│ │ │ │ │ ├── channels.py\n", + "│ │ │ │ │ ├── client.py\n", + "│ │ │ │ │ ├── constants.py\n", + "│ │ │ │ │ ├── ipkernel.py\n", + "│ │ │ │ │ ├── manager.py\n", + "│ │ │ │ │ └── socket.py\n", + "│ │ │ │ ├── iostream.py\n", + "│ │ │ │ ├── ipkernel.py\n", + "│ │ │ │ ├── jsonutil.py\n", + "│ │ │ │ ├── kernelapp.py\n", + "│ │ │ │ ├── kernelbase.py\n", + "│ │ │ │ ├── kernelspec.py\n", + "│ │ │ │ ├── log.py\n", + "│ │ │ │ ├── parentpoller.py\n", + "│ │ │ │ ├── pickleutil.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ ├── pylab\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── backend_inline.cpython-310.pyc\n", + "│ │ │ │ │ │ └── config.cpython-310.pyc\n", + "│ │ │ │ │ ├── backend_inline.py\n", + "│ │ │ │ │ └── config.py\n", + "│ │ │ │ ├── resources\n", + "│ │ │ │ │ ├── logo-32x32.png\n", + "│ │ │ │ │ ├── logo-64x64.png\n", + "│ │ │ │ │ └── logo-svg.svg\n", + "│ │ │ │ ├── serialize.py\n", + "│ │ │ │ ├── trio_runner.py\n", + "│ │ │ │ └── zmqshell.py\n", + "│ │ │ ├── ipykernel-6.29.4.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── REQUESTED\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── licenses\n", + "│ │ │ │ └── LICENSE\n", + "│ │ │ ├── ipykernel_launcher.py\n", + "│ │ │ ├── ipython-8.24.0.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ ├── entry_points.txt\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── jedi\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __main__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _compatibility.cpython-310.pyc\n", + "│ │ │ │ │ ├── cache.cpython-310.pyc\n", + "│ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ ├── debug.cpython-310.pyc\n", + "│ │ │ │ │ ├── file_io.cpython-310.pyc\n", + "│ │ │ │ │ ├── parser_utils.cpython-310.pyc\n", + "│ │ │ │ │ ├── settings.cpython-310.pyc\n", + "│ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ ├── _compatibility.py\n", + "│ │ │ │ ├── api\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── classes.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── completion.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── completion_cache.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── environment.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── errors.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── exceptions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── file_name.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── helpers.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── interpreter.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── keywords.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── project.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── replstartup.cpython-310.pyc\n", + "│ │ │ │ │ │ └── strings.cpython-310.pyc\n", + "│ │ │ │ │ ├── classes.py\n", + "│ │ │ │ │ ├── completion.py\n", + "│ │ │ │ │ ├── completion_cache.py\n", + "│ │ │ │ │ ├── environment.py\n", + "│ │ │ │ │ ├── errors.py\n", + "│ │ │ │ │ ├── exceptions.py\n", + "│ │ │ │ │ ├── file_name.py\n", + "│ │ │ │ │ ├── helpers.py\n", + "│ │ │ │ │ ├── interpreter.py\n", + "│ │ │ │ │ ├── keywords.py\n", + "│ │ │ │ │ ├── project.py\n", + "│ │ │ │ │ ├── refactoring\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── extract.cpython-310.pyc\n", + "│ │ │ │ │ │ └── extract.py\n", + "│ │ │ │ │ ├── replstartup.py\n", + "│ │ │ │ │ └── strings.py\n", + "│ │ │ │ ├── cache.py\n", + "│ │ │ │ ├── common.py\n", + "│ │ │ │ ├── debug.py\n", + "│ │ │ │ ├── file_io.py\n", + "│ │ │ │ ├── inference\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── analysis.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── arguments.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base_value.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cache.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── context.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── docstring_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── docstrings.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dynamic_params.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── filters.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── finder.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── flow_analysis.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── helpers.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── imports.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── lazy_value.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── names.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── param.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── parser_cache.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── recursion.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── references.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── signature.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── star_args.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── syntax_tree.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── sys_path.cpython-310.pyc\n", + "│ │ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ │ ├── analysis.py\n", + "│ │ │ │ │ ├── arguments.py\n", + "│ │ │ │ │ ├── base_value.py\n", + "│ │ │ │ │ ├── cache.py\n", + "│ │ │ │ │ ├── compiled\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── access.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── getattr_static.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── mixed.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── value.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── access.py\n", + "│ │ │ │ │ │ ├── getattr_static.py\n", + "│ │ │ │ │ │ ├── mixed.py\n", + "│ │ │ │ │ │ ├── subprocess\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __main__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── functions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── functions.py\n", + "│ │ │ │ │ │ └── value.py\n", + "│ │ │ │ │ ├── context.py\n", + "│ │ │ │ │ ├── docstring_utils.py\n", + "│ │ │ │ │ ├── docstrings.py\n", + "│ │ │ │ │ ├── dynamic_params.py\n", + "│ │ │ │ │ ├── filters.py\n", + "│ │ │ │ │ ├── finder.py\n", + "│ │ │ │ │ ├── flow_analysis.py\n", + "│ │ │ │ │ ├── gradual\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── annotation.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conversion.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── generics.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── stub_value.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── type_var.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── typeshed.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── typing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── annotation.py\n", + "│ │ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ │ ├── conversion.py\n", + "│ │ │ │ │ │ ├── generics.py\n", + "│ │ │ │ │ │ ├── stub_value.py\n", + "│ │ │ │ │ │ ├── type_var.py\n", + "│ │ │ │ │ │ ├── typeshed.py\n", + "│ │ │ │ │ │ ├── typing.py\n", + "│ │ │ │ │ │ └── utils.py\n", + "│ │ │ │ │ ├── helpers.py\n", + "│ │ │ │ │ ├── imports.py\n", + "│ │ │ │ │ ├── lazy_value.py\n", + "│ │ │ │ │ ├── names.py\n", + "│ │ │ │ │ ├── param.py\n", + "│ │ │ │ │ ├── parser_cache.py\n", + "│ │ │ │ │ ├── recursion.py\n", + "│ │ │ │ │ ├── references.py\n", + "│ │ │ │ │ ├── signature.py\n", + "│ │ │ │ │ ├── star_args.py\n", + "│ │ │ │ │ ├── syntax_tree.py\n", + "│ │ │ │ │ ├── sys_path.py\n", + "│ │ │ │ │ ├── utils.py\n", + "│ │ │ │ │ └── value\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── decorator.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dynamic_arrays.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── function.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── instance.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── iterable.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── klass.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── module.cpython-310.pyc\n", + "│ │ │ │ │ │ └── namespace.cpython-310.pyc\n", + "│ │ │ │ │ ├── decorator.py\n", + "│ │ │ │ │ ├── dynamic_arrays.py\n", + "│ │ │ │ │ ├── function.py\n", + "│ │ │ │ │ ├── instance.py\n", + "│ │ │ │ │ ├── iterable.py\n", + "│ │ │ │ │ ├── klass.py\n", + "│ │ │ │ │ ├── module.py\n", + "│ │ │ │ │ └── namespace.py\n", + "│ │ │ │ ├── parser_utils.py\n", + "│ │ │ │ ├── plugins\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── django.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── flask.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pytest.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── registry.cpython-310.pyc\n", + "│ │ │ │ │ │ └── stdlib.cpython-310.pyc\n", + "│ │ │ │ │ ├── django.py\n", + "│ │ │ │ │ ├── flask.py\n", + "│ │ │ │ │ ├── pytest.py\n", + "│ │ │ │ │ ├── registry.py\n", + "│ │ │ │ │ └── stdlib.py\n", + "│ │ │ │ ├── settings.py\n", + "│ │ │ │ ├── third_party\n", + "│ │ │ │ │ ├── django-stubs\n", + "│ │ │ │ │ │ ├── LICENSE.txt\n", + "│ │ │ │ │ │ └── django-stubs\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ ├── apps\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── config.pyi\n", + "│ │ │ │ │ │ │ └── registry.pyi\n", + "│ │ │ │ │ │ ├── conf\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── global_settings.pyi\n", + "│ │ │ │ │ │ │ ├── locale\n", + "│ │ │ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ │ │ └── urls\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── i18n.pyi\n", + "│ │ │ │ │ │ │ └── static.pyi\n", + "│ │ │ │ │ │ ├── contrib\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── admin\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── actions.pyi\n", + "│ │ │ │ │ │ │ │ ├── apps.pyi\n", + "│ │ │ │ │ │ │ │ ├── checks.pyi\n", + "│ │ │ │ │ │ │ │ ├── decorators.pyi\n", + "│ │ │ │ │ │ │ │ ├── filters.pyi\n", + "│ │ │ │ │ │ │ │ ├── forms.pyi\n", + "│ │ │ │ │ │ │ │ ├── helpers.pyi\n", + "│ │ │ │ │ │ │ │ ├── models.pyi\n", + "│ │ │ │ │ │ │ │ ├── options.pyi\n", + "│ │ │ │ │ │ │ │ ├── sites.pyi\n", + "│ │ │ │ │ │ │ │ ├── templatetags\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── admin_list.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── admin_modify.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── admin_static.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── admin_urls.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ │ └── log.pyi\n", + "│ │ │ │ │ │ │ │ ├── tests.pyi\n", + "│ │ │ │ │ │ │ │ ├── utils.pyi\n", + "│ │ │ │ │ │ │ │ ├── views\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── autocomplete.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── decorators.pyi\n", + "│ │ │ │ │ │ │ │ │ └── main.pyi\n", + "│ │ │ │ │ │ │ │ └── widgets.pyi\n", + "│ │ │ │ │ │ │ ├── admindocs\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── middleware.pyi\n", + "│ │ │ │ │ │ │ │ ├── urls.pyi\n", + "│ │ │ │ │ │ │ │ ├── utils.pyi\n", + "│ │ │ │ │ │ │ │ └── views.pyi\n", + "│ │ │ │ │ │ │ ├── auth\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── admin.pyi\n", + "│ │ │ │ │ │ │ │ ├── apps.pyi\n", + "│ │ │ │ │ │ │ │ ├── backends.pyi\n", + "│ │ │ │ │ │ │ │ ├── base_user.pyi\n", + "│ │ │ │ │ │ │ │ ├── checks.pyi\n", + "│ │ │ │ │ │ │ │ ├── context_processors.pyi\n", + "│ │ │ │ │ │ │ │ ├── decorators.pyi\n", + "│ │ │ │ │ │ │ │ ├── forms.pyi\n", + "│ │ │ │ │ │ │ │ ├── handlers\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── modwsgi.pyi\n", + "│ │ │ │ │ │ │ │ ├── hashers.pyi\n", + "│ │ │ │ │ │ │ │ ├── management\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── commands\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── changepassword.pyi\n", + "│ │ │ │ │ │ │ │ │ └── createsuperuser.pyi\n", + "│ │ │ │ │ │ │ │ ├── middleware.pyi\n", + "│ │ │ │ │ │ │ │ ├── mixins.pyi\n", + "│ │ │ │ │ │ │ │ ├── models.pyi\n", + "│ │ │ │ │ │ │ │ ├── password_validation.pyi\n", + "│ │ │ │ │ │ │ │ ├── signals.pyi\n", + "│ │ │ │ │ │ │ │ ├── tokens.pyi\n", + "│ │ │ │ │ │ │ │ ├── urls.pyi\n", + "│ │ │ │ │ │ │ │ ├── validators.pyi\n", + "│ │ │ │ │ │ │ │ └── views.pyi\n", + "│ │ │ │ │ │ │ ├── contenttypes\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── admin.pyi\n", + "│ │ │ │ │ │ │ │ ├── apps.pyi\n", + "│ │ │ │ │ │ │ │ ├── checks.pyi\n", + "│ │ │ │ │ │ │ │ ├── fields.pyi\n", + "│ │ │ │ │ │ │ │ ├── forms.pyi\n", + "│ │ │ │ │ │ │ │ ├── management\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── commands\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── remove_stale_contenttypes.pyi\n", + "│ │ │ │ │ │ │ │ ├── models.pyi\n", + "│ │ │ │ │ │ │ │ └── views.pyi\n", + "│ │ │ │ │ │ │ ├── flatpages\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── forms.pyi\n", + "│ │ │ │ │ │ │ │ ├── middleware.pyi\n", + "│ │ │ │ │ │ │ │ ├── models.pyi\n", + "│ │ │ │ │ │ │ │ ├── sitemaps.pyi\n", + "│ │ │ │ │ │ │ │ ├── templatetags\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── flatpages.pyi\n", + "│ │ │ │ │ │ │ │ ├── urls.pyi\n", + "│ │ │ │ │ │ │ │ └── views.pyi\n", + "│ │ │ │ │ │ │ ├── gis\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── db\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── models\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── fields.pyi\n", + "│ │ │ │ │ │ │ ├── humanize\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── templatetags\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── humanize.pyi\n", + "│ │ │ │ │ │ │ ├── messages\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── api.pyi\n", + "│ │ │ │ │ │ │ │ ├── constants.pyi\n", + "│ │ │ │ │ │ │ │ ├── context_processors.pyi\n", + "│ │ │ │ │ │ │ │ ├── middleware.pyi\n", + "│ │ │ │ │ │ │ │ ├── storage\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── cookie.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── fallback.pyi\n", + "│ │ │ │ │ │ │ │ │ └── session.pyi\n", + "│ │ │ │ │ │ │ │ ├── utils.pyi\n", + "│ │ │ │ │ │ │ │ └── views.pyi\n", + "│ │ │ │ │ │ │ ├── postgres\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── aggregates\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── general.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── mixins.pyi\n", + "│ │ │ │ │ │ │ │ │ └── statistics.pyi\n", + "│ │ │ │ │ │ │ │ ├── constraints.pyi\n", + "│ │ │ │ │ │ │ │ ├── fields\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── array.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── citext.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── hstore.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── jsonb.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── mixins.pyi\n", + "│ │ │ │ │ │ │ │ │ └── ranges.pyi\n", + "│ │ │ │ │ │ │ │ ├── functions.pyi\n", + "│ │ │ │ │ │ │ │ ├── indexes.pyi\n", + "│ │ │ │ │ │ │ │ ├── lookups.pyi\n", + "│ │ │ │ │ │ │ │ ├── operations.pyi\n", + "│ │ │ │ │ │ │ │ ├── search.pyi\n", + "│ │ │ │ │ │ │ │ ├── signals.pyi\n", + "│ │ │ │ │ │ │ │ └── validators.pyi\n", + "│ │ │ │ │ │ │ ├── redirects\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── middleware.pyi\n", + "│ │ │ │ │ │ │ │ └── models.pyi\n", + "│ │ │ │ │ │ │ ├── sessions\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── backends\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── cache.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── cached_db.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── db.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── file.pyi\n", + "│ │ │ │ │ │ │ │ │ └── signed_cookies.pyi\n", + "│ │ │ │ │ │ │ │ ├── base_session.pyi\n", + "│ │ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ │ ├── management\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── commands\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── clearsessions.pyi\n", + "│ │ │ │ │ │ │ │ ├── middleware.pyi\n", + "│ │ │ │ │ │ │ │ ├── models.pyi\n", + "│ │ │ │ │ │ │ │ └── serializers.pyi\n", + "│ │ │ │ │ │ │ ├── sitemaps\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── management\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── commands\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── ping_google.pyi\n", + "│ │ │ │ │ │ │ │ └── views.pyi\n", + "│ │ │ │ │ │ │ ├── sites\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── apps.pyi\n", + "│ │ │ │ │ │ │ │ ├── management.pyi\n", + "│ │ │ │ │ │ │ │ ├── managers.pyi\n", + "│ │ │ │ │ │ │ │ ├── middleware.pyi\n", + "│ │ │ │ │ │ │ │ ├── models.pyi\n", + "│ │ │ │ │ │ │ │ ├── requests.pyi\n", + "│ │ │ │ │ │ │ │ └── shortcuts.pyi\n", + "│ │ │ │ │ │ │ ├── staticfiles\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── apps.pyi\n", + "│ │ │ │ │ │ │ │ ├── checks.pyi\n", + "│ │ │ │ │ │ │ │ ├── finders.pyi\n", + "│ │ │ │ │ │ │ │ ├── handlers.pyi\n", + "│ │ │ │ │ │ │ │ ├── management\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── commands\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── collectstatic.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── findstatic.pyi\n", + "│ │ │ │ │ │ │ │ │ └── runserver.pyi\n", + "│ │ │ │ │ │ │ │ ├── storage.pyi\n", + "│ │ │ │ │ │ │ │ ├── templatetags\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── staticfiles.pyi\n", + "│ │ │ │ │ │ │ │ ├── testing.pyi\n", + "│ │ │ │ │ │ │ │ ├── urls.pyi\n", + "│ │ │ │ │ │ │ │ ├── utils.pyi\n", + "│ │ │ │ │ │ │ │ └── views.pyi\n", + "│ │ │ │ │ │ │ └── syndication\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ └── views.pyi\n", + "│ │ │ │ │ │ ├── core\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── cache\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── backends\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── db.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── dummy.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── filebased.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── locmem.pyi\n", + "│ │ │ │ │ │ │ │ │ └── memcached.pyi\n", + "│ │ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ │ ├── checks\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── caches.pyi\n", + "│ │ │ │ │ │ │ │ ├── database.pyi\n", + "│ │ │ │ │ │ │ │ ├── messages.pyi\n", + "│ │ │ │ │ │ │ │ ├── model_checks.pyi\n", + "│ │ │ │ │ │ │ │ ├── registry.pyi\n", + "│ │ │ │ │ │ │ │ ├── security\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── csrf.pyi\n", + "│ │ │ │ │ │ │ │ │ └── sessions.pyi\n", + "│ │ │ │ │ │ │ │ ├── templates.pyi\n", + "│ │ │ │ │ │ │ │ ├── translation.pyi\n", + "│ │ │ │ │ │ │ │ └── urls.pyi\n", + "│ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ ├── files\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ ├── images.pyi\n", + "│ │ │ │ │ │ │ │ ├── locks.pyi\n", + "│ │ │ │ │ │ │ │ ├── move.pyi\n", + "│ │ │ │ │ │ │ │ ├── storage.pyi\n", + "│ │ │ │ │ │ │ │ ├── temp.pyi\n", + "│ │ │ │ │ │ │ │ ├── uploadedfile.pyi\n", + "│ │ │ │ │ │ │ │ ├── uploadhandler.pyi\n", + "│ │ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ │ ├── handlers\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ ├── exception.pyi\n", + "│ │ │ │ │ │ │ │ └── wsgi.pyi\n", + "│ │ │ │ │ │ │ ├── mail\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── backends\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── console.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── dummy.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── filebased.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── locmem.pyi\n", + "│ │ │ │ │ │ │ │ │ └── smtp.pyi\n", + "│ │ │ │ │ │ │ │ ├── message.pyi\n", + "│ │ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ │ ├── management\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ ├── color.pyi\n", + "│ │ │ │ │ │ │ │ ├── commands\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── dumpdata.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── loaddata.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── makemessages.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── runserver.pyi\n", + "│ │ │ │ │ │ │ │ │ └── testserver.pyi\n", + "│ │ │ │ │ │ │ │ ├── sql.pyi\n", + "│ │ │ │ │ │ │ │ ├── templates.pyi\n", + "│ │ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ │ ├── paginator.pyi\n", + "│ │ │ │ │ │ │ ├── serializers\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ ├── json.pyi\n", + "│ │ │ │ │ │ │ │ └── python.pyi\n", + "│ │ │ │ │ │ │ ├── servers\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── basehttp.pyi\n", + "│ │ │ │ │ │ │ ├── signals.pyi\n", + "│ │ │ │ │ │ │ ├── signing.pyi\n", + "│ │ │ │ │ │ │ ├── validators.pyi\n", + "│ │ │ │ │ │ │ └── wsgi.pyi\n", + "│ │ │ │ │ │ ├── db\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── backends\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── base\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── client.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── creation.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── features.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── introspection.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── operations.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── schema.pyi\n", + "│ │ │ │ │ │ │ │ │ └── validation.pyi\n", + "│ │ │ │ │ │ │ │ ├── ddl_references.pyi\n", + "│ │ │ │ │ │ │ │ ├── dummy\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── base.pyi\n", + "│ │ │ │ │ │ │ │ ├── mysql\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── client.pyi\n", + "│ │ │ │ │ │ │ │ ├── postgresql\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── client.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── creation.pyi\n", + "│ │ │ │ │ │ │ │ │ └── operations.pyi\n", + "│ │ │ │ │ │ │ │ ├── signals.pyi\n", + "│ │ │ │ │ │ │ │ ├── sqlite3\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── creation.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── features.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── introspection.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── operations.pyi\n", + "│ │ │ │ │ │ │ │ │ └── schema.pyi\n", + "│ │ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ │ ├── migrations\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── autodetector.pyi\n", + "│ │ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ │ ├── executor.pyi\n", + "│ │ │ │ │ │ │ │ ├── graph.pyi\n", + "│ │ │ │ │ │ │ │ ├── loader.pyi\n", + "│ │ │ │ │ │ │ │ ├── migration.pyi\n", + "│ │ │ │ │ │ │ │ ├── operations\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── fields.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── models.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── special.pyi\n", + "│ │ │ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ │ │ ├── optimizer.pyi\n", + "│ │ │ │ │ │ │ │ ├── questioner.pyi\n", + "│ │ │ │ │ │ │ │ ├── recorder.pyi\n", + "│ │ │ │ │ │ │ │ ├── serializer.pyi\n", + "│ │ │ │ │ │ │ │ ├── state.pyi\n", + "│ │ │ │ │ │ │ │ ├── topological_sort.pyi\n", + "│ │ │ │ │ │ │ │ ├── utils.pyi\n", + "│ │ │ │ │ │ │ │ └── writer.pyi\n", + "│ │ │ │ │ │ │ ├── models\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── aggregates.pyi\n", + "│ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ ├── constraints.pyi\n", + "│ │ │ │ │ │ │ │ ├── deletion.pyi\n", + "│ │ │ │ │ │ │ │ ├── enums.pyi\n", + "│ │ │ │ │ │ │ │ ├── expressions.pyi\n", + "│ │ │ │ │ │ │ │ ├── fields\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── files.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── mixins.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── proxy.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── related.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── related_descriptors.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── related_lookups.pyi\n", + "│ │ │ │ │ │ │ │ │ └── reverse_related.pyi\n", + "│ │ │ │ │ │ │ │ ├── functions\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── comparison.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── datetime.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── math.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── mixins.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── text.pyi\n", + "│ │ │ │ │ │ │ │ │ └── window.pyi\n", + "│ │ │ │ │ │ │ │ ├── indexes.pyi\n", + "│ │ │ │ │ │ │ │ ├── lookups.pyi\n", + "│ │ │ │ │ │ │ │ ├── manager.pyi\n", + "│ │ │ │ │ │ │ │ ├── options.pyi\n", + "│ │ │ │ │ │ │ │ ├── query.pyi\n", + "│ │ │ │ │ │ │ │ ├── query_utils.pyi\n", + "│ │ │ │ │ │ │ │ ├── signals.pyi\n", + "│ │ │ │ │ │ │ │ ├── sql\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── compiler.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── constants.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── datastructures.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── query.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── subqueries.pyi\n", + "│ │ │ │ │ │ │ │ │ └── where.pyi\n", + "│ │ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ │ ├── transaction.pyi\n", + "│ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ ├── dispatch\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ └── dispatcher.pyi\n", + "│ │ │ │ │ │ ├── forms\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── boundfield.pyi\n", + "│ │ │ │ │ │ │ ├── fields.pyi\n", + "│ │ │ │ │ │ │ ├── forms.pyi\n", + "│ │ │ │ │ │ │ ├── formsets.pyi\n", + "│ │ │ │ │ │ │ ├── models.pyi\n", + "│ │ │ │ │ │ │ ├── renderers.pyi\n", + "│ │ │ │ │ │ │ ├── utils.pyi\n", + "│ │ │ │ │ │ │ └── widgets.pyi\n", + "│ │ │ │ │ │ ├── http\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── cookie.pyi\n", + "│ │ │ │ │ │ │ ├── multipartparser.pyi\n", + "│ │ │ │ │ │ │ ├── request.pyi\n", + "│ │ │ │ │ │ │ └── response.pyi\n", + "│ │ │ │ │ │ ├── middleware\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── cache.pyi\n", + "│ │ │ │ │ │ │ ├── clickjacking.pyi\n", + "│ │ │ │ │ │ │ ├── common.pyi\n", + "│ │ │ │ │ │ │ ├── csrf.pyi\n", + "│ │ │ │ │ │ │ ├── gzip.pyi\n", + "│ │ │ │ │ │ │ ├── http.pyi\n", + "│ │ │ │ │ │ │ ├── locale.pyi\n", + "│ │ │ │ │ │ │ └── security.pyi\n", + "│ │ │ │ │ │ ├── shortcuts.pyi\n", + "│ │ │ │ │ │ ├── template\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── backends\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ ├── django.pyi\n", + "│ │ │ │ │ │ │ │ ├── dummy.pyi\n", + "│ │ │ │ │ │ │ │ ├── jinja2.pyi\n", + "│ │ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ ├── context.pyi\n", + "│ │ │ │ │ │ │ ├── context_processors.pyi\n", + "│ │ │ │ │ │ │ ├── defaultfilters.pyi\n", + "│ │ │ │ │ │ │ ├── defaulttags.pyi\n", + "│ │ │ │ │ │ │ ├── engine.pyi\n", + "│ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ ├── library.pyi\n", + "│ │ │ │ │ │ │ ├── loader.pyi\n", + "│ │ │ │ │ │ │ ├── loader_tags.pyi\n", + "│ │ │ │ │ │ │ ├── loaders\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── app_directories.pyi\n", + "│ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ ├── cached.pyi\n", + "│ │ │ │ │ │ │ │ ├── filesystem.pyi\n", + "│ │ │ │ │ │ │ │ └── locmem.pyi\n", + "│ │ │ │ │ │ │ ├── response.pyi\n", + "│ │ │ │ │ │ │ ├── smartif.pyi\n", + "│ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ ├── templatetags\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── cache.pyi\n", + "│ │ │ │ │ │ │ ├── i18n.pyi\n", + "│ │ │ │ │ │ │ ├── l10n.pyi\n", + "│ │ │ │ │ │ │ ├── static.pyi\n", + "│ │ │ │ │ │ │ └── tz.pyi\n", + "│ │ │ │ │ │ ├── test\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── client.pyi\n", + "│ │ │ │ │ │ │ ├── html.pyi\n", + "│ │ │ │ │ │ │ ├── runner.pyi\n", + "│ │ │ │ │ │ │ ├── selenium.pyi\n", + "│ │ │ │ │ │ │ ├── signals.pyi\n", + "│ │ │ │ │ │ │ ├── testcases.pyi\n", + "│ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ ├── urls\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ ├── conf.pyi\n", + "│ │ │ │ │ │ │ ├── converters.pyi\n", + "│ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ ├── resolvers.pyi\n", + "│ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ ├── utils\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── _os.pyi\n", + "│ │ │ │ │ │ │ ├── archive.pyi\n", + "│ │ │ │ │ │ │ ├── autoreload.pyi\n", + "│ │ │ │ │ │ │ ├── baseconv.pyi\n", + "│ │ │ │ │ │ │ ├── cache.pyi\n", + "│ │ │ │ │ │ │ ├── crypto.pyi\n", + "│ │ │ │ │ │ │ ├── datastructures.pyi\n", + "│ │ │ │ │ │ │ ├── dateformat.pyi\n", + "│ │ │ │ │ │ │ ├── dateparse.pyi\n", + "│ │ │ │ │ │ │ ├── dates.pyi\n", + "│ │ │ │ │ │ │ ├── datetime_safe.pyi\n", + "│ │ │ │ │ │ │ ├── deconstruct.pyi\n", + "│ │ │ │ │ │ │ ├── decorators.pyi\n", + "│ │ │ │ │ │ │ ├── deprecation.pyi\n", + "│ │ │ │ │ │ │ ├── duration.pyi\n", + "│ │ │ │ │ │ │ ├── encoding.pyi\n", + "│ │ │ │ │ │ │ ├── feedgenerator.pyi\n", + "│ │ │ │ │ │ │ ├── formats.pyi\n", + "│ │ │ │ │ │ │ ├── functional.pyi\n", + "│ │ │ │ │ │ │ ├── hashable.pyi\n", + "│ │ │ │ │ │ │ ├── html.pyi\n", + "│ │ │ │ │ │ │ ├── http.pyi\n", + "│ │ │ │ │ │ │ ├── inspect.pyi\n", + "│ │ │ │ │ │ │ ├── ipv6.pyi\n", + "│ │ │ │ │ │ │ ├── itercompat.pyi\n", + "│ │ │ │ │ │ │ ├── jslex.pyi\n", + "│ │ │ │ │ │ │ ├── log.pyi\n", + "│ │ │ │ │ │ │ ├── lorem_ipsum.pyi\n", + "│ │ │ │ │ │ │ ├── module_loading.pyi\n", + "│ │ │ │ │ │ │ ├── numberformat.pyi\n", + "│ │ │ │ │ │ │ ├── regex_helper.pyi\n", + "│ │ │ │ │ │ │ ├── safestring.pyi\n", + "│ │ │ │ │ │ │ ├── six.pyi\n", + "│ │ │ │ │ │ │ ├── termcolors.pyi\n", + "│ │ │ │ │ │ │ ├── text.pyi\n", + "│ │ │ │ │ │ │ ├── timesince.pyi\n", + "│ │ │ │ │ │ │ ├── timezone.pyi\n", + "│ │ │ │ │ │ │ ├── topological_sort.pyi\n", + "│ │ │ │ │ │ │ ├── translation\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── reloader.pyi\n", + "│ │ │ │ │ │ │ │ ├── template.pyi\n", + "│ │ │ │ │ │ │ │ ├── trans_null.pyi\n", + "│ │ │ │ │ │ │ │ └── trans_real.pyi\n", + "│ │ │ │ │ │ │ ├── tree.pyi\n", + "│ │ │ │ │ │ │ ├── version.pyi\n", + "│ │ │ │ │ │ │ └── xmlutils.pyi\n", + "│ │ │ │ │ │ └── views\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ ├── csrf.pyi\n", + "│ │ │ │ │ │ ├── debug.pyi\n", + "│ │ │ │ │ │ ├── decorators\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── cache.pyi\n", + "│ │ │ │ │ │ │ ├── clickjacking.pyi\n", + "│ │ │ │ │ │ │ ├── csrf.pyi\n", + "│ │ │ │ │ │ │ ├── debug.pyi\n", + "│ │ │ │ │ │ │ ├── gzip.pyi\n", + "│ │ │ │ │ │ │ ├── http.pyi\n", + "│ │ │ │ │ │ │ └── vary.pyi\n", + "│ │ │ │ │ │ ├── defaults.pyi\n", + "│ │ │ │ │ │ ├── generic\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ ├── dates.pyi\n", + "│ │ │ │ │ │ │ ├── detail.pyi\n", + "│ │ │ │ │ │ │ ├── edit.pyi\n", + "│ │ │ │ │ │ │ └── list.pyi\n", + "│ │ │ │ │ │ ├── i18n.pyi\n", + "│ │ │ │ │ │ └── static.pyi\n", + "│ │ │ │ │ └── typeshed\n", + "│ │ │ │ │ ├── LICENSE\n", + "│ │ │ │ │ ├── stdlib\n", + "│ │ │ │ │ │ ├── 2\n", + "│ │ │ │ │ │ │ ├── BaseHTTPServer.pyi\n", + "│ │ │ │ │ │ │ ├── CGIHTTPServer.pyi\n", + "│ │ │ │ │ │ │ ├── ConfigParser.pyi\n", + "│ │ │ │ │ │ │ ├── Cookie.pyi\n", + "│ │ │ │ │ │ │ ├── HTMLParser.pyi\n", + "│ │ │ │ │ │ │ ├── Queue.pyi\n", + "│ │ │ │ │ │ │ ├── SimpleHTTPServer.pyi\n", + "│ │ │ │ │ │ │ ├── SocketServer.pyi\n", + "│ │ │ │ │ │ │ ├── StringIO.pyi\n", + "│ │ │ │ │ │ │ ├── UserDict.pyi\n", + "│ │ │ │ │ │ │ ├── UserList.pyi\n", + "│ │ │ │ │ │ │ ├── UserString.pyi\n", + "│ │ │ │ │ │ │ ├── __builtin__.pyi\n", + "│ │ │ │ │ │ │ ├── _ast.pyi\n", + "│ │ │ │ │ │ │ ├── _collections.pyi\n", + "│ │ │ │ │ │ │ ├── _functools.pyi\n", + "│ │ │ │ │ │ │ ├── _hotshot.pyi\n", + "│ │ │ │ │ │ │ ├── _io.pyi\n", + "│ │ │ │ │ │ │ ├── _json.pyi\n", + "│ │ │ │ │ │ │ ├── _md5.pyi\n", + "│ │ │ │ │ │ │ ├── _sha.pyi\n", + "│ │ │ │ │ │ │ ├── _sha256.pyi\n", + "│ │ │ │ │ │ │ ├── _sha512.pyi\n", + "│ │ │ │ │ │ │ ├── _socket.pyi\n", + "│ │ │ │ │ │ │ ├── _sre.pyi\n", + "│ │ │ │ │ │ │ ├── _struct.pyi\n", + "│ │ │ │ │ │ │ ├── _symtable.pyi\n", + "│ │ │ │ │ │ │ ├── _threading_local.pyi\n", + "│ │ │ │ │ │ │ ├── _winreg.pyi\n", + "│ │ │ │ │ │ │ ├── abc.pyi\n", + "│ │ │ │ │ │ │ ├── ast.pyi\n", + "│ │ │ │ │ │ │ ├── atexit.pyi\n", + "│ │ │ │ │ │ │ ├── builtins.pyi\n", + "│ │ │ │ │ │ │ ├── cPickle.pyi\n", + "│ │ │ │ │ │ │ ├── cStringIO.pyi\n", + "│ │ │ │ │ │ │ ├── collections.pyi\n", + "│ │ │ │ │ │ │ ├── commands.pyi\n", + "│ │ │ │ │ │ │ ├── compileall.pyi\n", + "│ │ │ │ │ │ │ ├── cookielib.pyi\n", + "│ │ │ │ │ │ │ ├── copy_reg.pyi\n", + "│ │ │ │ │ │ │ ├── dircache.pyi\n", + "│ │ │ │ │ │ │ ├── distutils\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── archive_util.pyi\n", + "│ │ │ │ │ │ │ │ ├── bcppcompiler.pyi\n", + "│ │ │ │ │ │ │ │ ├── ccompiler.pyi\n", + "│ │ │ │ │ │ │ │ ├── cmd.pyi\n", + "│ │ │ │ │ │ │ │ ├── command\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── bdist.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── bdist_dumb.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── bdist_msi.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── bdist_packager.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── bdist_rpm.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── bdist_wininst.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── build.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── build_clib.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── build_ext.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── build_py.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── build_scripts.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── check.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── clean.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── config.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── install.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── install_data.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── install_egg_info.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── install_headers.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── install_lib.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── install_scripts.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── register.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── sdist.pyi\n", + "│ │ │ │ │ │ │ │ │ └── upload.pyi\n", + "│ │ │ │ │ │ │ │ ├── config.pyi\n", + "│ │ │ │ │ │ │ │ ├── core.pyi\n", + "│ │ │ │ │ │ │ │ ├── cygwinccompiler.pyi\n", + "│ │ │ │ │ │ │ │ ├── debug.pyi\n", + "│ │ │ │ │ │ │ │ ├── dep_util.pyi\n", + "│ │ │ │ │ │ │ │ ├── dir_util.pyi\n", + "│ │ │ │ │ │ │ │ ├── dist.pyi\n", + "│ │ │ │ │ │ │ │ ├── emxccompiler.pyi\n", + "│ │ │ │ │ │ │ │ ├── errors.pyi\n", + "│ │ │ │ │ │ │ │ ├── extension.pyi\n", + "│ │ │ │ │ │ │ │ ├── fancy_getopt.pyi\n", + "│ │ │ │ │ │ │ │ ├── file_util.pyi\n", + "│ │ │ │ │ │ │ │ ├── filelist.pyi\n", + "│ │ │ │ │ │ │ │ ├── log.pyi\n", + "│ │ │ │ │ │ │ │ ├── msvccompiler.pyi\n", + "│ │ │ │ │ │ │ │ ├── spawn.pyi\n", + "│ │ │ │ │ │ │ │ ├── sysconfig.pyi\n", + "│ │ │ │ │ │ │ │ ├── text_file.pyi\n", + "│ │ │ │ │ │ │ │ ├── unixccompiler.pyi\n", + "│ │ │ │ │ │ │ │ ├── util.pyi\n", + "│ │ │ │ │ │ │ │ └── version.pyi\n", + "│ │ │ │ │ │ │ ├── dummy_thread.pyi\n", + "│ │ │ │ │ │ │ ├── email\n", + "│ │ │ │ │ │ │ │ ├── MIMEText.pyi\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── _parseaddr.pyi\n", + "│ │ │ │ │ │ │ │ ├── base64mime.pyi\n", + "│ │ │ │ │ │ │ │ ├── charset.pyi\n", + "│ │ │ │ │ │ │ │ ├── encoders.pyi\n", + "│ │ │ │ │ │ │ │ ├── feedparser.pyi\n", + "│ │ │ │ │ │ │ │ ├── generator.pyi\n", + "│ │ │ │ │ │ │ │ ├── header.pyi\n", + "│ │ │ │ │ │ │ │ ├── iterators.pyi\n", + "│ │ │ │ │ │ │ │ ├── message.pyi\n", + "│ │ │ │ │ │ │ │ ├── mime\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── application.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── audio.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── image.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── message.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── multipart.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── nonmultipart.pyi\n", + "│ │ │ │ │ │ │ │ │ └── text.pyi\n", + "│ │ │ │ │ │ │ │ ├── parser.pyi\n", + "│ │ │ │ │ │ │ │ ├── quoprimime.pyi\n", + "│ │ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ │ ├── encodings\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── utf_8.pyi\n", + "│ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ ├── fcntl.pyi\n", + "│ │ │ │ │ │ │ ├── fnmatch.pyi\n", + "│ │ │ │ │ │ │ ├── functools.pyi\n", + "│ │ │ │ │ │ │ ├── future_builtins.pyi\n", + "│ │ │ │ │ │ │ ├── gc.pyi\n", + "│ │ │ │ │ │ │ ├── getopt.pyi\n", + "│ │ │ │ │ │ │ ├── getpass.pyi\n", + "│ │ │ │ │ │ │ ├── gettext.pyi\n", + "│ │ │ │ │ │ │ ├── glob.pyi\n", + "│ │ │ │ │ │ │ ├── gzip.pyi\n", + "│ │ │ │ │ │ │ ├── hashlib.pyi\n", + "│ │ │ │ │ │ │ ├── heapq.pyi\n", + "│ │ │ │ │ │ │ ├── htmlentitydefs.pyi\n", + "│ │ │ │ │ │ │ ├── httplib.pyi\n", + "│ │ │ │ │ │ │ ├── imp.pyi\n", + "│ │ │ │ │ │ │ ├── importlib.pyi\n", + "│ │ │ │ │ │ │ ├── inspect.pyi\n", + "│ │ │ │ │ │ │ ├── io.pyi\n", + "│ │ │ │ │ │ │ ├── itertools.pyi\n", + "│ │ │ │ │ │ │ ├── json.pyi\n", + "│ │ │ │ │ │ │ ├── markupbase.pyi\n", + "│ │ │ │ │ │ │ ├── md5.pyi\n", + "│ │ │ │ │ │ │ ├── mimetools.pyi\n", + "│ │ │ │ │ │ │ ├── multiprocessing\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── dummy\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── connection.pyi\n", + "│ │ │ │ │ │ │ │ ├── pool.pyi\n", + "│ │ │ │ │ │ │ │ ├── process.pyi\n", + "│ │ │ │ │ │ │ │ └── util.pyi\n", + "│ │ │ │ │ │ │ ├── mutex.pyi\n", + "│ │ │ │ │ │ │ ├── ntpath.pyi\n", + "│ │ │ │ │ │ │ ├── nturl2path.pyi\n", + "│ │ │ │ │ │ │ ├── os\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── path.pyi\n", + "│ │ │ │ │ │ │ ├── os2emxpath.pyi\n", + "│ │ │ │ │ │ │ ├── pipes.pyi\n", + "│ │ │ │ │ │ │ ├── platform.pyi\n", + "│ │ │ │ │ │ │ ├── popen2.pyi\n", + "│ │ │ │ │ │ │ ├── posix.pyi\n", + "│ │ │ │ │ │ │ ├── posixpath.pyi\n", + "│ │ │ │ │ │ │ ├── random.pyi\n", + "│ │ │ │ │ │ │ ├── re.pyi\n", + "│ │ │ │ │ │ │ ├── repr.pyi\n", + "│ │ │ │ │ │ │ ├── resource.pyi\n", + "│ │ │ │ │ │ │ ├── rfc822.pyi\n", + "│ │ │ │ │ │ │ ├── robotparser.pyi\n", + "│ │ │ │ │ │ │ ├── runpy.pyi\n", + "│ │ │ │ │ │ │ ├── sets.pyi\n", + "│ │ │ │ │ │ │ ├── sha.pyi\n", + "│ │ │ │ │ │ │ ├── shelve.pyi\n", + "│ │ │ │ │ │ │ ├── shlex.pyi\n", + "│ │ │ │ │ │ │ ├── signal.pyi\n", + "│ │ │ │ │ │ │ ├── smtplib.pyi\n", + "│ │ │ │ │ │ │ ├── spwd.pyi\n", + "│ │ │ │ │ │ │ ├── sre_constants.pyi\n", + "│ │ │ │ │ │ │ ├── sre_parse.pyi\n", + "│ │ │ │ │ │ │ ├── stat.pyi\n", + "│ │ │ │ │ │ │ ├── string.pyi\n", + "│ │ │ │ │ │ │ ├── stringold.pyi\n", + "│ │ │ │ │ │ │ ├── strop.pyi\n", + "│ │ │ │ │ │ │ ├── subprocess.pyi\n", + "│ │ │ │ │ │ │ ├── symbol.pyi\n", + "│ │ │ │ │ │ │ ├── sys.pyi\n", + "│ │ │ │ │ │ │ ├── tempfile.pyi\n", + "│ │ │ │ │ │ │ ├── textwrap.pyi\n", + "│ │ │ │ │ │ │ ├── thread.pyi\n", + "│ │ │ │ │ │ │ ├── toaiff.pyi\n", + "│ │ │ │ │ │ │ ├── tokenize.pyi\n", + "│ │ │ │ │ │ │ ├── types.pyi\n", + "│ │ │ │ │ │ │ ├── typing.pyi\n", + "│ │ │ │ │ │ │ ├── unittest.pyi\n", + "│ │ │ │ │ │ │ ├── urllib.pyi\n", + "│ │ │ │ │ │ │ ├── urllib2.pyi\n", + "│ │ │ │ │ │ │ ├── urlparse.pyi\n", + "│ │ │ │ │ │ │ ├── user.pyi\n", + "│ │ │ │ │ │ │ ├── whichdb.pyi\n", + "│ │ │ │ │ │ │ └── xmlrpclib.pyi\n", + "│ │ │ │ │ │ ├── 2and3\n", + "│ │ │ │ │ │ │ ├── __future__.pyi\n", + "│ │ │ │ │ │ │ ├── _bisect.pyi\n", + "│ │ │ │ │ │ │ ├── _codecs.pyi\n", + "│ │ │ │ │ │ │ ├── _csv.pyi\n", + "│ │ │ │ │ │ │ ├── _curses.pyi\n", + "│ │ │ │ │ │ │ ├── _dummy_threading.pyi\n", + "│ │ │ │ │ │ │ ├── _heapq.pyi\n", + "│ │ │ │ │ │ │ ├── _msi.pyi\n", + "│ │ │ │ │ │ │ ├── _random.pyi\n", + "│ │ │ │ │ │ │ ├── _typeshed\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── wsgi.pyi\n", + "│ │ │ │ │ │ │ │ └── xml.pyi\n", + "│ │ │ │ │ │ │ ├── _warnings.pyi\n", + "│ │ │ │ │ │ │ ├── _weakref.pyi\n", + "│ │ │ │ │ │ │ ├── _weakrefset.pyi\n", + "│ │ │ │ │ │ │ ├── aifc.pyi\n", + "│ │ │ │ │ │ │ ├── antigravity.pyi\n", + "│ │ │ │ │ │ │ ├── argparse.pyi\n", + "│ │ │ │ │ │ │ ├── array.pyi\n", + "│ │ │ │ │ │ │ ├── asynchat.pyi\n", + "│ │ │ │ │ │ │ ├── asyncore.pyi\n", + "│ │ │ │ │ │ │ ├── audioop.pyi\n", + "│ │ │ │ │ │ │ ├── base64.pyi\n", + "│ │ │ │ │ │ │ ├── bdb.pyi\n", + "│ │ │ │ │ │ │ ├── binascii.pyi\n", + "│ │ │ │ │ │ │ ├── binhex.pyi\n", + "│ │ │ │ │ │ │ ├── bisect.pyi\n", + "│ │ │ │ │ │ │ ├── bz2.pyi\n", + "│ │ │ │ │ │ │ ├── cProfile.pyi\n", + "│ │ │ │ │ │ │ ├── calendar.pyi\n", + "│ │ │ │ │ │ │ ├── cgi.pyi\n", + "│ │ │ │ │ │ │ ├── cgitb.pyi\n", + "│ │ │ │ │ │ │ ├── chunk.pyi\n", + "│ │ │ │ │ │ │ ├── cmath.pyi\n", + "│ │ │ │ │ │ │ ├── cmd.pyi\n", + "│ │ │ │ │ │ │ ├── code.pyi\n", + "│ │ │ │ │ │ │ ├── codecs.pyi\n", + "│ │ │ │ │ │ │ ├── codeop.pyi\n", + "│ │ │ │ │ │ │ ├── colorsys.pyi\n", + "│ │ │ │ │ │ │ ├── contextlib.pyi\n", + "│ │ │ │ │ │ │ ├── copy.pyi\n", + "│ │ │ │ │ │ │ ├── crypt.pyi\n", + "│ │ │ │ │ │ │ ├── csv.pyi\n", + "│ │ │ │ │ │ │ ├── ctypes\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── util.pyi\n", + "│ │ │ │ │ │ │ │ └── wintypes.pyi\n", + "│ │ │ │ │ │ │ ├── curses\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── ascii.pyi\n", + "│ │ │ │ │ │ │ │ ├── panel.pyi\n", + "│ │ │ │ │ │ │ │ └── textpad.pyi\n", + "│ │ │ │ │ │ │ ├── datetime.pyi\n", + "│ │ │ │ │ │ │ ├── decimal.pyi\n", + "│ │ │ │ │ │ │ ├── difflib.pyi\n", + "│ │ │ │ │ │ │ ├── dis.pyi\n", + "│ │ │ │ │ │ │ ├── doctest.pyi\n", + "│ │ │ │ │ │ │ ├── dummy_threading.pyi\n", + "│ │ │ │ │ │ │ ├── ensurepip\n", + "│ │ │ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── errno.pyi\n", + "│ │ │ │ │ │ │ ├── filecmp.pyi\n", + "│ │ │ │ │ │ │ ├── fileinput.pyi\n", + "│ │ │ │ │ │ │ ├── formatter.pyi\n", + "│ │ │ │ │ │ │ ├── fractions.pyi\n", + "│ │ │ │ │ │ │ ├── ftplib.pyi\n", + "│ │ │ │ │ │ │ ├── genericpath.pyi\n", + "│ │ │ │ │ │ │ ├── grp.pyi\n", + "│ │ │ │ │ │ │ ├── hmac.pyi\n", + "│ │ │ │ │ │ │ ├── imaplib.pyi\n", + "│ │ │ │ │ │ │ ├── imghdr.pyi\n", + "│ │ │ │ │ │ │ ├── keyword.pyi\n", + "│ │ │ │ │ │ │ ├── lib2to3\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── pgen2\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── driver.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── grammar.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── literals.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── parse.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── pgen.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── token.pyi\n", + "│ │ │ │ │ │ │ │ │ └── tokenize.pyi\n", + "│ │ │ │ │ │ │ │ ├── pygram.pyi\n", + "│ │ │ │ │ │ │ │ └── pytree.pyi\n", + "│ │ │ │ │ │ │ ├── linecache.pyi\n", + "│ │ │ │ │ │ │ ├── locale.pyi\n", + "│ │ │ │ │ │ │ ├── logging\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── config.pyi\n", + "│ │ │ │ │ │ │ │ └── handlers.pyi\n", + "│ │ │ │ │ │ │ ├── macpath.pyi\n", + "│ │ │ │ │ │ │ ├── mailbox.pyi\n", + "│ │ │ │ │ │ │ ├── mailcap.pyi\n", + "│ │ │ │ │ │ │ ├── marshal.pyi\n", + "│ │ │ │ │ │ │ ├── math.pyi\n", + "│ │ │ │ │ │ │ ├── mimetypes.pyi\n", + "│ │ │ │ │ │ │ ├── mmap.pyi\n", + "│ │ │ │ │ │ │ ├── modulefinder.pyi\n", + "│ │ │ │ │ │ │ ├── msilib\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── schema.pyi\n", + "│ │ │ │ │ │ │ │ ├── sequence.pyi\n", + "│ │ │ │ │ │ │ │ └── text.pyi\n", + "│ │ │ │ │ │ │ ├── msvcrt.pyi\n", + "│ │ │ │ │ │ │ ├── netrc.pyi\n", + "│ │ │ │ │ │ │ ├── nis.pyi\n", + "│ │ │ │ │ │ │ ├── numbers.pyi\n", + "│ │ │ │ │ │ │ ├── opcode.pyi\n", + "│ │ │ │ │ │ │ ├── operator.pyi\n", + "│ │ │ │ │ │ │ ├── optparse.pyi\n", + "│ │ │ │ │ │ │ ├── parser.pyi\n", + "│ │ │ │ │ │ │ ├── pdb.pyi\n", + "│ │ │ │ │ │ │ ├── pickle.pyi\n", + "│ │ │ │ │ │ │ ├── pickletools.pyi\n", + "│ │ │ │ │ │ │ ├── pkgutil.pyi\n", + "│ │ │ │ │ │ │ ├── plistlib.pyi\n", + "│ │ │ │ │ │ │ ├── poplib.pyi\n", + "│ │ │ │ │ │ │ ├── pprint.pyi\n", + "│ │ │ │ │ │ │ ├── profile.pyi\n", + "│ │ │ │ │ │ │ ├── pstats.pyi\n", + "│ │ │ │ │ │ │ ├── pty.pyi\n", + "│ │ │ │ │ │ │ ├── pwd.pyi\n", + "│ │ │ │ │ │ │ ├── py_compile.pyi\n", + "│ │ │ │ │ │ │ ├── pyclbr.pyi\n", + "│ │ │ │ │ │ │ ├── pydoc.pyi\n", + "│ │ │ │ │ │ │ ├── pydoc_data\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── topics.pyi\n", + "│ │ │ │ │ │ │ ├── pyexpat\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── errors.pyi\n", + "│ │ │ │ │ │ │ │ └── model.pyi\n", + "│ │ │ │ │ │ │ ├── quopri.pyi\n", + "│ │ │ │ │ │ │ ├── readline.pyi\n", + "│ │ │ │ │ │ │ ├── rlcompleter.pyi\n", + "│ │ │ │ │ │ │ ├── sched.pyi\n", + "│ │ │ │ │ │ │ ├── select.pyi\n", + "│ │ │ │ │ │ │ ├── shutil.pyi\n", + "│ │ │ │ │ │ │ ├── site.pyi\n", + "│ │ │ │ │ │ │ ├── smtpd.pyi\n", + "│ │ │ │ │ │ │ ├── sndhdr.pyi\n", + "│ │ │ │ │ │ │ ├── socket.pyi\n", + "│ │ │ │ │ │ │ ├── sqlite3\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── dbapi2.pyi\n", + "│ │ │ │ │ │ │ ├── sre_compile.pyi\n", + "│ │ │ │ │ │ │ ├── ssl.pyi\n", + "│ │ │ │ │ │ │ ├── stringprep.pyi\n", + "│ │ │ │ │ │ │ ├── struct.pyi\n", + "│ │ │ │ │ │ │ ├── sunau.pyi\n", + "│ │ │ │ │ │ │ ├── symtable.pyi\n", + "│ │ │ │ │ │ │ ├── sysconfig.pyi\n", + "│ │ │ │ │ │ │ ├── syslog.pyi\n", + "│ │ │ │ │ │ │ ├── tabnanny.pyi\n", + "│ │ │ │ │ │ │ ├── tarfile.pyi\n", + "│ │ │ │ │ │ │ ├── telnetlib.pyi\n", + "│ │ │ │ │ │ │ ├── termios.pyi\n", + "│ │ │ │ │ │ │ ├── this.pyi\n", + "│ │ │ │ │ │ │ ├── threading.pyi\n", + "│ │ │ │ │ │ │ ├── time.pyi\n", + "│ │ │ │ │ │ │ ├── timeit.pyi\n", + "│ │ │ │ │ │ │ ├── token.pyi\n", + "│ │ │ │ │ │ │ ├── trace.pyi\n", + "│ │ │ │ │ │ │ ├── traceback.pyi\n", + "│ │ │ │ │ │ │ ├── tty.pyi\n", + "│ │ │ │ │ │ │ ├── turtle.pyi\n", + "│ │ │ │ │ │ │ ├── unicodedata.pyi\n", + "│ │ │ │ │ │ │ ├── uu.pyi\n", + "│ │ │ │ │ │ │ ├── uuid.pyi\n", + "│ │ │ │ │ │ │ ├── warnings.pyi\n", + "│ │ │ │ │ │ │ ├── wave.pyi\n", + "│ │ │ │ │ │ │ ├── weakref.pyi\n", + "│ │ │ │ │ │ │ ├── webbrowser.pyi\n", + "│ │ │ │ │ │ │ ├── winsound.pyi\n", + "│ │ │ │ │ │ │ ├── wsgiref\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── handlers.pyi\n", + "│ │ │ │ │ │ │ │ ├── headers.pyi\n", + "│ │ │ │ │ │ │ │ ├── simple_server.pyi\n", + "│ │ │ │ │ │ │ │ ├── types.pyi\n", + "│ │ │ │ │ │ │ │ ├── util.pyi\n", + "│ │ │ │ │ │ │ │ └── validate.pyi\n", + "│ │ │ │ │ │ │ ├── xdrlib.pyi\n", + "│ │ │ │ │ │ │ ├── xml\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── dom\n", + "│ │ │ │ │ │ │ │ │ ├── NodeFilter.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── domreg.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── expatbuilder.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── minicompat.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── minidom.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── pulldom.pyi\n", + "│ │ │ │ │ │ │ │ │ └── xmlbuilder.pyi\n", + "│ │ │ │ │ │ │ │ ├── etree\n", + "│ │ │ │ │ │ │ │ │ ├── ElementInclude.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── ElementPath.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── ElementTree.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── cElementTree.pyi\n", + "│ │ │ │ │ │ │ │ ├── parsers\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── expat\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── errors.pyi\n", + "│ │ │ │ │ │ │ │ │ └── model.pyi\n", + "│ │ │ │ │ │ │ │ └── sax\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── handler.pyi\n", + "│ │ │ │ │ │ │ │ ├── saxutils.pyi\n", + "│ │ │ │ │ │ │ │ └── xmlreader.pyi\n", + "│ │ │ │ │ │ │ ├── zipfile.pyi\n", + "│ │ │ │ │ │ │ ├── zipimport.pyi\n", + "│ │ │ │ │ │ │ └── zlib.pyi\n", + "│ │ │ │ │ │ ├── 3\n", + "│ │ │ │ │ │ │ ├── _ast.pyi\n", + "│ │ │ │ │ │ │ ├── _bootlocale.pyi\n", + "│ │ │ │ │ │ │ ├── _compat_pickle.pyi\n", + "│ │ │ │ │ │ │ ├── _compression.pyi\n", + "│ │ │ │ │ │ │ ├── _decimal.pyi\n", + "│ │ │ │ │ │ │ ├── _dummy_thread.pyi\n", + "│ │ │ │ │ │ │ ├── _imp.pyi\n", + "│ │ │ │ │ │ │ ├── _importlib_modulespec.pyi\n", + "│ │ │ │ │ │ │ ├── _json.pyi\n", + "│ │ │ │ │ │ │ ├── _markupbase.pyi\n", + "│ │ │ │ │ │ │ ├── _operator.pyi\n", + "│ │ │ │ │ │ │ ├── _osx_support.pyi\n", + "│ │ │ │ │ │ │ ├── _posixsubprocess.pyi\n", + "│ │ │ │ │ │ │ ├── _pydecimal.pyi\n", + "│ │ │ │ │ │ │ ├── _sitebuiltins.pyi\n", + "│ │ │ │ │ │ │ ├── _stat.pyi\n", + "│ │ │ │ │ │ │ ├── _thread.pyi\n", + "│ │ │ │ │ │ │ ├── _threading_local.pyi\n", + "│ │ │ │ │ │ │ ├── _tkinter.pyi\n", + "│ │ │ │ │ │ │ ├── _tracemalloc.pyi\n", + "│ │ │ │ │ │ │ ├── _winapi.pyi\n", + "│ │ │ │ │ │ │ ├── abc.pyi\n", + "│ │ │ │ │ │ │ ├── ast.pyi\n", + "│ │ │ │ │ │ │ ├── asyncio\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── base_events.pyi\n", + "│ │ │ │ │ │ │ │ ├── base_futures.pyi\n", + "│ │ │ │ │ │ │ │ ├── base_subprocess.pyi\n", + "│ │ │ │ │ │ │ │ ├── base_tasks.pyi\n", + "│ │ │ │ │ │ │ │ ├── compat.pyi\n", + "│ │ │ │ │ │ │ │ ├── constants.pyi\n", + "│ │ │ │ │ │ │ │ ├── coroutines.pyi\n", + "│ │ │ │ │ │ │ │ ├── events.pyi\n", + "│ │ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ │ ├── format_helpers.pyi\n", + "│ │ │ │ │ │ │ │ ├── futures.pyi\n", + "│ │ │ │ │ │ │ │ ├── locks.pyi\n", + "│ │ │ │ │ │ │ │ ├── log.pyi\n", + "│ │ │ │ │ │ │ │ ├── proactor_events.pyi\n", + "│ │ │ │ │ │ │ │ ├── protocols.pyi\n", + "│ │ │ │ │ │ │ │ ├── queues.pyi\n", + "│ │ │ │ │ │ │ │ ├── runners.pyi\n", + "│ │ │ │ │ │ │ │ ├── selector_events.pyi\n", + "│ │ │ │ │ │ │ │ ├── sslproto.pyi\n", + "│ │ │ │ │ │ │ │ ├── staggered.pyi\n", + "│ │ │ │ │ │ │ │ ├── streams.pyi\n", + "│ │ │ │ │ │ │ │ ├── subprocess.pyi\n", + "│ │ │ │ │ │ │ │ ├── tasks.pyi\n", + "│ │ │ │ │ │ │ │ ├── threads.pyi\n", + "│ │ │ │ │ │ │ │ ├── transports.pyi\n", + "│ │ │ │ │ │ │ │ ├── trsock.pyi\n", + "│ │ │ │ │ │ │ │ ├── unix_events.pyi\n", + "│ │ │ │ │ │ │ │ ├── windows_events.pyi\n", + "│ │ │ │ │ │ │ │ └── windows_utils.pyi\n", + "│ │ │ │ │ │ │ ├── atexit.pyi\n", + "│ │ │ │ │ │ │ ├── builtins.pyi\n", + "│ │ │ │ │ │ │ ├── collections\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── abc.pyi\n", + "│ │ │ │ │ │ │ ├── compileall.pyi\n", + "│ │ │ │ │ │ │ ├── concurrent\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── futures\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── _base.pyi\n", + "│ │ │ │ │ │ │ │ ├── process.pyi\n", + "│ │ │ │ │ │ │ │ └── thread.pyi\n", + "│ │ │ │ │ │ │ ├── configparser.pyi\n", + "│ │ │ │ │ │ │ ├── copyreg.pyi\n", + "│ │ │ │ │ │ │ ├── dbm\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── dumb.pyi\n", + "│ │ │ │ │ │ │ │ ├── gnu.pyi\n", + "│ │ │ │ │ │ │ │ └── ndbm.pyi\n", + "│ │ │ │ │ │ │ ├── distutils\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── archive_util.pyi\n", + "│ │ │ │ │ │ │ │ ├── bcppcompiler.pyi\n", + "│ │ │ │ │ │ │ │ ├── ccompiler.pyi\n", + "│ │ │ │ │ │ │ │ ├── cmd.pyi\n", + "│ │ │ │ │ │ │ │ ├── command\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── bdist.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── bdist_dumb.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── bdist_msi.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── bdist_packager.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── bdist_rpm.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── bdist_wininst.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── build.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── build_clib.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── build_ext.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── build_py.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── build_scripts.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── check.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── clean.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── config.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── install.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── install_data.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── install_egg_info.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── install_headers.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── install_lib.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── install_scripts.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── register.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── sdist.pyi\n", + "│ │ │ │ │ │ │ │ │ └── upload.pyi\n", + "│ │ │ │ │ │ │ │ ├── config.pyi\n", + "│ │ │ │ │ │ │ │ ├── core.pyi\n", + "│ │ │ │ │ │ │ │ ├── cygwinccompiler.pyi\n", + "│ │ │ │ │ │ │ │ ├── debug.pyi\n", + "│ │ │ │ │ │ │ │ ├── dep_util.pyi\n", + "│ │ │ │ │ │ │ │ ├── dir_util.pyi\n", + "│ │ │ │ │ │ │ │ ├── dist.pyi\n", + "│ │ │ │ │ │ │ │ ├── errors.pyi\n", + "│ │ │ │ │ │ │ │ ├── extension.pyi\n", + "│ │ │ │ │ │ │ │ ├── fancy_getopt.pyi\n", + "│ │ │ │ │ │ │ │ ├── file_util.pyi\n", + "│ │ │ │ │ │ │ │ ├── filelist.pyi\n", + "│ │ │ │ │ │ │ │ ├── log.pyi\n", + "│ │ │ │ │ │ │ │ ├── msvccompiler.pyi\n", + "│ │ │ │ │ │ │ │ ├── spawn.pyi\n", + "│ │ │ │ │ │ │ │ ├── sysconfig.pyi\n", + "│ │ │ │ │ │ │ │ ├── text_file.pyi\n", + "│ │ │ │ │ │ │ │ ├── unixccompiler.pyi\n", + "│ │ │ │ │ │ │ │ ├── util.pyi\n", + "│ │ │ │ │ │ │ │ └── version.pyi\n", + "│ │ │ │ │ │ │ ├── email\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── charset.pyi\n", + "│ │ │ │ │ │ │ │ ├── contentmanager.pyi\n", + "│ │ │ │ │ │ │ │ ├── encoders.pyi\n", + "│ │ │ │ │ │ │ │ ├── errors.pyi\n", + "│ │ │ │ │ │ │ │ ├── feedparser.pyi\n", + "│ │ │ │ │ │ │ │ ├── generator.pyi\n", + "│ │ │ │ │ │ │ │ ├── header.pyi\n", + "│ │ │ │ │ │ │ │ ├── headerregistry.pyi\n", + "│ │ │ │ │ │ │ │ ├── iterators.pyi\n", + "│ │ │ │ │ │ │ │ ├── message.pyi\n", + "│ │ │ │ │ │ │ │ ├── mime\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── application.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── audio.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── image.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── message.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── multipart.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── nonmultipart.pyi\n", + "│ │ │ │ │ │ │ │ │ └── text.pyi\n", + "│ │ │ │ │ │ │ │ ├── parser.pyi\n", + "│ │ │ │ │ │ │ │ ├── policy.pyi\n", + "│ │ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ │ ├── encodings\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── utf_8.pyi\n", + "│ │ │ │ │ │ │ ├── enum.pyi\n", + "│ │ │ │ │ │ │ ├── faulthandler.pyi\n", + "│ │ │ │ │ │ │ ├── fcntl.pyi\n", + "│ │ │ │ │ │ │ ├── fnmatch.pyi\n", + "│ │ │ │ │ │ │ ├── functools.pyi\n", + "│ │ │ │ │ │ │ ├── gc.pyi\n", + "│ │ │ │ │ │ │ ├── getopt.pyi\n", + "│ │ │ │ │ │ │ ├── getpass.pyi\n", + "│ │ │ │ │ │ │ ├── gettext.pyi\n", + "│ │ │ │ │ │ │ ├── glob.pyi\n", + "│ │ │ │ │ │ │ ├── gzip.pyi\n", + "│ │ │ │ │ │ │ ├── hashlib.pyi\n", + "│ │ │ │ │ │ │ ├── heapq.pyi\n", + "│ │ │ │ │ │ │ ├── html\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── entities.pyi\n", + "│ │ │ │ │ │ │ │ └── parser.pyi\n", + "│ │ │ │ │ │ │ ├── http\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── client.pyi\n", + "│ │ │ │ │ │ │ │ ├── cookiejar.pyi\n", + "│ │ │ │ │ │ │ │ ├── cookies.pyi\n", + "│ │ │ │ │ │ │ │ └── server.pyi\n", + "│ │ │ │ │ │ │ ├── imp.pyi\n", + "│ │ │ │ │ │ │ ├── importlib\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── abc.pyi\n", + "│ │ │ │ │ │ │ │ ├── machinery.pyi\n", + "│ │ │ │ │ │ │ │ ├── metadata.pyi\n", + "│ │ │ │ │ │ │ │ ├── resources.pyi\n", + "│ │ │ │ │ │ │ │ └── util.pyi\n", + "│ │ │ │ │ │ │ ├── inspect.pyi\n", + "│ │ │ │ │ │ │ ├── io.pyi\n", + "│ │ │ │ │ │ │ ├── ipaddress.pyi\n", + "│ │ │ │ │ │ │ ├── itertools.pyi\n", + "│ │ │ │ │ │ │ ├── json\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── decoder.pyi\n", + "│ │ │ │ │ │ │ │ ├── encoder.pyi\n", + "│ │ │ │ │ │ │ │ └── tool.pyi\n", + "│ │ │ │ │ │ │ ├── lzma.pyi\n", + "│ │ │ │ │ │ │ ├── macurl2path.pyi\n", + "│ │ │ │ │ │ │ ├── multiprocessing\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── connection.pyi\n", + "│ │ │ │ │ │ │ │ ├── context.pyi\n", + "│ │ │ │ │ │ │ │ ├── dummy\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── connection.pyi\n", + "│ │ │ │ │ │ │ │ ├── managers.pyi\n", + "│ │ │ │ │ │ │ │ ├── pool.pyi\n", + "│ │ │ │ │ │ │ │ ├── process.pyi\n", + "│ │ │ │ │ │ │ │ ├── queues.pyi\n", + "│ │ │ │ │ │ │ │ ├── shared_memory.pyi\n", + "│ │ │ │ │ │ │ │ ├── sharedctypes.pyi\n", + "│ │ │ │ │ │ │ │ ├── spawn.pyi\n", + "│ │ │ │ │ │ │ │ └── synchronize.pyi\n", + "│ │ │ │ │ │ │ ├── nntplib.pyi\n", + "│ │ │ │ │ │ │ ├── ntpath.pyi\n", + "│ │ │ │ │ │ │ ├── nturl2path.pyi\n", + "│ │ │ │ │ │ │ ├── os\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── path.pyi\n", + "│ │ │ │ │ │ │ ├── pathlib.pyi\n", + "│ │ │ │ │ │ │ ├── pipes.pyi\n", + "│ │ │ │ │ │ │ ├── platform.pyi\n", + "│ │ │ │ │ │ │ ├── posix.pyi\n", + "│ │ │ │ │ │ │ ├── posixpath.pyi\n", + "│ │ │ │ │ │ │ ├── queue.pyi\n", + "│ │ │ │ │ │ │ ├── random.pyi\n", + "│ │ │ │ │ │ │ ├── re.pyi\n", + "│ │ │ │ │ │ │ ├── reprlib.pyi\n", + "│ │ │ │ │ │ │ ├── resource.pyi\n", + "│ │ │ │ │ │ │ ├── runpy.pyi\n", + "│ │ │ │ │ │ │ ├── secrets.pyi\n", + "│ │ │ │ │ │ │ ├── selectors.pyi\n", + "│ │ │ │ │ │ │ ├── shelve.pyi\n", + "│ │ │ │ │ │ │ ├── shlex.pyi\n", + "│ │ │ │ │ │ │ ├── signal.pyi\n", + "│ │ │ │ │ │ │ ├── smtplib.pyi\n", + "│ │ │ │ │ │ │ ├── socketserver.pyi\n", + "│ │ │ │ │ │ │ ├── spwd.pyi\n", + "│ │ │ │ │ │ │ ├── sre_constants.pyi\n", + "│ │ │ │ │ │ │ ├── sre_parse.pyi\n", + "│ │ │ │ │ │ │ ├── stat.pyi\n", + "│ │ │ │ │ │ │ ├── statistics.pyi\n", + "│ │ │ │ │ │ │ ├── string.pyi\n", + "│ │ │ │ │ │ │ ├── subprocess.pyi\n", + "│ │ │ │ │ │ │ ├── symbol.pyi\n", + "│ │ │ │ │ │ │ ├── sys.pyi\n", + "│ │ │ │ │ │ │ ├── tempfile.pyi\n", + "│ │ │ │ │ │ │ ├── textwrap.pyi\n", + "│ │ │ │ │ │ │ ├── tkinter\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── commondialog.pyi\n", + "│ │ │ │ │ │ │ │ ├── constants.pyi\n", + "│ │ │ │ │ │ │ │ ├── dialog.pyi\n", + "│ │ │ │ │ │ │ │ ├── filedialog.pyi\n", + "│ │ │ │ │ │ │ │ ├── font.pyi\n", + "│ │ │ │ │ │ │ │ ├── messagebox.pyi\n", + "│ │ │ │ │ │ │ │ └── ttk.pyi\n", + "│ │ │ │ │ │ │ ├── tokenize.pyi\n", + "│ │ │ │ │ │ │ ├── tracemalloc.pyi\n", + "│ │ │ │ │ │ │ ├── types.pyi\n", + "│ │ │ │ │ │ │ ├── typing.pyi\n", + "│ │ │ │ │ │ │ ├── unittest\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── async_case.pyi\n", + "│ │ │ │ │ │ │ │ ├── case.pyi\n", + "│ │ │ │ │ │ │ │ ├── loader.pyi\n", + "│ │ │ │ │ │ │ │ ├── main.pyi\n", + "│ │ │ │ │ │ │ │ ├── mock.pyi\n", + "│ │ │ │ │ │ │ │ ├── result.pyi\n", + "│ │ │ │ │ │ │ │ ├── runner.pyi\n", + "│ │ │ │ │ │ │ │ ├── signals.pyi\n", + "│ │ │ │ │ │ │ │ ├── suite.pyi\n", + "│ │ │ │ │ │ │ │ └── util.pyi\n", + "│ │ │ │ │ │ │ ├── urllib\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── error.pyi\n", + "│ │ │ │ │ │ │ │ ├── parse.pyi\n", + "│ │ │ │ │ │ │ │ ├── request.pyi\n", + "│ │ │ │ │ │ │ │ ├── response.pyi\n", + "│ │ │ │ │ │ │ │ └── robotparser.pyi\n", + "│ │ │ │ │ │ │ ├── venv\n", + "│ │ │ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── winreg.pyi\n", + "│ │ │ │ │ │ │ ├── xmlrpc\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── client.pyi\n", + "│ │ │ │ │ │ │ │ └── server.pyi\n", + "│ │ │ │ │ │ │ ├── xxlimited.pyi\n", + "│ │ │ │ │ │ │ └── zipapp.pyi\n", + "│ │ │ │ │ │ ├── 3.7\n", + "│ │ │ │ │ │ │ ├── _py_abc.pyi\n", + "│ │ │ │ │ │ │ ├── contextvars.pyi\n", + "│ │ │ │ │ │ │ └── dataclasses.pyi\n", + "│ │ │ │ │ │ └── 3.9\n", + "│ │ │ │ │ │ ├── graphlib.pyi\n", + "│ │ │ │ │ │ └── zoneinfo\n", + "│ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ └── third_party\n", + "│ │ │ │ │ ├── 2\n", + "│ │ │ │ │ │ ├── OpenSSL\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ └── crypto.pyi\n", + "│ │ │ │ │ │ ├── concurrent\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ └── futures\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── _base.pyi\n", + "│ │ │ │ │ │ │ ├── process.pyi\n", + "│ │ │ │ │ │ │ └── thread.pyi\n", + "│ │ │ │ │ │ ├── enum.pyi\n", + "│ │ │ │ │ │ ├── fb303\n", + "│ │ │ │ │ │ │ ├── FacebookService.pyi\n", + "│ │ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ │ ├── ipaddress.pyi\n", + "│ │ │ │ │ │ ├── kazoo\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── client.pyi\n", + "│ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ └── recipe\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ └── watchers.pyi\n", + "│ │ │ │ │ │ ├── pathlib2.pyi\n", + "│ │ │ │ │ │ ├── pymssql.pyi\n", + "│ │ │ │ │ │ ├── routes\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── mapper.pyi\n", + "│ │ │ │ │ │ │ └── util.pyi\n", + "│ │ │ │ │ │ ├── scribe\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── scribe.pyi\n", + "│ │ │ │ │ │ │ └── ttypes.pyi\n", + "│ │ │ │ │ │ ├── six\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ └── moves\n", + "│ │ │ │ │ │ │ ├── BaseHTTPServer.pyi\n", + "│ │ │ │ │ │ │ ├── CGIHTTPServer.pyi\n", + "│ │ │ │ │ │ │ ├── SimpleHTTPServer.pyi\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── _dummy_thread.pyi\n", + "│ │ │ │ │ │ │ ├── _thread.pyi\n", + "│ │ │ │ │ │ │ ├── cPickle.pyi\n", + "│ │ │ │ │ │ │ ├── collections_abc.pyi\n", + "│ │ │ │ │ │ │ ├── configparser.pyi\n", + "│ │ │ │ │ │ │ ├── email_mime_base.pyi\n", + "│ │ │ │ │ │ │ ├── email_mime_multipart.pyi\n", + "│ │ │ │ │ │ │ ├── email_mime_nonmultipart.pyi\n", + "│ │ │ │ │ │ │ ├── email_mime_text.pyi\n", + "│ │ │ │ │ │ │ ├── html_entities.pyi\n", + "│ │ │ │ │ │ │ ├── html_parser.pyi\n", + "│ │ │ │ │ │ │ ├── http_client.pyi\n", + "│ │ │ │ │ │ │ ├── http_cookiejar.pyi\n", + "│ │ │ │ │ │ │ ├── http_cookies.pyi\n", + "│ │ │ │ │ │ │ ├── queue.pyi\n", + "│ │ │ │ │ │ │ ├── reprlib.pyi\n", + "│ │ │ │ │ │ │ ├── socketserver.pyi\n", + "│ │ │ │ │ │ │ ├── urllib\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── error.pyi\n", + "│ │ │ │ │ │ │ │ ├── parse.pyi\n", + "│ │ │ │ │ │ │ │ ├── request.pyi\n", + "│ │ │ │ │ │ │ │ ├── response.pyi\n", + "│ │ │ │ │ │ │ │ └── robotparser.pyi\n", + "│ │ │ │ │ │ │ ├── urllib_error.pyi\n", + "│ │ │ │ │ │ │ ├── urllib_parse.pyi\n", + "│ │ │ │ │ │ │ ├── urllib_request.pyi\n", + "│ │ │ │ │ │ │ ├── urllib_response.pyi\n", + "│ │ │ │ │ │ │ ├── urllib_robotparser.pyi\n", + "│ │ │ │ │ │ │ └── xmlrpc_client.pyi\n", + "│ │ │ │ │ │ └── tornado\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ ├── concurrent.pyi\n", + "│ │ │ │ │ │ ├── gen.pyi\n", + "│ │ │ │ │ │ ├── httpclient.pyi\n", + "│ │ │ │ │ │ ├── httpserver.pyi\n", + "│ │ │ │ │ │ ├── httputil.pyi\n", + "│ │ │ │ │ │ ├── ioloop.pyi\n", + "│ │ │ │ │ │ ├── locks.pyi\n", + "│ │ │ │ │ │ ├── netutil.pyi\n", + "│ │ │ │ │ │ ├── process.pyi\n", + "│ │ │ │ │ │ ├── tcpserver.pyi\n", + "│ │ │ │ │ │ ├── testing.pyi\n", + "│ │ │ │ │ │ ├── util.pyi\n", + "│ │ │ │ │ │ └── web.pyi\n", + "│ │ │ │ │ ├── 2and3\n", + "│ │ │ │ │ │ ├── atomicwrites\n", + "│ │ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ │ ├── attr\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── _version_info.pyi\n", + "│ │ │ │ │ │ │ ├── converters.pyi\n", + "│ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ ├── filters.pyi\n", + "│ │ │ │ │ │ │ └── validators.pyi\n", + "│ │ │ │ │ │ ├── backports\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ └── ssl_match_hostname.pyi\n", + "│ │ │ │ │ │ ├── backports_abc.pyi\n", + "│ │ │ │ │ │ ├── bleach\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── callbacks.pyi\n", + "│ │ │ │ │ │ │ ├── linkifier.pyi\n", + "│ │ │ │ │ │ │ ├── sanitizer.pyi\n", + "│ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ ├── boto\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── auth.pyi\n", + "│ │ │ │ │ │ │ ├── auth_handler.pyi\n", + "│ │ │ │ │ │ │ ├── compat.pyi\n", + "│ │ │ │ │ │ │ ├── connection.pyi\n", + "│ │ │ │ │ │ │ ├── ec2\n", + "│ │ │ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── elb\n", + "│ │ │ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── exception.pyi\n", + "│ │ │ │ │ │ │ ├── kms\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ │ └── layer1.pyi\n", + "│ │ │ │ │ │ │ ├── plugin.pyi\n", + "│ │ │ │ │ │ │ ├── regioninfo.pyi\n", + "│ │ │ │ │ │ │ ├── s3\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── acl.pyi\n", + "│ │ │ │ │ │ │ │ ├── bucket.pyi\n", + "│ │ │ │ │ │ │ │ ├── bucketlistresultset.pyi\n", + "│ │ │ │ │ │ │ │ ├── bucketlogging.pyi\n", + "│ │ │ │ │ │ │ │ ├── connection.pyi\n", + "│ │ │ │ │ │ │ │ ├── cors.pyi\n", + "│ │ │ │ │ │ │ │ ├── deletemarker.pyi\n", + "│ │ │ │ │ │ │ │ ├── key.pyi\n", + "│ │ │ │ │ │ │ │ ├── keyfile.pyi\n", + "│ │ │ │ │ │ │ │ ├── lifecycle.pyi\n", + "│ │ │ │ │ │ │ │ ├── multidelete.pyi\n", + "│ │ │ │ │ │ │ │ ├── multipart.pyi\n", + "│ │ │ │ │ │ │ │ ├── prefix.pyi\n", + "│ │ │ │ │ │ │ │ ├── tagging.pyi\n", + "│ │ │ │ │ │ │ │ ├── user.pyi\n", + "│ │ │ │ │ │ │ │ └── website.pyi\n", + "│ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ ├── cachetools\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── abc.pyi\n", + "│ │ │ │ │ │ │ ├── cache.pyi\n", + "│ │ │ │ │ │ │ ├── decorators.pyi\n", + "│ │ │ │ │ │ │ ├── func.pyi\n", + "│ │ │ │ │ │ │ ├── lfu.pyi\n", + "│ │ │ │ │ │ │ ├── lru.pyi\n", + "│ │ │ │ │ │ │ ├── rr.pyi\n", + "│ │ │ │ │ │ │ └── ttl.pyi\n", + "│ │ │ │ │ │ ├── certifi.pyi\n", + "│ │ │ │ │ │ ├── characteristic\n", + "│ │ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ │ ├── chardet\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── enums.pyi\n", + "│ │ │ │ │ │ │ ├── langbulgarianmodel.pyi\n", + "│ │ │ │ │ │ │ ├── langcyrillicmodel.pyi\n", + "│ │ │ │ │ │ │ ├── langgreekmodel.pyi\n", + "│ │ │ │ │ │ │ ├── langhebrewmodel.pyi\n", + "│ │ │ │ │ │ │ ├── langhungarianmodel.pyi\n", + "│ │ │ │ │ │ │ ├── langthaimodel.pyi\n", + "│ │ │ │ │ │ │ ├── langturkishmodel.pyi\n", + "│ │ │ │ │ │ │ ├── universaldetector.pyi\n", + "│ │ │ │ │ │ │ └── version.pyi\n", + "│ │ │ │ │ │ ├── click\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── _termui_impl.pyi\n", + "│ │ │ │ │ │ │ ├── core.pyi\n", + "│ │ │ │ │ │ │ ├── decorators.pyi\n", + "│ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ ├── formatting.pyi\n", + "│ │ │ │ │ │ │ ├── globals.pyi\n", + "│ │ │ │ │ │ │ ├── parser.pyi\n", + "│ │ │ │ │ │ │ ├── termui.pyi\n", + "│ │ │ │ │ │ │ ├── testing.pyi\n", + "│ │ │ │ │ │ │ ├── types.pyi\n", + "│ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ ├── croniter.pyi\n", + "│ │ │ │ │ │ ├── cryptography\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ ├── fernet.pyi\n", + "│ │ │ │ │ │ │ ├── hazmat\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── backends\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── interfaces.pyi\n", + "│ │ │ │ │ │ │ │ ├── bindings\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── openssl\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── binding.pyi\n", + "│ │ │ │ │ │ │ │ └── primitives\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── asymmetric\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── dh.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── dsa.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── ec.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── ed25519.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── ed448.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── padding.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── rsa.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── utils.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── x25519.pyi\n", + "│ │ │ │ │ │ │ │ │ └── x448.pyi\n", + "│ │ │ │ │ │ │ │ ├── ciphers\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── aead.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── algorithms.pyi\n", + "│ │ │ │ │ │ │ │ │ └── modes.pyi\n", + "│ │ │ │ │ │ │ │ ├── cmac.pyi\n", + "│ │ │ │ │ │ │ │ ├── constant_time.pyi\n", + "│ │ │ │ │ │ │ │ ├── hashes.pyi\n", + "│ │ │ │ │ │ │ │ ├── hmac.pyi\n", + "│ │ │ │ │ │ │ │ ├── kdf\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── concatkdf.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── hkdf.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── kbkdf.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── pbkdf2.pyi\n", + "│ │ │ │ │ │ │ │ │ ├── scrypt.pyi\n", + "│ │ │ │ │ │ │ │ │ └── x963kdf.pyi\n", + "│ │ │ │ │ │ │ │ ├── keywrap.pyi\n", + "│ │ │ │ │ │ │ │ ├── padding.pyi\n", + "│ │ │ │ │ │ │ │ ├── poly1305.pyi\n", + "│ │ │ │ │ │ │ │ ├── serialization\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── pkcs12.pyi\n", + "│ │ │ │ │ │ │ │ └── twofactor\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── hotp.pyi\n", + "│ │ │ │ │ │ │ │ └── totp.pyi\n", + "│ │ │ │ │ │ │ └── x509\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── extensions.pyi\n", + "│ │ │ │ │ │ │ └── oid.pyi\n", + "│ │ │ │ │ │ ├── dateparser.pyi\n", + "│ │ │ │ │ │ ├── datetimerange\n", + "│ │ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ │ ├── dateutil\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── _common.pyi\n", + "│ │ │ │ │ │ │ ├── easter.pyi\n", + "│ │ │ │ │ │ │ ├── parser.pyi\n", + "│ │ │ │ │ │ │ ├── relativedelta.pyi\n", + "│ │ │ │ │ │ │ ├── rrule.pyi\n", + "│ │ │ │ │ │ │ ├── tz\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── _common.pyi\n", + "│ │ │ │ │ │ │ │ └── tz.pyi\n", + "│ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ ├── decorator.pyi\n", + "│ │ │ │ │ │ ├── deprecated\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── classic.pyi\n", + "│ │ │ │ │ │ │ └── sphinx.pyi\n", + "│ │ │ │ │ │ ├── emoji\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── core.pyi\n", + "│ │ │ │ │ │ │ └── unicode_codes.pyi\n", + "│ │ │ │ │ │ ├── first.pyi\n", + "│ │ │ │ │ │ ├── flask\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── app.pyi\n", + "│ │ │ │ │ │ │ ├── blueprints.pyi\n", + "│ │ │ │ │ │ │ ├── cli.pyi\n", + "│ │ │ │ │ │ │ ├── config.pyi\n", + "│ │ │ │ │ │ │ ├── ctx.pyi\n", + "│ │ │ │ │ │ │ ├── debughelpers.pyi\n", + "│ │ │ │ │ │ │ ├── globals.pyi\n", + "│ │ │ │ │ │ │ ├── helpers.pyi\n", + "│ │ │ │ │ │ │ ├── json\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── tag.pyi\n", + "│ │ │ │ │ │ │ ├── logging.pyi\n", + "│ │ │ │ │ │ │ ├── sessions.pyi\n", + "│ │ │ │ │ │ │ ├── signals.pyi\n", + "│ │ │ │ │ │ │ ├── templating.pyi\n", + "│ │ │ │ │ │ │ ├── testing.pyi\n", + "│ │ │ │ │ │ │ ├── views.pyi\n", + "│ │ │ │ │ │ │ └── wrappers.pyi\n", + "│ │ │ │ │ │ ├── geoip2\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── database.pyi\n", + "│ │ │ │ │ │ │ ├── errors.pyi\n", + "│ │ │ │ │ │ │ ├── mixins.pyi\n", + "│ │ │ │ │ │ │ ├── models.pyi\n", + "│ │ │ │ │ │ │ └── records.pyi\n", + "│ │ │ │ │ │ ├── gflags.pyi\n", + "│ │ │ │ │ │ ├── google\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ └── protobuf\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── any_pb2.pyi\n", + "│ │ │ │ │ │ │ ├── api_pb2.pyi\n", + "│ │ │ │ │ │ │ ├── compiler\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── plugin_pb2.pyi\n", + "│ │ │ │ │ │ │ ├── descriptor.pyi\n", + "│ │ │ │ │ │ │ ├── descriptor_pb2.pyi\n", + "│ │ │ │ │ │ │ ├── descriptor_pool.pyi\n", + "│ │ │ │ │ │ │ ├── duration_pb2.pyi\n", + "│ │ │ │ │ │ │ ├── empty_pb2.pyi\n", + "│ │ │ │ │ │ │ ├── field_mask_pb2.pyi\n", + "│ │ │ │ │ │ │ ├── internal\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── containers.pyi\n", + "│ │ │ │ │ │ │ │ ├── decoder.pyi\n", + "│ │ │ │ │ │ │ │ ├── encoder.pyi\n", + "│ │ │ │ │ │ │ │ ├── enum_type_wrapper.pyi\n", + "│ │ │ │ │ │ │ │ ├── extension_dict.pyi\n", + "│ │ │ │ │ │ │ │ ├── message_listener.pyi\n", + "│ │ │ │ │ │ │ │ ├── python_message.pyi\n", + "│ │ │ │ │ │ │ │ ├── well_known_types.pyi\n", + "│ │ │ │ │ │ │ │ └── wire_format.pyi\n", + "│ │ │ │ │ │ │ ├── json_format.pyi\n", + "│ │ │ │ │ │ │ ├── message.pyi\n", + "│ │ │ │ │ │ │ ├── message_factory.pyi\n", + "│ │ │ │ │ │ │ ├── reflection.pyi\n", + "│ │ │ │ │ │ │ ├── service.pyi\n", + "│ │ │ │ │ │ │ ├── source_context_pb2.pyi\n", + "│ │ │ │ │ │ │ ├── struct_pb2.pyi\n", + "│ │ │ │ │ │ │ ├── symbol_database.pyi\n", + "│ │ │ │ │ │ │ ├── timestamp_pb2.pyi\n", + "│ │ │ │ │ │ │ ├── type_pb2.pyi\n", + "│ │ │ │ │ │ │ ├── util\n", + "│ │ │ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ │ │ └── wrappers_pb2.pyi\n", + "│ │ │ │ │ │ ├── itsdangerous.pyi\n", + "│ │ │ │ │ │ ├── jinja2\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── _compat.pyi\n", + "│ │ │ │ │ │ │ ├── _stringdefs.pyi\n", + "│ │ │ │ │ │ │ ├── bccache.pyi\n", + "│ │ │ │ │ │ │ ├── compiler.pyi\n", + "│ │ │ │ │ │ │ ├── constants.pyi\n", + "│ │ │ │ │ │ │ ├── debug.pyi\n", + "│ │ │ │ │ │ │ ├── defaults.pyi\n", + "│ │ │ │ │ │ │ ├── environment.pyi\n", + "│ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ ├── ext.pyi\n", + "│ │ │ │ │ │ │ ├── filters.pyi\n", + "│ │ │ │ │ │ │ ├── lexer.pyi\n", + "│ │ │ │ │ │ │ ├── loaders.pyi\n", + "│ │ │ │ │ │ │ ├── meta.pyi\n", + "│ │ │ │ │ │ │ ├── nodes.pyi\n", + "│ │ │ │ │ │ │ ├── optimizer.pyi\n", + "│ │ │ │ │ │ │ ├── parser.pyi\n", + "│ │ │ │ │ │ │ ├── runtime.pyi\n", + "│ │ │ │ │ │ │ ├── sandbox.pyi\n", + "│ │ │ │ │ │ │ ├── tests.pyi\n", + "│ │ │ │ │ │ │ ├── utils.pyi\n", + "│ │ │ │ │ │ │ └── visitor.pyi\n", + "│ │ │ │ │ │ ├── markdown\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── __meta__.pyi\n", + "│ │ │ │ │ │ │ ├── blockparser.pyi\n", + "│ │ │ │ │ │ │ ├── blockprocessors.pyi\n", + "│ │ │ │ │ │ │ ├── core.pyi\n", + "│ │ │ │ │ │ │ ├── extensions\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── abbr.pyi\n", + "│ │ │ │ │ │ │ │ ├── admonition.pyi\n", + "│ │ │ │ │ │ │ │ ├── attr_list.pyi\n", + "│ │ │ │ │ │ │ │ ├── codehilite.pyi\n", + "│ │ │ │ │ │ │ │ ├── def_list.pyi\n", + "│ │ │ │ │ │ │ │ ├── extra.pyi\n", + "│ │ │ │ │ │ │ │ ├── fenced_code.pyi\n", + "│ │ │ │ │ │ │ │ ├── footnotes.pyi\n", + "│ │ │ │ │ │ │ │ ├── legacy_attrs.pyi\n", + "│ │ │ │ │ │ │ │ ├── legacy_em.pyi\n", + "│ │ │ │ │ │ │ │ ├── md_in_html.pyi\n", + "│ │ │ │ │ │ │ │ ├── meta.pyi\n", + "│ │ │ │ │ │ │ │ ├── nl2br.pyi\n", + "│ │ │ │ │ │ │ │ ├── sane_lists.pyi\n", + "│ │ │ │ │ │ │ │ ├── smarty.pyi\n", + "│ │ │ │ │ │ │ │ ├── tables.pyi\n", + "│ │ │ │ │ │ │ │ ├── toc.pyi\n", + "│ │ │ │ │ │ │ │ └── wikilinks.pyi\n", + "│ │ │ │ │ │ │ ├── inlinepatterns.pyi\n", + "│ │ │ │ │ │ │ ├── pep562.pyi\n", + "│ │ │ │ │ │ │ ├── postprocessors.pyi\n", + "│ │ │ │ │ │ │ ├── preprocessors.pyi\n", + "│ │ │ │ │ │ │ ├── serializers.pyi\n", + "│ │ │ │ │ │ │ ├── treeprocessors.pyi\n", + "│ │ │ │ │ │ │ └── util.pyi\n", + "│ │ │ │ │ │ ├── markupsafe\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── _compat.pyi\n", + "│ │ │ │ │ │ │ ├── _constants.pyi\n", + "│ │ │ │ │ │ │ ├── _native.pyi\n", + "│ │ │ │ │ │ │ └── _speedups.pyi\n", + "│ │ │ │ │ │ ├── maxminddb\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── compat.pyi\n", + "│ │ │ │ │ │ │ ├── const.pyi\n", + "│ │ │ │ │ │ │ ├── decoder.pyi\n", + "│ │ │ │ │ │ │ ├── errors.pyi\n", + "│ │ │ │ │ │ │ ├── extension.pyi\n", + "│ │ │ │ │ │ │ └── reader.pyi\n", + "│ │ │ │ │ │ ├── mock.pyi\n", + "│ │ │ │ │ │ ├── mypy_extensions.pyi\n", + "│ │ │ │ │ │ ├── nmap\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ └── nmap.pyi\n", + "│ │ │ │ │ │ ├── paramiko\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── _version.pyi\n", + "│ │ │ │ │ │ │ ├── _winapi.pyi\n", + "│ │ │ │ │ │ │ ├── agent.pyi\n", + "│ │ │ │ │ │ │ ├── auth_handler.pyi\n", + "│ │ │ │ │ │ │ ├── ber.pyi\n", + "│ │ │ │ │ │ │ ├── buffered_pipe.pyi\n", + "│ │ │ │ │ │ │ ├── channel.pyi\n", + "│ │ │ │ │ │ │ ├── client.pyi\n", + "│ │ │ │ │ │ │ ├── common.pyi\n", + "│ │ │ │ │ │ │ ├── compress.pyi\n", + "│ │ │ │ │ │ │ ├── config.pyi\n", + "│ │ │ │ │ │ │ ├── dsskey.pyi\n", + "│ │ │ │ │ │ │ ├── ecdsakey.pyi\n", + "│ │ │ │ │ │ │ ├── ed25519key.pyi\n", + "│ │ │ │ │ │ │ ├── file.pyi\n", + "│ │ │ │ │ │ │ ├── hostkeys.pyi\n", + "│ │ │ │ │ │ │ ├── kex_curve25519.pyi\n", + "│ │ │ │ │ │ │ ├── kex_ecdh_nist.pyi\n", + "│ │ │ │ │ │ │ ├── kex_gex.pyi\n", + "│ │ │ │ │ │ │ ├── kex_group1.pyi\n", + "│ │ │ │ │ │ │ ├── kex_group14.pyi\n", + "│ │ │ │ │ │ │ ├── kex_group16.pyi\n", + "│ │ │ │ │ │ │ ├── kex_gss.pyi\n", + "│ │ │ │ │ │ │ ├── message.pyi\n", + "│ │ │ │ │ │ │ ├── packet.pyi\n", + "│ │ │ │ │ │ │ ├── pipe.pyi\n", + "│ │ │ │ │ │ │ ├── pkey.pyi\n", + "│ │ │ │ │ │ │ ├── primes.pyi\n", + "│ │ │ │ │ │ │ ├── proxy.pyi\n", + "│ │ │ │ │ │ │ ├── py3compat.pyi\n", + "│ │ │ │ │ │ │ ├── rsakey.pyi\n", + "│ │ │ │ │ │ │ ├── server.pyi\n", + "│ │ │ │ │ │ │ ├── sftp.pyi\n", + "│ │ │ │ │ │ │ ├── sftp_attr.pyi\n", + "│ │ │ │ │ │ │ ├── sftp_client.pyi\n", + "│ │ │ │ │ │ │ ├── sftp_file.pyi\n", + "│ │ │ │ │ │ │ ├── sftp_handle.pyi\n", + "│ │ │ │ │ │ │ ├── sftp_server.pyi\n", + "│ │ │ │ │ │ │ ├── sftp_si.pyi\n", + "│ │ │ │ │ │ │ ├── ssh_exception.pyi\n", + "│ │ │ │ │ │ │ ├── ssh_gss.pyi\n", + "│ │ │ │ │ │ │ ├── transport.pyi\n", + "│ │ │ │ │ │ │ ├── util.pyi\n", + "│ │ │ │ │ │ │ └── win_pageant.pyi\n", + "│ │ │ │ │ │ ├── polib.pyi\n", + "│ │ │ │ │ │ ├── pyVmomi\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── vim\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── event.pyi\n", + "│ │ │ │ │ │ │ │ ├── fault.pyi\n", + "│ │ │ │ │ │ │ │ ├── option.pyi\n", + "│ │ │ │ │ │ │ │ └── view.pyi\n", + "│ │ │ │ │ │ │ └── vmodl\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── fault.pyi\n", + "│ │ │ │ │ │ │ └── query.pyi\n", + "│ │ │ │ │ │ ├── pycurl.pyi\n", + "│ │ │ │ │ │ ├── pymysql\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── charset.pyi\n", + "│ │ │ │ │ │ │ ├── connections.pyi\n", + "│ │ │ │ │ │ │ ├── constants\n", + "│ │ │ │ │ │ │ │ ├── CLIENT.pyi\n", + "│ │ │ │ │ │ │ │ ├── COMMAND.pyi\n", + "│ │ │ │ │ │ │ │ ├── ER.pyi\n", + "│ │ │ │ │ │ │ │ ├── FIELD_TYPE.pyi\n", + "│ │ │ │ │ │ │ │ ├── FLAG.pyi\n", + "│ │ │ │ │ │ │ │ ├── SERVER_STATUS.pyi\n", + "│ │ │ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── converters.pyi\n", + "│ │ │ │ │ │ │ ├── cursors.pyi\n", + "│ │ │ │ │ │ │ ├── err.pyi\n", + "│ │ │ │ │ │ │ ├── times.pyi\n", + "│ │ │ │ │ │ │ └── util.pyi\n", + "│ │ │ │ │ │ ├── pynamodb\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── attributes.pyi\n", + "│ │ │ │ │ │ │ ├── connection\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ │ │ ├── table.pyi\n", + "│ │ │ │ │ │ │ │ └── util.pyi\n", + "│ │ │ │ │ │ │ ├── constants.pyi\n", + "│ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ ├── indexes.pyi\n", + "│ │ │ │ │ │ │ ├── models.pyi\n", + "│ │ │ │ │ │ │ ├── settings.pyi\n", + "│ │ │ │ │ │ │ ├── throttle.pyi\n", + "│ │ │ │ │ │ │ └── types.pyi\n", + "│ │ │ │ │ │ ├── pyre_extensions.pyi\n", + "│ │ │ │ │ │ ├── pytz\n", + "│ │ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ │ ├── redis\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── client.pyi\n", + "│ │ │ │ │ │ │ ├── connection.pyi\n", + "│ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ ├── requests\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── adapters.pyi\n", + "│ │ │ │ │ │ │ ├── api.pyi\n", + "│ │ │ │ │ │ │ ├── auth.pyi\n", + "│ │ │ │ │ │ │ ├── compat.pyi\n", + "│ │ │ │ │ │ │ ├── cookies.pyi\n", + "│ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ ├── hooks.pyi\n", + "│ │ │ │ │ │ │ ├── models.pyi\n", + "│ │ │ │ │ │ │ ├── packages\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ └── urllib3\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── _collections.pyi\n", + "│ │ │ │ │ │ │ │ ├── connection.pyi\n", + "│ │ │ │ │ │ │ │ ├── connectionpool.pyi\n", + "│ │ │ │ │ │ │ │ ├── contrib\n", + "│ │ │ │ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ │ ├── fields.pyi\n", + "│ │ │ │ │ │ │ │ ├── filepost.pyi\n", + "│ │ │ │ │ │ │ │ ├── packages\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── ssl_match_hostname\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ │ └── _implementation.pyi\n", + "│ │ │ │ │ │ │ │ ├── poolmanager.pyi\n", + "│ │ │ │ │ │ │ │ ├── request.pyi\n", + "│ │ │ │ │ │ │ │ ├── response.pyi\n", + "│ │ │ │ │ │ │ │ └── util\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── connection.pyi\n", + "│ │ │ │ │ │ │ │ ├── request.pyi\n", + "│ │ │ │ │ │ │ │ ├── response.pyi\n", + "│ │ │ │ │ │ │ │ ├── retry.pyi\n", + "│ │ │ │ │ │ │ │ ├── ssl_.pyi\n", + "│ │ │ │ │ │ │ │ ├── timeout.pyi\n", + "│ │ │ │ │ │ │ │ └── url.pyi\n", + "│ │ │ │ │ │ │ ├── sessions.pyi\n", + "│ │ │ │ │ │ │ ├── status_codes.pyi\n", + "│ │ │ │ │ │ │ ├── structures.pyi\n", + "│ │ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ │ ├── retry\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ └── api.pyi\n", + "│ │ │ │ │ │ ├── simplejson\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── decoder.pyi\n", + "│ │ │ │ │ │ │ ├── encoder.pyi\n", + "│ │ │ │ │ │ │ └── scanner.pyi\n", + "│ │ │ │ │ │ ├── singledispatch.pyi\n", + "│ │ │ │ │ │ ├── slugify\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── slugify.pyi\n", + "│ │ │ │ │ │ │ └── special.pyi\n", + "│ │ │ │ │ │ ├── tabulate.pyi\n", + "│ │ │ │ │ │ ├── termcolor.pyi\n", + "│ │ │ │ │ │ ├── toml.pyi\n", + "│ │ │ │ │ │ ├── typing_extensions.pyi\n", + "│ │ │ │ │ │ ├── tzlocal\n", + "│ │ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ │ ├── ujson.pyi\n", + "│ │ │ │ │ │ ├── werkzeug\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── _compat.pyi\n", + "│ │ │ │ │ │ │ ├── _internal.pyi\n", + "│ │ │ │ │ │ │ ├── _reloader.pyi\n", + "│ │ │ │ │ │ │ ├── contrib\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── atom.pyi\n", + "│ │ │ │ │ │ │ │ ├── cache.pyi\n", + "│ │ │ │ │ │ │ │ ├── fixers.pyi\n", + "│ │ │ │ │ │ │ │ ├── iterio.pyi\n", + "│ │ │ │ │ │ │ │ ├── jsrouting.pyi\n", + "│ │ │ │ │ │ │ │ ├── limiter.pyi\n", + "│ │ │ │ │ │ │ │ ├── lint.pyi\n", + "│ │ │ │ │ │ │ │ ├── profiler.pyi\n", + "│ │ │ │ │ │ │ │ ├── securecookie.pyi\n", + "│ │ │ │ │ │ │ │ ├── sessions.pyi\n", + "│ │ │ │ │ │ │ │ ├── testtools.pyi\n", + "│ │ │ │ │ │ │ │ └── wrappers.pyi\n", + "│ │ │ │ │ │ │ ├── datastructures.pyi\n", + "│ │ │ │ │ │ │ ├── debug\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── console.pyi\n", + "│ │ │ │ │ │ │ │ ├── repr.pyi\n", + "│ │ │ │ │ │ │ │ └── tbtools.pyi\n", + "│ │ │ │ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ │ │ │ ├── filesystem.pyi\n", + "│ │ │ │ │ │ │ ├── formparser.pyi\n", + "│ │ │ │ │ │ │ ├── http.pyi\n", + "│ │ │ │ │ │ │ ├── local.pyi\n", + "│ │ │ │ │ │ │ ├── middleware\n", + "│ │ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ │ ├── dispatcher.pyi\n", + "│ │ │ │ │ │ │ │ ├── http_proxy.pyi\n", + "│ │ │ │ │ │ │ │ ├── lint.pyi\n", + "│ │ │ │ │ │ │ │ ├── profiler.pyi\n", + "│ │ │ │ │ │ │ │ ├── proxy_fix.pyi\n", + "│ │ │ │ │ │ │ │ └── shared_data.pyi\n", + "│ │ │ │ │ │ │ ├── posixemulation.pyi\n", + "│ │ │ │ │ │ │ ├── routing.pyi\n", + "│ │ │ │ │ │ │ ├── script.pyi\n", + "│ │ │ │ │ │ │ ├── security.pyi\n", + "│ │ │ │ │ │ │ ├── serving.pyi\n", + "│ │ │ │ │ │ │ ├── test.pyi\n", + "│ │ │ │ │ │ │ ├── testapp.pyi\n", + "│ │ │ │ │ │ │ ├── urls.pyi\n", + "│ │ │ │ │ │ │ ├── useragents.pyi\n", + "│ │ │ │ │ │ │ ├── utils.pyi\n", + "│ │ │ │ │ │ │ ├── wrappers.pyi\n", + "│ │ │ │ │ │ │ └── wsgi.pyi\n", + "│ │ │ │ │ │ └── yaml\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ ├── composer.pyi\n", + "│ │ │ │ │ │ ├── constructor.pyi\n", + "│ │ │ │ │ │ ├── cyaml.pyi\n", + "│ │ │ │ │ │ ├── dumper.pyi\n", + "│ │ │ │ │ │ ├── emitter.pyi\n", + "│ │ │ │ │ │ ├── error.pyi\n", + "│ │ │ │ │ │ ├── events.pyi\n", + "│ │ │ │ │ │ ├── loader.pyi\n", + "│ │ │ │ │ │ ├── nodes.pyi\n", + "│ │ │ │ │ │ ├── parser.pyi\n", + "│ │ │ │ │ │ ├── reader.pyi\n", + "│ │ │ │ │ │ ├── representer.pyi\n", + "│ │ │ │ │ │ ├── resolver.pyi\n", + "│ │ │ │ │ │ ├── scanner.pyi\n", + "│ │ │ │ │ │ ├── serializer.pyi\n", + "│ │ │ │ │ │ └── tokens.pyi\n", + "│ │ │ │ │ └── 3\n", + "│ │ │ │ │ ├── aiofiles\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ ├── base.pyi\n", + "│ │ │ │ │ │ ├── os.pyi\n", + "│ │ │ │ │ │ └── threadpool\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ ├── binary.pyi\n", + "│ │ │ │ │ │ └── text.pyi\n", + "│ │ │ │ │ ├── contextvars.pyi\n", + "│ │ │ │ │ ├── dataclasses.pyi\n", + "│ │ │ │ │ ├── docutils\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ ├── examples.pyi\n", + "│ │ │ │ │ │ ├── nodes.pyi\n", + "│ │ │ │ │ │ └── parsers\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ └── rst\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ ├── nodes.pyi\n", + "│ │ │ │ │ │ ├── roles.pyi\n", + "│ │ │ │ │ │ └── states.pyi\n", + "│ │ │ │ │ ├── filelock\n", + "│ │ │ │ │ │ └── __init__.pyi\n", + "│ │ │ │ │ ├── freezegun\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ └── api.pyi\n", + "│ │ │ │ │ ├── frozendict.pyi\n", + "│ │ │ │ │ ├── jwt\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ ├── algorithms.pyi\n", + "│ │ │ │ │ │ └── contrib\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ └── algorithms\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ ├── py_ecdsa.pyi\n", + "│ │ │ │ │ │ └── pycrypto.pyi\n", + "│ │ │ │ │ ├── orjson.pyi\n", + "│ │ │ │ │ ├── pkg_resources\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ └── py31compat.pyi\n", + "│ │ │ │ │ ├── pyrfc3339\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ ├── generator.pyi\n", + "│ │ │ │ │ │ ├── parser.pyi\n", + "│ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ ├── six\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ └── moves\n", + "│ │ │ │ │ │ ├── BaseHTTPServer.pyi\n", + "│ │ │ │ │ │ ├── CGIHTTPServer.pyi\n", + "│ │ │ │ │ │ ├── SimpleHTTPServer.pyi\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ ├── _dummy_thread.pyi\n", + "│ │ │ │ │ │ ├── _thread.pyi\n", + "│ │ │ │ │ │ ├── builtins.pyi\n", + "│ │ │ │ │ │ ├── cPickle.pyi\n", + "│ │ │ │ │ │ ├── collections_abc.pyi\n", + "│ │ │ │ │ │ ├── configparser.pyi\n", + "│ │ │ │ │ │ ├── email_mime_base.pyi\n", + "│ │ │ │ │ │ ├── email_mime_multipart.pyi\n", + "│ │ │ │ │ │ ├── email_mime_nonmultipart.pyi\n", + "│ │ │ │ │ │ ├── email_mime_text.pyi\n", + "│ │ │ │ │ │ ├── html_entities.pyi\n", + "│ │ │ │ │ │ ├── html_parser.pyi\n", + "│ │ │ │ │ │ ├── http_client.pyi\n", + "│ │ │ │ │ │ ├── http_cookiejar.pyi\n", + "│ │ │ │ │ │ ├── http_cookies.pyi\n", + "│ │ │ │ │ │ ├── queue.pyi\n", + "│ │ │ │ │ │ ├── reprlib.pyi\n", + "│ │ │ │ │ │ ├── socketserver.pyi\n", + "│ │ │ │ │ │ ├── tkinter.pyi\n", + "│ │ │ │ │ │ ├── tkinter_commondialog.pyi\n", + "│ │ │ │ │ │ ├── tkinter_constants.pyi\n", + "│ │ │ │ │ │ ├── tkinter_dialog.pyi\n", + "│ │ │ │ │ │ ├── tkinter_filedialog.pyi\n", + "│ │ │ │ │ │ ├── tkinter_tkfiledialog.pyi\n", + "│ │ │ │ │ │ ├── tkinter_ttk.pyi\n", + "│ │ │ │ │ │ ├── urllib\n", + "│ │ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ │ ├── error.pyi\n", + "│ │ │ │ │ │ │ ├── parse.pyi\n", + "│ │ │ │ │ │ │ ├── request.pyi\n", + "│ │ │ │ │ │ │ ├── response.pyi\n", + "│ │ │ │ │ │ │ └── robotparser.pyi\n", + "│ │ │ │ │ │ ├── urllib_error.pyi\n", + "│ │ │ │ │ │ ├── urllib_parse.pyi\n", + "│ │ │ │ │ │ ├── urllib_request.pyi\n", + "│ │ │ │ │ │ ├── urllib_response.pyi\n", + "│ │ │ │ │ │ └── urllib_robotparser.pyi\n", + "│ │ │ │ │ ├── typed_ast\n", + "│ │ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ │ ├── ast27.pyi\n", + "│ │ │ │ │ │ ├── ast3.pyi\n", + "│ │ │ │ │ │ └── conversions.pyi\n", + "│ │ │ │ │ └── waitress\n", + "│ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ ├── adjustments.pyi\n", + "│ │ │ │ │ ├── buffers.pyi\n", + "│ │ │ │ │ ├── channel.pyi\n", + "│ │ │ │ │ ├── compat.pyi\n", + "│ │ │ │ │ ├── parser.pyi\n", + "│ │ │ │ │ ├── proxy_headers.pyi\n", + "│ │ │ │ │ ├── receiver.pyi\n", + "│ │ │ │ │ ├── rfc7230.pyi\n", + "│ │ │ │ │ ├── runner.pyi\n", + "│ │ │ │ │ ├── server.pyi\n", + "│ │ │ │ │ ├── task.pyi\n", + "│ │ │ │ │ ├── trigger.pyi\n", + "│ │ │ │ │ ├── utilities.pyi\n", + "│ │ │ │ │ └── wasyncore.pyi\n", + "│ │ │ │ └── utils.py\n", + "│ │ │ ├── jedi-0.19.1.dist-info\n", + "│ │ │ │ ├── AUTHORS.txt\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE.txt\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── jupyter.py\n", + "│ │ │ ├── jupyter_client\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _version.cpython-310.pyc\n", + "│ │ │ │ │ ├── adapter.cpython-310.pyc\n", + "│ │ │ │ │ ├── channels.cpython-310.pyc\n", + "│ │ │ │ │ ├── channelsabc.cpython-310.pyc\n", + "│ │ │ │ │ ├── client.cpython-310.pyc\n", + "│ │ │ │ │ ├── clientabc.cpython-310.pyc\n", + "│ │ │ │ │ ├── connect.cpython-310.pyc\n", + "│ │ │ │ │ ├── consoleapp.cpython-310.pyc\n", + "│ │ │ │ │ ├── jsonutil.cpython-310.pyc\n", + "│ │ │ │ │ ├── kernelapp.cpython-310.pyc\n", + "│ │ │ │ │ ├── kernelspec.cpython-310.pyc\n", + "│ │ │ │ │ ├── kernelspecapp.cpython-310.pyc\n", + "│ │ │ │ │ ├── launcher.cpython-310.pyc\n", + "│ │ │ │ │ ├── localinterfaces.cpython-310.pyc\n", + "│ │ │ │ │ ├── manager.cpython-310.pyc\n", + "│ │ │ │ │ ├── managerabc.cpython-310.pyc\n", + "│ │ │ │ │ ├── multikernelmanager.cpython-310.pyc\n", + "│ │ │ │ │ ├── restarter.cpython-310.pyc\n", + "│ │ │ │ │ ├── runapp.cpython-310.pyc\n", + "│ │ │ │ │ ├── session.cpython-310.pyc\n", + "│ │ │ │ │ ├── threaded.cpython-310.pyc\n", + "│ │ │ │ │ ├── utils.cpython-310.pyc\n", + "│ │ │ │ │ └── win_interrupt.cpython-310.pyc\n", + "│ │ │ │ ├── _version.py\n", + "│ │ │ │ ├── adapter.py\n", + "│ │ │ │ ├── asynchronous\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ └── client.cpython-310.pyc\n", + "│ │ │ │ │ └── client.py\n", + "│ │ │ │ ├── blocking\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ └── client.cpython-310.pyc\n", + "│ │ │ │ │ └── client.py\n", + "│ │ │ │ ├── channels.py\n", + "│ │ │ │ ├── channelsabc.py\n", + "│ │ │ │ ├── client.py\n", + "│ │ │ │ ├── clientabc.py\n", + "│ │ │ │ ├── connect.py\n", + "│ │ │ │ ├── consoleapp.py\n", + "│ │ │ │ ├── ioloop\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── manager.cpython-310.pyc\n", + "│ │ │ │ │ │ └── restarter.cpython-310.pyc\n", + "│ │ │ │ │ ├── manager.py\n", + "│ │ │ │ │ └── restarter.py\n", + "│ │ │ │ ├── jsonutil.py\n", + "│ │ │ │ ├── kernelapp.py\n", + "│ │ │ │ ├── kernelspec.py\n", + "│ │ │ │ ├── kernelspecapp.py\n", + "│ │ │ │ ├── launcher.py\n", + "│ │ │ │ ├── localinterfaces.py\n", + "│ │ │ │ ├── manager.py\n", + "│ │ │ │ ├── managerabc.py\n", + "│ │ │ │ ├── multikernelmanager.py\n", + "│ │ │ │ ├── provisioning\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── factory.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── local_provisioner.cpython-310.pyc\n", + "│ │ │ │ │ │ └── provisioner_base.cpython-310.pyc\n", + "│ │ │ │ │ ├── factory.py\n", + "│ │ │ │ │ ├── local_provisioner.py\n", + "│ │ │ │ │ └── provisioner_base.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ ├── restarter.py\n", + "│ │ │ │ ├── runapp.py\n", + "│ │ │ │ ├── session.py\n", + "│ │ │ │ ├── ssh\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── forward.cpython-310.pyc\n", + "│ │ │ │ │ │ └── tunnel.cpython-310.pyc\n", + "│ │ │ │ │ ├── forward.py\n", + "│ │ │ │ │ └── tunnel.py\n", + "│ │ │ │ ├── threaded.py\n", + "│ │ │ │ ├── utils.py\n", + "│ │ │ │ └── win_interrupt.py\n", + "│ │ │ ├── jupyter_client-8.6.1.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ ├── entry_points.txt\n", + "│ │ │ │ └── licenses\n", + "│ │ │ │ └── LICENSE\n", + "│ │ │ ├── jupyter_core\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __main__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ ├── application.cpython-310.pyc\n", + "│ │ │ │ │ ├── command.cpython-310.pyc\n", + "│ │ │ │ │ ├── migrate.cpython-310.pyc\n", + "│ │ │ │ │ ├── paths.cpython-310.pyc\n", + "│ │ │ │ │ ├── troubleshoot.cpython-310.pyc\n", + "│ │ │ │ │ └── version.cpython-310.pyc\n", + "│ │ │ │ ├── application.py\n", + "│ │ │ │ ├── command.py\n", + "│ │ │ │ ├── migrate.py\n", + "│ │ │ │ ├── paths.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ ├── troubleshoot.py\n", + "│ │ │ │ ├── utils\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ └── version.py\n", + "│ │ │ ├── jupyter_core-5.7.2.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ ├── entry_points.txt\n", + "│ │ │ │ └── licenses\n", + "│ │ │ │ └── LICENSE\n", + "│ │ │ ├── matplotlib_inline\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── backend_inline.cpython-310.pyc\n", + "│ │ │ │ │ └── config.cpython-310.pyc\n", + "│ │ │ │ ├── backend_inline.py\n", + "│ │ │ │ └── config.py\n", + "│ │ │ ├── matplotlib_inline-0.1.7.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ ├── entry_points.txt\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── nest_asyncio-1.6.0.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── nest_asyncio.py\n", + "│ │ │ ├── numpy\n", + "│ │ │ │ ├── __config__.py\n", + "│ │ │ │ ├── __init__.cython-30.pxd\n", + "│ │ │ │ ├── __init__.pxd\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __config__.cpython-310.pyc\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _distributor_init.cpython-310.pyc\n", + "│ │ │ │ │ ├── _globals.cpython-310.pyc\n", + "│ │ │ │ │ ├── _pytesttester.cpython-310.pyc\n", + "│ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ ├── ctypeslib.cpython-310.pyc\n", + "│ │ │ │ │ ├── dtypes.cpython-310.pyc\n", + "│ │ │ │ │ ├── exceptions.cpython-310.pyc\n", + "│ │ │ │ │ ├── matlib.cpython-310.pyc\n", + "│ │ │ │ │ └── version.cpython-310.pyc\n", + "│ │ │ │ ├── _core\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _dtype.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _dtype_ctypes.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _internal.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _multiarray_umath.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── multiarray.cpython-310.pyc\n", + "│ │ │ │ │ │ └── umath.cpython-310.pyc\n", + "│ │ │ │ │ ├── _dtype.py\n", + "│ │ │ │ │ ├── _dtype_ctypes.py\n", + "│ │ │ │ │ ├── _internal.py\n", + "│ │ │ │ │ ├── _multiarray_umath.py\n", + "│ │ │ │ │ ├── multiarray.py\n", + "│ │ │ │ │ └── umath.py\n", + "│ │ │ │ ├── _distributor_init.py\n", + "│ │ │ │ ├── _globals.py\n", + "│ │ │ │ ├── _pyinstaller\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── hook-numpy.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pyinstaller-smoke.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_pyinstaller.cpython-310.pyc\n", + "│ │ │ │ │ ├── hook-numpy.py\n", + "│ │ │ │ │ ├── pyinstaller-smoke.py\n", + "│ │ │ │ │ └── test_pyinstaller.py\n", + "│ │ │ │ ├── _pytesttester.py\n", + "│ │ │ │ ├── _pytesttester.pyi\n", + "│ │ │ │ ├── _typing\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _add_docstring.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _array_like.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _char_codes.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _dtype_like.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _extended_precision.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _nbit.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _nested_sequence.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _scalars.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _shape.cpython-310.pyc\n", + "│ │ │ │ │ │ └── setup.cpython-310.pyc\n", + "│ │ │ │ │ ├── _add_docstring.py\n", + "│ │ │ │ │ ├── _array_like.py\n", + "│ │ │ │ │ ├── _callable.pyi\n", + "│ │ │ │ │ ├── _char_codes.py\n", + "│ │ │ │ │ ├── _dtype_like.py\n", + "│ │ │ │ │ ├── _extended_precision.py\n", + "│ │ │ │ │ ├── _nbit.py\n", + "│ │ │ │ │ ├── _nested_sequence.py\n", + "│ │ │ │ │ ├── _scalars.py\n", + "│ │ │ │ │ ├── _shape.py\n", + "│ │ │ │ │ ├── _ufunc.pyi\n", + "│ │ │ │ │ └── setup.py\n", + "│ │ │ │ ├── _utils\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _convertions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _inspect.cpython-310.pyc\n", + "│ │ │ │ │ │ └── _pep440.cpython-310.pyc\n", + "│ │ │ │ │ ├── _convertions.py\n", + "│ │ │ │ │ ├── _inspect.py\n", + "│ │ │ │ │ └── _pep440.py\n", + "│ │ │ │ ├── array_api\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _array_object.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _constants.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _creation_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _data_type_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _dtypes.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _elementwise_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _indexing_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _manipulation_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _searching_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _set_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _sorting_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _statistical_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _typing.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _utility_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── linalg.cpython-310.pyc\n", + "│ │ │ │ │ │ └── setup.cpython-310.pyc\n", + "│ │ │ │ │ ├── _array_object.py\n", + "│ │ │ │ │ ├── _constants.py\n", + "│ │ │ │ │ ├── _creation_functions.py\n", + "│ │ │ │ │ ├── _data_type_functions.py\n", + "│ │ │ │ │ ├── _dtypes.py\n", + "│ │ │ │ │ ├── _elementwise_functions.py\n", + "│ │ │ │ │ ├── _indexing_functions.py\n", + "│ │ │ │ │ ├── _manipulation_functions.py\n", + "│ │ │ │ │ ├── _searching_functions.py\n", + "│ │ │ │ │ ├── _set_functions.py\n", + "│ │ │ │ │ ├── _sorting_functions.py\n", + "│ │ │ │ │ ├── _statistical_functions.py\n", + "│ │ │ │ │ ├── _typing.py\n", + "│ │ │ │ │ ├── _utility_functions.py\n", + "│ │ │ │ │ ├── linalg.py\n", + "│ │ │ │ │ ├── setup.py\n", + "│ │ │ │ │ └── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_array_object.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_creation_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_data_type_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_elementwise_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_indexing_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_manipulation_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_set_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_sorting_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_validation.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_array_object.py\n", + "│ │ │ │ │ ├── test_creation_functions.py\n", + "│ │ │ │ │ ├── test_data_type_functions.py\n", + "│ │ │ │ │ ├── test_elementwise_functions.py\n", + "│ │ │ │ │ ├── test_indexing_functions.py\n", + "│ │ │ │ │ ├── test_manipulation_functions.py\n", + "│ │ │ │ │ ├── test_set_functions.py\n", + "│ │ │ │ │ ├── test_sorting_functions.py\n", + "│ │ │ │ │ └── test_validation.py\n", + "│ │ │ │ ├── compat\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── py3k.cpython-310.pyc\n", + "│ │ │ │ │ │ └── setup.cpython-310.pyc\n", + "│ │ │ │ │ ├── py3k.py\n", + "│ │ │ │ │ ├── setup.py\n", + "│ │ │ │ │ └── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_compat.cpython-310.pyc\n", + "│ │ │ │ │ └── test_compat.py\n", + "│ │ │ │ ├── conftest.py\n", + "│ │ │ │ ├── core\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _add_newdocs.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _add_newdocs_scalars.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _asarray.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _dtype.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _dtype_ctypes.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _exceptions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _internal.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _machar.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _methods.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _string_helpers.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _type_aliases.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _ufunc_config.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── arrayprint.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cversions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── defchararray.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── einsumfunc.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── fromnumeric.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── function_base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── getlimits.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── memmap.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── multiarray.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── numeric.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── numerictypes.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── overrides.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── records.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── shape_base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── umath.cpython-310.pyc\n", + "│ │ │ │ │ │ └── umath_tests.cpython-310.pyc\n", + "│ │ │ │ │ ├── _add_newdocs.py\n", + "│ │ │ │ │ ├── _add_newdocs_scalars.py\n", + "│ │ │ │ │ ├── _asarray.py\n", + "│ │ │ │ │ ├── _asarray.pyi\n", + "│ │ │ │ │ ├── _dtype.py\n", + "│ │ │ │ │ ├── _dtype_ctypes.py\n", + "│ │ │ │ │ ├── _exceptions.py\n", + "│ │ │ │ │ ├── _internal.py\n", + "│ │ │ │ │ ├── _internal.pyi\n", + "│ │ │ │ │ ├── _machar.py\n", + "│ │ │ │ │ ├── _methods.py\n", + "│ │ │ │ │ ├── _multiarray_tests.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── _multiarray_umath.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── _operand_flag_tests.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── _rational_tests.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── _simd.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── _string_helpers.py\n", + "│ │ │ │ │ ├── _struct_ufunc_tests.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── _type_aliases.py\n", + "│ │ │ │ │ ├── _type_aliases.pyi\n", + "│ │ │ │ │ ├── _ufunc_config.py\n", + "│ │ │ │ │ ├── _ufunc_config.pyi\n", + "│ │ │ │ │ ├── _umath_tests.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── arrayprint.py\n", + "│ │ │ │ │ ├── arrayprint.pyi\n", + "│ │ │ │ │ ├── cversions.py\n", + "│ │ │ │ │ ├── defchararray.py\n", + "│ │ │ │ │ ├── defchararray.pyi\n", + "│ │ │ │ │ ├── einsumfunc.py\n", + "│ │ │ │ │ ├── einsumfunc.pyi\n", + "│ │ │ │ │ ├── fromnumeric.py\n", + "│ │ │ │ │ ├── fromnumeric.pyi\n", + "│ │ │ │ │ ├── function_base.py\n", + "│ │ │ │ │ ├── function_base.pyi\n", + "│ │ │ │ │ ├── getlimits.py\n", + "│ │ │ │ │ ├── getlimits.pyi\n", + "│ │ │ │ │ ├── include\n", + "│ │ │ │ │ │ └── numpy\n", + "│ │ │ │ │ │ ├── __multiarray_api.c\n", + "│ │ │ │ │ │ ├── __multiarray_api.h\n", + "│ │ │ │ │ │ ├── __ufunc_api.c\n", + "│ │ │ │ │ │ ├── __ufunc_api.h\n", + "│ │ │ │ │ │ ├── _dtype_api.h\n", + "│ │ │ │ │ │ ├── _neighborhood_iterator_imp.h\n", + "│ │ │ │ │ │ ├── _numpyconfig.h\n", + "│ │ │ │ │ │ ├── arrayobject.h\n", + "│ │ │ │ │ │ ├── arrayscalars.h\n", + "│ │ │ │ │ │ ├── experimental_dtype_api.h\n", + "│ │ │ │ │ │ ├── halffloat.h\n", + "│ │ │ │ │ │ ├── ndarrayobject.h\n", + "│ │ │ │ │ │ ├── ndarraytypes.h\n", + "│ │ │ │ │ │ ├── noprefix.h\n", + "│ │ │ │ │ │ ├── npy_1_7_deprecated_api.h\n", + "│ │ │ │ │ │ ├── npy_3kcompat.h\n", + "│ │ │ │ │ │ ├── npy_common.h\n", + "│ │ │ │ │ │ ├── npy_cpu.h\n", + "│ │ │ │ │ │ ├── npy_endian.h\n", + "│ │ │ │ │ │ ├── npy_interrupt.h\n", + "│ │ │ │ │ │ ├── npy_math.h\n", + "│ │ │ │ │ │ ├── npy_no_deprecated_api.h\n", + "│ │ │ │ │ │ ├── npy_os.h\n", + "│ │ │ │ │ │ ├── numpyconfig.h\n", + "│ │ │ │ │ │ ├── old_defines.h\n", + "│ │ │ │ │ │ ├── random\n", + "│ │ │ │ │ │ │ ├── LICENSE.txt\n", + "│ │ │ │ │ │ │ ├── bitgen.h\n", + "│ │ │ │ │ │ │ ├── distributions.h\n", + "│ │ │ │ │ │ │ └── libdivide.h\n", + "│ │ │ │ │ │ ├── ufuncobject.h\n", + "│ │ │ │ │ │ └── utils.h\n", + "│ │ │ │ │ ├── lib\n", + "│ │ │ │ │ │ ├── libnpymath.a\n", + "│ │ │ │ │ │ └── npy-pkg-config\n", + "│ │ │ │ │ │ ├── mlib.ini\n", + "│ │ │ │ │ │ └── npymath.ini\n", + "│ │ │ │ │ ├── memmap.py\n", + "│ │ │ │ │ ├── memmap.pyi\n", + "│ │ │ │ │ ├── multiarray.py\n", + "│ │ │ │ │ ├── multiarray.pyi\n", + "│ │ │ │ │ ├── numeric.py\n", + "│ │ │ │ │ ├── numeric.pyi\n", + "│ │ │ │ │ ├── numerictypes.py\n", + "│ │ │ │ │ ├── numerictypes.pyi\n", + "│ │ │ │ │ ├── overrides.py\n", + "│ │ │ │ │ ├── records.py\n", + "│ │ │ │ │ ├── records.pyi\n", + "│ │ │ │ │ ├── shape_base.py\n", + "│ │ │ │ │ ├── shape_base.pyi\n", + "│ │ │ │ │ ├── tests\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _locales.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test__exceptions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_abc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_argparse.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_array_coercion.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_array_interface.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arraymethod.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arrayprint.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_casting_floatingpoint_errors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_casting_unittests.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_conversion_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_cpu_dispatcher.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_cpu_features.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_custom_dtypes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_cython.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_datetime.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_defchararray.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_deprecations.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_dlpack.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_dtype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_einsum.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_errstate.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_extint128.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_function_base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_getlimits.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_half.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_hashtable.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_indexerrors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_item_selection.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_limited_api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_longdouble.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_machar.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_mem_overlap.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_mem_policy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_memmap.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_multiarray.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_nditer.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_nep50_promotions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_numeric.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_numerictypes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_numpy_2_0_compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_overrides.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_print.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_protocols.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_records.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_regression.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_scalar_ctors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_scalar_methods.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_scalarbuffer.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_scalarinherit.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_scalarmath.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_scalarprint.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_shape_base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_simd.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_simd_module.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_strings.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_ufunc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_umath.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_umath_accuracy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_umath_complex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_unicode.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _locales.py\n", + "│ │ │ │ │ │ ├── data\n", + "│ │ │ │ │ │ │ ├── astype_copy.pkl\n", + "│ │ │ │ │ │ │ ├── generate_umath_validation_data.cpp\n", + "│ │ │ │ │ │ │ ├── numpy_2_0_array.pkl\n", + "│ │ │ │ │ │ │ ├── recarray_from_file.fits\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-README.txt\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-arccos.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-arccosh.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-arcsin.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-arcsinh.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-arctan.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-arctanh.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-cbrt.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-cos.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-cosh.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-exp.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-exp2.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-expm1.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-log.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-log10.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-log1p.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-log2.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-sin.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-sinh.csv\n", + "│ │ │ │ │ │ │ ├── umath-validation-set-tan.csv\n", + "│ │ │ │ │ │ │ └── umath-validation-set-tanh.csv\n", + "│ │ │ │ │ │ ├── examples\n", + "│ │ │ │ │ │ │ ├── cython\n", + "│ │ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ │ └── setup.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── checks.pyx\n", + "│ │ │ │ │ │ │ │ ├── meson.build\n", + "│ │ │ │ │ │ │ │ └── setup.py\n", + "│ │ │ │ │ │ │ └── limited_api\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ └── setup.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── limited_api.c\n", + "│ │ │ │ │ │ │ └── setup.py\n", + "│ │ │ │ │ │ ├── test__exceptions.py\n", + "│ │ │ │ │ │ ├── test_abc.py\n", + "│ │ │ │ │ │ ├── test_api.py\n", + "│ │ │ │ │ │ ├── test_argparse.py\n", + "│ │ │ │ │ │ ├── test_array_coercion.py\n", + "│ │ │ │ │ │ ├── test_array_interface.py\n", + "│ │ │ │ │ │ ├── test_arraymethod.py\n", + "│ │ │ │ │ │ ├── test_arrayprint.py\n", + "│ │ │ │ │ │ ├── test_casting_floatingpoint_errors.py\n", + "│ │ │ │ │ │ ├── test_casting_unittests.py\n", + "│ │ │ │ │ │ ├── test_conversion_utils.py\n", + "│ │ │ │ │ │ ├── test_cpu_dispatcher.py\n", + "│ │ │ │ │ │ ├── test_cpu_features.py\n", + "│ │ │ │ │ │ ├── test_custom_dtypes.py\n", + "│ │ │ │ │ │ ├── test_cython.py\n", + "│ │ │ │ │ │ ├── test_datetime.py\n", + "│ │ │ │ │ │ ├── test_defchararray.py\n", + "│ │ │ │ │ │ ├── test_deprecations.py\n", + "│ │ │ │ │ │ ├── test_dlpack.py\n", + "│ │ │ │ │ │ ├── test_dtype.py\n", + "│ │ │ │ │ │ ├── test_einsum.py\n", + "│ │ │ │ │ │ ├── test_errstate.py\n", + "│ │ │ │ │ │ ├── test_extint128.py\n", + "│ │ │ │ │ │ ├── test_function_base.py\n", + "│ │ │ │ │ │ ├── test_getlimits.py\n", + "│ │ │ │ │ │ ├── test_half.py\n", + "│ │ │ │ │ │ ├── test_hashtable.py\n", + "│ │ │ │ │ │ ├── test_indexerrors.py\n", + "│ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ ├── test_item_selection.py\n", + "│ │ │ │ │ │ ├── test_limited_api.py\n", + "│ │ │ │ │ │ ├── test_longdouble.py\n", + "│ │ │ │ │ │ ├── test_machar.py\n", + "│ │ │ │ │ │ ├── test_mem_overlap.py\n", + "│ │ │ │ │ │ ├── test_mem_policy.py\n", + "│ │ │ │ │ │ ├── test_memmap.py\n", + "│ │ │ │ │ │ ├── test_multiarray.py\n", + "│ │ │ │ │ │ ├── test_nditer.py\n", + "│ │ │ │ │ │ ├── test_nep50_promotions.py\n", + "│ │ │ │ │ │ ├── test_numeric.py\n", + "│ │ │ │ │ │ ├── test_numerictypes.py\n", + "│ │ │ │ │ │ ├── test_numpy_2_0_compat.py\n", + "│ │ │ │ │ │ ├── test_overrides.py\n", + "│ │ │ │ │ │ ├── test_print.py\n", + "│ │ │ │ │ │ ├── test_protocols.py\n", + "│ │ │ │ │ │ ├── test_records.py\n", + "│ │ │ │ │ │ ├── test_regression.py\n", + "│ │ │ │ │ │ ├── test_scalar_ctors.py\n", + "│ │ │ │ │ │ ├── test_scalar_methods.py\n", + "│ │ │ │ │ │ ├── test_scalarbuffer.py\n", + "│ │ │ │ │ │ ├── test_scalarinherit.py\n", + "│ │ │ │ │ │ ├── test_scalarmath.py\n", + "│ │ │ │ │ │ ├── test_scalarprint.py\n", + "│ │ │ │ │ │ ├── test_shape_base.py\n", + "│ │ │ │ │ │ ├── test_simd.py\n", + "│ │ │ │ │ │ ├── test_simd_module.py\n", + "│ │ │ │ │ │ ├── test_strings.py\n", + "│ │ │ │ │ │ ├── test_ufunc.py\n", + "│ │ │ │ │ │ ├── test_umath.py\n", + "│ │ │ │ │ │ ├── test_umath_accuracy.py\n", + "│ │ │ │ │ │ ├── test_umath_complex.py\n", + "│ │ │ │ │ │ └── test_unicode.py\n", + "│ │ │ │ │ ├── umath.py\n", + "│ │ │ │ │ └── umath_tests.py\n", + "│ │ │ │ ├── ctypeslib.py\n", + "│ │ │ │ ├── ctypeslib.pyi\n", + "│ │ │ │ ├── distutils\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _shell_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── armccompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ccompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ccompiler_opt.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── conv_template.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── core.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cpuinfo.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── exec_command.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── extension.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── from_template.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── fujitsuccompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── intelccompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── lib2def.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── line_endings.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── log.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── mingw32ccompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── misc_util.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── msvc9compiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── msvccompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── npy_pkg_config.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── numpy_distribution.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pathccompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── setup.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── system_info.cpython-310.pyc\n", + "│ │ │ │ │ │ └── unixccompiler.cpython-310.pyc\n", + "│ │ │ │ │ ├── _shell_utils.py\n", + "│ │ │ │ │ ├── armccompiler.py\n", + "│ │ │ │ │ ├── ccompiler.py\n", + "│ │ │ │ │ ├── ccompiler_opt.py\n", + "│ │ │ │ │ ├── checks\n", + "│ │ │ │ │ │ ├── cpu_asimd.c\n", + "│ │ │ │ │ │ ├── cpu_asimddp.c\n", + "│ │ │ │ │ │ ├── cpu_asimdfhm.c\n", + "│ │ │ │ │ │ ├── cpu_asimdhp.c\n", + "│ │ │ │ │ │ ├── cpu_avx.c\n", + "│ │ │ │ │ │ ├── cpu_avx2.c\n", + "│ │ │ │ │ │ ├── cpu_avx512_clx.c\n", + "│ │ │ │ │ │ ├── cpu_avx512_cnl.c\n", + "│ │ │ │ │ │ ├── cpu_avx512_icl.c\n", + "│ │ │ │ │ │ ├── cpu_avx512_knl.c\n", + "│ │ │ │ │ │ ├── cpu_avx512_knm.c\n", + "│ │ │ │ │ │ ├── cpu_avx512_skx.c\n", + "│ │ │ │ │ │ ├── cpu_avx512_spr.c\n", + "│ │ │ │ │ │ ├── cpu_avx512cd.c\n", + "│ │ │ │ │ │ ├── cpu_avx512f.c\n", + "│ │ │ │ │ │ ├── cpu_f16c.c\n", + "│ │ │ │ │ │ ├── cpu_fma3.c\n", + "│ │ │ │ │ │ ├── cpu_fma4.c\n", + "│ │ │ │ │ │ ├── cpu_neon.c\n", + "│ │ │ │ │ │ ├── cpu_neon_fp16.c\n", + "│ │ │ │ │ │ ├── cpu_neon_vfpv4.c\n", + "│ │ │ │ │ │ ├── cpu_popcnt.c\n", + "│ │ │ │ │ │ ├── cpu_sse.c\n", + "│ │ │ │ │ │ ├── cpu_sse2.c\n", + "│ │ │ │ │ │ ├── cpu_sse3.c\n", + "│ │ │ │ │ │ ├── cpu_sse41.c\n", + "│ │ │ │ │ │ ├── cpu_sse42.c\n", + "│ │ │ │ │ │ ├── cpu_ssse3.c\n", + "│ │ │ │ │ │ ├── cpu_vsx.c\n", + "│ │ │ │ │ │ ├── cpu_vsx2.c\n", + "│ │ │ │ │ │ ├── cpu_vsx3.c\n", + "│ │ │ │ │ │ ├── cpu_vsx4.c\n", + "│ │ │ │ │ │ ├── cpu_vx.c\n", + "│ │ │ │ │ │ ├── cpu_vxe.c\n", + "│ │ │ │ │ │ ├── cpu_vxe2.c\n", + "│ │ │ │ │ │ ├── cpu_xop.c\n", + "│ │ │ │ │ │ ├── extra_avx512bw_mask.c\n", + "│ │ │ │ │ │ ├── extra_avx512dq_mask.c\n", + "│ │ │ │ │ │ ├── extra_avx512f_reduce.c\n", + "│ │ │ │ │ │ ├── extra_vsx3_half_double.c\n", + "│ │ │ │ │ │ ├── extra_vsx4_mma.c\n", + "│ │ │ │ │ │ ├── extra_vsx_asm.c\n", + "│ │ │ │ │ │ └── test_flags.c\n", + "│ │ │ │ │ ├── command\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── autodist.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── bdist_rpm.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── build.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── build_clib.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── build_ext.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── build_py.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── build_scripts.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── build_src.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── config.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── config_compiler.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── develop.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── egg_info.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── install.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── install_clib.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── install_data.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── install_headers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── sdist.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── autodist.py\n", + "│ │ │ │ │ │ ├── bdist_rpm.py\n", + "│ │ │ │ │ │ ├── build.py\n", + "│ │ │ │ │ │ ├── build_clib.py\n", + "│ │ │ │ │ │ ├── build_ext.py\n", + "│ │ │ │ │ │ ├── build_py.py\n", + "│ │ │ │ │ │ ├── build_scripts.py\n", + "│ │ │ │ │ │ ├── build_src.py\n", + "│ │ │ │ │ │ ├── config.py\n", + "│ │ │ │ │ │ ├── config_compiler.py\n", + "│ │ │ │ │ │ ├── develop.py\n", + "│ │ │ │ │ │ ├── egg_info.py\n", + "│ │ │ │ │ │ ├── install.py\n", + "│ │ │ │ │ │ ├── install_clib.py\n", + "│ │ │ │ │ │ ├── install_data.py\n", + "│ │ │ │ │ │ ├── install_headers.py\n", + "│ │ │ │ │ │ └── sdist.py\n", + "│ │ │ │ │ ├── conv_template.py\n", + "│ │ │ │ │ ├── core.py\n", + "│ │ │ │ │ ├── cpuinfo.py\n", + "│ │ │ │ │ ├── exec_command.py\n", + "│ │ │ │ │ ├── extension.py\n", + "│ │ │ │ │ ├── fcompiler\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── absoft.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── arm.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── compaq.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── environment.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── fujitsu.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── g95.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── gnu.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── hpux.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── ibm.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── intel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── lahey.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── mips.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── nag.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── none.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── nv.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pathf95.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pg.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── sun.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── vast.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── absoft.py\n", + "│ │ │ │ │ │ ├── arm.py\n", + "│ │ │ │ │ │ ├── compaq.py\n", + "│ │ │ │ │ │ ├── environment.py\n", + "│ │ │ │ │ │ ├── fujitsu.py\n", + "│ │ │ │ │ │ ├── g95.py\n", + "│ │ │ │ │ │ ├── gnu.py\n", + "│ │ │ │ │ │ ├── hpux.py\n", + "│ │ │ │ │ │ ├── ibm.py\n", + "│ │ │ │ │ │ ├── intel.py\n", + "│ │ │ │ │ │ ├── lahey.py\n", + "│ │ │ │ │ │ ├── mips.py\n", + "│ │ │ │ │ │ ├── nag.py\n", + "│ │ │ │ │ │ ├── none.py\n", + "│ │ │ │ │ │ ├── nv.py\n", + "│ │ │ │ │ │ ├── pathf95.py\n", + "│ │ │ │ │ │ ├── pg.py\n", + "│ │ │ │ │ │ ├── sun.py\n", + "│ │ │ │ │ │ └── vast.py\n", + "│ │ │ │ │ ├── from_template.py\n", + "│ │ │ │ │ ├── fujitsuccompiler.py\n", + "│ │ │ │ │ ├── intelccompiler.py\n", + "│ │ │ │ │ ├── lib2def.py\n", + "│ │ │ │ │ ├── line_endings.py\n", + "│ │ │ │ │ ├── log.py\n", + "│ │ │ │ │ ├── mingw\n", + "│ │ │ │ │ │ └── gfortran_vs2003_hack.c\n", + "│ │ │ │ │ ├── mingw32ccompiler.py\n", + "│ │ │ │ │ ├── misc_util.py\n", + "│ │ │ │ │ ├── msvc9compiler.py\n", + "│ │ │ │ │ ├── msvccompiler.py\n", + "│ │ │ │ │ ├── npy_pkg_config.py\n", + "│ │ │ │ │ ├── numpy_distribution.py\n", + "│ │ │ │ │ ├── pathccompiler.py\n", + "│ │ │ │ │ ├── setup.py\n", + "│ │ │ │ │ ├── system_info.py\n", + "│ │ │ │ │ ├── tests\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_build_ext.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_ccompiler_opt.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_ccompiler_opt_conf.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_exec_command.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_fcompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_fcompiler_gnu.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_fcompiler_intel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_fcompiler_nagfor.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_from_template.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_log.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_mingw32ccompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_misc_util.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_npy_pkg_config.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_shell_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_system_info.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_build_ext.py\n", + "│ │ │ │ │ │ ├── test_ccompiler_opt.py\n", + "│ │ │ │ │ │ ├── test_ccompiler_opt_conf.py\n", + "│ │ │ │ │ │ ├── test_exec_command.py\n", + "│ │ │ │ │ │ ├── test_fcompiler.py\n", + "│ │ │ │ │ │ ├── test_fcompiler_gnu.py\n", + "│ │ │ │ │ │ ├── test_fcompiler_intel.py\n", + "│ │ │ │ │ │ ├── test_fcompiler_nagfor.py\n", + "│ │ │ │ │ │ ├── test_from_template.py\n", + "│ │ │ │ │ │ ├── test_log.py\n", + "│ │ │ │ │ │ ├── test_mingw32ccompiler.py\n", + "│ │ │ │ │ │ ├── test_misc_util.py\n", + "│ │ │ │ │ │ ├── test_npy_pkg_config.py\n", + "│ │ │ │ │ │ ├── test_shell_utils.py\n", + "│ │ │ │ │ │ └── test_system_info.py\n", + "│ │ │ │ │ └── unixccompiler.py\n", + "│ │ │ │ ├── doc\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── constants.cpython-310.pyc\n", + "│ │ │ │ │ │ └── ufuncs.cpython-310.pyc\n", + "│ │ │ │ │ ├── constants.py\n", + "│ │ │ │ │ └── ufuncs.py\n", + "│ │ │ │ ├── dtypes.py\n", + "│ │ │ │ ├── dtypes.pyi\n", + "│ │ │ │ ├── exceptions.py\n", + "│ │ │ │ ├── exceptions.pyi\n", + "│ │ │ │ ├── f2py\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ ├── __main__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── __version__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _isocbind.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _src_pyf.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── auxfuncs.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── capi_maps.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cb_rules.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cfuncs.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── common_rules.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── crackfortran.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── diagnose.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── f2py2e.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── f90mod_rules.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── func2subr.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── rules.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── setup.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── symbolic.cpython-310.pyc\n", + "│ │ │ │ │ │ └── use_rules.cpython-310.pyc\n", + "│ │ │ │ │ ├── __version__.py\n", + "│ │ │ │ │ ├── _backends\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _backend.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _distutils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── _meson.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _backend.py\n", + "│ │ │ │ │ │ ├── _distutils.py\n", + "│ │ │ │ │ │ ├── _meson.py\n", + "│ │ │ │ │ │ └── meson.build.template\n", + "│ │ │ │ │ ├── _isocbind.py\n", + "│ │ │ │ │ ├── _src_pyf.py\n", + "│ │ │ │ │ ├── auxfuncs.py\n", + "│ │ │ │ │ ├── capi_maps.py\n", + "│ │ │ │ │ ├── cb_rules.py\n", + "│ │ │ │ │ ├── cfuncs.py\n", + "│ │ │ │ │ ├── common_rules.py\n", + "│ │ │ │ │ ├── crackfortran.py\n", + "│ │ │ │ │ ├── diagnose.py\n", + "│ │ │ │ │ ├── f2py2e.py\n", + "│ │ │ │ │ ├── f90mod_rules.py\n", + "│ │ │ │ │ ├── func2subr.py\n", + "│ │ │ │ │ ├── rules.py\n", + "│ │ │ │ │ ├── setup.cfg\n", + "│ │ │ │ │ ├── setup.py\n", + "│ │ │ │ │ ├── src\n", + "│ │ │ │ │ │ ├── fortranobject.c\n", + "│ │ │ │ │ │ └── fortranobject.h\n", + "│ │ │ │ │ ├── symbolic.py\n", + "│ │ │ │ │ ├── tests\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_abstract_interface.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_array_from_pyobj.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_assumed_shape.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_block_docstring.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_callback.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_character.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_compile_function.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_crackfortran.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_data.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_docs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_f2cmap.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_f2py2e.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_isoc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_kind.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_mixed.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_module_doc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_parameter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_pyf_src.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_quoted_character.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_regression.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_return_character.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_return_complex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_return_integer.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_return_logical.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_return_real.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_semicolon_split.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_size.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_string.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_symbolic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_value_attrspec.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── util.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── src\n", + "│ │ │ │ │ │ │ ├── abstract_interface\n", + "│ │ │ │ │ │ │ │ ├── foo.f90\n", + "│ │ │ │ │ │ │ │ └── gh18403_mod.f90\n", + "│ │ │ │ │ │ │ ├── array_from_pyobj\n", + "│ │ │ │ │ │ │ │ └── wrapmodule.c\n", + "│ │ │ │ │ │ │ ├── assumed_shape\n", + "│ │ │ │ │ │ │ │ ├── .f2py_f2cmap\n", + "│ │ │ │ │ │ │ │ ├── foo_free.f90\n", + "│ │ │ │ │ │ │ │ ├── foo_mod.f90\n", + "│ │ │ │ │ │ │ │ ├── foo_use.f90\n", + "│ │ │ │ │ │ │ │ └── precision.f90\n", + "│ │ │ │ │ │ │ ├── block_docstring\n", + "│ │ │ │ │ │ │ │ └── foo.f\n", + "│ │ │ │ │ │ │ ├── callback\n", + "│ │ │ │ │ │ │ │ ├── foo.f\n", + "│ │ │ │ │ │ │ │ ├── gh17797.f90\n", + "│ │ │ │ │ │ │ │ ├── gh18335.f90\n", + "│ │ │ │ │ │ │ │ ├── gh25211.f\n", + "│ │ │ │ │ │ │ │ └── gh25211.pyf\n", + "│ │ │ │ │ │ │ ├── cli\n", + "│ │ │ │ │ │ │ │ ├── gh_22819.pyf\n", + "│ │ │ │ │ │ │ │ ├── hi77.f\n", + "│ │ │ │ │ │ │ │ └── hiworld.f90\n", + "│ │ │ │ │ │ │ ├── common\n", + "│ │ │ │ │ │ │ │ ├── block.f\n", + "│ │ │ │ │ │ │ │ └── gh19161.f90\n", + "│ │ │ │ │ │ │ ├── crackfortran\n", + "│ │ │ │ │ │ │ │ ├── accesstype.f90\n", + "│ │ │ │ │ │ │ │ ├── data_common.f\n", + "│ │ │ │ │ │ │ │ ├── data_multiplier.f\n", + "│ │ │ │ │ │ │ │ ├── data_stmts.f90\n", + "│ │ │ │ │ │ │ │ ├── data_with_comments.f\n", + "│ │ │ │ │ │ │ │ ├── foo_deps.f90\n", + "│ │ │ │ │ │ │ │ ├── gh15035.f\n", + "│ │ │ │ │ │ │ │ ├── gh17859.f\n", + "│ │ │ │ │ │ │ │ ├── gh22648.pyf\n", + "│ │ │ │ │ │ │ │ ├── gh23533.f\n", + "│ │ │ │ │ │ │ │ ├── gh23598.f90\n", + "│ │ │ │ │ │ │ │ ├── gh23598Warn.f90\n", + "│ │ │ │ │ │ │ │ ├── gh23879.f90\n", + "│ │ │ │ │ │ │ │ ├── gh2848.f90\n", + "│ │ │ │ │ │ │ │ ├── operators.f90\n", + "│ │ │ │ │ │ │ │ ├── privatemod.f90\n", + "│ │ │ │ │ │ │ │ ├── publicmod.f90\n", + "│ │ │ │ │ │ │ │ ├── pubprivmod.f90\n", + "│ │ │ │ │ │ │ │ └── unicode_comment.f90\n", + "│ │ │ │ │ │ │ ├── f2cmap\n", + "│ │ │ │ │ │ │ │ ├── .f2py_f2cmap\n", + "│ │ │ │ │ │ │ │ └── isoFortranEnvMap.f90\n", + "│ │ │ │ │ │ │ ├── isocintrin\n", + "│ │ │ │ │ │ │ │ └── isoCtests.f90\n", + "│ │ │ │ │ │ │ ├── kind\n", + "│ │ │ │ │ │ │ │ └── foo.f90\n", + "│ │ │ │ │ │ │ ├── mixed\n", + "│ │ │ │ │ │ │ │ ├── foo.f\n", + "│ │ │ │ │ │ │ │ ├── foo_fixed.f90\n", + "│ │ │ │ │ │ │ │ └── foo_free.f90\n", + "│ │ │ │ │ │ │ ├── module_data\n", + "│ │ │ │ │ │ │ │ ├── mod.mod\n", + "│ │ │ │ │ │ │ │ └── module_data_docstring.f90\n", + "│ │ │ │ │ │ │ ├── negative_bounds\n", + "│ │ │ │ │ │ │ │ └── issue_20853.f90\n", + "│ │ │ │ │ │ │ ├── parameter\n", + "│ │ │ │ │ │ │ │ ├── constant_both.f90\n", + "│ │ │ │ │ │ │ │ ├── constant_compound.f90\n", + "│ │ │ │ │ │ │ │ ├── constant_integer.f90\n", + "│ │ │ │ │ │ │ │ ├── constant_non_compound.f90\n", + "│ │ │ │ │ │ │ │ └── constant_real.f90\n", + "│ │ │ │ │ │ │ ├── quoted_character\n", + "│ │ │ │ │ │ │ │ └── foo.f\n", + "│ │ │ │ │ │ │ ├── regression\n", + "│ │ │ │ │ │ │ │ ├── gh25337\n", + "│ │ │ │ │ │ │ │ │ ├── data.f90\n", + "│ │ │ │ │ │ │ │ │ └── use_data.f90\n", + "│ │ │ │ │ │ │ │ └── inout.f90\n", + "│ │ │ │ │ │ │ ├── return_character\n", + "│ │ │ │ │ │ │ │ ├── foo77.f\n", + "│ │ │ │ │ │ │ │ └── foo90.f90\n", + "│ │ │ │ │ │ │ ├── return_complex\n", + "│ │ │ │ │ │ │ │ ├── foo77.f\n", + "│ │ │ │ │ │ │ │ └── foo90.f90\n", + "│ │ │ │ │ │ │ ├── return_integer\n", + "│ │ │ │ │ │ │ │ ├── foo77.f\n", + "│ │ │ │ │ │ │ │ └── foo90.f90\n", + "│ │ │ │ │ │ │ ├── return_logical\n", + "│ │ │ │ │ │ │ │ ├── foo77.f\n", + "│ │ │ │ │ │ │ │ └── foo90.f90\n", + "│ │ │ │ │ │ │ ├── return_real\n", + "│ │ │ │ │ │ │ │ ├── foo77.f\n", + "│ │ │ │ │ │ │ │ └── foo90.f90\n", + "│ │ │ │ │ │ │ ├── size\n", + "│ │ │ │ │ │ │ │ └── foo.f90\n", + "│ │ │ │ │ │ │ ├── string\n", + "│ │ │ │ │ │ │ │ ├── char.f90\n", + "│ │ │ │ │ │ │ │ ├── fixed_string.f90\n", + "│ │ │ │ │ │ │ │ ├── gh24008.f\n", + "│ │ │ │ │ │ │ │ ├── gh24662.f90\n", + "│ │ │ │ │ │ │ │ ├── gh25286.f90\n", + "│ │ │ │ │ │ │ │ ├── gh25286.pyf\n", + "│ │ │ │ │ │ │ │ ├── gh25286_bc.pyf\n", + "│ │ │ │ │ │ │ │ ├── scalar_string.f90\n", + "│ │ │ │ │ │ │ │ └── string.f\n", + "│ │ │ │ │ │ │ └── value_attrspec\n", + "│ │ │ │ │ │ │ └── gh21665.f90\n", + "│ │ │ │ │ │ ├── test_abstract_interface.py\n", + "│ │ │ │ │ │ ├── test_array_from_pyobj.py\n", + "│ │ │ │ │ │ ├── test_assumed_shape.py\n", + "│ │ │ │ │ │ ├── test_block_docstring.py\n", + "│ │ │ │ │ │ ├── test_callback.py\n", + "│ │ │ │ │ │ ├── test_character.py\n", + "│ │ │ │ │ │ ├── test_common.py\n", + "│ │ │ │ │ │ ├── test_compile_function.py\n", + "│ │ │ │ │ │ ├── test_crackfortran.py\n", + "│ │ │ │ │ │ ├── test_data.py\n", + "│ │ │ │ │ │ ├── test_docs.py\n", + "│ │ │ │ │ │ ├── test_f2cmap.py\n", + "│ │ │ │ │ │ ├── test_f2py2e.py\n", + "│ │ │ │ │ │ ├── test_isoc.py\n", + "│ │ │ │ │ │ ├── test_kind.py\n", + "│ │ │ │ │ │ ├── test_mixed.py\n", + "│ │ │ │ │ │ ├── test_module_doc.py\n", + "│ │ │ │ │ │ ├── test_parameter.py\n", + "│ │ │ │ │ │ ├── test_pyf_src.py\n", + "│ │ │ │ │ │ ├── test_quoted_character.py\n", + "│ │ │ │ │ │ ├── test_regression.py\n", + "│ │ │ │ │ │ ├── test_return_character.py\n", + "│ │ │ │ │ │ ├── test_return_complex.py\n", + "│ │ │ │ │ │ ├── test_return_integer.py\n", + "│ │ │ │ │ │ ├── test_return_logical.py\n", + "│ │ │ │ │ │ ├── test_return_real.py\n", + "│ │ │ │ │ │ ├── test_semicolon_split.py\n", + "│ │ │ │ │ │ ├── test_size.py\n", + "│ │ │ │ │ │ ├── test_string.py\n", + "│ │ │ │ │ │ ├── test_symbolic.py\n", + "│ │ │ │ │ │ ├── test_value_attrspec.py\n", + "│ │ │ │ │ │ └── util.py\n", + "│ │ │ │ │ └── use_rules.py\n", + "│ │ │ │ ├── fft\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _pocketfft.cpython-310.pyc\n", + "│ │ │ │ │ │ └── helper.cpython-310.pyc\n", + "│ │ │ │ │ ├── _pocketfft.py\n", + "│ │ │ │ │ ├── _pocketfft.pyi\n", + "│ │ │ │ │ ├── _pocketfft_internal.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── helper.py\n", + "│ │ │ │ │ ├── helper.pyi\n", + "│ │ │ │ │ └── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_helper.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_pocketfft.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_helper.py\n", + "│ │ │ │ │ └── test_pocketfft.py\n", + "│ │ │ │ ├── lib\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _datasource.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _iotools.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _version.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── arraypad.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── arraysetops.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── arrayterator.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── format.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── function_base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── histograms.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── index_tricks.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── mixins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── nanfunctions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── npyio.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── polynomial.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── recfunctions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── scimath.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── setup.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── shape_base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── stride_tricks.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── twodim_base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── type_check.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ufunclike.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── user_array.cpython-310.pyc\n", + "│ │ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ │ ├── _datasource.py\n", + "│ │ │ │ │ ├── _iotools.py\n", + "│ │ │ │ │ ├── _version.py\n", + "│ │ │ │ │ ├── _version.pyi\n", + "│ │ │ │ │ ├── arraypad.py\n", + "│ │ │ │ │ ├── arraypad.pyi\n", + "│ │ │ │ │ ├── arraysetops.py\n", + "│ │ │ │ │ ├── arraysetops.pyi\n", + "│ │ │ │ │ ├── arrayterator.py\n", + "│ │ │ │ │ ├── arrayterator.pyi\n", + "│ │ │ │ │ ├── format.py\n", + "│ │ │ │ │ ├── format.pyi\n", + "│ │ │ │ │ ├── function_base.py\n", + "│ │ │ │ │ ├── function_base.pyi\n", + "│ │ │ │ │ ├── histograms.py\n", + "│ │ │ │ │ ├── histograms.pyi\n", + "│ │ │ │ │ ├── index_tricks.py\n", + "│ │ │ │ │ ├── index_tricks.pyi\n", + "│ │ │ │ │ ├── mixins.py\n", + "│ │ │ │ │ ├── mixins.pyi\n", + "│ │ │ │ │ ├── nanfunctions.py\n", + "│ │ │ │ │ ├── nanfunctions.pyi\n", + "│ │ │ │ │ ├── npyio.py\n", + "│ │ │ │ │ ├── npyio.pyi\n", + "│ │ │ │ │ ├── polynomial.py\n", + "│ │ │ │ │ ├── polynomial.pyi\n", + "│ │ │ │ │ ├── recfunctions.py\n", + "│ │ │ │ │ ├── scimath.py\n", + "│ │ │ │ │ ├── scimath.pyi\n", + "│ │ │ │ │ ├── setup.py\n", + "│ │ │ │ │ ├── shape_base.py\n", + "│ │ │ │ │ ├── shape_base.pyi\n", + "│ │ │ │ │ ├── stride_tricks.py\n", + "│ │ │ │ │ ├── stride_tricks.pyi\n", + "│ │ │ │ │ ├── tests\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test__datasource.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test__iotools.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test__version.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arraypad.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arraysetops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arrayterator.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_financial_expired.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_format.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_function_base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_histograms.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_index_tricks.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_io.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_loadtxt.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_mixins.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_nanfunctions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_packbits.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_polynomial.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_recfunctions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_regression.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_shape_base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_stride_tricks.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_twodim_base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_type_check.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_ufunclike.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── data\n", + "│ │ │ │ │ │ │ ├── py2-objarr.npy\n", + "│ │ │ │ │ │ │ ├── py2-objarr.npz\n", + "│ │ │ │ │ │ │ ├── py3-objarr.npy\n", + "│ │ │ │ │ │ │ ├── py3-objarr.npz\n", + "│ │ │ │ │ │ │ ├── python3.npy\n", + "│ │ │ │ │ │ │ └── win64python2.npy\n", + "│ │ │ │ │ │ ├── test__datasource.py\n", + "│ │ │ │ │ │ ├── test__iotools.py\n", + "│ │ │ │ │ │ ├── test__version.py\n", + "│ │ │ │ │ │ ├── test_arraypad.py\n", + "│ │ │ │ │ │ ├── test_arraysetops.py\n", + "│ │ │ │ │ │ ├── test_arrayterator.py\n", + "│ │ │ │ │ │ ├── test_financial_expired.py\n", + "│ │ │ │ │ │ ├── test_format.py\n", + "│ │ │ │ │ │ ├── test_function_base.py\n", + "│ │ │ │ │ │ ├── test_histograms.py\n", + "│ │ │ │ │ │ ├── test_index_tricks.py\n", + "│ │ │ │ │ │ ├── test_io.py\n", + "│ │ │ │ │ │ ├── test_loadtxt.py\n", + "│ │ │ │ │ │ ├── test_mixins.py\n", + "│ │ │ │ │ │ ├── test_nanfunctions.py\n", + "│ │ │ │ │ │ ├── test_packbits.py\n", + "│ │ │ │ │ │ ├── test_polynomial.py\n", + "│ │ │ │ │ │ ├── test_recfunctions.py\n", + "│ │ │ │ │ │ ├── test_regression.py\n", + "│ │ │ │ │ │ ├── test_shape_base.py\n", + "│ │ │ │ │ │ ├── test_stride_tricks.py\n", + "│ │ │ │ │ │ ├── test_twodim_base.py\n", + "│ │ │ │ │ │ ├── test_type_check.py\n", + "│ │ │ │ │ │ ├── test_ufunclike.py\n", + "│ │ │ │ │ │ └── test_utils.py\n", + "│ │ │ │ │ ├── twodim_base.py\n", + "│ │ │ │ │ ├── twodim_base.pyi\n", + "│ │ │ │ │ ├── type_check.py\n", + "│ │ │ │ │ ├── type_check.pyi\n", + "│ │ │ │ │ ├── ufunclike.py\n", + "│ │ │ │ │ ├── ufunclike.pyi\n", + "│ │ │ │ │ ├── user_array.py\n", + "│ │ │ │ │ ├── utils.py\n", + "│ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ ├── linalg\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ └── linalg.cpython-310.pyc\n", + "│ │ │ │ │ ├── _umath_linalg.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── lapack_lite.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── linalg.py\n", + "│ │ │ │ │ ├── linalg.pyi\n", + "│ │ │ │ │ └── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_deprecations.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_linalg.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_regression.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_deprecations.py\n", + "│ │ │ │ │ ├── test_linalg.py\n", + "│ │ │ │ │ └── test_regression.py\n", + "│ │ │ │ ├── ma\n", + "│ │ │ │ │ ├── API_CHANGES.txt\n", + "│ │ │ │ │ ├── LICENSE\n", + "│ │ │ │ │ ├── README.rst\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── core.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── extras.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── mrecords.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── setup.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── testutils.cpython-310.pyc\n", + "│ │ │ │ │ │ └── timer_comparison.cpython-310.pyc\n", + "│ │ │ │ │ ├── core.py\n", + "│ │ │ │ │ ├── core.pyi\n", + "│ │ │ │ │ ├── extras.py\n", + "│ │ │ │ │ ├── extras.pyi\n", + "│ │ │ │ │ ├── mrecords.py\n", + "│ │ │ │ │ ├── mrecords.pyi\n", + "│ │ │ │ │ ├── setup.py\n", + "│ │ │ │ │ ├── tests\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_core.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_deprecations.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_extras.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_mrecords.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_old_ma.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_regression.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_subclassing.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_core.py\n", + "│ │ │ │ │ │ ├── test_deprecations.py\n", + "│ │ │ │ │ │ ├── test_extras.py\n", + "│ │ │ │ │ │ ├── test_mrecords.py\n", + "│ │ │ │ │ │ ├── test_old_ma.py\n", + "│ │ │ │ │ │ ├── test_regression.py\n", + "│ │ │ │ │ │ └── test_subclassing.py\n", + "│ │ │ │ │ ├── testutils.py\n", + "│ │ │ │ │ └── timer_comparison.py\n", + "│ │ │ │ ├── matlib.py\n", + "│ │ │ │ ├── matrixlib\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── defmatrix.cpython-310.pyc\n", + "│ │ │ │ │ │ └── setup.cpython-310.pyc\n", + "│ │ │ │ │ ├── defmatrix.py\n", + "│ │ │ │ │ ├── defmatrix.pyi\n", + "│ │ │ │ │ ├── setup.py\n", + "│ │ │ │ │ └── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_defmatrix.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_interaction.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_masked_matrix.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_matrix_linalg.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_multiarray.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_numeric.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_regression.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_defmatrix.py\n", + "│ │ │ │ │ ├── test_interaction.py\n", + "│ │ │ │ │ ├── test_masked_matrix.py\n", + "│ │ │ │ │ ├── test_matrix_linalg.py\n", + "│ │ │ │ │ ├── test_multiarray.py\n", + "│ │ │ │ │ ├── test_numeric.py\n", + "│ │ │ │ │ └── test_regression.py\n", + "│ │ │ │ ├── polynomial\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _polybase.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── chebyshev.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── hermite.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── hermite_e.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── laguerre.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── legendre.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── polynomial.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── polyutils.cpython-310.pyc\n", + "│ │ │ │ │ │ └── setup.cpython-310.pyc\n", + "│ │ │ │ │ ├── _polybase.py\n", + "│ │ │ │ │ ├── _polybase.pyi\n", + "│ │ │ │ │ ├── chebyshev.py\n", + "│ │ │ │ │ ├── chebyshev.pyi\n", + "│ │ │ │ │ ├── hermite.py\n", + "│ │ │ │ │ ├── hermite.pyi\n", + "│ │ │ │ │ ├── hermite_e.py\n", + "│ │ │ │ │ ├── hermite_e.pyi\n", + "│ │ │ │ │ ├── laguerre.py\n", + "│ │ │ │ │ ├── laguerre.pyi\n", + "│ │ │ │ │ ├── legendre.py\n", + "│ │ │ │ │ ├── legendre.pyi\n", + "│ │ │ │ │ ├── polynomial.py\n", + "│ │ │ │ │ ├── polynomial.pyi\n", + "│ │ │ │ │ ├── polyutils.py\n", + "│ │ │ │ │ ├── polyutils.pyi\n", + "│ │ │ │ │ ├── setup.py\n", + "│ │ │ │ │ └── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_chebyshev.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_classes.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_hermite.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_hermite_e.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_laguerre.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_legendre.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_polynomial.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_polyutils.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_printing.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_symbol.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_chebyshev.py\n", + "│ │ │ │ │ ├── test_classes.py\n", + "│ │ │ │ │ ├── test_hermite.py\n", + "│ │ │ │ │ ├── test_hermite_e.py\n", + "│ │ │ │ │ ├── test_laguerre.py\n", + "│ │ │ │ │ ├── test_legendre.py\n", + "│ │ │ │ │ ├── test_polynomial.py\n", + "│ │ │ │ │ ├── test_polyutils.py\n", + "│ │ │ │ │ ├── test_printing.py\n", + "│ │ │ │ │ └── test_symbol.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ ├── random\n", + "│ │ │ │ │ ├── LICENSE.md\n", + "│ │ │ │ │ ├── __init__.pxd\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ └── _pickle.cpython-310.pyc\n", + "│ │ │ │ │ ├── _bounded_integers.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── _bounded_integers.pxd\n", + "│ │ │ │ │ ├── _common.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── _common.pxd\n", + "│ │ │ │ │ ├── _examples\n", + "│ │ │ │ │ │ ├── cffi\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── extending.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── parse.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── extending.py\n", + "│ │ │ │ │ │ │ └── parse.py\n", + "│ │ │ │ │ │ ├── cython\n", + "│ │ │ │ │ │ │ ├── extending.pyx\n", + "│ │ │ │ │ │ │ ├── extending_distributions.pyx\n", + "│ │ │ │ │ │ │ └── meson.build\n", + "│ │ │ │ │ │ └── numba\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── extending.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── extending_distributions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── extending.py\n", + "│ │ │ │ │ │ └── extending_distributions.py\n", + "│ │ │ │ │ ├── _generator.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── _generator.pyi\n", + "│ │ │ │ │ ├── _mt19937.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── _mt19937.pyi\n", + "│ │ │ │ │ ├── _pcg64.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── _pcg64.pyi\n", + "│ │ │ │ │ ├── _philox.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── _philox.pyi\n", + "│ │ │ │ │ ├── _pickle.py\n", + "│ │ │ │ │ ├── _sfc64.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── _sfc64.pyi\n", + "│ │ │ │ │ ├── bit_generator.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── bit_generator.pxd\n", + "│ │ │ │ │ ├── bit_generator.pyi\n", + "│ │ │ │ │ ├── c_distributions.pxd\n", + "│ │ │ │ │ ├── lib\n", + "│ │ │ │ │ │ └── libnpyrandom.a\n", + "│ │ │ │ │ ├── mtrand.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── mtrand.pyi\n", + "│ │ │ │ │ └── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_direct.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_extending.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_generator_mt19937.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_generator_mt19937_regressions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_random.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_randomstate.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_randomstate_regression.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_regression.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_seed_sequence.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_smoke.cpython-310.pyc\n", + "│ │ │ │ │ ├── data\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── mt19937-testset-1.csv\n", + "│ │ │ │ │ │ ├── mt19937-testset-2.csv\n", + "│ │ │ │ │ │ ├── pcg64-testset-1.csv\n", + "│ │ │ │ │ │ ├── pcg64-testset-2.csv\n", + "│ │ │ │ │ │ ├── pcg64dxsm-testset-1.csv\n", + "│ │ │ │ │ │ ├── pcg64dxsm-testset-2.csv\n", + "│ │ │ │ │ │ ├── philox-testset-1.csv\n", + "│ │ │ │ │ │ ├── philox-testset-2.csv\n", + "│ │ │ │ │ │ ├── sfc64-testset-1.csv\n", + "│ │ │ │ │ │ └── sfc64-testset-2.csv\n", + "│ │ │ │ │ ├── test_direct.py\n", + "│ │ │ │ │ ├── test_extending.py\n", + "│ │ │ │ │ ├── test_generator_mt19937.py\n", + "│ │ │ │ │ ├── test_generator_mt19937_regressions.py\n", + "│ │ │ │ │ ├── test_random.py\n", + "│ │ │ │ │ ├── test_randomstate.py\n", + "│ │ │ │ │ ├── test_randomstate_regression.py\n", + "│ │ │ │ │ ├── test_regression.py\n", + "│ │ │ │ │ ├── test_seed_sequence.py\n", + "│ │ │ │ │ └── test_smoke.py\n", + "│ │ │ │ ├── testing\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── overrides.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── print_coercion_tables.cpython-310.pyc\n", + "│ │ │ │ │ │ └── setup.cpython-310.pyc\n", + "│ │ │ │ │ ├── _private\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── extbuild.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── extbuild.py\n", + "│ │ │ │ │ │ ├── utils.py\n", + "│ │ │ │ │ │ └── utils.pyi\n", + "│ │ │ │ │ ├── overrides.py\n", + "│ │ │ │ │ ├── print_coercion_tables.py\n", + "│ │ │ │ │ ├── setup.py\n", + "│ │ │ │ │ └── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_utils.cpython-310.pyc\n", + "│ │ │ │ │ └── test_utils.py\n", + "│ │ │ │ ├── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test__all__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_ctypeslib.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_lazyloading.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_matlib.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_numpy_config.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_numpy_version.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_public_api.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_reloading.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_scripts.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_warnings.cpython-310.pyc\n", + "│ │ │ │ │ ├── test__all__.py\n", + "│ │ │ │ │ ├── test_ctypeslib.py\n", + "│ │ │ │ │ ├── test_lazyloading.py\n", + "│ │ │ │ │ ├── test_matlib.py\n", + "│ │ │ │ │ ├── test_numpy_config.py\n", + "│ │ │ │ │ ├── test_numpy_version.py\n", + "│ │ │ │ │ ├── test_public_api.py\n", + "│ │ │ │ │ ├── test_reloading.py\n", + "│ │ │ │ │ ├── test_scripts.py\n", + "│ │ │ │ │ └── test_warnings.py\n", + "│ │ │ │ ├── typing\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── mypy_plugin.cpython-310.pyc\n", + "│ │ │ │ │ │ └── setup.cpython-310.pyc\n", + "│ │ │ │ │ ├── mypy_plugin.py\n", + "│ │ │ │ │ ├── setup.py\n", + "│ │ │ │ │ └── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_isfile.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_runtime.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_typing.cpython-310.pyc\n", + "│ │ │ │ │ ├── data\n", + "│ │ │ │ │ │ ├── fail\n", + "│ │ │ │ │ │ │ ├── arithmetic.pyi\n", + "│ │ │ │ │ │ │ ├── array_constructors.pyi\n", + "│ │ │ │ │ │ │ ├── array_like.pyi\n", + "│ │ │ │ │ │ │ ├── array_pad.pyi\n", + "│ │ │ │ │ │ │ ├── arrayprint.pyi\n", + "│ │ │ │ │ │ │ ├── arrayterator.pyi\n", + "│ │ │ │ │ │ │ ├── bitwise_ops.pyi\n", + "│ │ │ │ │ │ │ ├── char.pyi\n", + "│ │ │ │ │ │ │ ├── chararray.pyi\n", + "│ │ │ │ │ │ │ ├── comparisons.pyi\n", + "│ │ │ │ │ │ │ ├── constants.pyi\n", + "│ │ │ │ │ │ │ ├── datasource.pyi\n", + "│ │ │ │ │ │ │ ├── dtype.pyi\n", + "│ │ │ │ │ │ │ ├── einsumfunc.pyi\n", + "│ │ │ │ │ │ │ ├── false_positives.pyi\n", + "│ │ │ │ │ │ │ ├── flatiter.pyi\n", + "│ │ │ │ │ │ │ ├── fromnumeric.pyi\n", + "│ │ │ │ │ │ │ ├── histograms.pyi\n", + "│ │ │ │ │ │ │ ├── index_tricks.pyi\n", + "│ │ │ │ │ │ │ ├── lib_function_base.pyi\n", + "│ │ │ │ │ │ │ ├── lib_polynomial.pyi\n", + "│ │ │ │ │ │ │ ├── lib_utils.pyi\n", + "│ │ │ │ │ │ │ ├── lib_version.pyi\n", + "│ │ │ │ │ │ │ ├── linalg.pyi\n", + "│ │ │ │ │ │ │ ├── memmap.pyi\n", + "│ │ │ │ │ │ │ ├── modules.pyi\n", + "│ │ │ │ │ │ │ ├── multiarray.pyi\n", + "│ │ │ │ │ │ │ ├── ndarray.pyi\n", + "│ │ │ │ │ │ │ ├── ndarray_misc.pyi\n", + "│ │ │ │ │ │ │ ├── nditer.pyi\n", + "│ │ │ │ │ │ │ ├── nested_sequence.pyi\n", + "│ │ │ │ │ │ │ ├── npyio.pyi\n", + "│ │ │ │ │ │ │ ├── numerictypes.pyi\n", + "│ │ │ │ │ │ │ ├── random.pyi\n", + "│ │ │ │ │ │ │ ├── rec.pyi\n", + "│ │ │ │ │ │ │ ├── scalars.pyi\n", + "│ │ │ │ │ │ │ ├── shape_base.pyi\n", + "│ │ │ │ │ │ │ ├── stride_tricks.pyi\n", + "│ │ │ │ │ │ │ ├── testing.pyi\n", + "│ │ │ │ │ │ │ ├── twodim_base.pyi\n", + "│ │ │ │ │ │ │ ├── type_check.pyi\n", + "│ │ │ │ │ │ │ ├── ufunc_config.pyi\n", + "│ │ │ │ │ │ │ ├── ufunclike.pyi\n", + "│ │ │ │ │ │ │ ├── ufuncs.pyi\n", + "│ │ │ │ │ │ │ └── warnings_and_errors.pyi\n", + "│ │ │ │ │ │ ├── misc\n", + "│ │ │ │ │ │ │ └── extended_precision.pyi\n", + "│ │ │ │ │ │ ├── mypy.ini\n", + "│ │ │ │ │ │ ├── pass\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── arithmetic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── array_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── array_like.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── arrayprint.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── arrayterator.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── bitwise_ops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── comparisons.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── dtype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── einsumfunc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── flatiter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── fromnumeric.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── index_tricks.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── lib_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── lib_version.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── literal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── mod.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── modules.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── multiarray.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── ndarray_conversion.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── ndarray_misc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── ndarray_shape_manipulation.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── numeric.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── numerictypes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── random.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── scalars.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── simple.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── simple_py3.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── ufunc_config.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── ufunclike.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── ufuncs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── warnings_and_errors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── arithmetic.py\n", + "│ │ │ │ │ │ │ ├── array_constructors.py\n", + "│ │ │ │ │ │ │ ├── array_like.py\n", + "│ │ │ │ │ │ │ ├── arrayprint.py\n", + "│ │ │ │ │ │ │ ├── arrayterator.py\n", + "│ │ │ │ │ │ │ ├── bitwise_ops.py\n", + "│ │ │ │ │ │ │ ├── comparisons.py\n", + "│ │ │ │ │ │ │ ├── dtype.py\n", + "│ │ │ │ │ │ │ ├── einsumfunc.py\n", + "│ │ │ │ │ │ │ ├── flatiter.py\n", + "│ │ │ │ │ │ │ ├── fromnumeric.py\n", + "│ │ │ │ │ │ │ ├── index_tricks.py\n", + "│ │ │ │ │ │ │ ├── lib_utils.py\n", + "│ │ │ │ │ │ │ ├── lib_version.py\n", + "│ │ │ │ │ │ │ ├── literal.py\n", + "│ │ │ │ │ │ │ ├── mod.py\n", + "│ │ │ │ │ │ │ ├── modules.py\n", + "│ │ │ │ │ │ │ ├── multiarray.py\n", + "│ │ │ │ │ │ │ ├── ndarray_conversion.py\n", + "│ │ │ │ │ │ │ ├── ndarray_misc.py\n", + "│ │ │ │ │ │ │ ├── ndarray_shape_manipulation.py\n", + "│ │ │ │ │ │ │ ├── numeric.py\n", + "│ │ │ │ │ │ │ ├── numerictypes.py\n", + "│ │ │ │ │ │ │ ├── random.py\n", + "│ │ │ │ │ │ │ ├── scalars.py\n", + "│ │ │ │ │ │ │ ├── simple.py\n", + "│ │ │ │ │ │ │ ├── simple_py3.py\n", + "│ │ │ │ │ │ │ ├── ufunc_config.py\n", + "│ │ │ │ │ │ │ ├── ufunclike.py\n", + "│ │ │ │ │ │ │ ├── ufuncs.py\n", + "│ │ │ │ │ │ │ └── warnings_and_errors.py\n", + "│ │ │ │ │ │ └── reveal\n", + "│ │ │ │ │ │ ├── arithmetic.pyi\n", + "│ │ │ │ │ │ ├── array_constructors.pyi\n", + "│ │ │ │ │ │ ├── arraypad.pyi\n", + "│ │ │ │ │ │ ├── arrayprint.pyi\n", + "│ │ │ │ │ │ ├── arraysetops.pyi\n", + "│ │ │ │ │ │ ├── arrayterator.pyi\n", + "│ │ │ │ │ │ ├── bitwise_ops.pyi\n", + "│ │ │ │ │ │ ├── char.pyi\n", + "│ │ │ │ │ │ ├── chararray.pyi\n", + "│ │ │ │ │ │ ├── comparisons.pyi\n", + "│ │ │ │ │ │ ├── constants.pyi\n", + "│ │ │ │ │ │ ├── ctypeslib.pyi\n", + "│ │ │ │ │ │ ├── datasource.pyi\n", + "│ │ │ │ │ │ ├── dtype.pyi\n", + "│ │ │ │ │ │ ├── einsumfunc.pyi\n", + "│ │ │ │ │ │ ├── emath.pyi\n", + "│ │ │ │ │ │ ├── false_positives.pyi\n", + "│ │ │ │ │ │ ├── fft.pyi\n", + "│ │ │ │ │ │ ├── flatiter.pyi\n", + "│ │ │ │ │ │ ├── fromnumeric.pyi\n", + "│ │ │ │ │ │ ├── getlimits.pyi\n", + "│ │ │ │ │ │ ├── histograms.pyi\n", + "│ │ │ │ │ │ ├── index_tricks.pyi\n", + "│ │ │ │ │ │ ├── lib_function_base.pyi\n", + "│ │ │ │ │ │ ├── lib_polynomial.pyi\n", + "│ │ │ │ │ │ ├── lib_utils.pyi\n", + "│ │ │ │ │ │ ├── lib_version.pyi\n", + "│ │ │ │ │ │ ├── linalg.pyi\n", + "│ │ │ │ │ │ ├── matrix.pyi\n", + "│ │ │ │ │ │ ├── memmap.pyi\n", + "│ │ │ │ │ │ ├── mod.pyi\n", + "│ │ │ │ │ │ ├── modules.pyi\n", + "│ │ │ │ │ │ ├── multiarray.pyi\n", + "│ │ │ │ │ │ ├── nbit_base_example.pyi\n", + "│ │ │ │ │ │ ├── ndarray_conversion.pyi\n", + "│ │ │ │ │ │ ├── ndarray_misc.pyi\n", + "│ │ │ │ │ │ ├── ndarray_shape_manipulation.pyi\n", + "│ │ │ │ │ │ ├── nditer.pyi\n", + "│ │ │ │ │ │ ├── nested_sequence.pyi\n", + "│ │ │ │ │ │ ├── npyio.pyi\n", + "│ │ │ │ │ │ ├── numeric.pyi\n", + "│ │ │ │ │ │ ├── numerictypes.pyi\n", + "│ │ │ │ │ │ ├── random.pyi\n", + "│ │ │ │ │ │ ├── rec.pyi\n", + "│ │ │ │ │ │ ├── scalars.pyi\n", + "│ │ │ │ │ │ ├── shape_base.pyi\n", + "│ │ │ │ │ │ ├── stride_tricks.pyi\n", + "│ │ │ │ │ │ ├── testing.pyi\n", + "│ │ │ │ │ │ ├── twodim_base.pyi\n", + "│ │ │ │ │ │ ├── type_check.pyi\n", + "│ │ │ │ │ │ ├── ufunc_config.pyi\n", + "│ │ │ │ │ │ ├── ufunclike.pyi\n", + "│ │ │ │ │ │ ├── ufuncs.pyi\n", + "│ │ │ │ │ │ └── warnings_and_errors.pyi\n", + "│ │ │ │ │ ├── test_isfile.py\n", + "│ │ │ │ │ ├── test_runtime.py\n", + "│ │ │ │ │ └── test_typing.py\n", + "│ │ │ │ └── version.py\n", + "│ │ │ ├── numpy-1.26.4.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE.txt\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── entry_points.txt\n", + "│ │ │ ├── numpy.libs\n", + "│ │ │ │ ├── libgfortran-040039e1.so.5.0.0\n", + "│ │ │ │ ├── libopenblas64_p-r0-0cf96a72.3.23.dev.so\n", + "│ │ │ │ └── libquadmath-96973f99.so.0.0.0\n", + "│ │ │ ├── packaging\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _elffile.cpython-310.pyc\n", + "│ │ │ │ │ ├── _manylinux.cpython-310.pyc\n", + "│ │ │ │ │ ├── _musllinux.cpython-310.pyc\n", + "│ │ │ │ │ ├── _parser.cpython-310.pyc\n", + "│ │ │ │ │ ├── _structures.cpython-310.pyc\n", + "│ │ │ │ │ ├── _tokenizer.cpython-310.pyc\n", + "│ │ │ │ │ ├── markers.cpython-310.pyc\n", + "│ │ │ │ │ ├── metadata.cpython-310.pyc\n", + "│ │ │ │ │ ├── requirements.cpython-310.pyc\n", + "│ │ │ │ │ ├── specifiers.cpython-310.pyc\n", + "│ │ │ │ │ ├── tags.cpython-310.pyc\n", + "│ │ │ │ │ ├── utils.cpython-310.pyc\n", + "│ │ │ │ │ └── version.cpython-310.pyc\n", + "│ │ │ │ ├── _elffile.py\n", + "│ │ │ │ ├── _manylinux.py\n", + "│ │ │ │ ├── _musllinux.py\n", + "│ │ │ │ ├── _parser.py\n", + "│ │ │ │ ├── _structures.py\n", + "│ │ │ │ ├── _tokenizer.py\n", + "│ │ │ │ ├── markers.py\n", + "│ │ │ │ ├── metadata.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ ├── requirements.py\n", + "│ │ │ │ ├── specifiers.py\n", + "│ │ │ │ ├── tags.py\n", + "│ │ │ │ ├── utils.py\n", + "│ │ │ │ └── version.py\n", + "│ │ │ ├── packaging-24.0.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── LICENSE.APACHE\n", + "│ │ │ │ ├── LICENSE.BSD\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ └── WHEEL\n", + "│ │ │ ├── pandas\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _typing.cpython-310.pyc\n", + "│ │ │ │ │ ├── _version.cpython-310.pyc\n", + "│ │ │ │ │ ├── _version_meson.cpython-310.pyc\n", + "│ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ └── testing.cpython-310.pyc\n", + "│ │ │ │ ├── _config\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── config.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dates.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── display.cpython-310.pyc\n", + "│ │ │ │ │ │ └── localization.cpython-310.pyc\n", + "│ │ │ │ │ ├── config.py\n", + "│ │ │ │ │ ├── dates.py\n", + "│ │ │ │ │ ├── display.py\n", + "│ │ │ │ │ └── localization.py\n", + "│ │ │ │ ├── _libs\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── algos.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── algos.pyi\n", + "│ │ │ │ │ ├── arrays.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── arrays.pyi\n", + "│ │ │ │ │ ├── byteswap.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── byteswap.pyi\n", + "│ │ │ │ │ ├── groupby.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── groupby.pyi\n", + "│ │ │ │ │ ├── hashing.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── hashing.pyi\n", + "│ │ │ │ │ ├── hashtable.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── hashtable.pyi\n", + "│ │ │ │ │ ├── index.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── index.pyi\n", + "│ │ │ │ │ ├── indexing.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── indexing.pyi\n", + "│ │ │ │ │ ├── internals.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── internals.pyi\n", + "│ │ │ │ │ ├── interval.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── interval.pyi\n", + "│ │ │ │ │ ├── join.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── join.pyi\n", + "│ │ │ │ │ ├── json.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── json.pyi\n", + "│ │ │ │ │ ├── lib.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── lib.pyi\n", + "│ │ │ │ │ ├── missing.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── missing.pyi\n", + "│ │ │ │ │ ├── ops.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── ops.pyi\n", + "│ │ │ │ │ ├── ops_dispatch.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── ops_dispatch.pyi\n", + "│ │ │ │ │ ├── pandas_datetime.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── pandas_parser.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── parsers.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── parsers.pyi\n", + "│ │ │ │ │ ├── properties.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── properties.pyi\n", + "│ │ │ │ │ ├── reshape.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── reshape.pyi\n", + "│ │ │ │ │ ├── sas.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── sas.pyi\n", + "│ │ │ │ │ ├── sparse.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── sparse.pyi\n", + "│ │ │ │ │ ├── testing.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── testing.pyi\n", + "│ │ │ │ │ ├── tslib.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── tslib.pyi\n", + "│ │ │ │ │ ├── tslibs\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── ccalendar.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── ccalendar.pyi\n", + "│ │ │ │ │ │ ├── conversion.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── conversion.pyi\n", + "│ │ │ │ │ │ ├── dtypes.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── dtypes.pyi\n", + "│ │ │ │ │ │ ├── fields.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── fields.pyi\n", + "│ │ │ │ │ │ ├── nattype.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── nattype.pyi\n", + "│ │ │ │ │ │ ├── np_datetime.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── np_datetime.pyi\n", + "│ │ │ │ │ │ ├── offsets.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── offsets.pyi\n", + "│ │ │ │ │ │ ├── parsing.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── parsing.pyi\n", + "│ │ │ │ │ │ ├── period.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── period.pyi\n", + "│ │ │ │ │ │ ├── strptime.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── strptime.pyi\n", + "│ │ │ │ │ │ ├── timedeltas.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── timedeltas.pyi\n", + "│ │ │ │ │ │ ├── timestamps.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── timestamps.pyi\n", + "│ │ │ │ │ │ ├── timezones.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── timezones.pyi\n", + "│ │ │ │ │ │ ├── tzconversion.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── tzconversion.pyi\n", + "│ │ │ │ │ │ ├── vectorized.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ └── vectorized.pyi\n", + "│ │ │ │ │ ├── window\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── aggregations.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ ├── aggregations.pyi\n", + "│ │ │ │ │ │ ├── indexers.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ │ └── indexers.pyi\n", + "│ │ │ │ │ ├── writers.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ └── writers.pyi\n", + "│ │ │ │ ├── _testing\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _hypothesis.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _io.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _warnings.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── asserters.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── compat.cpython-310.pyc\n", + "│ │ │ │ │ │ └── contexts.cpython-310.pyc\n", + "│ │ │ │ │ ├── _hypothesis.py\n", + "│ │ │ │ │ ├── _io.py\n", + "│ │ │ │ │ ├── _warnings.py\n", + "│ │ │ │ │ ├── asserters.py\n", + "│ │ │ │ │ ├── compat.py\n", + "│ │ │ │ │ └── contexts.py\n", + "│ │ │ │ ├── _typing.py\n", + "│ │ │ │ ├── _version.py\n", + "│ │ │ │ ├── _version_meson.py\n", + "│ │ │ │ ├── api\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── extensions\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── indexers\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── interchange\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── types\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ └── typing\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ ├── arrays\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ ├── compat\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _constants.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _optional.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── compressors.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pickle_compat.cpython-310.pyc\n", + "│ │ │ │ │ │ └── pyarrow.cpython-310.pyc\n", + "│ │ │ │ │ ├── _constants.py\n", + "│ │ │ │ │ ├── _optional.py\n", + "│ │ │ │ │ ├── compressors.py\n", + "│ │ │ │ │ ├── numpy\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── function.cpython-310.pyc\n", + "│ │ │ │ │ │ └── function.py\n", + "│ │ │ │ │ ├── pickle_compat.py\n", + "│ │ │ │ │ └── pyarrow.py\n", + "│ │ │ │ ├── conftest.py\n", + "│ │ │ │ ├── core\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── accessor.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── algorithms.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── api.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── apply.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── arraylike.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── config_init.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── construction.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── flags.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── frame.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── generic.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── missing.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── nanops.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── resample.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── roperator.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── sample.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── series.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── shared_docs.cpython-310.pyc\n", + "│ │ │ │ │ │ └── sorting.cpython-310.pyc\n", + "│ │ │ │ │ ├── _numba\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── executor.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── extensions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── executor.py\n", + "│ │ │ │ │ │ ├── extensions.py\n", + "│ │ │ │ │ │ └── kernels\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── mean_.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── min_max_.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── shared.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── sum_.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── var_.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── mean_.py\n", + "│ │ │ │ │ │ ├── min_max_.py\n", + "│ │ │ │ │ │ ├── shared.py\n", + "│ │ │ │ │ │ ├── sum_.py\n", + "│ │ │ │ │ │ └── var_.py\n", + "│ │ │ │ │ ├── accessor.py\n", + "│ │ │ │ │ ├── algorithms.py\n", + "│ │ │ │ │ ├── api.py\n", + "│ │ │ │ │ ├── apply.py\n", + "│ │ │ │ │ ├── array_algos\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── datetimelike_accumulations.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── masked_accumulations.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── masked_reductions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── putmask.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── quantile.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── replace.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── take.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── transforms.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── datetimelike_accumulations.py\n", + "│ │ │ │ │ │ ├── masked_accumulations.py\n", + "│ │ │ │ │ │ ├── masked_reductions.py\n", + "│ │ │ │ │ │ ├── putmask.py\n", + "│ │ │ │ │ │ ├── quantile.py\n", + "│ │ │ │ │ │ ├── replace.py\n", + "│ │ │ │ │ │ ├── take.py\n", + "│ │ │ │ │ │ └── transforms.py\n", + "│ │ │ │ │ ├── arraylike.py\n", + "│ │ │ │ │ ├── arrays\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _arrow_string_mixins.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _mixins.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _ranges.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── boolean.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── categorical.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── datetimelike.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── datetimes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── floating.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── integer.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── interval.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── masked.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── numeric.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── numpy_.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── period.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── string_.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── string_arrow.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── timedeltas.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _arrow_string_mixins.py\n", + "│ │ │ │ │ │ ├── _mixins.py\n", + "│ │ │ │ │ │ ├── _ranges.py\n", + "│ │ │ │ │ │ ├── _utils.py\n", + "│ │ │ │ │ │ ├── arrow\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── _arrow_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── accessors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── array.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── extension_types.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _arrow_utils.py\n", + "│ │ │ │ │ │ │ ├── accessors.py\n", + "│ │ │ │ │ │ │ ├── array.py\n", + "│ │ │ │ │ │ │ └── extension_types.py\n", + "│ │ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ │ ├── boolean.py\n", + "│ │ │ │ │ │ ├── categorical.py\n", + "│ │ │ │ │ │ ├── datetimelike.py\n", + "│ │ │ │ │ │ ├── datetimes.py\n", + "│ │ │ │ │ │ ├── floating.py\n", + "│ │ │ │ │ │ ├── integer.py\n", + "│ │ │ │ │ │ ├── interval.py\n", + "│ │ │ │ │ │ ├── masked.py\n", + "│ │ │ │ │ │ ├── numeric.py\n", + "│ │ │ │ │ │ ├── numpy_.py\n", + "│ │ │ │ │ │ ├── period.py\n", + "│ │ │ │ │ │ ├── sparse\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── accessor.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── array.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── scipy_sparse.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── accessor.py\n", + "│ │ │ │ │ │ │ ├── array.py\n", + "│ │ │ │ │ │ │ └── scipy_sparse.py\n", + "│ │ │ │ │ │ ├── string_.py\n", + "│ │ │ │ │ │ ├── string_arrow.py\n", + "│ │ │ │ │ │ └── timedeltas.py\n", + "│ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ ├── computation\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── align.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── check.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── engines.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── eval.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── expr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── expressions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── ops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── parsing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pytables.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── scope.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── align.py\n", + "│ │ │ │ │ │ ├── api.py\n", + "│ │ │ │ │ │ ├── check.py\n", + "│ │ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ │ ├── engines.py\n", + "│ │ │ │ │ │ ├── eval.py\n", + "│ │ │ │ │ │ ├── expr.py\n", + "│ │ │ │ │ │ ├── expressions.py\n", + "│ │ │ │ │ │ ├── ops.py\n", + "│ │ │ │ │ │ ├── parsing.py\n", + "│ │ │ │ │ │ ├── pytables.py\n", + "│ │ │ │ │ │ └── scope.py\n", + "│ │ │ │ │ ├── config_init.py\n", + "│ │ │ │ │ ├── construction.py\n", + "│ │ │ │ │ ├── dtypes\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── cast.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── concat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── dtypes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── generic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── inference.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── missing.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── api.py\n", + "│ │ │ │ │ │ ├── astype.py\n", + "│ │ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ │ ├── cast.py\n", + "│ │ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ │ ├── concat.py\n", + "│ │ │ │ │ │ ├── dtypes.py\n", + "│ │ │ │ │ │ ├── generic.py\n", + "│ │ │ │ │ │ ├── inference.py\n", + "│ │ │ │ │ │ └── missing.py\n", + "│ │ │ │ │ ├── flags.py\n", + "│ │ │ │ │ ├── frame.py\n", + "│ │ │ │ │ ├── generic.py\n", + "│ │ │ │ │ ├── groupby\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── categorical.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── generic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── groupby.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── grouper.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── numba_.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── ops.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ │ ├── categorical.py\n", + "│ │ │ │ │ │ ├── generic.py\n", + "│ │ │ │ │ │ ├── groupby.py\n", + "│ │ │ │ │ │ ├── grouper.py\n", + "│ │ │ │ │ │ ├── indexing.py\n", + "│ │ │ │ │ │ ├── numba_.py\n", + "│ │ │ │ │ │ └── ops.py\n", + "│ │ │ │ │ ├── indexers\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── objects.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── objects.py\n", + "│ │ │ │ │ │ └── utils.py\n", + "│ │ │ │ │ ├── indexes\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── accessors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── category.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── datetimelike.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── datetimes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── extension.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── frozen.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── interval.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── multi.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── period.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── range.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── timedeltas.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── accessors.py\n", + "│ │ │ │ │ │ ├── api.py\n", + "│ │ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ │ ├── category.py\n", + "│ │ │ │ │ │ ├── datetimelike.py\n", + "│ │ │ │ │ │ ├── datetimes.py\n", + "│ │ │ │ │ │ ├── extension.py\n", + "│ │ │ │ │ │ ├── frozen.py\n", + "│ │ │ │ │ │ ├── interval.py\n", + "│ │ │ │ │ │ ├── multi.py\n", + "│ │ │ │ │ │ ├── period.py\n", + "│ │ │ │ │ │ ├── range.py\n", + "│ │ │ │ │ │ └── timedeltas.py\n", + "│ │ │ │ │ ├── indexing.py\n", + "│ │ │ │ │ ├── interchange\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── buffer.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── column.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── dataframe.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── dataframe_protocol.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── from_dataframe.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── buffer.py\n", + "│ │ │ │ │ │ ├── column.py\n", + "│ │ │ │ │ │ ├── dataframe.py\n", + "│ │ │ │ │ │ ├── dataframe_protocol.py\n", + "│ │ │ │ │ │ ├── from_dataframe.py\n", + "│ │ │ │ │ │ └── utils.py\n", + "│ │ │ │ │ ├── internals\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── array_manager.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── blocks.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── concat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── construction.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── managers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── ops.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── api.py\n", + "│ │ │ │ │ │ ├── array_manager.py\n", + "│ │ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ │ ├── blocks.py\n", + "│ │ │ │ │ │ ├── concat.py\n", + "│ │ │ │ │ │ ├── construction.py\n", + "│ │ │ │ │ │ ├── managers.py\n", + "│ │ │ │ │ │ └── ops.py\n", + "│ │ │ │ │ ├── methods\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── describe.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── selectn.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── to_dict.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── describe.py\n", + "│ │ │ │ │ │ ├── selectn.py\n", + "│ │ │ │ │ │ └── to_dict.py\n", + "│ │ │ │ │ ├── missing.py\n", + "│ │ │ │ │ ├── nanops.py\n", + "│ │ │ │ │ ├── ops\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── array_ops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── dispatch.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── docstrings.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── invalid.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── mask_ops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── missing.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── array_ops.py\n", + "│ │ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ │ ├── dispatch.py\n", + "│ │ │ │ │ │ ├── docstrings.py\n", + "│ │ │ │ │ │ ├── invalid.py\n", + "│ │ │ │ │ │ ├── mask_ops.py\n", + "│ │ │ │ │ │ └── missing.py\n", + "│ │ │ │ │ ├── resample.py\n", + "│ │ │ │ │ ├── reshape\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── concat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── encoding.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── melt.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── merge.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pivot.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── reshape.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── tile.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── util.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── api.py\n", + "│ │ │ │ │ │ ├── concat.py\n", + "│ │ │ │ │ │ ├── encoding.py\n", + "│ │ │ │ │ │ ├── melt.py\n", + "│ │ │ │ │ │ ├── merge.py\n", + "│ │ │ │ │ │ ├── pivot.py\n", + "│ │ │ │ │ │ ├── reshape.py\n", + "│ │ │ │ │ │ ├── tile.py\n", + "│ │ │ │ │ │ └── util.py\n", + "│ │ │ │ │ ├── roperator.py\n", + "│ │ │ │ │ ├── sample.py\n", + "│ │ │ │ │ ├── series.py\n", + "│ │ │ │ │ ├── shared_docs.py\n", + "│ │ │ │ │ ├── sorting.py\n", + "│ │ │ │ │ ├── sparse\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── api.cpython-310.pyc\n", + "│ │ │ │ │ │ └── api.py\n", + "│ │ │ │ │ ├── strings\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── accessor.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── object_array.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── accessor.py\n", + "│ │ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ │ └── object_array.py\n", + "│ │ │ │ │ ├── tools\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── datetimes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── numeric.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── timedeltas.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── times.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── datetimes.py\n", + "│ │ │ │ │ │ ├── numeric.py\n", + "│ │ │ │ │ │ ├── timedeltas.py\n", + "│ │ │ │ │ │ └── times.py\n", + "│ │ │ │ │ ├── util\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── hashing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── numba_.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── hashing.py\n", + "│ │ │ │ │ │ └── numba_.py\n", + "│ │ │ │ │ └── window\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── doc.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ewm.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── expanding.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── numba_.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── online.cpython-310.pyc\n", + "│ │ │ │ │ │ └── rolling.cpython-310.pyc\n", + "│ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ ├── doc.py\n", + "│ │ │ │ │ ├── ewm.py\n", + "│ │ │ │ │ ├── expanding.py\n", + "│ │ │ │ │ ├── numba_.py\n", + "│ │ │ │ │ ├── online.py\n", + "│ │ │ │ │ └── rolling.py\n", + "│ │ │ │ ├── errors\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ ├── io\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _util.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── api.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── clipboards.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── feather_format.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── gbq.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── html.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── orc.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── parquet.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pickle.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pytables.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── spss.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── sql.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── stata.cpython-310.pyc\n", + "│ │ │ │ │ │ └── xml.cpython-310.pyc\n", + "│ │ │ │ │ ├── _util.py\n", + "│ │ │ │ │ ├── api.py\n", + "│ │ │ │ │ ├── clipboard\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── clipboards.py\n", + "│ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ ├── excel\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _calamine.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _odfreader.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _odswriter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _openpyxl.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _pyxlsb.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _util.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _xlrd.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── _xlsxwriter.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _base.py\n", + "│ │ │ │ │ │ ├── _calamine.py\n", + "│ │ │ │ │ │ ├── _odfreader.py\n", + "│ │ │ │ │ │ ├── _odswriter.py\n", + "│ │ │ │ │ │ ├── _openpyxl.py\n", + "│ │ │ │ │ │ ├── _pyxlsb.py\n", + "│ │ │ │ │ │ ├── _util.py\n", + "│ │ │ │ │ │ ├── _xlrd.py\n", + "│ │ │ │ │ │ └── _xlsxwriter.py\n", + "│ │ │ │ │ ├── feather_format.py\n", + "│ │ │ │ │ ├── formats\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _color_data.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── console.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── css.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── csvs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── excel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── format.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── html.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── info.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── printing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── string.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── style.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── style_render.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── xml.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _color_data.py\n", + "│ │ │ │ │ │ ├── console.py\n", + "│ │ │ │ │ │ ├── css.py\n", + "│ │ │ │ │ │ ├── csvs.py\n", + "│ │ │ │ │ │ ├── excel.py\n", + "│ │ │ │ │ │ ├── format.py\n", + "│ │ │ │ │ │ ├── html.py\n", + "│ │ │ │ │ │ ├── info.py\n", + "│ │ │ │ │ │ ├── printing.py\n", + "│ │ │ │ │ │ ├── string.py\n", + "│ │ │ │ │ │ ├── style.py\n", + "│ │ │ │ │ │ ├── style_render.py\n", + "│ │ │ │ │ │ ├── templates\n", + "│ │ │ │ │ │ │ ├── html.tpl\n", + "│ │ │ │ │ │ │ ├── html_style.tpl\n", + "│ │ │ │ │ │ │ ├── html_table.tpl\n", + "│ │ │ │ │ │ │ ├── latex.tpl\n", + "│ │ │ │ │ │ │ ├── latex_longtable.tpl\n", + "│ │ │ │ │ │ │ ├── latex_table.tpl\n", + "│ │ │ │ │ │ │ └── string.tpl\n", + "│ │ │ │ │ │ └── xml.py\n", + "│ │ │ │ │ ├── gbq.py\n", + "│ │ │ │ │ ├── html.py\n", + "│ │ │ │ │ ├── json\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _json.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _normalize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── _table_schema.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _json.py\n", + "│ │ │ │ │ │ ├── _normalize.py\n", + "│ │ │ │ │ │ └── _table_schema.py\n", + "│ │ │ │ │ ├── orc.py\n", + "│ │ │ │ │ ├── parquet.py\n", + "│ │ │ │ │ ├── parsers\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── arrow_parser_wrapper.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── base_parser.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── c_parser_wrapper.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── python_parser.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── readers.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── arrow_parser_wrapper.py\n", + "│ │ │ │ │ │ ├── base_parser.py\n", + "│ │ │ │ │ │ ├── c_parser_wrapper.py\n", + "│ │ │ │ │ │ ├── python_parser.py\n", + "│ │ │ │ │ │ └── readers.py\n", + "│ │ │ │ │ ├── pickle.py\n", + "│ │ │ │ │ ├── pytables.py\n", + "│ │ │ │ │ ├── sas\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── sas7bdat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── sas_constants.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── sas_xport.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── sasreader.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── sas7bdat.py\n", + "│ │ │ │ │ │ ├── sas_constants.py\n", + "│ │ │ │ │ │ ├── sas_xport.py\n", + "│ │ │ │ │ │ └── sasreader.py\n", + "│ │ │ │ │ ├── spss.py\n", + "│ │ │ │ │ ├── sql.py\n", + "│ │ │ │ │ ├── stata.py\n", + "│ │ │ │ │ └── xml.py\n", + "│ │ │ │ ├── plotting\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _core.cpython-310.pyc\n", + "│ │ │ │ │ │ └── _misc.cpython-310.pyc\n", + "│ │ │ │ │ ├── _core.py\n", + "│ │ │ │ │ ├── _matplotlib\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── boxplot.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── converter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── core.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── groupby.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── hist.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── misc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── style.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── timeseries.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── tools.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── boxplot.py\n", + "│ │ │ │ │ │ ├── converter.py\n", + "│ │ │ │ │ │ ├── core.py\n", + "│ │ │ │ │ │ ├── groupby.py\n", + "│ │ │ │ │ │ ├── hist.py\n", + "│ │ │ │ │ │ ├── misc.py\n", + "│ │ │ │ │ │ ├── style.py\n", + "│ │ │ │ │ │ ├── timeseries.py\n", + "│ │ │ │ │ │ └── tools.py\n", + "│ │ │ │ │ └── _misc.py\n", + "│ │ │ │ ├── pyproject.toml\n", + "│ │ │ │ ├── testing.py\n", + "│ │ │ │ ├── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_aggregation.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_algos.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_common.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_downstream.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_errors.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_expressions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_flags.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_multilevel.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_nanops.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_optional_dependency.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_register_accessor.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_sorting.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_take.cpython-310.pyc\n", + "│ │ │ │ │ ├── api\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_types.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_api.py\n", + "│ │ │ │ │ │ └── test_types.py\n", + "│ │ │ │ │ ├── apply\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_frame_apply.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_frame_apply_relabeling.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_frame_transform.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_invalid_arg.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_numba.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_series_apply.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_series_apply_relabeling.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_series_transform.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_str.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ │ ├── test_frame_apply.py\n", + "│ │ │ │ │ │ ├── test_frame_apply_relabeling.py\n", + "│ │ │ │ │ │ ├── test_frame_transform.py\n", + "│ │ │ │ │ │ ├── test_invalid_arg.py\n", + "│ │ │ │ │ │ ├── test_numba.py\n", + "│ │ │ │ │ │ ├── test_series_apply.py\n", + "│ │ │ │ │ │ ├── test_series_apply_relabeling.py\n", + "│ │ │ │ │ │ ├── test_series_transform.py\n", + "│ │ │ │ │ │ └── test_str.py\n", + "│ │ │ │ │ ├── arithmetic\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_array_ops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_categorical.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_datetime64.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_interval.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_numeric.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_object.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_period.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_timedelta64.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ ├── test_array_ops.py\n", + "│ │ │ │ │ │ ├── test_categorical.py\n", + "│ │ │ │ │ │ ├── test_datetime64.py\n", + "│ │ │ │ │ │ ├── test_interval.py\n", + "│ │ │ │ │ │ ├── test_numeric.py\n", + "│ │ │ │ │ │ ├── test_object.py\n", + "│ │ │ │ │ │ ├── test_period.py\n", + "│ │ │ │ │ │ └── test_timedelta64.py\n", + "│ │ │ │ │ ├── arrays\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── masked_shared.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_array.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_datetimelike.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_datetimes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_ndarray_backed.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_period.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_timedeltas.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── boolean\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_arithmetic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_comparison.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_construction.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_function.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_logical.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_ops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reduction.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_repr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arithmetic.py\n", + "│ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ ├── test_comparison.py\n", + "│ │ │ │ │ │ │ ├── test_construction.py\n", + "│ │ │ │ │ │ │ ├── test_function.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_logical.py\n", + "│ │ │ │ │ │ │ ├── test_ops.py\n", + "│ │ │ │ │ │ │ ├── test_reduction.py\n", + "│ │ │ │ │ │ │ └── test_repr.py\n", + "│ │ │ │ │ │ ├── categorical\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_algos.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_analytics.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_dtypes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_map.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_missing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_operators.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_replace.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_repr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_sorting.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_subclass.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_take.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_warnings.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_algos.py\n", + "│ │ │ │ │ │ │ ├── test_analytics.py\n", + "│ │ │ │ │ │ │ ├── test_api.py\n", + "│ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ │ ├── test_dtypes.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_map.py\n", + "│ │ │ │ │ │ │ ├── test_missing.py\n", + "│ │ │ │ │ │ │ ├── test_operators.py\n", + "│ │ │ │ │ │ │ ├── test_replace.py\n", + "│ │ │ │ │ │ │ ├── test_repr.py\n", + "│ │ │ │ │ │ │ ├── test_sorting.py\n", + "│ │ │ │ │ │ │ ├── test_subclass.py\n", + "│ │ │ │ │ │ │ ├── test_take.py\n", + "│ │ │ │ │ │ │ └── test_warnings.py\n", + "│ │ │ │ │ │ ├── datetimes\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_cumulative.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_reductions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ │ ├── test_cumulative.py\n", + "│ │ │ │ │ │ │ └── test_reductions.py\n", + "│ │ │ │ │ │ ├── floating\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_arithmetic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_comparison.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_concat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_construction.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_contains.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_function.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_repr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_to_numpy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ │ ├── test_arithmetic.py\n", + "│ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ ├── test_comparison.py\n", + "│ │ │ │ │ │ │ ├── test_concat.py\n", + "│ │ │ │ │ │ │ ├── test_construction.py\n", + "│ │ │ │ │ │ │ ├── test_contains.py\n", + "│ │ │ │ │ │ │ ├── test_function.py\n", + "│ │ │ │ │ │ │ ├── test_repr.py\n", + "│ │ │ │ │ │ │ └── test_to_numpy.py\n", + "│ │ │ │ │ │ ├── integer\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_arithmetic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_comparison.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_concat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_construction.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_dtypes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_function.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reduction.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_repr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ │ ├── test_arithmetic.py\n", + "│ │ │ │ │ │ │ ├── test_comparison.py\n", + "│ │ │ │ │ │ │ ├── test_concat.py\n", + "│ │ │ │ │ │ │ ├── test_construction.py\n", + "│ │ │ │ │ │ │ ├── test_dtypes.py\n", + "│ │ │ │ │ │ │ ├── test_function.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_reduction.py\n", + "│ │ │ │ │ │ │ └── test_repr.py\n", + "│ │ │ │ │ │ ├── interval\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_formats.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_interval.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_interval_pyarrow.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_overlaps.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ ├── test_formats.py\n", + "│ │ │ │ │ │ │ ├── test_interval.py\n", + "│ │ │ │ │ │ │ ├── test_interval_pyarrow.py\n", + "│ │ │ │ │ │ │ └── test_overlaps.py\n", + "│ │ │ │ │ │ ├── masked\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_arithmetic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_arrow_compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_function.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arithmetic.py\n", + "│ │ │ │ │ │ │ ├── test_arrow_compat.py\n", + "│ │ │ │ │ │ │ ├── test_function.py\n", + "│ │ │ │ │ │ │ └── test_indexing.py\n", + "│ │ │ │ │ │ ├── masked_shared.py\n", + "│ │ │ │ │ │ ├── numpy_\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_numpy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ └── test_numpy.py\n", + "│ │ │ │ │ │ ├── period\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_arrow_compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_reductions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arrow_compat.py\n", + "│ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ │ └── test_reductions.py\n", + "│ │ │ │ │ │ ├── sparse\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_accessor.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_arithmetics.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_array.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_combine_concat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_dtype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_libsparse.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reductions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_unary.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_accessor.py\n", + "│ │ │ │ │ │ │ ├── test_arithmetics.py\n", + "│ │ │ │ │ │ │ ├── test_array.py\n", + "│ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ ├── test_combine_concat.py\n", + "│ │ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ │ ├── test_dtype.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_libsparse.py\n", + "│ │ │ │ │ │ │ ├── test_reductions.py\n", + "│ │ │ │ │ │ │ └── test_unary.py\n", + "│ │ │ │ │ │ ├── string_\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_string.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_string_arrow.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_string.py\n", + "│ │ │ │ │ │ │ └── test_string_arrow.py\n", + "│ │ │ │ │ │ ├── test_array.py\n", + "│ │ │ │ │ │ ├── test_datetimelike.py\n", + "│ │ │ │ │ │ ├── test_datetimes.py\n", + "│ │ │ │ │ │ ├── test_ndarray_backed.py\n", + "│ │ │ │ │ │ ├── test_period.py\n", + "│ │ │ │ │ │ ├── test_timedeltas.py\n", + "│ │ │ │ │ │ └── timedeltas\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_cumulative.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_reductions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ ├── test_cumulative.py\n", + "│ │ │ │ │ │ └── test_reductions.py\n", + "│ │ │ │ │ ├── base\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_conversion.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_fillna.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_misc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_transpose.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_unique.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_value_counts.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ ├── test_conversion.py\n", + "│ │ │ │ │ │ ├── test_fillna.py\n", + "│ │ │ │ │ │ ├── test_misc.py\n", + "│ │ │ │ │ │ ├── test_transpose.py\n", + "│ │ │ │ │ │ ├── test_unique.py\n", + "│ │ │ │ │ │ └── test_value_counts.py\n", + "│ │ │ │ │ ├── computation\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_eval.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_compat.py\n", + "│ │ │ │ │ │ └── test_eval.py\n", + "│ │ │ │ │ ├── config\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_config.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_localization.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_config.py\n", + "│ │ │ │ │ │ └── test_localization.py\n", + "│ │ │ │ │ ├── construction\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_extract_array.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_extract_array.py\n", + "│ │ │ │ │ ├── copy_view\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_array.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_chained_assignment_deprecation.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_clip.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_core_functionalities.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_internals.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_interp_fillna.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_methods.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_replace.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_setitem.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_util.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── util.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── index\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_datetimeindex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_periodindex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_timedeltaindex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_datetimeindex.py\n", + "│ │ │ │ │ │ │ ├── test_index.py\n", + "│ │ │ │ │ │ │ ├── test_periodindex.py\n", + "│ │ │ │ │ │ │ └── test_timedeltaindex.py\n", + "│ │ │ │ │ │ ├── test_array.py\n", + "│ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ ├── test_chained_assignment_deprecation.py\n", + "│ │ │ │ │ │ ├── test_clip.py\n", + "│ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ ├── test_core_functionalities.py\n", + "│ │ │ │ │ │ ├── test_functions.py\n", + "│ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ ├── test_internals.py\n", + "│ │ │ │ │ │ ├── test_interp_fillna.py\n", + "│ │ │ │ │ │ ├── test_methods.py\n", + "│ │ │ │ │ │ ├── test_replace.py\n", + "│ │ │ │ │ │ ├── test_setitem.py\n", + "│ │ │ │ │ │ ├── test_util.py\n", + "│ │ │ │ │ │ └── util.py\n", + "│ │ │ │ │ ├── dtypes\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_concat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_dtypes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_generic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_inference.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_missing.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cast\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_can_hold_element.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_construct_from_scalar.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_construct_ndarray.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_construct_object_arr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_dict_compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_downcast.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_find_common_type.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_infer_datetimelike.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_infer_dtype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_maybe_box_native.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_promote.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_can_hold_element.py\n", + "│ │ │ │ │ │ │ ├── test_construct_from_scalar.py\n", + "│ │ │ │ │ │ │ ├── test_construct_ndarray.py\n", + "│ │ │ │ │ │ │ ├── test_construct_object_arr.py\n", + "│ │ │ │ │ │ │ ├── test_dict_compat.py\n", + "│ │ │ │ │ │ │ ├── test_downcast.py\n", + "│ │ │ │ │ │ │ ├── test_find_common_type.py\n", + "│ │ │ │ │ │ │ ├── test_infer_datetimelike.py\n", + "│ │ │ │ │ │ │ ├── test_infer_dtype.py\n", + "│ │ │ │ │ │ │ ├── test_maybe_box_native.py\n", + "│ │ │ │ │ │ │ └── test_promote.py\n", + "│ │ │ │ │ │ ├── test_common.py\n", + "│ │ │ │ │ │ ├── test_concat.py\n", + "│ │ │ │ │ │ ├── test_dtypes.py\n", + "│ │ │ │ │ │ ├── test_generic.py\n", + "│ │ │ │ │ │ ├── test_inference.py\n", + "│ │ │ │ │ │ └── test_missing.py\n", + "│ │ │ │ │ ├── extension\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arrow.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_categorical.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_datetime.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_extension.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_interval.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_masked.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_numpy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_period.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_sparse.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_string.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── array_with_attr\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── array.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_array_with_attr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── array.py\n", + "│ │ │ │ │ │ │ └── test_array_with_attr.py\n", + "│ │ │ │ │ │ ├── base\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── accumulate.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── casting.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── dim2.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── dtype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── getitem.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── groupby.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── interface.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── io.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── methods.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── missing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── ops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── printing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── reduce.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── reshaping.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── setitem.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── accumulate.py\n", + "│ │ │ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ │ │ ├── casting.py\n", + "│ │ │ │ │ │ │ ├── constructors.py\n", + "│ │ │ │ │ │ │ ├── dim2.py\n", + "│ │ │ │ │ │ │ ├── dtype.py\n", + "│ │ │ │ │ │ │ ├── getitem.py\n", + "│ │ │ │ │ │ │ ├── groupby.py\n", + "│ │ │ │ │ │ │ ├── index.py\n", + "│ │ │ │ │ │ │ ├── interface.py\n", + "│ │ │ │ │ │ │ ├── io.py\n", + "│ │ │ │ │ │ │ ├── methods.py\n", + "│ │ │ │ │ │ │ ├── missing.py\n", + "│ │ │ │ │ │ │ ├── ops.py\n", + "│ │ │ │ │ │ │ ├── printing.py\n", + "│ │ │ │ │ │ │ ├── reduce.py\n", + "│ │ │ │ │ │ │ ├── reshaping.py\n", + "│ │ │ │ │ │ │ └── setitem.py\n", + "│ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ ├── date\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── array.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── array.py\n", + "│ │ │ │ │ │ ├── decimal\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── array.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_decimal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── array.py\n", + "│ │ │ │ │ │ │ └── test_decimal.py\n", + "│ │ │ │ │ │ ├── json\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── array.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_json.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── array.py\n", + "│ │ │ │ │ │ │ └── test_json.py\n", + "│ │ │ │ │ │ ├── list\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── array.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_list.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── array.py\n", + "│ │ │ │ │ │ │ └── test_list.py\n", + "│ │ │ │ │ │ ├── test_arrow.py\n", + "│ │ │ │ │ │ ├── test_categorical.py\n", + "│ │ │ │ │ │ ├── test_common.py\n", + "│ │ │ │ │ │ ├── test_datetime.py\n", + "│ │ │ │ │ │ ├── test_extension.py\n", + "│ │ │ │ │ │ ├── test_interval.py\n", + "│ │ │ │ │ │ ├── test_masked.py\n", + "│ │ │ │ │ │ ├── test_numpy.py\n", + "│ │ │ │ │ │ ├── test_period.py\n", + "│ │ │ │ │ │ ├── test_sparse.py\n", + "│ │ │ │ │ │ └── test_string.py\n", + "│ │ │ │ │ ├── frame\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_alter_axes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arithmetic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arrow_interface.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_block_internals.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_cumulative.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_iteration.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_logical_ops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_nonunique_indexes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_npfuncs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_query_eval.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_reductions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_repr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_stack_unstack.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_subclass.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_ufunc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_unary.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_validate.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ ├── constructors\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_from_dict.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_from_records.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_from_dict.py\n", + "│ │ │ │ │ │ │ └── test_from_records.py\n", + "│ │ │ │ │ │ ├── indexing\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_coercion.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_delitem.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_get.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_get_value.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_getitem.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_insert.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_mask.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_set_value.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_setitem.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_take.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_where.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_xs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_coercion.py\n", + "│ │ │ │ │ │ │ ├── test_delitem.py\n", + "│ │ │ │ │ │ │ ├── test_get.py\n", + "│ │ │ │ │ │ │ ├── test_get_value.py\n", + "│ │ │ │ │ │ │ ├── test_getitem.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_insert.py\n", + "│ │ │ │ │ │ │ ├── test_mask.py\n", + "│ │ │ │ │ │ │ ├── test_set_value.py\n", + "│ │ │ │ │ │ │ ├── test_setitem.py\n", + "│ │ │ │ │ │ │ ├── test_take.py\n", + "│ │ │ │ │ │ │ ├── test_where.py\n", + "│ │ │ │ │ │ │ └── test_xs.py\n", + "│ │ │ │ │ │ ├── methods\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_add_prefix_suffix.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_align.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_asfreq.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_asof.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_assign.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_at_time.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_between_time.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_clip.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_combine.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_combine_first.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_compare.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_convert_dtypes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_copy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_count.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_cov_corr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_describe.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_diff.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_dot.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_drop.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_drop_duplicates.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_droplevel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_dropna.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_dtypes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_duplicated.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_equals.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_explode.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_fillna.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_filter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_first_and_last.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_first_valid_index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_get_numeric_data.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_head_tail.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_infer_objects.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_info.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_interpolate.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_is_homogeneous_dtype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_isetitem.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_isin.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_iterrows.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_join.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_map.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_matmul.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_nlargest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_pct_change.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_pipe.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_pop.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_quantile.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_rank.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reindex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reindex_like.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_rename.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_rename_axis.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reorder_levels.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_replace.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reset_index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_round.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_sample.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_select_dtypes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_set_axis.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_set_index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_shift.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_size.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_sort_index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_sort_values.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_swapaxes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_swaplevel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_csv.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_dict.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_dict_of_blocks.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_numpy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_period.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_records.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_timestamp.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_transpose.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_truncate.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_tz_convert.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_tz_localize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_update.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_value_counts.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_values.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_add_prefix_suffix.py\n", + "│ │ │ │ │ │ │ ├── test_align.py\n", + "│ │ │ │ │ │ │ ├── test_asfreq.py\n", + "│ │ │ │ │ │ │ ├── test_asof.py\n", + "│ │ │ │ │ │ │ ├── test_assign.py\n", + "│ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ ├── test_at_time.py\n", + "│ │ │ │ │ │ │ ├── test_between_time.py\n", + "│ │ │ │ │ │ │ ├── test_clip.py\n", + "│ │ │ │ │ │ │ ├── test_combine.py\n", + "│ │ │ │ │ │ │ ├── test_combine_first.py\n", + "│ │ │ │ │ │ │ ├── test_compare.py\n", + "│ │ │ │ │ │ │ ├── test_convert_dtypes.py\n", + "│ │ │ │ │ │ │ ├── test_copy.py\n", + "│ │ │ │ │ │ │ ├── test_count.py\n", + "│ │ │ │ │ │ │ ├── test_cov_corr.py\n", + "│ │ │ │ │ │ │ ├── test_describe.py\n", + "│ │ │ │ │ │ │ ├── test_diff.py\n", + "│ │ │ │ │ │ │ ├── test_dot.py\n", + "│ │ │ │ │ │ │ ├── test_drop.py\n", + "│ │ │ │ │ │ │ ├── test_drop_duplicates.py\n", + "│ │ │ │ │ │ │ ├── test_droplevel.py\n", + "│ │ │ │ │ │ │ ├── test_dropna.py\n", + "│ │ │ │ │ │ │ ├── test_dtypes.py\n", + "│ │ │ │ │ │ │ ├── test_duplicated.py\n", + "│ │ │ │ │ │ │ ├── test_equals.py\n", + "│ │ │ │ │ │ │ ├── test_explode.py\n", + "│ │ │ │ │ │ │ ├── test_fillna.py\n", + "│ │ │ │ │ │ │ ├── test_filter.py\n", + "│ │ │ │ │ │ │ ├── test_first_and_last.py\n", + "│ │ │ │ │ │ │ ├── test_first_valid_index.py\n", + "│ │ │ │ │ │ │ ├── test_get_numeric_data.py\n", + "│ │ │ │ │ │ │ ├── test_head_tail.py\n", + "│ │ │ │ │ │ │ ├── test_infer_objects.py\n", + "│ │ │ │ │ │ │ ├── test_info.py\n", + "│ │ │ │ │ │ │ ├── test_interpolate.py\n", + "│ │ │ │ │ │ │ ├── test_is_homogeneous_dtype.py\n", + "│ │ │ │ │ │ │ ├── test_isetitem.py\n", + "│ │ │ │ │ │ │ ├── test_isin.py\n", + "│ │ │ │ │ │ │ ├── test_iterrows.py\n", + "│ │ │ │ │ │ │ ├── test_join.py\n", + "│ │ │ │ │ │ │ ├── test_map.py\n", + "│ │ │ │ │ │ │ ├── test_matmul.py\n", + "│ │ │ │ │ │ │ ├── test_nlargest.py\n", + "│ │ │ │ │ │ │ ├── test_pct_change.py\n", + "│ │ │ │ │ │ │ ├── test_pipe.py\n", + "│ │ │ │ │ │ │ ├── test_pop.py\n", + "│ │ │ │ │ │ │ ├── test_quantile.py\n", + "│ │ │ │ │ │ │ ├── test_rank.py\n", + "│ │ │ │ │ │ │ ├── test_reindex.py\n", + "│ │ │ │ │ │ │ ├── test_reindex_like.py\n", + "│ │ │ │ │ │ │ ├── test_rename.py\n", + "│ │ │ │ │ │ │ ├── test_rename_axis.py\n", + "│ │ │ │ │ │ │ ├── test_reorder_levels.py\n", + "│ │ │ │ │ │ │ ├── test_replace.py\n", + "│ │ │ │ │ │ │ ├── test_reset_index.py\n", + "│ │ │ │ │ │ │ ├── test_round.py\n", + "│ │ │ │ │ │ │ ├── test_sample.py\n", + "│ │ │ │ │ │ │ ├── test_select_dtypes.py\n", + "│ │ │ │ │ │ │ ├── test_set_axis.py\n", + "│ │ │ │ │ │ │ ├── test_set_index.py\n", + "│ │ │ │ │ │ │ ├── test_shift.py\n", + "│ │ │ │ │ │ │ ├── test_size.py\n", + "│ │ │ │ │ │ │ ├── test_sort_index.py\n", + "│ │ │ │ │ │ │ ├── test_sort_values.py\n", + "│ │ │ │ │ │ │ ├── test_swapaxes.py\n", + "│ │ │ │ │ │ │ ├── test_swaplevel.py\n", + "│ │ │ │ │ │ │ ├── test_to_csv.py\n", + "│ │ │ │ │ │ │ ├── test_to_dict.py\n", + "│ │ │ │ │ │ │ ├── test_to_dict_of_blocks.py\n", + "│ │ │ │ │ │ │ ├── test_to_numpy.py\n", + "│ │ │ │ │ │ │ ├── test_to_period.py\n", + "│ │ │ │ │ │ │ ├── test_to_records.py\n", + "│ │ │ │ │ │ │ ├── test_to_timestamp.py\n", + "│ │ │ │ │ │ │ ├── test_transpose.py\n", + "│ │ │ │ │ │ │ ├── test_truncate.py\n", + "│ │ │ │ │ │ │ ├── test_tz_convert.py\n", + "│ │ │ │ │ │ │ ├── test_tz_localize.py\n", + "│ │ │ │ │ │ │ ├── test_update.py\n", + "│ │ │ │ │ │ │ ├── test_value_counts.py\n", + "│ │ │ │ │ │ │ └── test_values.py\n", + "│ │ │ │ │ │ ├── test_alter_axes.py\n", + "│ │ │ │ │ │ ├── test_api.py\n", + "│ │ │ │ │ │ ├── test_arithmetic.py\n", + "│ │ │ │ │ │ ├── test_arrow_interface.py\n", + "│ │ │ │ │ │ ├── test_block_internals.py\n", + "│ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ ├── test_cumulative.py\n", + "│ │ │ │ │ │ ├── test_iteration.py\n", + "│ │ │ │ │ │ ├── test_logical_ops.py\n", + "│ │ │ │ │ │ ├── test_nonunique_indexes.py\n", + "│ │ │ │ │ │ ├── test_npfuncs.py\n", + "│ │ │ │ │ │ ├── test_query_eval.py\n", + "│ │ │ │ │ │ ├── test_reductions.py\n", + "│ │ │ │ │ │ ├── test_repr.py\n", + "│ │ │ │ │ │ ├── test_stack_unstack.py\n", + "│ │ │ │ │ │ ├── test_subclass.py\n", + "│ │ │ │ │ │ ├── test_ufunc.py\n", + "│ │ │ │ │ │ ├── test_unary.py\n", + "│ │ │ │ │ │ └── test_validate.py\n", + "│ │ │ │ │ ├── generic\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_duplicate_labels.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_finalize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_frame.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_generic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_label_or_level_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_series.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_to_xarray.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_duplicate_labels.py\n", + "│ │ │ │ │ │ ├── test_finalize.py\n", + "│ │ │ │ │ │ ├── test_frame.py\n", + "│ │ │ │ │ │ ├── test_generic.py\n", + "│ │ │ │ │ │ ├── test_label_or_level_utils.py\n", + "│ │ │ │ │ │ ├── test_series.py\n", + "│ │ │ │ │ │ └── test_to_xarray.py\n", + "│ │ │ │ │ ├── groupby\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_all_methods.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_apply.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_apply_mutate.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_bin_groupby.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_categorical.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_counting.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_cumulative.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_filters.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_groupby.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_groupby_dropna.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_groupby_subclass.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_grouping.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_index_as_string.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_libgroupby.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_missing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_numba.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_numeric_only.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_pipe.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_raises.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_reductions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_timegrouper.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── aggregate\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_aggregate.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_cython.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_numba.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_other.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_aggregate.py\n", + "│ │ │ │ │ │ │ ├── test_cython.py\n", + "│ │ │ │ │ │ │ ├── test_numba.py\n", + "│ │ │ │ │ │ │ └── test_other.py\n", + "│ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ ├── methods\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_corrwith.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_describe.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_groupby_shift_diff.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_is_monotonic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_nlargest_nsmallest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_nth.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_quantile.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_rank.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_sample.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_size.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_skew.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_value_counts.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_corrwith.py\n", + "│ │ │ │ │ │ │ ├── test_describe.py\n", + "│ │ │ │ │ │ │ ├── test_groupby_shift_diff.py\n", + "│ │ │ │ │ │ │ ├── test_is_monotonic.py\n", + "│ │ │ │ │ │ │ ├── test_nlargest_nsmallest.py\n", + "│ │ │ │ │ │ │ ├── test_nth.py\n", + "│ │ │ │ │ │ │ ├── test_quantile.py\n", + "│ │ │ │ │ │ │ ├── test_rank.py\n", + "│ │ │ │ │ │ │ ├── test_sample.py\n", + "│ │ │ │ │ │ │ ├── test_size.py\n", + "│ │ │ │ │ │ │ ├── test_skew.py\n", + "│ │ │ │ │ │ │ └── test_value_counts.py\n", + "│ │ │ │ │ │ ├── test_all_methods.py\n", + "│ │ │ │ │ │ ├── test_api.py\n", + "│ │ │ │ │ │ ├── test_apply.py\n", + "│ │ │ │ │ │ ├── test_apply_mutate.py\n", + "│ │ │ │ │ │ ├── test_bin_groupby.py\n", + "│ │ │ │ │ │ ├── test_categorical.py\n", + "│ │ │ │ │ │ ├── test_counting.py\n", + "│ │ │ │ │ │ ├── test_cumulative.py\n", + "│ │ │ │ │ │ ├── test_filters.py\n", + "│ │ │ │ │ │ ├── test_groupby.py\n", + "│ │ │ │ │ │ ├── test_groupby_dropna.py\n", + "│ │ │ │ │ │ ├── test_groupby_subclass.py\n", + "│ │ │ │ │ │ ├── test_grouping.py\n", + "│ │ │ │ │ │ ├── test_index_as_string.py\n", + "│ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ ├── test_libgroupby.py\n", + "│ │ │ │ │ │ ├── test_missing.py\n", + "│ │ │ │ │ │ ├── test_numba.py\n", + "│ │ │ │ │ │ ├── test_numeric_only.py\n", + "│ │ │ │ │ │ ├── test_pipe.py\n", + "│ │ │ │ │ │ ├── test_raises.py\n", + "│ │ │ │ │ │ ├── test_reductions.py\n", + "│ │ │ │ │ │ ├── test_timegrouper.py\n", + "│ │ │ │ │ │ └── transform\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_numba.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_transform.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_numba.py\n", + "│ │ │ │ │ │ └── test_transform.py\n", + "│ │ │ │ │ ├── indexes\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_any_index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_datetimelike.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_engines.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_frozen.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_index_new.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_numpy_compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_old_base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_setops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_subclass.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base_class\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_formats.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_pickle.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reshape.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_setops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_where.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ │ ├── test_formats.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_pickle.py\n", + "│ │ │ │ │ │ │ ├── test_reshape.py\n", + "│ │ │ │ │ │ │ ├── test_setops.py\n", + "│ │ │ │ │ │ │ └── test_where.py\n", + "│ │ │ │ │ │ ├── categorical\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_append.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_category.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_equals.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_fillna.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_formats.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_map.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reindex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_setops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_append.py\n", + "│ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ ├── test_category.py\n", + "│ │ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ │ ├── test_equals.py\n", + "│ │ │ │ │ │ │ ├── test_fillna.py\n", + "│ │ │ │ │ │ │ ├── test_formats.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_map.py\n", + "│ │ │ │ │ │ │ ├── test_reindex.py\n", + "│ │ │ │ │ │ │ └── test_setops.py\n", + "│ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ ├── datetimelike_\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_drop_duplicates.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_equals.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_is_monotonic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_nat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_sort_values.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_value_counts.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_drop_duplicates.py\n", + "│ │ │ │ │ │ │ ├── test_equals.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_is_monotonic.py\n", + "│ │ │ │ │ │ │ ├── test_nat.py\n", + "│ │ │ │ │ │ │ ├── test_sort_values.py\n", + "│ │ │ │ │ │ │ └── test_value_counts.py\n", + "│ │ │ │ │ │ ├── datetimes\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_arithmetic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_date_range.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_datetime.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_formats.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_freq_attr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_iter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_join.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_npfuncs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_ops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_partial_slicing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_pickle.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reindex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_scalar_compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_setops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_timezones.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── methods\n", + "│ │ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_asof.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_delete.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_factorize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_fillna.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_insert.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_isocalendar.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_map.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_normalize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_repeat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_resolution.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_round.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_shift.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_snap.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_to_frame.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_to_julian_date.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_to_period.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_to_pydatetime.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_to_series.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_tz_convert.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_tz_localize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ └── test_unique.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_asof.py\n", + "│ │ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ │ ├── test_delete.py\n", + "│ │ │ │ │ │ │ │ ├── test_factorize.py\n", + "│ │ │ │ │ │ │ │ ├── test_fillna.py\n", + "│ │ │ │ │ │ │ │ ├── test_insert.py\n", + "│ │ │ │ │ │ │ │ ├── test_isocalendar.py\n", + "│ │ │ │ │ │ │ │ ├── test_map.py\n", + "│ │ │ │ │ │ │ │ ├── test_normalize.py\n", + "│ │ │ │ │ │ │ │ ├── test_repeat.py\n", + "│ │ │ │ │ │ │ │ ├── test_resolution.py\n", + "│ │ │ │ │ │ │ │ ├── test_round.py\n", + "│ │ │ │ │ │ │ │ ├── test_shift.py\n", + "│ │ │ │ │ │ │ │ ├── test_snap.py\n", + "│ │ │ │ │ │ │ │ ├── test_to_frame.py\n", + "│ │ │ │ │ │ │ │ ├── test_to_julian_date.py\n", + "│ │ │ │ │ │ │ │ ├── test_to_period.py\n", + "│ │ │ │ │ │ │ │ ├── test_to_pydatetime.py\n", + "│ │ │ │ │ │ │ │ ├── test_to_series.py\n", + "│ │ │ │ │ │ │ │ ├── test_tz_convert.py\n", + "│ │ │ │ │ │ │ │ ├── test_tz_localize.py\n", + "│ │ │ │ │ │ │ │ └── test_unique.py\n", + "│ │ │ │ │ │ │ ├── test_arithmetic.py\n", + "│ │ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ │ ├── test_date_range.py\n", + "│ │ │ │ │ │ │ ├── test_datetime.py\n", + "│ │ │ │ │ │ │ ├── test_formats.py\n", + "│ │ │ │ │ │ │ ├── test_freq_attr.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_iter.py\n", + "│ │ │ │ │ │ │ ├── test_join.py\n", + "│ │ │ │ │ │ │ ├── test_npfuncs.py\n", + "│ │ │ │ │ │ │ ├── test_ops.py\n", + "│ │ │ │ │ │ │ ├── test_partial_slicing.py\n", + "│ │ │ │ │ │ │ ├── test_pickle.py\n", + "│ │ │ │ │ │ │ ├── test_reindex.py\n", + "│ │ │ │ │ │ │ ├── test_scalar_compat.py\n", + "│ │ │ │ │ │ │ ├── test_setops.py\n", + "│ │ │ │ │ │ │ └── test_timezones.py\n", + "│ │ │ │ │ │ ├── interval\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_equals.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_formats.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_interval.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_interval_range.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_interval_tree.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_join.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_pickle.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_setops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ │ ├── test_equals.py\n", + "│ │ │ │ │ │ │ ├── test_formats.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_interval.py\n", + "│ │ │ │ │ │ │ ├── test_interval_range.py\n", + "│ │ │ │ │ │ │ ├── test_interval_tree.py\n", + "│ │ │ │ │ │ │ ├── test_join.py\n", + "│ │ │ │ │ │ │ ├── test_pickle.py\n", + "│ │ │ │ │ │ │ └── test_setops.py\n", + "│ │ │ │ │ │ ├── multi\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_analytics.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_conversion.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_copy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_drop.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_duplicates.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_equivalence.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_formats.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_get_level_values.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_get_set.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_integrity.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_isin.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_join.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_lexsort.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_missing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_monotonic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_names.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_partial_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_pickle.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reindex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reshape.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_setops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_sorting.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_take.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ │ ├── test_analytics.py\n", + "│ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ ├── test_compat.py\n", + "│ │ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ │ ├── test_conversion.py\n", + "│ │ │ │ │ │ │ ├── test_copy.py\n", + "│ │ │ │ │ │ │ ├── test_drop.py\n", + "│ │ │ │ │ │ │ ├── test_duplicates.py\n", + "│ │ │ │ │ │ │ ├── test_equivalence.py\n", + "│ │ │ │ │ │ │ ├── test_formats.py\n", + "│ │ │ │ │ │ │ ├── test_get_level_values.py\n", + "│ │ │ │ │ │ │ ├── test_get_set.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_integrity.py\n", + "│ │ │ │ │ │ │ ├── test_isin.py\n", + "│ │ │ │ │ │ │ ├── test_join.py\n", + "│ │ │ │ │ │ │ ├── test_lexsort.py\n", + "│ │ │ │ │ │ │ ├── test_missing.py\n", + "│ │ │ │ │ │ │ ├── test_monotonic.py\n", + "│ │ │ │ │ │ │ ├── test_names.py\n", + "│ │ │ │ │ │ │ ├── test_partial_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_pickle.py\n", + "│ │ │ │ │ │ │ ├── test_reindex.py\n", + "│ │ │ │ │ │ │ ├── test_reshape.py\n", + "│ │ │ │ │ │ │ ├── test_setops.py\n", + "│ │ │ │ │ │ │ ├── test_sorting.py\n", + "│ │ │ │ │ │ │ └── test_take.py\n", + "│ │ │ │ │ │ ├── numeric\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_join.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_numeric.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_setops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_join.py\n", + "│ │ │ │ │ │ │ ├── test_numeric.py\n", + "│ │ │ │ │ │ │ └── test_setops.py\n", + "│ │ │ │ │ │ ├── object\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ └── test_indexing.py\n", + "│ │ │ │ │ │ ├── period\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_formats.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_freq_attr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_join.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_monotonic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_partial_slicing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_period.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_period_range.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_pickle.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_resolution.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_scalar_compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_searchsorted.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_setops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_tools.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── methods\n", + "│ │ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_asfreq.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_factorize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_fillna.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_insert.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_is_full.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_repeat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_shift.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ └── test_to_timestamp.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_asfreq.py\n", + "│ │ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ │ ├── test_factorize.py\n", + "│ │ │ │ │ │ │ │ ├── test_fillna.py\n", + "│ │ │ │ │ │ │ │ ├── test_insert.py\n", + "│ │ │ │ │ │ │ │ ├── test_is_full.py\n", + "│ │ │ │ │ │ │ │ ├── test_repeat.py\n", + "│ │ │ │ │ │ │ │ ├── test_shift.py\n", + "│ │ │ │ │ │ │ │ └── test_to_timestamp.py\n", + "│ │ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ │ ├── test_formats.py\n", + "│ │ │ │ │ │ │ ├── test_freq_attr.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_join.py\n", + "│ │ │ │ │ │ │ ├── test_monotonic.py\n", + "│ │ │ │ │ │ │ ├── test_partial_slicing.py\n", + "│ │ │ │ │ │ │ ├── test_period.py\n", + "│ │ │ │ │ │ │ ├── test_period_range.py\n", + "│ │ │ │ │ │ │ ├── test_pickle.py\n", + "│ │ │ │ │ │ │ ├── test_resolution.py\n", + "│ │ │ │ │ │ │ ├── test_scalar_compat.py\n", + "│ │ │ │ │ │ │ ├── test_searchsorted.py\n", + "│ │ │ │ │ │ │ ├── test_setops.py\n", + "│ │ │ │ │ │ │ └── test_tools.py\n", + "│ │ │ │ │ │ ├── ranges\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_join.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_range.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_setops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_join.py\n", + "│ │ │ │ │ │ │ ├── test_range.py\n", + "│ │ │ │ │ │ │ └── test_setops.py\n", + "│ │ │ │ │ │ ├── test_any_index.py\n", + "│ │ │ │ │ │ ├── test_base.py\n", + "│ │ │ │ │ │ ├── test_common.py\n", + "│ │ │ │ │ │ ├── test_datetimelike.py\n", + "│ │ │ │ │ │ ├── test_engines.py\n", + "│ │ │ │ │ │ ├── test_frozen.py\n", + "│ │ │ │ │ │ ├── test_index_new.py\n", + "│ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ ├── test_numpy_compat.py\n", + "│ │ │ │ │ │ ├── test_old_base.py\n", + "│ │ │ │ │ │ ├── test_setops.py\n", + "│ │ │ │ │ │ ├── test_subclass.py\n", + "│ │ │ │ │ │ └── timedeltas\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arithmetic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_delete.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_formats.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_freq_attr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_join.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_ops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_pickle.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_scalar_compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_searchsorted.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_setops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_timedelta.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_timedelta_range.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── methods\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_factorize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_fillna.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_insert.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_repeat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_shift.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ ├── test_factorize.py\n", + "│ │ │ │ │ │ │ ├── test_fillna.py\n", + "│ │ │ │ │ │ │ ├── test_insert.py\n", + "│ │ │ │ │ │ │ ├── test_repeat.py\n", + "│ │ │ │ │ │ │ └── test_shift.py\n", + "│ │ │ │ │ │ ├── test_arithmetic.py\n", + "│ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ ├── test_delete.py\n", + "│ │ │ │ │ │ ├── test_formats.py\n", + "│ │ │ │ │ │ ├── test_freq_attr.py\n", + "│ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ ├── test_join.py\n", + "│ │ │ │ │ │ ├── test_ops.py\n", + "│ │ │ │ │ │ ├── test_pickle.py\n", + "│ │ │ │ │ │ ├── test_scalar_compat.py\n", + "│ │ │ │ │ │ ├── test_searchsorted.py\n", + "│ │ │ │ │ │ ├── test_setops.py\n", + "│ │ │ │ │ │ ├── test_timedelta.py\n", + "│ │ │ │ │ │ └── test_timedelta_range.py\n", + "│ │ │ │ │ ├── indexing\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_at.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_categorical.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_chaining_and_caching.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_check_indexer.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_coercion.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_datetime.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_floats.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_iat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_iloc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_indexers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_loc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_na_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_partial.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_scalar.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ ├── interval\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_interval.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_interval_new.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_interval.py\n", + "│ │ │ │ │ │ │ └── test_interval_new.py\n", + "│ │ │ │ │ │ ├── multiindex\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_chaining_and_caching.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_datetime.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_getitem.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_iloc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing_slow.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_loc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_multiindex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_partial.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_setitem.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_slice.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_sorted.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_chaining_and_caching.py\n", + "│ │ │ │ │ │ │ ├── test_datetime.py\n", + "│ │ │ │ │ │ │ ├── test_getitem.py\n", + "│ │ │ │ │ │ │ ├── test_iloc.py\n", + "│ │ │ │ │ │ │ ├── test_indexing_slow.py\n", + "│ │ │ │ │ │ │ ├── test_loc.py\n", + "│ │ │ │ │ │ │ ├── test_multiindex.py\n", + "│ │ │ │ │ │ │ ├── test_partial.py\n", + "│ │ │ │ │ │ │ ├── test_setitem.py\n", + "│ │ │ │ │ │ │ ├── test_slice.py\n", + "│ │ │ │ │ │ │ └── test_sorted.py\n", + "│ │ │ │ │ │ ├── test_at.py\n", + "│ │ │ │ │ │ ├── test_categorical.py\n", + "│ │ │ │ │ │ ├── test_chaining_and_caching.py\n", + "│ │ │ │ │ │ ├── test_check_indexer.py\n", + "│ │ │ │ │ │ ├── test_coercion.py\n", + "│ │ │ │ │ │ ├── test_datetime.py\n", + "│ │ │ │ │ │ ├── test_floats.py\n", + "│ │ │ │ │ │ ├── test_iat.py\n", + "│ │ │ │ │ │ ├── test_iloc.py\n", + "│ │ │ │ │ │ ├── test_indexers.py\n", + "│ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ ├── test_loc.py\n", + "│ │ │ │ │ │ ├── test_na_indexing.py\n", + "│ │ │ │ │ │ ├── test_partial.py\n", + "│ │ │ │ │ │ └── test_scalar.py\n", + "│ │ │ │ │ ├── interchange\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_impl.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_spec_conformance.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_impl.py\n", + "│ │ │ │ │ │ ├── test_spec_conformance.py\n", + "│ │ │ │ │ │ └── test_utils.py\n", + "│ │ │ │ │ ├── internals\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_internals.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_managers.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_api.py\n", + "│ │ │ │ │ │ ├── test_internals.py\n", + "│ │ │ │ │ │ └── test_managers.py\n", + "│ │ │ │ │ ├── io\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── generate_legacy_storage_files.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_clipboard.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_compression.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_feather.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_fsspec.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_gbq.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_gcs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_html.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_http_headers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_orc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_parquet.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_pickle.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_s3.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_spss.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_sql.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_stata.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ ├── excel\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_odf.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_odswriter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_openpyxl.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_readers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_style.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_writers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_xlrd.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_xlsxwriter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_odf.py\n", + "│ │ │ │ │ │ │ ├── test_odswriter.py\n", + "│ │ │ │ │ │ │ ├── test_openpyxl.py\n", + "│ │ │ │ │ │ │ ├── test_readers.py\n", + "│ │ │ │ │ │ │ ├── test_style.py\n", + "│ │ │ │ │ │ │ ├── test_writers.py\n", + "│ │ │ │ │ │ │ ├── test_xlrd.py\n", + "│ │ │ │ │ │ │ └── test_xlsxwriter.py\n", + "│ │ │ │ │ │ ├── formats\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_console.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_css.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_eng_formatting.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_format.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_ipython_compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_printing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_csv.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_excel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_html.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_latex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_markdown.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_to_string.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── style\n", + "│ │ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_bar.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_exceptions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_format.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_highlight.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_html.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_matplotlib.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_non_unique.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_style.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_to_latex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_to_string.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ └── test_tooltip.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_bar.py\n", + "│ │ │ │ │ │ │ │ ├── test_exceptions.py\n", + "│ │ │ │ │ │ │ │ ├── test_format.py\n", + "│ │ │ │ │ │ │ │ ├── test_highlight.py\n", + "│ │ │ │ │ │ │ │ ├── test_html.py\n", + "│ │ │ │ │ │ │ │ ├── test_matplotlib.py\n", + "│ │ │ │ │ │ │ │ ├── test_non_unique.py\n", + "│ │ │ │ │ │ │ │ ├── test_style.py\n", + "│ │ │ │ │ │ │ │ ├── test_to_latex.py\n", + "│ │ │ │ │ │ │ │ ├── test_to_string.py\n", + "│ │ │ │ │ │ │ │ └── test_tooltip.py\n", + "│ │ │ │ │ │ │ ├── test_console.py\n", + "│ │ │ │ │ │ │ ├── test_css.py\n", + "│ │ │ │ │ │ │ ├── test_eng_formatting.py\n", + "│ │ │ │ │ │ │ ├── test_format.py\n", + "│ │ │ │ │ │ │ ├── test_ipython_compat.py\n", + "│ │ │ │ │ │ │ ├── test_printing.py\n", + "│ │ │ │ │ │ │ ├── test_to_csv.py\n", + "│ │ │ │ │ │ │ ├── test_to_excel.py\n", + "│ │ │ │ │ │ │ ├── test_to_html.py\n", + "│ │ │ │ │ │ │ ├── test_to_latex.py\n", + "│ │ │ │ │ │ │ ├── test_to_markdown.py\n", + "│ │ │ │ │ │ │ └── test_to_string.py\n", + "│ │ │ │ │ │ ├── generate_legacy_storage_files.py\n", + "│ │ │ │ │ │ ├── json\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_compression.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_deprecated_kwargs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_json_table_schema.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_json_table_schema_ext_dtype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_normalize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_pandas.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_readlines.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_ujson.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ │ ├── test_compression.py\n", + "│ │ │ │ │ │ │ ├── test_deprecated_kwargs.py\n", + "│ │ │ │ │ │ │ ├── test_json_table_schema.py\n", + "│ │ │ │ │ │ │ ├── test_json_table_schema_ext_dtype.py\n", + "│ │ │ │ │ │ │ ├── test_normalize.py\n", + "│ │ │ │ │ │ │ ├── test_pandas.py\n", + "│ │ │ │ │ │ │ ├── test_readlines.py\n", + "│ │ │ │ │ │ │ └── test_ujson.py\n", + "│ │ │ │ │ │ ├── parser\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_c_parser_only.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_comment.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_compression.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_concatenate_chunks.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_converters.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_dialect.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_encoding.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_header.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_index_col.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_mangle_dupes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_multi_thread.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_na_values.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_network.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_parse_dates.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_python_parser_only.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_quoting.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_read_fwf.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_skiprows.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_textreader.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_unsupported.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_upcast.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── common\n", + "│ │ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_chunksize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_common_basic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_data_list.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_decimal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_file_buffer_url.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_float.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_inf.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_ints.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_iterator.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_read_errors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ └── test_verbose.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_chunksize.py\n", + "│ │ │ │ │ │ │ │ ├── test_common_basic.py\n", + "│ │ │ │ │ │ │ │ ├── test_data_list.py\n", + "│ │ │ │ │ │ │ │ ├── test_decimal.py\n", + "│ │ │ │ │ │ │ │ ├── test_file_buffer_url.py\n", + "│ │ │ │ │ │ │ │ ├── test_float.py\n", + "│ │ │ │ │ │ │ │ ├── test_index.py\n", + "│ │ │ │ │ │ │ │ ├── test_inf.py\n", + "│ │ │ │ │ │ │ │ ├── test_ints.py\n", + "│ │ │ │ │ │ │ │ ├── test_iterator.py\n", + "│ │ │ │ │ │ │ │ ├── test_read_errors.py\n", + "│ │ │ │ │ │ │ │ └── test_verbose.py\n", + "│ │ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ │ ├── dtypes\n", + "│ │ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_categorical.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_dtypes_basic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ └── test_empty.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_categorical.py\n", + "│ │ │ │ │ │ │ │ ├── test_dtypes_basic.py\n", + "│ │ │ │ │ │ │ │ └── test_empty.py\n", + "│ │ │ │ │ │ │ ├── test_c_parser_only.py\n", + "│ │ │ │ │ │ │ ├── test_comment.py\n", + "│ │ │ │ │ │ │ ├── test_compression.py\n", + "│ │ │ │ │ │ │ ├── test_concatenate_chunks.py\n", + "│ │ │ │ │ │ │ ├── test_converters.py\n", + "│ │ │ │ │ │ │ ├── test_dialect.py\n", + "│ │ │ │ │ │ │ ├── test_encoding.py\n", + "│ │ │ │ │ │ │ ├── test_header.py\n", + "│ │ │ │ │ │ │ ├── test_index_col.py\n", + "│ │ │ │ │ │ │ ├── test_mangle_dupes.py\n", + "│ │ │ │ │ │ │ ├── test_multi_thread.py\n", + "│ │ │ │ │ │ │ ├── test_na_values.py\n", + "│ │ │ │ │ │ │ ├── test_network.py\n", + "│ │ │ │ │ │ │ ├── test_parse_dates.py\n", + "│ │ │ │ │ │ │ ├── test_python_parser_only.py\n", + "│ │ │ │ │ │ │ ├── test_quoting.py\n", + "│ │ │ │ │ │ │ ├── test_read_fwf.py\n", + "│ │ │ │ │ │ │ ├── test_skiprows.py\n", + "│ │ │ │ │ │ │ ├── test_textreader.py\n", + "│ │ │ │ │ │ │ ├── test_unsupported.py\n", + "│ │ │ │ │ │ │ ├── test_upcast.py\n", + "│ │ │ │ │ │ │ └── usecols\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_parse_dates.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_strings.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_usecols_basic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_parse_dates.py\n", + "│ │ │ │ │ │ │ ├── test_strings.py\n", + "│ │ │ │ │ │ │ └── test_usecols_basic.py\n", + "│ │ │ │ │ │ ├── pytables\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_append.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_categorical.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_complex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_errors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_file_handling.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_keys.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_put.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_pytables_missing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_read.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_retain_attributes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_round_trip.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_select.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_store.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_subclass.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_time_series.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_timezones.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ │ ├── test_append.py\n", + "│ │ │ │ │ │ │ ├── test_categorical.py\n", + "│ │ │ │ │ │ │ ├── test_compat.py\n", + "│ │ │ │ │ │ │ ├── test_complex.py\n", + "│ │ │ │ │ │ │ ├── test_errors.py\n", + "│ │ │ │ │ │ │ ├── test_file_handling.py\n", + "│ │ │ │ │ │ │ ├── test_keys.py\n", + "│ │ │ │ │ │ │ ├── test_put.py\n", + "│ │ │ │ │ │ │ ├── test_pytables_missing.py\n", + "│ │ │ │ │ │ │ ├── test_read.py\n", + "│ │ │ │ │ │ │ ├── test_retain_attributes.py\n", + "│ │ │ │ │ │ │ ├── test_round_trip.py\n", + "│ │ │ │ │ │ │ ├── test_select.py\n", + "│ │ │ │ │ │ │ ├── test_store.py\n", + "│ │ │ │ │ │ │ ├── test_subclass.py\n", + "│ │ │ │ │ │ │ ├── test_time_series.py\n", + "│ │ │ │ │ │ │ └── test_timezones.py\n", + "│ │ │ │ │ │ ├── sas\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_byteswap.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_sas.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_sas7bdat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_xport.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_byteswap.py\n", + "│ │ │ │ │ │ │ ├── test_sas.py\n", + "│ │ │ │ │ │ │ ├── test_sas7bdat.py\n", + "│ │ │ │ │ │ │ └── test_xport.py\n", + "│ │ │ │ │ │ ├── test_clipboard.py\n", + "│ │ │ │ │ │ ├── test_common.py\n", + "│ │ │ │ │ │ ├── test_compression.py\n", + "│ │ │ │ │ │ ├── test_feather.py\n", + "│ │ │ │ │ │ ├── test_fsspec.py\n", + "│ │ │ │ │ │ ├── test_gbq.py\n", + "│ │ │ │ │ │ ├── test_gcs.py\n", + "│ │ │ │ │ │ ├── test_html.py\n", + "│ │ │ │ │ │ ├── test_http_headers.py\n", + "│ │ │ │ │ │ ├── test_orc.py\n", + "│ │ │ │ │ │ ├── test_parquet.py\n", + "│ │ │ │ │ │ ├── test_pickle.py\n", + "│ │ │ │ │ │ ├── test_s3.py\n", + "│ │ │ │ │ │ ├── test_spss.py\n", + "│ │ │ │ │ │ ├── test_sql.py\n", + "│ │ │ │ │ │ ├── test_stata.py\n", + "│ │ │ │ │ │ └── xml\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_to_xml.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_xml.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_xml_dtypes.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ ├── test_to_xml.py\n", + "│ │ │ │ │ │ ├── test_xml.py\n", + "│ │ │ │ │ │ └── test_xml_dtypes.py\n", + "│ │ │ │ │ ├── libs\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_hashtable.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_join.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_lib.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_libalgos.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_hashtable.py\n", + "│ │ │ │ │ │ ├── test_join.py\n", + "│ │ │ │ │ │ ├── test_lib.py\n", + "│ │ │ │ │ │ └── test_libalgos.py\n", + "│ │ │ │ │ ├── plotting\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_backend.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_boxplot_method.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_converter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_datetimelike.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_groupby.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_hist_method.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_misc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_series.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_style.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ ├── frame\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_frame.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_frame_color.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_frame_groupby.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_frame_legend.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_frame_subplots.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_hist_box_by.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_frame.py\n", + "│ │ │ │ │ │ │ ├── test_frame_color.py\n", + "│ │ │ │ │ │ │ ├── test_frame_groupby.py\n", + "│ │ │ │ │ │ │ ├── test_frame_legend.py\n", + "│ │ │ │ │ │ │ ├── test_frame_subplots.py\n", + "│ │ │ │ │ │ │ └── test_hist_box_by.py\n", + "│ │ │ │ │ │ ├── test_backend.py\n", + "│ │ │ │ │ │ ├── test_boxplot_method.py\n", + "│ │ │ │ │ │ ├── test_common.py\n", + "│ │ │ │ │ │ ├── test_converter.py\n", + "│ │ │ │ │ │ ├── test_datetimelike.py\n", + "│ │ │ │ │ │ ├── test_groupby.py\n", + "│ │ │ │ │ │ ├── test_hist_method.py\n", + "│ │ │ │ │ │ ├── test_misc.py\n", + "│ │ │ │ │ │ ├── test_series.py\n", + "│ │ │ │ │ │ └── test_style.py\n", + "│ │ │ │ │ ├── reductions\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_reductions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_stat_reductions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_reductions.py\n", + "│ │ │ │ │ │ └── test_stat_reductions.py\n", + "│ │ │ │ │ ├── resample\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_datetime_index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_period_index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_resample_api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_resampler_grouper.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_time_grouper.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_timedelta.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ ├── test_base.py\n", + "│ │ │ │ │ │ ├── test_datetime_index.py\n", + "│ │ │ │ │ │ ├── test_period_index.py\n", + "│ │ │ │ │ │ ├── test_resample_api.py\n", + "│ │ │ │ │ │ ├── test_resampler_grouper.py\n", + "│ │ │ │ │ │ ├── test_time_grouper.py\n", + "│ │ │ │ │ │ └── test_timedelta.py\n", + "│ │ │ │ │ ├── reshape\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_crosstab.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_cut.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_from_dummies.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_get_dummies.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_melt.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_pivot.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_pivot_multilevel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_qcut.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_union_categoricals.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_util.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── concat\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_append.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_append_common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_categorical.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_concat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_dataframe.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_datetimes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_empty.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_invalid.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_series.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_sort.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ │ ├── test_append.py\n", + "│ │ │ │ │ │ │ ├── test_append_common.py\n", + "│ │ │ │ │ │ │ ├── test_categorical.py\n", + "│ │ │ │ │ │ │ ├── test_concat.py\n", + "│ │ │ │ │ │ │ ├── test_dataframe.py\n", + "│ │ │ │ │ │ │ ├── test_datetimes.py\n", + "│ │ │ │ │ │ │ ├── test_empty.py\n", + "│ │ │ │ │ │ │ ├── test_index.py\n", + "│ │ │ │ │ │ │ ├── test_invalid.py\n", + "│ │ │ │ │ │ │ ├── test_series.py\n", + "│ │ │ │ │ │ │ └── test_sort.py\n", + "│ │ │ │ │ │ ├── merge\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_join.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_merge.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_merge_asof.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_merge_cross.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_merge_index_as_string.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_merge_ordered.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_multi.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_join.py\n", + "│ │ │ │ │ │ │ ├── test_merge.py\n", + "│ │ │ │ │ │ │ ├── test_merge_asof.py\n", + "│ │ │ │ │ │ │ ├── test_merge_cross.py\n", + "│ │ │ │ │ │ │ ├── test_merge_index_as_string.py\n", + "│ │ │ │ │ │ │ ├── test_merge_ordered.py\n", + "│ │ │ │ │ │ │ └── test_multi.py\n", + "│ │ │ │ │ │ ├── test_crosstab.py\n", + "│ │ │ │ │ │ ├── test_cut.py\n", + "│ │ │ │ │ │ ├── test_from_dummies.py\n", + "│ │ │ │ │ │ ├── test_get_dummies.py\n", + "│ │ │ │ │ │ ├── test_melt.py\n", + "│ │ │ │ │ │ ├── test_pivot.py\n", + "│ │ │ │ │ │ ├── test_pivot_multilevel.py\n", + "│ │ │ │ │ │ ├── test_qcut.py\n", + "│ │ │ │ │ │ ├── test_union_categoricals.py\n", + "│ │ │ │ │ │ └── test_util.py\n", + "│ │ │ │ │ ├── scalar\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_na_scalar.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_nat.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── interval\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_arithmetic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_contains.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_formats.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_interval.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_overlaps.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arithmetic.py\n", + "│ │ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ │ ├── test_contains.py\n", + "│ │ │ │ │ │ │ ├── test_formats.py\n", + "│ │ │ │ │ │ │ ├── test_interval.py\n", + "│ │ │ │ │ │ │ └── test_overlaps.py\n", + "│ │ │ │ │ │ ├── period\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_arithmetic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_asfreq.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_period.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arithmetic.py\n", + "│ │ │ │ │ │ │ ├── test_asfreq.py\n", + "│ │ │ │ │ │ │ └── test_period.py\n", + "│ │ │ │ │ │ ├── test_na_scalar.py\n", + "│ │ │ │ │ │ ├── test_nat.py\n", + "│ │ │ │ │ │ ├── timedelta\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_arithmetic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_formats.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_timedelta.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── methods\n", + "│ │ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── test_as_unit.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ └── test_round.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_as_unit.py\n", + "│ │ │ │ │ │ │ │ └── test_round.py\n", + "│ │ │ │ │ │ │ ├── test_arithmetic.py\n", + "│ │ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ │ ├── test_formats.py\n", + "│ │ │ │ │ │ │ └── test_timedelta.py\n", + "│ │ │ │ │ │ └── timestamp\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arithmetic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_comparisons.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_formats.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_timestamp.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_timezones.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── methods\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_as_unit.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_normalize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_replace.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_round.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_timestamp_method.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_julian_date.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_pydatetime.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_tz_convert.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_tz_localize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_as_unit.py\n", + "│ │ │ │ │ │ │ ├── test_normalize.py\n", + "│ │ │ │ │ │ │ ├── test_replace.py\n", + "│ │ │ │ │ │ │ ├── test_round.py\n", + "│ │ │ │ │ │ │ ├── test_timestamp_method.py\n", + "│ │ │ │ │ │ │ ├── test_to_julian_date.py\n", + "│ │ │ │ │ │ │ ├── test_to_pydatetime.py\n", + "│ │ │ │ │ │ │ ├── test_tz_convert.py\n", + "│ │ │ │ │ │ │ └── test_tz_localize.py\n", + "│ │ │ │ │ │ ├── test_arithmetic.py\n", + "│ │ │ │ │ │ ├── test_comparisons.py\n", + "│ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ ├── test_formats.py\n", + "│ │ │ │ │ │ ├── test_timestamp.py\n", + "│ │ │ │ │ │ └── test_timezones.py\n", + "│ │ │ │ │ ├── series\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_arithmetic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_cumulative.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_formats.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_iteration.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_logical_ops.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_missing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_npfuncs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_reductions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_subclass.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_ufunc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_unary.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_validate.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── accessors\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_cat_accessor.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_dt_accessor.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_list_accessor.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_sparse_accessor.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_str_accessor.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_struct_accessor.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_cat_accessor.py\n", + "│ │ │ │ │ │ │ ├── test_dt_accessor.py\n", + "│ │ │ │ │ │ │ ├── test_list_accessor.py\n", + "│ │ │ │ │ │ │ ├── test_sparse_accessor.py\n", + "│ │ │ │ │ │ │ ├── test_str_accessor.py\n", + "│ │ │ │ │ │ │ └── test_struct_accessor.py\n", + "│ │ │ │ │ │ ├── indexing\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_datetime.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_delitem.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_get.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_getitem.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_indexing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_mask.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_set_value.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_setitem.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_take.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_where.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_xs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_datetime.py\n", + "│ │ │ │ │ │ │ ├── test_delitem.py\n", + "│ │ │ │ │ │ │ ├── test_get.py\n", + "│ │ │ │ │ │ │ ├── test_getitem.py\n", + "│ │ │ │ │ │ │ ├── test_indexing.py\n", + "│ │ │ │ │ │ │ ├── test_mask.py\n", + "│ │ │ │ │ │ │ ├── test_set_value.py\n", + "│ │ │ │ │ │ │ ├── test_setitem.py\n", + "│ │ │ │ │ │ │ ├── test_take.py\n", + "│ │ │ │ │ │ │ ├── test_where.py\n", + "│ │ │ │ │ │ │ └── test_xs.py\n", + "│ │ │ │ │ │ ├── methods\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_add_prefix_suffix.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_align.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_argsort.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_asof.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_astype.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_autocorr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_between.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_case_when.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_clip.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_combine.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_combine_first.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_compare.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_convert_dtypes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_copy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_count.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_cov_corr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_describe.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_diff.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_drop.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_drop_duplicates.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_dropna.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_dtypes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_duplicated.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_equals.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_explode.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_fillna.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_get_numeric_data.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_head_tail.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_infer_objects.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_info.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_interpolate.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_is_monotonic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_is_unique.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_isin.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_isna.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_item.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_map.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_matmul.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_nlargest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_nunique.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_pct_change.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_pop.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_quantile.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_rank.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reindex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reindex_like.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_rename.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_rename_axis.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_repeat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_replace.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_reset_index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_round.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_searchsorted.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_set_name.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_size.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_sort_index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_sort_values.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_csv.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_dict.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_frame.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_to_numpy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_tolist.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_truncate.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_tz_localize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_unique.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_unstack.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_update.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_value_counts.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_values.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_view.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_add_prefix_suffix.py\n", + "│ │ │ │ │ │ │ ├── test_align.py\n", + "│ │ │ │ │ │ │ ├── test_argsort.py\n", + "│ │ │ │ │ │ │ ├── test_asof.py\n", + "│ │ │ │ │ │ │ ├── test_astype.py\n", + "│ │ │ │ │ │ │ ├── test_autocorr.py\n", + "│ │ │ │ │ │ │ ├── test_between.py\n", + "│ │ │ │ │ │ │ ├── test_case_when.py\n", + "│ │ │ │ │ │ │ ├── test_clip.py\n", + "│ │ │ │ │ │ │ ├── test_combine.py\n", + "│ │ │ │ │ │ │ ├── test_combine_first.py\n", + "│ │ │ │ │ │ │ ├── test_compare.py\n", + "│ │ │ │ │ │ │ ├── test_convert_dtypes.py\n", + "│ │ │ │ │ │ │ ├── test_copy.py\n", + "│ │ │ │ │ │ │ ├── test_count.py\n", + "│ │ │ │ │ │ │ ├── test_cov_corr.py\n", + "│ │ │ │ │ │ │ ├── test_describe.py\n", + "│ │ │ │ │ │ │ ├── test_diff.py\n", + "│ │ │ │ │ │ │ ├── test_drop.py\n", + "│ │ │ │ │ │ │ ├── test_drop_duplicates.py\n", + "│ │ │ │ │ │ │ ├── test_dropna.py\n", + "│ │ │ │ │ │ │ ├── test_dtypes.py\n", + "│ │ │ │ │ │ │ ├── test_duplicated.py\n", + "│ │ │ │ │ │ │ ├── test_equals.py\n", + "│ │ │ │ │ │ │ ├── test_explode.py\n", + "│ │ │ │ │ │ │ ├── test_fillna.py\n", + "│ │ │ │ │ │ │ ├── test_get_numeric_data.py\n", + "│ │ │ │ │ │ │ ├── test_head_tail.py\n", + "│ │ │ │ │ │ │ ├── test_infer_objects.py\n", + "│ │ │ │ │ │ │ ├── test_info.py\n", + "│ │ │ │ │ │ │ ├── test_interpolate.py\n", + "│ │ │ │ │ │ │ ├── test_is_monotonic.py\n", + "│ │ │ │ │ │ │ ├── test_is_unique.py\n", + "│ │ │ │ │ │ │ ├── test_isin.py\n", + "│ │ │ │ │ │ │ ├── test_isna.py\n", + "│ │ │ │ │ │ │ ├── test_item.py\n", + "│ │ │ │ │ │ │ ├── test_map.py\n", + "│ │ │ │ │ │ │ ├── test_matmul.py\n", + "│ │ │ │ │ │ │ ├── test_nlargest.py\n", + "│ │ │ │ │ │ │ ├── test_nunique.py\n", + "│ │ │ │ │ │ │ ├── test_pct_change.py\n", + "│ │ │ │ │ │ │ ├── test_pop.py\n", + "│ │ │ │ │ │ │ ├── test_quantile.py\n", + "│ │ │ │ │ │ │ ├── test_rank.py\n", + "│ │ │ │ │ │ │ ├── test_reindex.py\n", + "│ │ │ │ │ │ │ ├── test_reindex_like.py\n", + "│ │ │ │ │ │ │ ├── test_rename.py\n", + "│ │ │ │ │ │ │ ├── test_rename_axis.py\n", + "│ │ │ │ │ │ │ ├── test_repeat.py\n", + "│ │ │ │ │ │ │ ├── test_replace.py\n", + "│ │ │ │ │ │ │ ├── test_reset_index.py\n", + "│ │ │ │ │ │ │ ├── test_round.py\n", + "│ │ │ │ │ │ │ ├── test_searchsorted.py\n", + "│ │ │ │ │ │ │ ├── test_set_name.py\n", + "│ │ │ │ │ │ │ ├── test_size.py\n", + "│ │ │ │ │ │ │ ├── test_sort_index.py\n", + "│ │ │ │ │ │ │ ├── test_sort_values.py\n", + "│ │ │ │ │ │ │ ├── test_to_csv.py\n", + "│ │ │ │ │ │ │ ├── test_to_dict.py\n", + "│ │ │ │ │ │ │ ├── test_to_frame.py\n", + "│ │ │ │ │ │ │ ├── test_to_numpy.py\n", + "│ │ │ │ │ │ │ ├── test_tolist.py\n", + "│ │ │ │ │ │ │ ├── test_truncate.py\n", + "│ │ │ │ │ │ │ ├── test_tz_localize.py\n", + "│ │ │ │ │ │ │ ├── test_unique.py\n", + "│ │ │ │ │ │ │ ├── test_unstack.py\n", + "│ │ │ │ │ │ │ ├── test_update.py\n", + "│ │ │ │ │ │ │ ├── test_value_counts.py\n", + "│ │ │ │ │ │ │ ├── test_values.py\n", + "│ │ │ │ │ │ │ └── test_view.py\n", + "│ │ │ │ │ │ ├── test_api.py\n", + "│ │ │ │ │ │ ├── test_arithmetic.py\n", + "│ │ │ │ │ │ ├── test_constructors.py\n", + "│ │ │ │ │ │ ├── test_cumulative.py\n", + "│ │ │ │ │ │ ├── test_formats.py\n", + "│ │ │ │ │ │ ├── test_iteration.py\n", + "│ │ │ │ │ │ ├── test_logical_ops.py\n", + "│ │ │ │ │ │ ├── test_missing.py\n", + "│ │ │ │ │ │ ├── test_npfuncs.py\n", + "│ │ │ │ │ │ ├── test_reductions.py\n", + "│ │ │ │ │ │ ├── test_subclass.py\n", + "│ │ │ │ │ │ ├── test_ufunc.py\n", + "│ │ │ │ │ │ ├── test_unary.py\n", + "│ │ │ │ │ │ └── test_validate.py\n", + "│ │ │ │ │ ├── strings\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_case_justify.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_cat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_extract.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_find_replace.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_get_dummies.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_split_partition.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_string_array.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_strings.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ ├── test_api.py\n", + "│ │ │ │ │ │ ├── test_case_justify.py\n", + "│ │ │ │ │ │ ├── test_cat.py\n", + "│ │ │ │ │ │ ├── test_extract.py\n", + "│ │ │ │ │ │ ├── test_find_replace.py\n", + "│ │ │ │ │ │ ├── test_get_dummies.py\n", + "│ │ │ │ │ │ ├── test_split_partition.py\n", + "│ │ │ │ │ │ ├── test_string_array.py\n", + "│ │ │ │ │ │ └── test_strings.py\n", + "│ │ │ │ │ ├── test_aggregation.py\n", + "│ │ │ │ │ ├── test_algos.py\n", + "│ │ │ │ │ ├── test_common.py\n", + "│ │ │ │ │ ├── test_downstream.py\n", + "│ │ │ │ │ ├── test_errors.py\n", + "│ │ │ │ │ ├── test_expressions.py\n", + "│ │ │ │ │ ├── test_flags.py\n", + "│ │ │ │ │ ├── test_multilevel.py\n", + "│ │ │ │ │ ├── test_nanops.py\n", + "│ │ │ │ │ ├── test_optional_dependency.py\n", + "│ │ │ │ │ ├── test_register_accessor.py\n", + "│ │ │ │ │ ├── test_sorting.py\n", + "│ │ │ │ │ ├── test_take.py\n", + "│ │ │ │ │ ├── tools\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_to_datetime.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_to_numeric.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_to_time.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_to_timedelta.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_to_datetime.py\n", + "│ │ │ │ │ │ ├── test_to_numeric.py\n", + "│ │ │ │ │ │ ├── test_to_time.py\n", + "│ │ │ │ │ │ └── test_to_timedelta.py\n", + "│ │ │ │ │ ├── tseries\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── frequencies\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_freq_code.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_frequencies.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_inference.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_freq_code.py\n", + "│ │ │ │ │ │ │ ├── test_frequencies.py\n", + "│ │ │ │ │ │ │ └── test_inference.py\n", + "│ │ │ │ │ │ ├── holiday\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_calendar.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_federal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── test_holiday.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── test_observance.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_calendar.py\n", + "│ │ │ │ │ │ │ ├── test_federal.py\n", + "│ │ │ │ │ │ │ ├── test_holiday.py\n", + "│ │ │ │ │ │ │ └── test_observance.py\n", + "│ │ │ │ │ │ └── offsets\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_business_day.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_business_hour.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_business_month.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_business_quarter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_business_year.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_custom_business_day.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_custom_business_hour.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_custom_business_month.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_dst.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_easter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_fiscal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_month.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_offsets.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_offsets_properties.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_quarter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_ticks.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_week.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_year.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ │ ├── test_business_day.py\n", + "│ │ │ │ │ │ ├── test_business_hour.py\n", + "│ │ │ │ │ │ ├── test_business_month.py\n", + "│ │ │ │ │ │ ├── test_business_quarter.py\n", + "│ │ │ │ │ │ ├── test_business_year.py\n", + "│ │ │ │ │ │ ├── test_common.py\n", + "│ │ │ │ │ │ ├── test_custom_business_day.py\n", + "│ │ │ │ │ │ ├── test_custom_business_hour.py\n", + "│ │ │ │ │ │ ├── test_custom_business_month.py\n", + "│ │ │ │ │ │ ├── test_dst.py\n", + "│ │ │ │ │ │ ├── test_easter.py\n", + "│ │ │ │ │ │ ├── test_fiscal.py\n", + "│ │ │ │ │ │ ├── test_index.py\n", + "│ │ │ │ │ │ ├── test_month.py\n", + "│ │ │ │ │ │ ├── test_offsets.py\n", + "│ │ │ │ │ │ ├── test_offsets_properties.py\n", + "│ │ │ │ │ │ ├── test_quarter.py\n", + "│ │ │ │ │ │ ├── test_ticks.py\n", + "│ │ │ │ │ │ ├── test_week.py\n", + "│ │ │ │ │ │ └── test_year.py\n", + "│ │ │ │ │ ├── tslibs\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_array_to_datetime.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_ccalendar.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_conversion.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_fields.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_libfrequencies.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_liboffsets.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_np_datetime.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_npy_units.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_parse_iso8601.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_parsing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_period.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_resolution.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_strptime.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_timedeltas.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_timezones.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_to_offset.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_tzconversion.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_api.py\n", + "│ │ │ │ │ │ ├── test_array_to_datetime.py\n", + "│ │ │ │ │ │ ├── test_ccalendar.py\n", + "│ │ │ │ │ │ ├── test_conversion.py\n", + "│ │ │ │ │ │ ├── test_fields.py\n", + "│ │ │ │ │ │ ├── test_libfrequencies.py\n", + "│ │ │ │ │ │ ├── test_liboffsets.py\n", + "│ │ │ │ │ │ ├── test_np_datetime.py\n", + "│ │ │ │ │ │ ├── test_npy_units.py\n", + "│ │ │ │ │ │ ├── test_parse_iso8601.py\n", + "│ │ │ │ │ │ ├── test_parsing.py\n", + "│ │ │ │ │ │ ├── test_period.py\n", + "│ │ │ │ │ │ ├── test_resolution.py\n", + "│ │ │ │ │ │ ├── test_strptime.py\n", + "│ │ │ │ │ │ ├── test_timedeltas.py\n", + "│ │ │ │ │ │ ├── test_timezones.py\n", + "│ │ │ │ │ │ ├── test_to_offset.py\n", + "│ │ │ │ │ │ └── test_tzconversion.py\n", + "│ │ │ │ │ ├── util\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_assert_almost_equal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_assert_attr_equal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_assert_categorical_equal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_assert_extension_array_equal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_assert_frame_equal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_assert_index_equal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_assert_interval_array_equal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_assert_numpy_array_equal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_assert_produces_warning.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_assert_series_equal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_deprecate.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_deprecate_kwarg.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_deprecate_nonkeyword_arguments.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_doc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_hashing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_numba.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_rewrite_warning.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_shares_memory.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_show_versions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_util.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_validate_args.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_validate_args_and_kwargs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_validate_inclusive.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_validate_kwargs.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ ├── test_assert_almost_equal.py\n", + "│ │ │ │ │ │ ├── test_assert_attr_equal.py\n", + "│ │ │ │ │ │ ├── test_assert_categorical_equal.py\n", + "│ │ │ │ │ │ ├── test_assert_extension_array_equal.py\n", + "│ │ │ │ │ │ ├── test_assert_frame_equal.py\n", + "│ │ │ │ │ │ ├── test_assert_index_equal.py\n", + "│ │ │ │ │ │ ├── test_assert_interval_array_equal.py\n", + "│ │ │ │ │ │ ├── test_assert_numpy_array_equal.py\n", + "│ │ │ │ │ │ ├── test_assert_produces_warning.py\n", + "│ │ │ │ │ │ ├── test_assert_series_equal.py\n", + "│ │ │ │ │ │ ├── test_deprecate.py\n", + "│ │ │ │ │ │ ├── test_deprecate_kwarg.py\n", + "│ │ │ │ │ │ ├── test_deprecate_nonkeyword_arguments.py\n", + "│ │ │ │ │ │ ├── test_doc.py\n", + "│ │ │ │ │ │ ├── test_hashing.py\n", + "│ │ │ │ │ │ ├── test_numba.py\n", + "│ │ │ │ │ │ ├── test_rewrite_warning.py\n", + "│ │ │ │ │ │ ├── test_shares_memory.py\n", + "│ │ │ │ │ │ ├── test_show_versions.py\n", + "│ │ │ │ │ │ ├── test_util.py\n", + "│ │ │ │ │ │ ├── test_validate_args.py\n", + "│ │ │ │ │ │ ├── test_validate_args_and_kwargs.py\n", + "│ │ │ │ │ │ ├── test_validate_inclusive.py\n", + "│ │ │ │ │ │ └── test_validate_kwargs.py\n", + "│ │ │ │ │ └── window\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_api.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_apply.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_base_indexer.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_cython_aggregations.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_dtypes.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_ewm.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_expanding.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_groupby.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_numba.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_online.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_pairwise.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_rolling.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_rolling_functions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_rolling_quantile.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_rolling_skew_kurt.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_timeseries_window.cpython-310.pyc\n", + "│ │ │ │ │ │ └── test_win_type.cpython-310.pyc\n", + "│ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ ├── moments\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_moments_consistency_ewm.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── test_moments_consistency_expanding.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── test_moments_consistency_rolling.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── conftest.py\n", + "│ │ │ │ │ │ ├── test_moments_consistency_ewm.py\n", + "│ │ │ │ │ │ ├── test_moments_consistency_expanding.py\n", + "│ │ │ │ │ │ └── test_moments_consistency_rolling.py\n", + "│ │ │ │ │ ├── test_api.py\n", + "│ │ │ │ │ ├── test_apply.py\n", + "│ │ │ │ │ ├── test_base_indexer.py\n", + "│ │ │ │ │ ├── test_cython_aggregations.py\n", + "│ │ │ │ │ ├── test_dtypes.py\n", + "│ │ │ │ │ ├── test_ewm.py\n", + "│ │ │ │ │ ├── test_expanding.py\n", + "│ │ │ │ │ ├── test_groupby.py\n", + "│ │ │ │ │ ├── test_numba.py\n", + "│ │ │ │ │ ├── test_online.py\n", + "│ │ │ │ │ ├── test_pairwise.py\n", + "│ │ │ │ │ ├── test_rolling.py\n", + "│ │ │ │ │ ├── test_rolling_functions.py\n", + "│ │ │ │ │ ├── test_rolling_quantile.py\n", + "│ │ │ │ │ ├── test_rolling_skew_kurt.py\n", + "│ │ │ │ │ ├── test_timeseries_window.py\n", + "│ │ │ │ │ └── test_win_type.py\n", + "│ │ │ │ ├── tseries\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── api.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── frequencies.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── holiday.cpython-310.pyc\n", + "│ │ │ │ │ │ └── offsets.cpython-310.pyc\n", + "│ │ │ │ │ ├── api.py\n", + "│ │ │ │ │ ├── frequencies.py\n", + "│ │ │ │ │ ├── holiday.py\n", + "│ │ │ │ │ └── offsets.py\n", + "│ │ │ │ └── util\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _decorators.cpython-310.pyc\n", + "│ │ │ │ │ ├── _doctools.cpython-310.pyc\n", + "│ │ │ │ │ ├── _exceptions.cpython-310.pyc\n", + "│ │ │ │ │ ├── _print_versions.cpython-310.pyc\n", + "│ │ │ │ │ ├── _test_decorators.cpython-310.pyc\n", + "│ │ │ │ │ ├── _tester.cpython-310.pyc\n", + "│ │ │ │ │ └── _validators.cpython-310.pyc\n", + "│ │ │ │ ├── _decorators.py\n", + "│ │ │ │ ├── _doctools.py\n", + "│ │ │ │ ├── _exceptions.py\n", + "│ │ │ │ ├── _print_versions.py\n", + "│ │ │ │ ├── _test_decorators.py\n", + "│ │ │ │ ├── _tester.py\n", + "│ │ │ │ ├── _validators.py\n", + "│ │ │ │ └── version\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ └── __pycache__\n", + "│ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ ├── pandas-2.2.2.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── REQUESTED\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── entry_points.txt\n", + "│ │ │ ├── parso\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _compatibility.cpython-310.pyc\n", + "│ │ │ │ │ ├── cache.cpython-310.pyc\n", + "│ │ │ │ │ ├── file_io.cpython-310.pyc\n", + "│ │ │ │ │ ├── grammar.cpython-310.pyc\n", + "│ │ │ │ │ ├── normalizer.cpython-310.pyc\n", + "│ │ │ │ │ ├── parser.cpython-310.pyc\n", + "│ │ │ │ │ ├── tree.cpython-310.pyc\n", + "│ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ ├── _compatibility.py\n", + "│ │ │ │ ├── cache.py\n", + "│ │ │ │ ├── file_io.py\n", + "│ │ │ │ ├── grammar.py\n", + "│ │ │ │ ├── normalizer.py\n", + "│ │ │ │ ├── parser.py\n", + "│ │ │ │ ├── pgen2\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── generator.cpython-310.pyc\n", + "│ │ │ │ │ │ └── grammar_parser.cpython-310.pyc\n", + "│ │ │ │ │ ├── generator.py\n", + "│ │ │ │ │ └── grammar_parser.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ ├── python\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── diff.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── errors.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── parser.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pep8.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── prefix.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── token.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── tokenize.cpython-310.pyc\n", + "│ │ │ │ │ │ └── tree.cpython-310.pyc\n", + "│ │ │ │ │ ├── diff.py\n", + "│ │ │ │ │ ├── errors.py\n", + "│ │ │ │ │ ├── grammar310.txt\n", + "│ │ │ │ │ ├── grammar311.txt\n", + "│ │ │ │ │ ├── grammar312.txt\n", + "│ │ │ │ │ ├── grammar313.txt\n", + "│ │ │ │ │ ├── grammar36.txt\n", + "│ │ │ │ │ ├── grammar37.txt\n", + "│ │ │ │ │ ├── grammar38.txt\n", + "│ │ │ │ │ ├── grammar39.txt\n", + "│ │ │ │ │ ├── parser.py\n", + "│ │ │ │ │ ├── pep8.py\n", + "│ │ │ │ │ ├── prefix.py\n", + "│ │ │ │ │ ├── token.py\n", + "│ │ │ │ │ ├── tokenize.py\n", + "│ │ │ │ │ └── tree.py\n", + "│ │ │ │ ├── tree.py\n", + "│ │ │ │ └── utils.py\n", + "│ │ │ ├── parso-0.8.4.dist-info\n", + "│ │ │ │ ├── AUTHORS.txt\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE.txt\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── pexpect\n", + "│ │ │ │ ├── ANSI.py\n", + "│ │ │ │ ├── FSM.py\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── ANSI.cpython-310.pyc\n", + "│ │ │ │ │ ├── FSM.cpython-310.pyc\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _async.cpython-310.pyc\n", + "│ │ │ │ │ ├── _async_pre_await.cpython-310.pyc\n", + "│ │ │ │ │ ├── _async_w_await.cpython-310.pyc\n", + "│ │ │ │ │ ├── exceptions.cpython-310.pyc\n", + "│ │ │ │ │ ├── expect.cpython-310.pyc\n", + "│ │ │ │ │ ├── fdpexpect.cpython-310.pyc\n", + "│ │ │ │ │ ├── popen_spawn.cpython-310.pyc\n", + "│ │ │ │ │ ├── pty_spawn.cpython-310.pyc\n", + "│ │ │ │ │ ├── pxssh.cpython-310.pyc\n", + "│ │ │ │ │ ├── replwrap.cpython-310.pyc\n", + "│ │ │ │ │ ├── run.cpython-310.pyc\n", + "│ │ │ │ │ ├── screen.cpython-310.pyc\n", + "│ │ │ │ │ ├── socket_pexpect.cpython-310.pyc\n", + "│ │ │ │ │ ├── spawnbase.cpython-310.pyc\n", + "│ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ ├── _async.py\n", + "│ │ │ │ ├── _async_pre_await.py\n", + "│ │ │ │ ├── _async_w_await.py\n", + "│ │ │ │ ├── bashrc.sh\n", + "│ │ │ │ ├── exceptions.py\n", + "│ │ │ │ ├── expect.py\n", + "│ │ │ │ ├── fdpexpect.py\n", + "│ │ │ │ ├── popen_spawn.py\n", + "│ │ │ │ ├── pty_spawn.py\n", + "│ │ │ │ ├── pxssh.py\n", + "│ │ │ │ ├── replwrap.py\n", + "│ │ │ │ ├── run.py\n", + "│ │ │ │ ├── screen.py\n", + "│ │ │ │ ├── socket_pexpect.py\n", + "│ │ │ │ ├── spawnbase.py\n", + "│ │ │ │ └── utils.py\n", + "│ │ │ ├── pexpect-4.9.0.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── pip\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __main__.py\n", + "│ │ │ │ ├── __pip-runner__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ └── __pip-runner__.cpython-310.pyc\n", + "│ │ │ │ ├── _internal\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── build_env.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cache.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── configuration.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── exceptions.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── main.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pyproject.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── self_outdated_check.cpython-310.pyc\n", + "│ │ │ │ │ │ └── wheel_builder.cpython-310.pyc\n", + "│ │ │ │ │ ├── build_env.py\n", + "│ │ │ │ │ ├── cache.py\n", + "│ │ │ │ │ ├── cli\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── autocompletion.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── base_command.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── cmdoptions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── command_context.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── main.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── main_parser.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── parser.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── progress_bars.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── req_command.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── spinners.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── status_codes.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── autocompletion.py\n", + "│ │ │ │ │ │ ├── base_command.py\n", + "│ │ │ │ │ │ ├── cmdoptions.py\n", + "│ │ │ │ │ │ ├── command_context.py\n", + "│ │ │ │ │ │ ├── main.py\n", + "│ │ │ │ │ │ ├── main_parser.py\n", + "│ │ │ │ │ │ ├── parser.py\n", + "│ │ │ │ │ │ ├── progress_bars.py\n", + "│ │ │ │ │ │ ├── req_command.py\n", + "│ │ │ │ │ │ ├── spinners.py\n", + "│ │ │ │ │ │ └── status_codes.py\n", + "│ │ │ │ │ ├── commands\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── cache.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── check.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── completion.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── configuration.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── debug.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── download.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── freeze.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── hash.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── help.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── inspect.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── install.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── list.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── search.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── show.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── uninstall.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── wheel.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cache.py\n", + "│ │ │ │ │ │ ├── check.py\n", + "│ │ │ │ │ │ ├── completion.py\n", + "│ │ │ │ │ │ ├── configuration.py\n", + "│ │ │ │ │ │ ├── debug.py\n", + "│ │ │ │ │ │ ├── download.py\n", + "│ │ │ │ │ │ ├── freeze.py\n", + "│ │ │ │ │ │ ├── hash.py\n", + "│ │ │ │ │ │ ├── help.py\n", + "│ │ │ │ │ │ ├── index.py\n", + "│ │ │ │ │ │ ├── inspect.py\n", + "│ │ │ │ │ │ ├── install.py\n", + "│ │ │ │ │ │ ├── list.py\n", + "│ │ │ │ │ │ ├── search.py\n", + "│ │ │ │ │ │ ├── show.py\n", + "│ │ │ │ │ │ ├── uninstall.py\n", + "│ │ │ │ │ │ └── wheel.py\n", + "│ │ │ │ │ ├── configuration.py\n", + "│ │ │ │ │ ├── distributions\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── installed.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── sdist.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── wheel.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ │ ├── installed.py\n", + "│ │ │ │ │ │ ├── sdist.py\n", + "│ │ │ │ │ │ └── wheel.py\n", + "│ │ │ │ │ ├── exceptions.py\n", + "│ │ │ │ │ ├── index\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── collector.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── package_finder.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── sources.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── collector.py\n", + "│ │ │ │ │ │ ├── package_finder.py\n", + "│ │ │ │ │ │ └── sources.py\n", + "│ │ │ │ │ ├── locations\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _distutils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _sysconfig.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _distutils.py\n", + "│ │ │ │ │ │ ├── _sysconfig.py\n", + "│ │ │ │ │ │ └── base.py\n", + "│ │ │ │ │ ├── main.py\n", + "│ │ │ │ │ ├── metadata\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _json.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── pkg_resources.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _json.py\n", + "│ │ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ │ ├── importlib\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── _compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── _dists.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── _envs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _compat.py\n", + "│ │ │ │ │ │ │ ├── _dists.py\n", + "│ │ │ │ │ │ │ └── _envs.py\n", + "│ │ │ │ │ │ └── pkg_resources.py\n", + "│ │ │ │ │ ├── models\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── candidate.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── direct_url.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── format_control.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── installation_report.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── link.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── scheme.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── search_scope.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── selection_prefs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── target_python.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── wheel.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── candidate.py\n", + "│ │ │ │ │ │ ├── direct_url.py\n", + "│ │ │ │ │ │ ├── format_control.py\n", + "│ │ │ │ │ │ ├── index.py\n", + "│ │ │ │ │ │ ├── installation_report.py\n", + "│ │ │ │ │ │ ├── link.py\n", + "│ │ │ │ │ │ ├── scheme.py\n", + "│ │ │ │ │ │ ├── search_scope.py\n", + "│ │ │ │ │ │ ├── selection_prefs.py\n", + "│ │ │ │ │ │ ├── target_python.py\n", + "│ │ │ │ │ │ └── wheel.py\n", + "│ │ │ │ │ ├── network\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── auth.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── cache.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── download.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── lazy_wheel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── session.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── xmlrpc.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── auth.py\n", + "│ │ │ │ │ │ ├── cache.py\n", + "│ │ │ │ │ │ ├── download.py\n", + "│ │ │ │ │ │ ├── lazy_wheel.py\n", + "│ │ │ │ │ │ ├── session.py\n", + "│ │ │ │ │ │ ├── utils.py\n", + "│ │ │ │ │ │ └── xmlrpc.py\n", + "│ │ │ │ │ ├── operations\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── check.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── freeze.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── prepare.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── build\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── build_tracker.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── metadata.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── metadata_editable.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── metadata_legacy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── wheel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── wheel_editable.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── wheel_legacy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── build_tracker.py\n", + "│ │ │ │ │ │ │ ├── metadata.py\n", + "│ │ │ │ │ │ │ ├── metadata_editable.py\n", + "│ │ │ │ │ │ │ ├── metadata_legacy.py\n", + "│ │ │ │ │ │ │ ├── wheel.py\n", + "│ │ │ │ │ │ │ ├── wheel_editable.py\n", + "│ │ │ │ │ │ │ └── wheel_legacy.py\n", + "│ │ │ │ │ │ ├── check.py\n", + "│ │ │ │ │ │ ├── freeze.py\n", + "│ │ │ │ │ │ ├── install\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── editable_legacy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── wheel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── editable_legacy.py\n", + "│ │ │ │ │ │ │ └── wheel.py\n", + "│ │ │ │ │ │ └── prepare.py\n", + "│ │ │ │ │ ├── pyproject.py\n", + "│ │ │ │ │ ├── req\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── constructors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── req_file.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── req_install.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── req_set.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── req_uninstall.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── constructors.py\n", + "│ │ │ │ │ │ ├── req_file.py\n", + "│ │ │ │ │ │ ├── req_install.py\n", + "│ │ │ │ │ │ ├── req_set.py\n", + "│ │ │ │ │ │ └── req_uninstall.py\n", + "│ │ │ │ │ ├── resolution\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ │ ├── legacy\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── resolver.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── resolver.py\n", + "│ │ │ │ │ │ └── resolvelib\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── candidates.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── factory.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── found_candidates.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── provider.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── reporter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── requirements.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── resolver.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ │ ├── candidates.py\n", + "│ │ │ │ │ │ ├── factory.py\n", + "│ │ │ │ │ │ ├── found_candidates.py\n", + "│ │ │ │ │ │ ├── provider.py\n", + "│ │ │ │ │ │ ├── reporter.py\n", + "│ │ │ │ │ │ ├── requirements.py\n", + "│ │ │ │ │ │ └── resolver.py\n", + "│ │ │ │ │ ├── self_outdated_check.py\n", + "│ │ │ │ │ ├── utils\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _jaraco_text.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _log.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── appdirs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── compatibility_tags.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── datetime.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── deprecation.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── direct_url_helpers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── egg_link.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── encoding.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── entrypoints.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── filesystem.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── filetypes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── glibc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── hashes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── logging.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── misc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── models.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── packaging.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── setuptools_build.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── subprocess.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── temp_dir.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── unpacking.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── urls.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── virtualenv.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── wheel.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _jaraco_text.py\n", + "│ │ │ │ │ │ ├── _log.py\n", + "│ │ │ │ │ │ ├── appdirs.py\n", + "│ │ │ │ │ │ ├── compat.py\n", + "│ │ │ │ │ │ ├── compatibility_tags.py\n", + "│ │ │ │ │ │ ├── datetime.py\n", + "│ │ │ │ │ │ ├── deprecation.py\n", + "│ │ │ │ │ │ ├── direct_url_helpers.py\n", + "│ │ │ │ │ │ ├── egg_link.py\n", + "│ │ │ │ │ │ ├── encoding.py\n", + "│ │ │ │ │ │ ├── entrypoints.py\n", + "│ │ │ │ │ │ ├── filesystem.py\n", + "│ │ │ │ │ │ ├── filetypes.py\n", + "│ │ │ │ │ │ ├── glibc.py\n", + "│ │ │ │ │ │ ├── hashes.py\n", + "│ │ │ │ │ │ ├── logging.py\n", + "│ │ │ │ │ │ ├── misc.py\n", + "│ │ │ │ │ │ ├── models.py\n", + "│ │ │ │ │ │ ├── packaging.py\n", + "│ │ │ │ │ │ ├── setuptools_build.py\n", + "│ │ │ │ │ │ ├── subprocess.py\n", + "│ │ │ │ │ │ ├── temp_dir.py\n", + "│ │ │ │ │ │ ├── unpacking.py\n", + "│ │ │ │ │ │ ├── urls.py\n", + "│ │ │ │ │ │ ├── virtualenv.py\n", + "│ │ │ │ │ │ └── wheel.py\n", + "│ │ │ │ │ ├── vcs\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── bazaar.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── git.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── mercurial.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── subversion.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── versioncontrol.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── bazaar.py\n", + "│ │ │ │ │ │ ├── git.py\n", + "│ │ │ │ │ │ ├── mercurial.py\n", + "│ │ │ │ │ │ ├── subversion.py\n", + "│ │ │ │ │ │ └── versioncontrol.py\n", + "│ │ │ │ │ └── wheel_builder.py\n", + "│ │ │ │ ├── _vendor\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── six.cpython-310.pyc\n", + "│ │ │ │ │ │ └── typing_extensions.cpython-310.pyc\n", + "│ │ │ │ │ ├── cachecontrol\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _cmd.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── adapter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── cache.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── controller.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── filewrapper.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── heuristics.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── serialize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── wrapper.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _cmd.py\n", + "│ │ │ │ │ │ ├── adapter.py\n", + "│ │ │ │ │ │ ├── cache.py\n", + "│ │ │ │ │ │ ├── caches\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── file_cache.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── redis_cache.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── file_cache.py\n", + "│ │ │ │ │ │ │ └── redis_cache.py\n", + "│ │ │ │ │ │ ├── controller.py\n", + "│ │ │ │ │ │ ├── filewrapper.py\n", + "│ │ │ │ │ │ ├── heuristics.py\n", + "│ │ │ │ │ │ ├── py.typed\n", + "│ │ │ │ │ │ ├── serialize.py\n", + "│ │ │ │ │ │ └── wrapper.py\n", + "│ │ │ │ │ ├── certifi\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __main__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── core.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cacert.pem\n", + "│ │ │ │ │ │ ├── core.py\n", + "│ │ │ │ │ │ └── py.typed\n", + "│ │ │ │ │ ├── chardet\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── big5freq.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── big5prober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── chardistribution.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── charsetgroupprober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── charsetprober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── codingstatemachine.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── codingstatemachinedict.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── cp949prober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── enums.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── escprober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── escsm.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── eucjpprober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── euckrfreq.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── euckrprober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── euctwfreq.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── euctwprober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── gb2312freq.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── gb2312prober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── hebrewprober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── jisfreq.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── johabfreq.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── johabprober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── jpcntx.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── langbulgarianmodel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── langgreekmodel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── langhebrewmodel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── langhungarianmodel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── langrussianmodel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── langthaimodel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── langturkishmodel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── latin1prober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── macromanprober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── mbcharsetprober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── mbcsgroupprober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── mbcssm.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── resultdict.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── sbcharsetprober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── sbcsgroupprober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── sjisprober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── universaldetector.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── utf1632prober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── utf8prober.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── version.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── big5freq.py\n", + "│ │ │ │ │ │ ├── big5prober.py\n", + "│ │ │ │ │ │ ├── chardistribution.py\n", + "│ │ │ │ │ │ ├── charsetgroupprober.py\n", + "│ │ │ │ │ │ ├── charsetprober.py\n", + "│ │ │ │ │ │ ├── cli\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── chardetect.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── chardetect.py\n", + "│ │ │ │ │ │ ├── codingstatemachine.py\n", + "│ │ │ │ │ │ ├── codingstatemachinedict.py\n", + "│ │ │ │ │ │ ├── cp949prober.py\n", + "│ │ │ │ │ │ ├── enums.py\n", + "│ │ │ │ │ │ ├── escprober.py\n", + "│ │ │ │ │ │ ├── escsm.py\n", + "│ │ │ │ │ │ ├── eucjpprober.py\n", + "│ │ │ │ │ │ ├── euckrfreq.py\n", + "│ │ │ │ │ │ ├── euckrprober.py\n", + "│ │ │ │ │ │ ├── euctwfreq.py\n", + "│ │ │ │ │ │ ├── euctwprober.py\n", + "│ │ │ │ │ │ ├── gb2312freq.py\n", + "│ │ │ │ │ │ ├── gb2312prober.py\n", + "│ │ │ │ │ │ ├── hebrewprober.py\n", + "│ │ │ │ │ │ ├── jisfreq.py\n", + "│ │ │ │ │ │ ├── johabfreq.py\n", + "│ │ │ │ │ │ ├── johabprober.py\n", + "│ │ │ │ │ │ ├── jpcntx.py\n", + "│ │ │ │ │ │ ├── langbulgarianmodel.py\n", + "│ │ │ │ │ │ ├── langgreekmodel.py\n", + "│ │ │ │ │ │ ├── langhebrewmodel.py\n", + "│ │ │ │ │ │ ├── langhungarianmodel.py\n", + "│ │ │ │ │ │ ├── langrussianmodel.py\n", + "│ │ │ │ │ │ ├── langthaimodel.py\n", + "│ │ │ │ │ │ ├── langturkishmodel.py\n", + "│ │ │ │ │ │ ├── latin1prober.py\n", + "│ │ │ │ │ │ ├── macromanprober.py\n", + "│ │ │ │ │ │ ├── mbcharsetprober.py\n", + "│ │ │ │ │ │ ├── mbcsgroupprober.py\n", + "│ │ │ │ │ │ ├── mbcssm.py\n", + "│ │ │ │ │ │ ├── metadata\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── languages.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── languages.py\n", + "│ │ │ │ │ │ ├── py.typed\n", + "│ │ │ │ │ │ ├── resultdict.py\n", + "│ │ │ │ │ │ ├── sbcharsetprober.py\n", + "│ │ │ │ │ │ ├── sbcsgroupprober.py\n", + "│ │ │ │ │ │ ├── sjisprober.py\n", + "│ │ │ │ │ │ ├── universaldetector.py\n", + "│ │ │ │ │ │ ├── utf1632prober.py\n", + "│ │ │ │ │ │ ├── utf8prober.py\n", + "│ │ │ │ │ │ └── version.py\n", + "│ │ │ │ │ ├── colorama\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── ansi.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── ansitowin32.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── initialise.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── win32.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── winterm.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ansi.py\n", + "│ │ │ │ │ │ ├── ansitowin32.py\n", + "│ │ │ │ │ │ ├── initialise.py\n", + "│ │ │ │ │ │ ├── tests\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── ansi_test.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── ansitowin32_test.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── initialise_test.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── isatty_test.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── winterm_test.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── ansi_test.py\n", + "│ │ │ │ │ │ │ ├── ansitowin32_test.py\n", + "│ │ │ │ │ │ │ ├── initialise_test.py\n", + "│ │ │ │ │ │ │ ├── isatty_test.py\n", + "│ │ │ │ │ │ │ ├── utils.py\n", + "│ │ │ │ │ │ │ └── winterm_test.py\n", + "│ │ │ │ │ │ ├── win32.py\n", + "│ │ │ │ │ │ └── winterm.py\n", + "│ │ │ │ │ ├── distlib\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── database.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── index.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── locators.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── manifest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── markers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── metadata.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── resources.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── scripts.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── util.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── version.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── wheel.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── compat.py\n", + "│ │ │ │ │ │ ├── database.py\n", + "│ │ │ │ │ │ ├── index.py\n", + "│ │ │ │ │ │ ├── locators.py\n", + "│ │ │ │ │ │ ├── manifest.py\n", + "│ │ │ │ │ │ ├── markers.py\n", + "│ │ │ │ │ │ ├── metadata.py\n", + "│ │ │ │ │ │ ├── resources.py\n", + "│ │ │ │ │ │ ├── scripts.py\n", + "│ │ │ │ │ │ ├── t32.exe\n", + "│ │ │ │ │ │ ├── t64-arm.exe\n", + "│ │ │ │ │ │ ├── t64.exe\n", + "│ │ │ │ │ │ ├── util.py\n", + "│ │ │ │ │ │ ├── version.py\n", + "│ │ │ │ │ │ ├── w32.exe\n", + "│ │ │ │ │ │ ├── w64-arm.exe\n", + "│ │ │ │ │ │ ├── w64.exe\n", + "│ │ │ │ │ │ └── wheel.py\n", + "│ │ │ │ │ ├── distro\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __main__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── distro.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── distro.py\n", + "│ │ │ │ │ │ └── py.typed\n", + "│ │ │ │ │ ├── idna\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── codec.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── core.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── idnadata.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── intranges.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── package_data.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── uts46data.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── codec.py\n", + "│ │ │ │ │ │ ├── compat.py\n", + "│ │ │ │ │ │ ├── core.py\n", + "│ │ │ │ │ │ ├── idnadata.py\n", + "│ │ │ │ │ │ ├── intranges.py\n", + "│ │ │ │ │ │ ├── package_data.py\n", + "│ │ │ │ │ │ ├── py.typed\n", + "│ │ │ │ │ │ └── uts46data.py\n", + "│ │ │ │ │ ├── msgpack\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── exceptions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── ext.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── fallback.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── exceptions.py\n", + "│ │ │ │ │ │ ├── ext.py\n", + "│ │ │ │ │ │ └── fallback.py\n", + "│ │ │ │ │ ├── packaging\n", + "│ │ │ │ │ │ ├── __about__.py\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __about__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _manylinux.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _musllinux.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _structures.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── markers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── requirements.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── specifiers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── tags.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── version.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _manylinux.py\n", + "│ │ │ │ │ │ ├── _musllinux.py\n", + "│ │ │ │ │ │ ├── _structures.py\n", + "│ │ │ │ │ │ ├── markers.py\n", + "│ │ │ │ │ │ ├── py.typed\n", + "│ │ │ │ │ │ ├── requirements.py\n", + "│ │ │ │ │ │ ├── specifiers.py\n", + "│ │ │ │ │ │ ├── tags.py\n", + "│ │ │ │ │ │ ├── utils.py\n", + "│ │ │ │ │ │ └── version.py\n", + "│ │ │ │ │ ├── pkg_resources\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── platformdirs\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __main__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── android.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── macos.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── unix.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── version.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── windows.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── android.py\n", + "│ │ │ │ │ │ ├── api.py\n", + "│ │ │ │ │ │ ├── macos.py\n", + "│ │ │ │ │ │ ├── py.typed\n", + "│ │ │ │ │ │ ├── unix.py\n", + "│ │ │ │ │ │ ├── version.py\n", + "│ │ │ │ │ │ └── windows.py\n", + "│ │ │ │ │ ├── pygments\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __main__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── cmdline.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── console.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── filter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── formatter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── lexer.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── modeline.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── plugin.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── regexopt.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── scanner.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── sphinxext.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── style.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── token.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── unistring.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── util.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cmdline.py\n", + "│ │ │ │ │ │ ├── console.py\n", + "│ │ │ │ │ │ ├── filter.py\n", + "│ │ │ │ │ │ ├── filters\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── formatter.py\n", + "│ │ │ │ │ │ ├── formatters\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── _mapping.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── bbcode.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── groff.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── html.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── img.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── irc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── latex.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── other.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── pangomarkup.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── rtf.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── svg.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── terminal.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── terminal256.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _mapping.py\n", + "│ │ │ │ │ │ │ ├── bbcode.py\n", + "│ │ │ │ │ │ │ ├── groff.py\n", + "│ │ │ │ │ │ │ ├── html.py\n", + "│ │ │ │ │ │ │ ├── img.py\n", + "│ │ │ │ │ │ │ ├── irc.py\n", + "│ │ │ │ │ │ │ ├── latex.py\n", + "│ │ │ │ │ │ │ ├── other.py\n", + "│ │ │ │ │ │ │ ├── pangomarkup.py\n", + "│ │ │ │ │ │ │ ├── rtf.py\n", + "│ │ │ │ │ │ │ ├── svg.py\n", + "│ │ │ │ │ │ │ ├── terminal.py\n", + "│ │ │ │ │ │ │ └── terminal256.py\n", + "│ │ │ │ │ │ ├── lexer.py\n", + "│ │ │ │ │ │ ├── lexers\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── _mapping.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── python.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _mapping.py\n", + "│ │ │ │ │ │ │ └── python.py\n", + "│ │ │ │ │ │ ├── modeline.py\n", + "│ │ │ │ │ │ ├── plugin.py\n", + "│ │ │ │ │ │ ├── regexopt.py\n", + "│ │ │ │ │ │ ├── scanner.py\n", + "│ │ │ │ │ │ ├── sphinxext.py\n", + "│ │ │ │ │ │ ├── style.py\n", + "│ │ │ │ │ │ ├── styles\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── token.py\n", + "│ │ │ │ │ │ ├── unistring.py\n", + "│ │ │ │ │ │ └── util.py\n", + "│ │ │ │ │ ├── pyparsing\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── actions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── common.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── core.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── exceptions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── helpers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── results.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── testing.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── unicode.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── util.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── actions.py\n", + "│ │ │ │ │ │ ├── common.py\n", + "│ │ │ │ │ │ ├── core.py\n", + "│ │ │ │ │ │ ├── diagram\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── exceptions.py\n", + "│ │ │ │ │ │ ├── helpers.py\n", + "│ │ │ │ │ │ ├── py.typed\n", + "│ │ │ │ │ │ ├── results.py\n", + "│ │ │ │ │ │ ├── testing.py\n", + "│ │ │ │ │ │ ├── unicode.py\n", + "│ │ │ │ │ │ └── util.py\n", + "│ │ │ │ │ ├── pyproject_hooks\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── _impl.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _compat.py\n", + "│ │ │ │ │ │ ├── _impl.py\n", + "│ │ │ │ │ │ └── _in_process\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── _in_process.cpython-310.pyc\n", + "│ │ │ │ │ │ └── _in_process.py\n", + "│ │ │ │ │ ├── requests\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── __version__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _internal_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── adapters.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── auth.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── certs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── cookies.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── exceptions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── help.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── hooks.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── models.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── packages.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── sessions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── status_codes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── structures.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── __version__.py\n", + "│ │ │ │ │ │ ├── _internal_utils.py\n", + "│ │ │ │ │ │ ├── adapters.py\n", + "│ │ │ │ │ │ ├── api.py\n", + "│ │ │ │ │ │ ├── auth.py\n", + "│ │ │ │ │ │ ├── certs.py\n", + "│ │ │ │ │ │ ├── compat.py\n", + "│ │ │ │ │ │ ├── cookies.py\n", + "│ │ │ │ │ │ ├── exceptions.py\n", + "│ │ │ │ │ │ ├── help.py\n", + "│ │ │ │ │ │ ├── hooks.py\n", + "│ │ │ │ │ │ ├── models.py\n", + "│ │ │ │ │ │ ├── packages.py\n", + "│ │ │ │ │ │ ├── sessions.py\n", + "│ │ │ │ │ │ ├── status_codes.py\n", + "│ │ │ │ │ │ ├── structures.py\n", + "│ │ │ │ │ │ └── utils.py\n", + "│ │ │ │ │ ├── resolvelib\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── providers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── reporters.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── resolvers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── structs.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── compat\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── collections_abc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── collections_abc.py\n", + "│ │ │ │ │ │ ├── providers.py\n", + "│ │ │ │ │ │ ├── py.typed\n", + "│ │ │ │ │ │ ├── reporters.py\n", + "│ │ │ │ │ │ ├── resolvers.py\n", + "│ │ │ │ │ │ └── structs.py\n", + "│ │ │ │ │ ├── rich\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __main__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _cell_widths.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _emoji_codes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _emoji_replace.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _export_format.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _extension.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _fileno.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _inspect.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _log_render.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _loop.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _null_file.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _palettes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _pick.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _ratio.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _spinners.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _stack.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _timer.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _win32_console.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _windows.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _windows_renderer.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _wrap.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── abc.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── align.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── ansi.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── bar.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── box.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── cells.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── color.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── color_triplet.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── columns.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── console.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── constrain.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── containers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── control.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── default_styles.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── diagnose.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── emoji.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── errors.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── file_proxy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── filesize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── highlighter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── json.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── jupyter.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── layout.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── live.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── live_render.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── logging.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── markup.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── measure.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── padding.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pager.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── palette.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── panel.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── pretty.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── progress.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── progress_bar.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── prompt.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── protocol.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── region.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── repr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── rule.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── scope.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── screen.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── segment.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── spinner.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── status.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── style.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── styled.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── syntax.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── table.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── terminal_theme.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── text.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── theme.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── themes.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── traceback.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── tree.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _cell_widths.py\n", + "│ │ │ │ │ │ ├── _emoji_codes.py\n", + "│ │ │ │ │ │ ├── _emoji_replace.py\n", + "│ │ │ │ │ │ ├── _export_format.py\n", + "│ │ │ │ │ │ ├── _extension.py\n", + "│ │ │ │ │ │ ├── _fileno.py\n", + "│ │ │ │ │ │ ├── _inspect.py\n", + "│ │ │ │ │ │ ├── _log_render.py\n", + "│ │ │ │ │ │ ├── _loop.py\n", + "│ │ │ │ │ │ ├── _null_file.py\n", + "│ │ │ │ │ │ ├── _palettes.py\n", + "│ │ │ │ │ │ ├── _pick.py\n", + "│ │ │ │ │ │ ├── _ratio.py\n", + "│ │ │ │ │ │ ├── _spinners.py\n", + "│ │ │ │ │ │ ├── _stack.py\n", + "│ │ │ │ │ │ ├── _timer.py\n", + "│ │ │ │ │ │ ├── _win32_console.py\n", + "│ │ │ │ │ │ ├── _windows.py\n", + "│ │ │ │ │ │ ├── _windows_renderer.py\n", + "│ │ │ │ │ │ ├── _wrap.py\n", + "│ │ │ │ │ │ ├── abc.py\n", + "│ │ │ │ │ │ ├── align.py\n", + "│ │ │ │ │ │ ├── ansi.py\n", + "│ │ │ │ │ │ ├── bar.py\n", + "│ │ │ │ │ │ ├── box.py\n", + "│ │ │ │ │ │ ├── cells.py\n", + "│ │ │ │ │ │ ├── color.py\n", + "│ │ │ │ │ │ ├── color_triplet.py\n", + "│ │ │ │ │ │ ├── columns.py\n", + "│ │ │ │ │ │ ├── console.py\n", + "│ │ │ │ │ │ ├── constrain.py\n", + "│ │ │ │ │ │ ├── containers.py\n", + "│ │ │ │ │ │ ├── control.py\n", + "│ │ │ │ │ │ ├── default_styles.py\n", + "│ │ │ │ │ │ ├── diagnose.py\n", + "│ │ │ │ │ │ ├── emoji.py\n", + "│ │ │ │ │ │ ├── errors.py\n", + "│ │ │ │ │ │ ├── file_proxy.py\n", + "│ │ │ │ │ │ ├── filesize.py\n", + "│ │ │ │ │ │ ├── highlighter.py\n", + "│ │ │ │ │ │ ├── json.py\n", + "│ │ │ │ │ │ ├── jupyter.py\n", + "│ │ │ │ │ │ ├── layout.py\n", + "│ │ │ │ │ │ ├── live.py\n", + "│ │ │ │ │ │ ├── live_render.py\n", + "│ │ │ │ │ │ ├── logging.py\n", + "│ │ │ │ │ │ ├── markup.py\n", + "│ │ │ │ │ │ ├── measure.py\n", + "│ │ │ │ │ │ ├── padding.py\n", + "│ │ │ │ │ │ ├── pager.py\n", + "│ │ │ │ │ │ ├── palette.py\n", + "│ │ │ │ │ │ ├── panel.py\n", + "│ │ │ │ │ │ ├── pretty.py\n", + "│ │ │ │ │ │ ├── progress.py\n", + "│ │ │ │ │ │ ├── progress_bar.py\n", + "│ │ │ │ │ │ ├── prompt.py\n", + "│ │ │ │ │ │ ├── protocol.py\n", + "│ │ │ │ │ │ ├── py.typed\n", + "│ │ │ │ │ │ ├── region.py\n", + "│ │ │ │ │ │ ├── repr.py\n", + "│ │ │ │ │ │ ├── rule.py\n", + "│ │ │ │ │ │ ├── scope.py\n", + "│ │ │ │ │ │ ├── screen.py\n", + "│ │ │ │ │ │ ├── segment.py\n", + "│ │ │ │ │ │ ├── spinner.py\n", + "│ │ │ │ │ │ ├── status.py\n", + "│ │ │ │ │ │ ├── style.py\n", + "│ │ │ │ │ │ ├── styled.py\n", + "│ │ │ │ │ │ ├── syntax.py\n", + "│ │ │ │ │ │ ├── table.py\n", + "│ │ │ │ │ │ ├── terminal_theme.py\n", + "│ │ │ │ │ │ ├── text.py\n", + "│ │ │ │ │ │ ├── theme.py\n", + "│ │ │ │ │ │ ├── themes.py\n", + "│ │ │ │ │ │ ├── traceback.py\n", + "│ │ │ │ │ │ └── tree.py\n", + "│ │ │ │ │ ├── six.py\n", + "│ │ │ │ │ ├── tenacity\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _asyncio.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── after.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── before.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── before_sleep.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── nap.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── retry.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── stop.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── tornadoweb.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── wait.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _asyncio.py\n", + "│ │ │ │ │ │ ├── _utils.py\n", + "│ │ │ │ │ │ ├── after.py\n", + "│ │ │ │ │ │ ├── before.py\n", + "│ │ │ │ │ │ ├── before_sleep.py\n", + "│ │ │ │ │ │ ├── nap.py\n", + "│ │ │ │ │ │ ├── py.typed\n", + "│ │ │ │ │ │ ├── retry.py\n", + "│ │ │ │ │ │ ├── stop.py\n", + "│ │ │ │ │ │ ├── tornadoweb.py\n", + "│ │ │ │ │ │ └── wait.py\n", + "│ │ │ │ │ ├── tomli\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _parser.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _re.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── _types.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _parser.py\n", + "│ │ │ │ │ │ ├── _re.py\n", + "│ │ │ │ │ │ ├── _types.py\n", + "│ │ │ │ │ │ └── py.typed\n", + "│ │ │ │ │ ├── truststore\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _api.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _macos.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _openssl.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _ssl_constants.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── _windows.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _api.py\n", + "│ │ │ │ │ │ ├── _macos.py\n", + "│ │ │ │ │ │ ├── _openssl.py\n", + "│ │ │ │ │ │ ├── _ssl_constants.py\n", + "│ │ │ │ │ │ ├── _windows.py\n", + "│ │ │ │ │ │ └── py.typed\n", + "│ │ │ │ │ ├── typing_extensions.py\n", + "│ │ │ │ │ ├── urllib3\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _collections.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _version.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── connection.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── connectionpool.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── exceptions.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── fields.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── filepost.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── poolmanager.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── request.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── response.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _collections.py\n", + "│ │ │ │ │ │ ├── _version.py\n", + "│ │ │ │ │ │ ├── connection.py\n", + "│ │ │ │ │ │ ├── connectionpool.py\n", + "│ │ │ │ │ │ ├── contrib\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── _appengine_environ.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── appengine.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── ntlmpool.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── pyopenssl.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── securetransport.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── socks.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _appengine_environ.py\n", + "│ │ │ │ │ │ │ ├── _securetransport\n", + "│ │ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── bindings.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ └── low_level.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── bindings.py\n", + "│ │ │ │ │ │ │ │ └── low_level.py\n", + "│ │ │ │ │ │ │ ├── appengine.py\n", + "│ │ │ │ │ │ │ ├── ntlmpool.py\n", + "│ │ │ │ │ │ │ ├── pyopenssl.py\n", + "│ │ │ │ │ │ │ ├── securetransport.py\n", + "│ │ │ │ │ │ │ └── socks.py\n", + "│ │ │ │ │ │ ├── exceptions.py\n", + "│ │ │ │ │ │ ├── fields.py\n", + "│ │ │ │ │ │ ├── filepost.py\n", + "│ │ │ │ │ │ ├── packages\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ └── six.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── backports\n", + "│ │ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ ├── makefile.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ │ └── weakref_finalize.cpython-310.pyc\n", + "│ │ │ │ │ │ │ │ ├── makefile.py\n", + "│ │ │ │ │ │ │ │ └── weakref_finalize.py\n", + "│ │ │ │ │ │ │ └── six.py\n", + "│ │ │ │ │ │ ├── poolmanager.py\n", + "│ │ │ │ │ │ ├── request.py\n", + "│ │ │ │ │ │ ├── response.py\n", + "│ │ │ │ │ │ └── util\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── connection.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── proxy.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── queue.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── request.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── response.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── retry.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── ssl_.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── ssl_match_hostname.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── ssltransport.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── timeout.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── url.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── wait.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── connection.py\n", + "│ │ │ │ │ │ ├── proxy.py\n", + "│ │ │ │ │ │ ├── queue.py\n", + "│ │ │ │ │ │ ├── request.py\n", + "│ │ │ │ │ │ ├── response.py\n", + "│ │ │ │ │ │ ├── retry.py\n", + "│ │ │ │ │ │ ├── ssl_.py\n", + "│ │ │ │ │ │ ├── ssl_match_hostname.py\n", + "│ │ │ │ │ │ ├── ssltransport.py\n", + "│ │ │ │ │ │ ├── timeout.py\n", + "│ │ │ │ │ │ ├── url.py\n", + "│ │ │ │ │ │ └── wait.py\n", + "│ │ │ │ │ ├── vendor.txt\n", + "│ │ │ │ │ └── webencodings\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── labels.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── mklabels.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── tests.cpython-310.pyc\n", + "│ │ │ │ │ │ └── x_user_defined.cpython-310.pyc\n", + "│ │ │ │ │ ├── labels.py\n", + "│ │ │ │ │ ├── mklabels.py\n", + "│ │ │ │ │ ├── tests.py\n", + "│ │ │ │ │ └── x_user_defined.py\n", + "│ │ │ │ └── py.typed\n", + "│ │ │ ├── pip-24.0.dist-info\n", + "│ │ │ │ ├── AUTHORS.txt\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE.txt\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── REQUESTED\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ ├── entry_points.txt\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── pkg_resources\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ ├── _vendor\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── appdirs.cpython-310.pyc\n", + "│ │ │ │ │ │ └── pyparsing.cpython-310.pyc\n", + "│ │ │ │ │ ├── appdirs.py\n", + "│ │ │ │ │ ├── packaging\n", + "│ │ │ │ │ │ ├── __about__.py\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __about__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _manylinux.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _musllinux.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _structures.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── markers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── requirements.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── specifiers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── tags.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── version.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _manylinux.py\n", + "│ │ │ │ │ │ ├── _musllinux.py\n", + "│ │ │ │ │ │ ├── _structures.py\n", + "│ │ │ │ │ │ ├── markers.py\n", + "│ │ │ │ │ │ ├── requirements.py\n", + "│ │ │ │ │ │ ├── specifiers.py\n", + "│ │ │ │ │ │ ├── tags.py\n", + "│ │ │ │ │ │ ├── utils.py\n", + "│ │ │ │ │ │ └── version.py\n", + "│ │ │ │ │ └── pyparsing.py\n", + "│ │ │ │ ├── extern\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ └── tests\n", + "│ │ │ │ └── data\n", + "│ │ │ │ └── my-test-package-source\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ └── setup.cpython-310.pyc\n", + "│ │ │ │ └── setup.py\n", + "│ │ │ ├── platformdirs\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __main__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ ├── android.cpython-310.pyc\n", + "│ │ │ │ │ ├── api.cpython-310.pyc\n", + "│ │ │ │ │ ├── macos.cpython-310.pyc\n", + "│ │ │ │ │ ├── unix.cpython-310.pyc\n", + "│ │ │ │ │ ├── version.cpython-310.pyc\n", + "│ │ │ │ │ └── windows.cpython-310.pyc\n", + "│ │ │ │ ├── android.py\n", + "│ │ │ │ ├── api.py\n", + "│ │ │ │ ├── macos.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ ├── unix.py\n", + "│ │ │ │ ├── version.py\n", + "│ │ │ │ └── windows.py\n", + "│ │ │ ├── platformdirs-4.2.1.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── licenses\n", + "│ │ │ │ └── LICENSE\n", + "│ │ │ ├── prompt_toolkit\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── auto_suggest.cpython-310.pyc\n", + "│ │ │ │ │ ├── buffer.cpython-310.pyc\n", + "│ │ │ │ │ ├── cache.cpython-310.pyc\n", + "│ │ │ │ │ ├── cursor_shapes.cpython-310.pyc\n", + "│ │ │ │ │ ├── data_structures.cpython-310.pyc\n", + "│ │ │ │ │ ├── document.cpython-310.pyc\n", + "│ │ │ │ │ ├── enums.cpython-310.pyc\n", + "│ │ │ │ │ ├── history.cpython-310.pyc\n", + "│ │ │ │ │ ├── keys.cpython-310.pyc\n", + "│ │ │ │ │ ├── log.cpython-310.pyc\n", + "│ │ │ │ │ ├── mouse_events.cpython-310.pyc\n", + "│ │ │ │ │ ├── patch_stdout.cpython-310.pyc\n", + "│ │ │ │ │ ├── renderer.cpython-310.pyc\n", + "│ │ │ │ │ ├── search.cpython-310.pyc\n", + "│ │ │ │ │ ├── selection.cpython-310.pyc\n", + "│ │ │ │ │ ├── token.cpython-310.pyc\n", + "│ │ │ │ │ ├── utils.cpython-310.pyc\n", + "│ │ │ │ │ ├── validation.cpython-310.pyc\n", + "│ │ │ │ │ └── win32_types.cpython-310.pyc\n", + "│ │ │ │ ├── application\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── application.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── current.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dummy.cpython-310.pyc\n", + "│ │ │ │ │ │ └── run_in_terminal.cpython-310.pyc\n", + "│ │ │ │ │ ├── application.py\n", + "│ │ │ │ │ ├── current.py\n", + "│ │ │ │ │ ├── dummy.py\n", + "│ │ │ │ │ └── run_in_terminal.py\n", + "│ │ │ │ ├── auto_suggest.py\n", + "│ │ │ │ ├── buffer.py\n", + "│ │ │ │ ├── cache.py\n", + "│ │ │ │ ├── clipboard\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── in_memory.cpython-310.pyc\n", + "│ │ │ │ │ │ └── pyperclip.cpython-310.pyc\n", + "│ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ ├── in_memory.py\n", + "│ │ │ │ │ └── pyperclip.py\n", + "│ │ │ │ ├── completion\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── deduplicate.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── filesystem.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── fuzzy_completer.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── nested.cpython-310.pyc\n", + "│ │ │ │ │ │ └── word_completer.cpython-310.pyc\n", + "│ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ ├── deduplicate.py\n", + "│ │ │ │ │ ├── filesystem.py\n", + "│ │ │ │ │ ├── fuzzy_completer.py\n", + "│ │ │ │ │ ├── nested.py\n", + "│ │ │ │ │ └── word_completer.py\n", + "│ │ │ │ ├── contrib\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── completers\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── system.cpython-310.pyc\n", + "│ │ │ │ │ │ └── system.py\n", + "│ │ │ │ │ ├── regular_languages\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── compiler.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── completion.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── lexer.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── regex_parser.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── validation.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── compiler.py\n", + "│ │ │ │ │ │ ├── completion.py\n", + "│ │ │ │ │ │ ├── lexer.py\n", + "│ │ │ │ │ │ ├── regex_parser.py\n", + "│ │ │ │ │ │ └── validation.py\n", + "│ │ │ │ │ ├── ssh\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── server.cpython-310.pyc\n", + "│ │ │ │ │ │ └── server.py\n", + "│ │ │ │ │ └── telnet\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── log.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── protocol.cpython-310.pyc\n", + "│ │ │ │ │ │ └── server.cpython-310.pyc\n", + "│ │ │ │ │ ├── log.py\n", + "│ │ │ │ │ ├── protocol.py\n", + "│ │ │ │ │ └── server.py\n", + "│ │ │ │ ├── cursor_shapes.py\n", + "│ │ │ │ ├── data_structures.py\n", + "│ │ │ │ ├── document.py\n", + "│ │ │ │ ├── enums.py\n", + "│ │ │ │ ├── eventloop\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── async_generator.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── inputhook.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── utils.cpython-310.pyc\n", + "│ │ │ │ │ │ └── win32.cpython-310.pyc\n", + "│ │ │ │ │ ├── async_generator.py\n", + "│ │ │ │ │ ├── inputhook.py\n", + "│ │ │ │ │ ├── utils.py\n", + "│ │ │ │ │ └── win32.py\n", + "│ │ │ │ ├── filters\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── app.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cli.cpython-310.pyc\n", + "│ │ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ │ ├── app.py\n", + "│ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ ├── cli.py\n", + "│ │ │ │ │ └── utils.py\n", + "│ │ │ │ ├── formatted_text\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ansi.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── html.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pygments.cpython-310.pyc\n", + "│ │ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ │ ├── ansi.py\n", + "│ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ ├── html.py\n", + "│ │ │ │ │ ├── pygments.py\n", + "│ │ │ │ │ └── utils.py\n", + "│ │ │ │ ├── history.py\n", + "│ │ │ │ ├── input\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ansi_escape_sequences.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── defaults.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── posix_pipe.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── posix_utils.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── typeahead.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── vt100.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── vt100_parser.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── win32.cpython-310.pyc\n", + "│ │ │ │ │ │ └── win32_pipe.cpython-310.pyc\n", + "│ │ │ │ │ ├── ansi_escape_sequences.py\n", + "│ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ ├── defaults.py\n", + "│ │ │ │ │ ├── posix_pipe.py\n", + "│ │ │ │ │ ├── posix_utils.py\n", + "│ │ │ │ │ ├── typeahead.py\n", + "│ │ │ │ │ ├── vt100.py\n", + "│ │ │ │ │ ├── vt100_parser.py\n", + "│ │ │ │ │ ├── win32.py\n", + "│ │ │ │ │ └── win32_pipe.py\n", + "│ │ │ │ ├── key_binding\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── defaults.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── digraphs.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── emacs_state.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── key_bindings.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── key_processor.cpython-310.pyc\n", + "│ │ │ │ │ │ └── vi_state.cpython-310.pyc\n", + "│ │ │ │ │ ├── bindings\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── auto_suggest.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── basic.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── completion.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── cpr.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── emacs.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── focus.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── mouse.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── named_commands.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── open_in_editor.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── page_navigation.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── scroll.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── search.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── vi.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── auto_suggest.py\n", + "│ │ │ │ │ │ ├── basic.py\n", + "│ │ │ │ │ │ ├── completion.py\n", + "│ │ │ │ │ │ ├── cpr.py\n", + "│ │ │ │ │ │ ├── emacs.py\n", + "│ │ │ │ │ │ ├── focus.py\n", + "│ │ │ │ │ │ ├── mouse.py\n", + "│ │ │ │ │ │ ├── named_commands.py\n", + "│ │ │ │ │ │ ├── open_in_editor.py\n", + "│ │ │ │ │ │ ├── page_navigation.py\n", + "│ │ │ │ │ │ ├── scroll.py\n", + "│ │ │ │ │ │ ├── search.py\n", + "│ │ │ │ │ │ └── vi.py\n", + "│ │ │ │ │ ├── defaults.py\n", + "│ │ │ │ │ ├── digraphs.py\n", + "│ │ │ │ │ ├── emacs_state.py\n", + "│ │ │ │ │ ├── key_bindings.py\n", + "│ │ │ │ │ ├── key_processor.py\n", + "│ │ │ │ │ └── vi_state.py\n", + "│ │ │ │ ├── keys.py\n", + "│ │ │ │ ├── layout\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── containers.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── controls.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dimension.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dummy.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── layout.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── margins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── menus.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── mouse_handlers.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── processors.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── screen.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── scrollable_pane.cpython-310.pyc\n", + "│ │ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ │ ├── containers.py\n", + "│ │ │ │ │ ├── controls.py\n", + "│ │ │ │ │ ├── dimension.py\n", + "│ │ │ │ │ ├── dummy.py\n", + "│ │ │ │ │ ├── layout.py\n", + "│ │ │ │ │ ├── margins.py\n", + "│ │ │ │ │ ├── menus.py\n", + "│ │ │ │ │ ├── mouse_handlers.py\n", + "│ │ │ │ │ ├── processors.py\n", + "│ │ │ │ │ ├── screen.py\n", + "│ │ │ │ │ ├── scrollable_pane.py\n", + "│ │ │ │ │ └── utils.py\n", + "│ │ │ │ ├── lexers\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ └── pygments.cpython-310.pyc\n", + "│ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ └── pygments.py\n", + "│ │ │ │ ├── log.py\n", + "│ │ │ │ ├── mouse_events.py\n", + "│ │ │ │ ├── output\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── color_depth.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── conemu.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── defaults.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── flush_stdout.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── plain_text.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── vt100.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── win32.cpython-310.pyc\n", + "│ │ │ │ │ │ └── windows10.cpython-310.pyc\n", + "│ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ ├── color_depth.py\n", + "│ │ │ │ │ ├── conemu.py\n", + "│ │ │ │ │ ├── defaults.py\n", + "│ │ │ │ │ ├── flush_stdout.py\n", + "│ │ │ │ │ ├── plain_text.py\n", + "│ │ │ │ │ ├── vt100.py\n", + "│ │ │ │ │ ├── win32.py\n", + "│ │ │ │ │ └── windows10.py\n", + "│ │ │ │ ├── patch_stdout.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ ├── renderer.py\n", + "│ │ │ │ ├── search.py\n", + "│ │ │ │ ├── selection.py\n", + "│ │ │ │ ├── shortcuts\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dialogs.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── prompt.cpython-310.pyc\n", + "│ │ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ │ ├── dialogs.py\n", + "│ │ │ │ │ ├── progress_bar\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── formatters.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ │ └── formatters.py\n", + "│ │ │ │ │ ├── prompt.py\n", + "│ │ │ │ │ └── utils.py\n", + "│ │ │ │ ├── styles\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── defaults.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── named_colors.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pygments.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── style.cpython-310.pyc\n", + "│ │ │ │ │ │ └── style_transformation.cpython-310.pyc\n", + "│ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ ├── defaults.py\n", + "│ │ │ │ │ ├── named_colors.py\n", + "│ │ │ │ │ ├── pygments.py\n", + "│ │ │ │ │ ├── style.py\n", + "│ │ │ │ │ └── style_transformation.py\n", + "│ │ │ │ ├── token.py\n", + "│ │ │ │ ├── utils.py\n", + "│ │ │ │ ├── validation.py\n", + "│ │ │ │ ├── widgets\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dialogs.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── menus.cpython-310.pyc\n", + "│ │ │ │ │ │ └── toolbars.cpython-310.pyc\n", + "│ │ │ │ │ ├── base.py\n", + "│ │ │ │ │ ├── dialogs.py\n", + "│ │ │ │ │ ├── menus.py\n", + "│ │ │ │ │ └── toolbars.py\n", + "│ │ │ │ └── win32_types.py\n", + "│ │ │ ├── prompt_toolkit-3.0.43.dist-info\n", + "│ │ │ │ ├── AUTHORS.rst\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── psutil\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _common.cpython-310.pyc\n", + "│ │ │ │ │ ├── _compat.cpython-310.pyc\n", + "│ │ │ │ │ ├── _psaix.cpython-310.pyc\n", + "│ │ │ │ │ ├── _psbsd.cpython-310.pyc\n", + "│ │ │ │ │ ├── _pslinux.cpython-310.pyc\n", + "│ │ │ │ │ ├── _psosx.cpython-310.pyc\n", + "│ │ │ │ │ ├── _psposix.cpython-310.pyc\n", + "│ │ │ │ │ ├── _pssunos.cpython-310.pyc\n", + "│ │ │ │ │ └── _pswindows.cpython-310.pyc\n", + "│ │ │ │ ├── _common.py\n", + "│ │ │ │ ├── _compat.py\n", + "│ │ │ │ ├── _psaix.py\n", + "│ │ │ │ ├── _psbsd.py\n", + "│ │ │ │ ├── _pslinux.py\n", + "│ │ │ │ ├── _psosx.py\n", + "│ │ │ │ ├── _psposix.py\n", + "│ │ │ │ ├── _pssunos.py\n", + "│ │ │ │ ├── _psutil_linux.abi3.so\n", + "│ │ │ │ ├── _psutil_posix.abi3.so\n", + "│ │ │ │ ├── _pswindows.py\n", + "│ │ │ │ └── tests\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __main__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ ├── runner.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_aix.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_bsd.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_connections.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_contracts.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_linux.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_memleaks.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_misc.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_osx.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_posix.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_process.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_process_all.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_sunos.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_system.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_testutils.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_unicode.cpython-310.pyc\n", + "│ │ │ │ │ └── test_windows.cpython-310.pyc\n", + "│ │ │ │ ├── runner.py\n", + "│ │ │ │ ├── test_aix.py\n", + "│ │ │ │ ├── test_bsd.py\n", + "│ │ │ │ ├── test_connections.py\n", + "│ │ │ │ ├── test_contracts.py\n", + "│ │ │ │ ├── test_linux.py\n", + "│ │ │ │ ├── test_memleaks.py\n", + "│ │ │ │ ├── test_misc.py\n", + "│ │ │ │ ├── test_osx.py\n", + "│ │ │ │ ├── test_posix.py\n", + "│ │ │ │ ├── test_process.py\n", + "│ │ │ │ ├── test_process_all.py\n", + "│ │ │ │ ├── test_sunos.py\n", + "│ │ │ │ ├── test_system.py\n", + "│ │ │ │ ├── test_testutils.py\n", + "│ │ │ │ ├── test_unicode.py\n", + "│ │ │ │ └── test_windows.py\n", + "│ │ │ ├── psutil-5.9.8.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── ptyprocess\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _fork_pty.cpython-310.pyc\n", + "│ │ │ │ │ ├── ptyprocess.cpython-310.pyc\n", + "│ │ │ │ │ └── util.cpython-310.pyc\n", + "│ │ │ │ ├── _fork_pty.py\n", + "│ │ │ │ ├── ptyprocess.py\n", + "│ │ │ │ └── util.py\n", + "│ │ │ ├── ptyprocess-0.7.0.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ └── WHEEL\n", + "│ │ │ ├── pure_eval\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── core.cpython-310.pyc\n", + "│ │ │ │ │ ├── my_getattr_static.cpython-310.pyc\n", + "│ │ │ │ │ ├── utils.cpython-310.pyc\n", + "│ │ │ │ │ └── version.cpython-310.pyc\n", + "│ │ │ │ ├── core.py\n", + "│ │ │ │ ├── my_getattr_static.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ ├── utils.py\n", + "│ │ │ │ └── version.py\n", + "│ │ │ ├── pure_eval-0.2.2.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE.txt\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── pygments\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __main__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ ├── cmdline.cpython-310.pyc\n", + "│ │ │ │ │ ├── console.cpython-310.pyc\n", + "│ │ │ │ │ ├── filter.cpython-310.pyc\n", + "│ │ │ │ │ ├── formatter.cpython-310.pyc\n", + "│ │ │ │ │ ├── lexer.cpython-310.pyc\n", + "│ │ │ │ │ ├── modeline.cpython-310.pyc\n", + "│ │ │ │ │ ├── plugin.cpython-310.pyc\n", + "│ │ │ │ │ ├── regexopt.cpython-310.pyc\n", + "│ │ │ │ │ ├── scanner.cpython-310.pyc\n", + "│ │ │ │ │ ├── sphinxext.cpython-310.pyc\n", + "│ │ │ │ │ ├── style.cpython-310.pyc\n", + "│ │ │ │ │ ├── token.cpython-310.pyc\n", + "│ │ │ │ │ ├── unistring.cpython-310.pyc\n", + "│ │ │ │ │ └── util.cpython-310.pyc\n", + "│ │ │ │ ├── cmdline.py\n", + "│ │ │ │ ├── console.py\n", + "│ │ │ │ ├── filter.py\n", + "│ │ │ │ ├── filters\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ ├── formatter.py\n", + "│ │ │ │ ├── formatters\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _mapping.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── bbcode.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── groff.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── html.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── img.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── irc.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── latex.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── other.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pangomarkup.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── rtf.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── svg.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── terminal.cpython-310.pyc\n", + "│ │ │ │ │ │ └── terminal256.cpython-310.pyc\n", + "│ │ │ │ │ ├── _mapping.py\n", + "│ │ │ │ │ ├── bbcode.py\n", + "│ │ │ │ │ ├── groff.py\n", + "│ │ │ │ │ ├── html.py\n", + "│ │ │ │ │ ├── img.py\n", + "│ │ │ │ │ ├── irc.py\n", + "│ │ │ │ │ ├── latex.py\n", + "│ │ │ │ │ ├── other.py\n", + "│ │ │ │ │ ├── pangomarkup.py\n", + "│ │ │ │ │ ├── rtf.py\n", + "│ │ │ │ │ ├── svg.py\n", + "│ │ │ │ │ ├── terminal.py\n", + "│ │ │ │ │ └── terminal256.py\n", + "│ │ │ │ ├── lexer.py\n", + "│ │ │ │ ├── lexers\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _ada_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _asy_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _cl_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _cocoa_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _csound_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _css_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _julia_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _lasso_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _lilypond_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _lua_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _mapping.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _mql_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _mysql_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _openedge_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _php_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _postgres_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _qlik_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _scheme_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _scilab_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _sourcemod_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _stan_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _stata_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _tsql_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _usd_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _vbscript_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _vim_builtins.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── actionscript.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ada.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── agile.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── algebra.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ambient.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── amdgpu.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ampl.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── apdlexer.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── apl.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── archetype.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── arrow.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── arturo.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── asc.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── asm.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── asn1.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── automation.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── bare.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── basic.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── bdd.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── berry.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── bibtex.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── blueprint.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── boa.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── bqn.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── business.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── c_cpp.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── c_like.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── capnproto.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── carbon.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cddl.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── chapel.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── clean.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── comal.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── compiled.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── configs.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── console.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cplint.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── crystal.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── csound.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── css.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── d.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dalvik.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── data.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dax.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── devicetree.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── diff.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dns.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dotnet.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dsls.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dylan.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ecl.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── eiffel.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── elm.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── elpi.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── email.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── erlang.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── esoteric.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ezhil.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── factor.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── fantom.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── felix.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── fift.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── floscript.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── forth.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── fortran.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── foxpro.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── freefem.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── func.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── functional.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── futhark.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── gcodelexer.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── gdscript.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── go.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── grammar_notation.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── graph.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── graphics.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── graphql.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── graphviz.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── gsql.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── haskell.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── haxe.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── hdl.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── hexdump.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── html.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── idl.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── igor.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── inferno.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── installers.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── int_fiction.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── iolang.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── j.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── javascript.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── jmespath.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── jslt.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── jsonnet.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── jsx.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── julia.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── jvm.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── kuin.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── kusto.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ldap.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── lean.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── lilypond.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── lisp.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── macaulay2.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── make.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── markup.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── math.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── matlab.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── maxima.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── meson.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── mime.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── minecraft.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── mips.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ml.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── modeling.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── modula2.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── monte.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── mosel.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ncl.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── nimrod.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── nit.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── nix.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── oberon.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── objective.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ooc.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── openscad.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── other.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── parasail.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── parsers.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pascal.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pawn.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── perl.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── phix.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── php.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pointless.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pony.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── praat.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── procfile.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── prolog.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── promql.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── prql.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ptx.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── python.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── q.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── qlik.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── qvt.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── r.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── rdf.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── rebol.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── resource.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ride.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── rita.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── rnc.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── roboconf.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── robotframework.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ruby.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── rust.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── sas.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── savi.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── scdoc.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── scripting.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── sgf.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── shell.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── sieve.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── slash.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── smalltalk.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── smithy.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── smv.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── snobol.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── solidity.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── sophia.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── special.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── spice.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── sql.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── srcinfo.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── stata.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── supercollider.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── tal.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── tcl.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── teal.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── templates.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── teraterm.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── testing.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── text.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── textedit.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── textfmts.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── theorem.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── thingsdb.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── tlb.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── tls.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── tnt.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── trafficscript.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── typoscript.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ul4.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── unicon.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── urbi.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── usd.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── varnish.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── verification.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── verifpal.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── vip.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── vyper.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── web.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── webassembly.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── webidl.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── webmisc.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── wgsl.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── whiley.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── wowtoc.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── wren.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── x10.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── xorg.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── yang.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── yara.cpython-310.pyc\n", + "│ │ │ │ │ │ └── zig.cpython-310.pyc\n", + "│ │ │ │ │ ├── _ada_builtins.py\n", + "│ │ │ │ │ ├── _asy_builtins.py\n", + "│ │ │ │ │ ├── _cl_builtins.py\n", + "│ │ │ │ │ ├── _cocoa_builtins.py\n", + "│ │ │ │ │ ├── _csound_builtins.py\n", + "│ │ │ │ │ ├── _css_builtins.py\n", + "│ │ │ │ │ ├── _julia_builtins.py\n", + "│ │ │ │ │ ├── _lasso_builtins.py\n", + "│ │ │ │ │ ├── _lilypond_builtins.py\n", + "│ │ │ │ │ ├── _lua_builtins.py\n", + "│ │ │ │ │ ├── _mapping.py\n", + "│ │ │ │ │ ├── _mql_builtins.py\n", + "│ │ │ │ │ ├── _mysql_builtins.py\n", + "│ │ │ │ │ ├── _openedge_builtins.py\n", + "│ │ │ │ │ ├── _php_builtins.py\n", + "│ │ │ │ │ ├── _postgres_builtins.py\n", + "│ │ │ │ │ ├── _qlik_builtins.py\n", + "│ │ │ │ │ ├── _scheme_builtins.py\n", + "│ │ │ │ │ ├── _scilab_builtins.py\n", + "│ │ │ │ │ ├── _sourcemod_builtins.py\n", + "│ │ │ │ │ ├── _stan_builtins.py\n", + "│ │ │ │ │ ├── _stata_builtins.py\n", + "│ │ │ │ │ ├── _tsql_builtins.py\n", + "│ │ │ │ │ ├── _usd_builtins.py\n", + "│ │ │ │ │ ├── _vbscript_builtins.py\n", + "│ │ │ │ │ ├── _vim_builtins.py\n", + "│ │ │ │ │ ├── actionscript.py\n", + "│ │ │ │ │ ├── ada.py\n", + "│ │ │ │ │ ├── agile.py\n", + "│ │ │ │ │ ├── algebra.py\n", + "│ │ │ │ │ ├── ambient.py\n", + "│ │ │ │ │ ├── amdgpu.py\n", + "│ │ │ │ │ ├── ampl.py\n", + "│ │ │ │ │ ├── apdlexer.py\n", + "│ │ │ │ │ ├── apl.py\n", + "│ │ │ │ │ ├── archetype.py\n", + "│ │ │ │ │ ├── arrow.py\n", + "│ │ │ │ │ ├── arturo.py\n", + "│ │ │ │ │ ├── asc.py\n", + "│ │ │ │ │ ├── asm.py\n", + "│ │ │ │ │ ├── asn1.py\n", + "│ │ │ │ │ ├── automation.py\n", + "│ │ │ │ │ ├── bare.py\n", + "│ │ │ │ │ ├── basic.py\n", + "│ │ │ │ │ ├── bdd.py\n", + "│ │ │ │ │ ├── berry.py\n", + "│ │ │ │ │ ├── bibtex.py\n", + "│ │ │ │ │ ├── blueprint.py\n", + "│ │ │ │ │ ├── boa.py\n", + "│ │ │ │ │ ├── bqn.py\n", + "│ │ │ │ │ ├── business.py\n", + "│ │ │ │ │ ├── c_cpp.py\n", + "│ │ │ │ │ ├── c_like.py\n", + "│ │ │ │ │ ├── capnproto.py\n", + "│ │ │ │ │ ├── carbon.py\n", + "│ │ │ │ │ ├── cddl.py\n", + "│ │ │ │ │ ├── chapel.py\n", + "│ │ │ │ │ ├── clean.py\n", + "│ │ │ │ │ ├── comal.py\n", + "│ │ │ │ │ ├── compiled.py\n", + "│ │ │ │ │ ├── configs.py\n", + "│ │ │ │ │ ├── console.py\n", + "│ │ │ │ │ ├── cplint.py\n", + "│ │ │ │ │ ├── crystal.py\n", + "│ │ │ │ │ ├── csound.py\n", + "│ │ │ │ │ ├── css.py\n", + "│ │ │ │ │ ├── d.py\n", + "│ │ │ │ │ ├── dalvik.py\n", + "│ │ │ │ │ ├── data.py\n", + "│ │ │ │ │ ├── dax.py\n", + "│ │ │ │ │ ├── devicetree.py\n", + "│ │ │ │ │ ├── diff.py\n", + "│ │ │ │ │ ├── dns.py\n", + "│ │ │ │ │ ├── dotnet.py\n", + "│ │ │ │ │ ├── dsls.py\n", + "│ │ │ │ │ ├── dylan.py\n", + "│ │ │ │ │ ├── ecl.py\n", + "│ │ │ │ │ ├── eiffel.py\n", + "│ │ │ │ │ ├── elm.py\n", + "│ │ │ │ │ ├── elpi.py\n", + "│ │ │ │ │ ├── email.py\n", + "│ │ │ │ │ ├── erlang.py\n", + "│ │ │ │ │ ├── esoteric.py\n", + "│ │ │ │ │ ├── ezhil.py\n", + "│ │ │ │ │ ├── factor.py\n", + "│ │ │ │ │ ├── fantom.py\n", + "│ │ │ │ │ ├── felix.py\n", + "│ │ │ │ │ ├── fift.py\n", + "│ │ │ │ │ ├── floscript.py\n", + "│ │ │ │ │ ├── forth.py\n", + "│ │ │ │ │ ├── fortran.py\n", + "│ │ │ │ │ ├── foxpro.py\n", + "│ │ │ │ │ ├── freefem.py\n", + "│ │ │ │ │ ├── func.py\n", + "│ │ │ │ │ ├── functional.py\n", + "│ │ │ │ │ ├── futhark.py\n", + "│ │ │ │ │ ├── gcodelexer.py\n", + "│ │ │ │ │ ├── gdscript.py\n", + "│ │ │ │ │ ├── go.py\n", + "│ │ │ │ │ ├── grammar_notation.py\n", + "│ │ │ │ │ ├── graph.py\n", + "│ │ │ │ │ ├── graphics.py\n", + "│ │ │ │ │ ├── graphql.py\n", + "│ │ │ │ │ ├── graphviz.py\n", + "│ │ │ │ │ ├── gsql.py\n", + "│ │ │ │ │ ├── haskell.py\n", + "│ │ │ │ │ ├── haxe.py\n", + "│ │ │ │ │ ├── hdl.py\n", + "│ │ │ │ │ ├── hexdump.py\n", + "│ │ │ │ │ ├── html.py\n", + "│ │ │ │ │ ├── idl.py\n", + "│ │ │ │ │ ├── igor.py\n", + "│ │ │ │ │ ├── inferno.py\n", + "│ │ │ │ │ ├── installers.py\n", + "│ │ │ │ │ ├── int_fiction.py\n", + "│ │ │ │ │ ├── iolang.py\n", + "│ │ │ │ │ ├── j.py\n", + "│ │ │ │ │ ├── javascript.py\n", + "│ │ │ │ │ ├── jmespath.py\n", + "│ │ │ │ │ ├── jslt.py\n", + "│ │ │ │ │ ├── jsonnet.py\n", + "│ │ │ │ │ ├── jsx.py\n", + "│ │ │ │ │ ├── julia.py\n", + "│ │ │ │ │ ├── jvm.py\n", + "│ │ │ │ │ ├── kuin.py\n", + "│ │ │ │ │ ├── kusto.py\n", + "│ │ │ │ │ ├── ldap.py\n", + "│ │ │ │ │ ├── lean.py\n", + "│ │ │ │ │ ├── lilypond.py\n", + "│ │ │ │ │ ├── lisp.py\n", + "│ │ │ │ │ ├── macaulay2.py\n", + "│ │ │ │ │ ├── make.py\n", + "│ │ │ │ │ ├── markup.py\n", + "│ │ │ │ │ ├── math.py\n", + "│ │ │ │ │ ├── matlab.py\n", + "│ │ │ │ │ ├── maxima.py\n", + "│ │ │ │ │ ├── meson.py\n", + "│ │ │ │ │ ├── mime.py\n", + "│ │ │ │ │ ├── minecraft.py\n", + "│ │ │ │ │ ├── mips.py\n", + "│ │ │ │ │ ├── ml.py\n", + "│ │ │ │ │ ├── modeling.py\n", + "│ │ │ │ │ ├── modula2.py\n", + "│ │ │ │ │ ├── monte.py\n", + "│ │ │ │ │ ├── mosel.py\n", + "│ │ │ │ │ ├── ncl.py\n", + "│ │ │ │ │ ├── nimrod.py\n", + "│ │ │ │ │ ├── nit.py\n", + "│ │ │ │ │ ├── nix.py\n", + "│ │ │ │ │ ├── oberon.py\n", + "│ │ │ │ │ ├── objective.py\n", + "│ │ │ │ │ ├── ooc.py\n", + "│ │ │ │ │ ├── openscad.py\n", + "│ │ │ │ │ ├── other.py\n", + "│ │ │ │ │ ├── parasail.py\n", + "│ │ │ │ │ ├── parsers.py\n", + "│ │ │ │ │ ├── pascal.py\n", + "│ │ │ │ │ ├── pawn.py\n", + "│ │ │ │ │ ├── perl.py\n", + "│ │ │ │ │ ├── phix.py\n", + "│ │ │ │ │ ├── php.py\n", + "│ │ │ │ │ ├── pointless.py\n", + "│ │ │ │ │ ├── pony.py\n", + "│ │ │ │ │ ├── praat.py\n", + "│ │ │ │ │ ├── procfile.py\n", + "│ │ │ │ │ ├── prolog.py\n", + "│ │ │ │ │ ├── promql.py\n", + "│ │ │ │ │ ├── prql.py\n", + "│ │ │ │ │ ├── ptx.py\n", + "│ │ │ │ │ ├── python.py\n", + "│ │ │ │ │ ├── q.py\n", + "│ │ │ │ │ ├── qlik.py\n", + "│ │ │ │ │ ├── qvt.py\n", + "│ │ │ │ │ ├── r.py\n", + "│ │ │ │ │ ├── rdf.py\n", + "│ │ │ │ │ ├── rebol.py\n", + "│ │ │ │ │ ├── resource.py\n", + "│ │ │ │ │ ├── ride.py\n", + "│ │ │ │ │ ├── rita.py\n", + "│ │ │ │ │ ├── rnc.py\n", + "│ │ │ │ │ ├── roboconf.py\n", + "│ │ │ │ │ ├── robotframework.py\n", + "│ │ │ │ │ ├── ruby.py\n", + "│ │ │ │ │ ├── rust.py\n", + "│ │ │ │ │ ├── sas.py\n", + "│ │ │ │ │ ├── savi.py\n", + "│ │ │ │ │ ├── scdoc.py\n", + "│ │ │ │ │ ├── scripting.py\n", + "│ │ │ │ │ ├── sgf.py\n", + "│ │ │ │ │ ├── shell.py\n", + "│ │ │ │ │ ├── sieve.py\n", + "│ │ │ │ │ ├── slash.py\n", + "│ │ │ │ │ ├── smalltalk.py\n", + "│ │ │ │ │ ├── smithy.py\n", + "│ │ │ │ │ ├── smv.py\n", + "│ │ │ │ │ ├── snobol.py\n", + "│ │ │ │ │ ├── solidity.py\n", + "│ │ │ │ │ ├── sophia.py\n", + "│ │ │ │ │ ├── special.py\n", + "│ │ │ │ │ ├── spice.py\n", + "│ │ │ │ │ ├── sql.py\n", + "│ │ │ │ │ ├── srcinfo.py\n", + "│ │ │ │ │ ├── stata.py\n", + "│ │ │ │ │ ├── supercollider.py\n", + "│ │ │ │ │ ├── tal.py\n", + "│ │ │ │ │ ├── tcl.py\n", + "│ │ │ │ │ ├── teal.py\n", + "│ │ │ │ │ ├── templates.py\n", + "│ │ │ │ │ ├── teraterm.py\n", + "│ │ │ │ │ ├── testing.py\n", + "│ │ │ │ │ ├── text.py\n", + "│ │ │ │ │ ├── textedit.py\n", + "│ │ │ │ │ ├── textfmts.py\n", + "│ │ │ │ │ ├── theorem.py\n", + "│ │ │ │ │ ├── thingsdb.py\n", + "│ │ │ │ │ ├── tlb.py\n", + "│ │ │ │ │ ├── tls.py\n", + "│ │ │ │ │ ├── tnt.py\n", + "│ │ │ │ │ ├── trafficscript.py\n", + "│ │ │ │ │ ├── typoscript.py\n", + "│ │ │ │ │ ├── ul4.py\n", + "│ │ │ │ │ ├── unicon.py\n", + "│ │ │ │ │ ├── urbi.py\n", + "│ │ │ │ │ ├── usd.py\n", + "│ │ │ │ │ ├── varnish.py\n", + "│ │ │ │ │ ├── verification.py\n", + "│ │ │ │ │ ├── verifpal.py\n", + "│ │ │ │ │ ├── vip.py\n", + "│ │ │ │ │ ├── vyper.py\n", + "│ │ │ │ │ ├── web.py\n", + "│ │ │ │ │ ├── webassembly.py\n", + "│ │ │ │ │ ├── webidl.py\n", + "│ │ │ │ │ ├── webmisc.py\n", + "│ │ │ │ │ ├── wgsl.py\n", + "│ │ │ │ │ ├── whiley.py\n", + "│ │ │ │ │ ├── wowtoc.py\n", + "│ │ │ │ │ ├── wren.py\n", + "│ │ │ │ │ ├── x10.py\n", + "│ │ │ │ │ ├── xorg.py\n", + "│ │ │ │ │ ├── yang.py\n", + "│ │ │ │ │ ├── yara.py\n", + "│ │ │ │ │ └── zig.py\n", + "│ │ │ │ ├── modeline.py\n", + "│ │ │ │ ├── plugin.py\n", + "│ │ │ │ ├── regexopt.py\n", + "│ │ │ │ ├── scanner.py\n", + "│ │ │ │ ├── sphinxext.py\n", + "│ │ │ │ ├── style.py\n", + "│ │ │ │ ├── styles\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _mapping.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── abap.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── algol.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── algol_nu.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── arduino.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── autumn.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── borland.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── bw.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── colorful.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── default.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dracula.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── emacs.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── friendly.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── friendly_grayscale.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── fruity.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── gh_dark.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── gruvbox.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── igor.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── inkpot.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── lightbulb.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── lilypond.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── lovelace.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── manni.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── material.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── monokai.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── murphy.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── native.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── nord.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── onedark.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── paraiso_dark.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── paraiso_light.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── pastie.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── perldoc.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── rainbow_dash.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── rrt.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── sas.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── solarized.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── staroffice.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── stata_dark.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── stata_light.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── tango.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── trac.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── vim.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── vs.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── xcode.cpython-310.pyc\n", + "│ │ │ │ │ │ └── zenburn.cpython-310.pyc\n", + "│ │ │ │ │ ├── _mapping.py\n", + "│ │ │ │ │ ├── abap.py\n", + "│ │ │ │ │ ├── algol.py\n", + "│ │ │ │ │ ├── algol_nu.py\n", + "│ │ │ │ │ ├── arduino.py\n", + "│ │ │ │ │ ├── autumn.py\n", + "│ │ │ │ │ ├── borland.py\n", + "│ │ │ │ │ ├── bw.py\n", + "│ │ │ │ │ ├── colorful.py\n", + "│ │ │ │ │ ├── default.py\n", + "│ │ │ │ │ ├── dracula.py\n", + "│ │ │ │ │ ├── emacs.py\n", + "│ │ │ │ │ ├── friendly.py\n", + "│ │ │ │ │ ├── friendly_grayscale.py\n", + "│ │ │ │ │ ├── fruity.py\n", + "│ │ │ │ │ ├── gh_dark.py\n", + "│ │ │ │ │ ├── gruvbox.py\n", + "│ │ │ │ │ ├── igor.py\n", + "│ │ │ │ │ ├── inkpot.py\n", + "│ │ │ │ │ ├── lightbulb.py\n", + "│ │ │ │ │ ├── lilypond.py\n", + "│ │ │ │ │ ├── lovelace.py\n", + "│ │ │ │ │ ├── manni.py\n", + "│ │ │ │ │ ├── material.py\n", + "│ │ │ │ │ ├── monokai.py\n", + "│ │ │ │ │ ├── murphy.py\n", + "│ │ │ │ │ ├── native.py\n", + "│ │ │ │ │ ├── nord.py\n", + "│ │ │ │ │ ├── onedark.py\n", + "│ │ │ │ │ ├── paraiso_dark.py\n", + "│ │ │ │ │ ├── paraiso_light.py\n", + "│ │ │ │ │ ├── pastie.py\n", + "│ │ │ │ │ ├── perldoc.py\n", + "│ │ │ │ │ ├── rainbow_dash.py\n", + "│ │ │ │ │ ├── rrt.py\n", + "│ │ │ │ │ ├── sas.py\n", + "│ │ │ │ │ ├── solarized.py\n", + "│ │ │ │ │ ├── staroffice.py\n", + "│ │ │ │ │ ├── stata_dark.py\n", + "│ │ │ │ │ ├── stata_light.py\n", + "│ │ │ │ │ ├── tango.py\n", + "│ │ │ │ │ ├── trac.py\n", + "│ │ │ │ │ ├── vim.py\n", + "│ │ │ │ │ ├── vs.py\n", + "│ │ │ │ │ ├── xcode.py\n", + "│ │ │ │ │ └── zenburn.py\n", + "│ │ │ │ ├── token.py\n", + "│ │ │ │ ├── unistring.py\n", + "│ │ │ │ └── util.py\n", + "│ │ │ ├── pygments-2.17.2.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ ├── entry_points.txt\n", + "│ │ │ │ └── licenses\n", + "│ │ │ │ ├── AUTHORS\n", + "│ │ │ │ └── LICENSE\n", + "│ │ │ ├── python_dateutil-2.9.0.post0.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ ├── top_level.txt\n", + "│ │ │ │ └── zip-safe\n", + "│ │ │ ├── pytz\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── exceptions.cpython-310.pyc\n", + "│ │ │ │ │ ├── lazy.cpython-310.pyc\n", + "│ │ │ │ │ ├── reference.cpython-310.pyc\n", + "│ │ │ │ │ ├── tzfile.cpython-310.pyc\n", + "│ │ │ │ │ └── tzinfo.cpython-310.pyc\n", + "│ │ │ │ ├── exceptions.py\n", + "│ │ │ │ ├── lazy.py\n", + "│ │ │ │ ├── reference.py\n", + "│ │ │ │ ├── tzfile.py\n", + "│ │ │ │ ├── tzinfo.py\n", + "│ │ │ │ └── zoneinfo\n", + "│ │ │ │ ├── Africa\n", + "│ │ │ │ │ ├── Abidjan\n", + "│ │ │ │ │ ├── Accra\n", + "│ │ │ │ │ ├── Addis_Ababa\n", + "│ │ │ │ │ ├── Algiers\n", + "│ │ │ │ │ ├── Asmara\n", + "│ │ │ │ │ ├── Asmera\n", + "│ │ │ │ │ ├── Bamako\n", + "│ │ │ │ │ ├── Bangui\n", + "│ │ │ │ │ ├── Banjul\n", + "│ │ │ │ │ ├── Bissau\n", + "│ │ │ │ │ ├── Blantyre\n", + "│ │ │ │ │ ├── Brazzaville\n", + "│ │ │ │ │ ├── Bujumbura\n", + "│ │ │ │ │ ├── Cairo\n", + "│ │ │ │ │ ├── Casablanca\n", + "│ │ │ │ │ ├── Ceuta\n", + "│ │ │ │ │ ├── Conakry\n", + "│ │ │ │ │ ├── Dakar\n", + "│ │ │ │ │ ├── Dar_es_Salaam\n", + "│ │ │ │ │ ├── Djibouti\n", + "│ │ │ │ │ ├── Douala\n", + "│ │ │ │ │ ├── El_Aaiun\n", + "│ │ │ │ │ ├── Freetown\n", + "│ │ │ │ │ ├── Gaborone\n", + "│ │ │ │ │ ├── Harare\n", + "│ │ │ │ │ ├── Johannesburg\n", + "│ │ │ │ │ ├── Juba\n", + "│ │ │ │ │ ├── Kampala\n", + "│ │ │ │ │ ├── Khartoum\n", + "│ │ │ │ │ ├── Kigali\n", + "│ │ │ │ │ ├── Kinshasa\n", + "│ │ │ │ │ ├── Lagos\n", + "│ │ │ │ │ ├── Libreville\n", + "│ │ │ │ │ ├── Lome\n", + "│ │ │ │ │ ├── Luanda\n", + "│ │ │ │ │ ├── Lubumbashi\n", + "│ │ │ │ │ ├── Lusaka\n", + "│ │ │ │ │ ├── Malabo\n", + "│ │ │ │ │ ├── Maputo\n", + "│ │ │ │ │ ├── Maseru\n", + "│ │ │ │ │ ├── Mbabane\n", + "│ │ │ │ │ ├── Mogadishu\n", + "│ │ │ │ │ ├── Monrovia\n", + "│ │ │ │ │ ├── Nairobi\n", + "│ │ │ │ │ ├── Ndjamena\n", + "│ │ │ │ │ ├── Niamey\n", + "│ │ │ │ │ ├── Nouakchott\n", + "│ │ │ │ │ ├── Ouagadougou\n", + "│ │ │ │ │ ├── Porto-Novo\n", + "│ │ │ │ │ ├── Sao_Tome\n", + "│ │ │ │ │ ├── Timbuktu\n", + "│ │ │ │ │ ├── Tripoli\n", + "│ │ │ │ │ ├── Tunis\n", + "│ │ │ │ │ └── Windhoek\n", + "│ │ │ │ ├── America\n", + "│ │ │ │ │ ├── Adak\n", + "│ │ │ │ │ ├── Anchorage\n", + "│ │ │ │ │ ├── Anguilla\n", + "│ │ │ │ │ ├── Antigua\n", + "│ │ │ │ │ ├── Araguaina\n", + "│ │ │ │ │ ├── Argentina\n", + "│ │ │ │ │ │ ├── Buenos_Aires\n", + "│ │ │ │ │ │ ├── Catamarca\n", + "│ │ │ │ │ │ ├── ComodRivadavia\n", + "│ │ │ │ │ │ ├── Cordoba\n", + "│ │ │ │ │ │ ├── Jujuy\n", + "│ │ │ │ │ │ ├── La_Rioja\n", + "│ │ │ │ │ │ ├── Mendoza\n", + "│ │ │ │ │ │ ├── Rio_Gallegos\n", + "│ │ │ │ │ │ ├── Salta\n", + "│ │ │ │ │ │ ├── San_Juan\n", + "│ │ │ │ │ │ ├── San_Luis\n", + "│ │ │ │ │ │ ├── Tucuman\n", + "│ │ │ │ │ │ └── Ushuaia\n", + "│ │ │ │ │ ├── Aruba\n", + "│ │ │ │ │ ├── Asuncion\n", + "│ │ │ │ │ ├── Atikokan\n", + "│ │ │ │ │ ├── Atka\n", + "│ │ │ │ │ ├── Bahia\n", + "│ │ │ │ │ ├── Bahia_Banderas\n", + "│ │ │ │ │ ├── Barbados\n", + "│ │ │ │ │ ├── Belem\n", + "│ │ │ │ │ ├── Belize\n", + "│ │ │ │ │ ├── Blanc-Sablon\n", + "│ │ │ │ │ ├── Boa_Vista\n", + "│ │ │ │ │ ├── Bogota\n", + "│ │ │ │ │ ├── Boise\n", + "│ │ │ │ │ ├── Buenos_Aires\n", + "│ │ │ │ │ ├── Cambridge_Bay\n", + "│ │ │ │ │ ├── Campo_Grande\n", + "│ │ │ │ │ ├── Cancun\n", + "│ │ │ │ │ ├── Caracas\n", + "│ │ │ │ │ ├── Catamarca\n", + "│ │ │ │ │ ├── Cayenne\n", + "│ │ │ │ │ ├── Cayman\n", + "│ │ │ │ │ ├── Chicago\n", + "│ │ │ │ │ ├── Chihuahua\n", + "│ │ │ │ │ ├── Ciudad_Juarez\n", + "│ │ │ │ │ ├── Coral_Harbour\n", + "│ │ │ │ │ ├── Cordoba\n", + "│ │ │ │ │ ├── Costa_Rica\n", + "│ │ │ │ │ ├── Creston\n", + "│ │ │ │ │ ├── Cuiaba\n", + "│ │ │ │ │ ├── Curacao\n", + "│ │ │ │ │ ├── Danmarkshavn\n", + "│ │ │ │ │ ├── Dawson\n", + "│ │ │ │ │ ├── Dawson_Creek\n", + "│ │ │ │ │ ├── Denver\n", + "│ │ │ │ │ ├── Detroit\n", + "│ │ │ │ │ ├── Dominica\n", + "│ │ │ │ │ ├── Edmonton\n", + "│ │ │ │ │ ├── Eirunepe\n", + "│ │ │ │ │ ├── El_Salvador\n", + "│ │ │ │ │ ├── Ensenada\n", + "│ │ │ │ │ ├── Fort_Nelson\n", + "│ │ │ │ │ ├── Fort_Wayne\n", + "│ │ │ │ │ ├── Fortaleza\n", + "│ │ │ │ │ ├── Glace_Bay\n", + "│ │ │ │ │ ├── Godthab\n", + "│ │ │ │ │ ├── Goose_Bay\n", + "│ │ │ │ │ ├── Grand_Turk\n", + "│ │ │ │ │ ├── Grenada\n", + "│ │ │ │ │ ├── Guadeloupe\n", + "│ │ │ │ │ ├── Guatemala\n", + "│ │ │ │ │ ├── Guayaquil\n", + "│ │ │ │ │ ├── Guyana\n", + "│ │ │ │ │ ├── Halifax\n", + "│ │ │ │ │ ├── Havana\n", + "│ │ │ │ │ ├── Hermosillo\n", + "│ │ │ │ │ ├── Indiana\n", + "│ │ │ │ │ │ ├── Indianapolis\n", + "│ │ │ │ │ │ ├── Knox\n", + "│ │ │ │ │ │ ├── Marengo\n", + "│ │ │ │ │ │ ├── Petersburg\n", + "│ │ │ │ │ │ ├── Tell_City\n", + "│ │ │ │ │ │ ├── Vevay\n", + "│ │ │ │ │ │ ├── Vincennes\n", + "│ │ │ │ │ │ └── Winamac\n", + "│ │ │ │ │ ├── Indianapolis\n", + "│ │ │ │ │ ├── Inuvik\n", + "│ │ │ │ │ ├── Iqaluit\n", + "│ │ │ │ │ ├── Jamaica\n", + "│ │ │ │ │ ├── Jujuy\n", + "│ │ │ │ │ ├── Juneau\n", + "│ │ │ │ │ ├── Kentucky\n", + "│ │ │ │ │ │ ├── Louisville\n", + "│ │ │ │ │ │ └── Monticello\n", + "│ │ │ │ │ ├── Knox_IN\n", + "│ │ │ │ │ ├── Kralendijk\n", + "│ │ │ │ │ ├── La_Paz\n", + "│ │ │ │ │ ├── Lima\n", + "│ │ │ │ │ ├── Los_Angeles\n", + "│ │ │ │ │ ├── Louisville\n", + "│ │ │ │ │ ├── Lower_Princes\n", + "│ │ │ │ │ ├── Maceio\n", + "│ │ │ │ │ ├── Managua\n", + "│ │ │ │ │ ├── Manaus\n", + "│ │ │ │ │ ├── Marigot\n", + "│ │ │ │ │ ├── Martinique\n", + "│ │ │ │ │ ├── Matamoros\n", + "│ │ │ │ │ ├── Mazatlan\n", + "│ │ │ │ │ ├── Mendoza\n", + "│ │ │ │ │ ├── Menominee\n", + "│ │ │ │ │ ├── Merida\n", + "│ │ │ │ │ ├── Metlakatla\n", + "│ │ │ │ │ ├── Mexico_City\n", + "│ │ │ │ │ ├── Miquelon\n", + "│ │ │ │ │ ├── Moncton\n", + "│ │ │ │ │ ├── Monterrey\n", + "│ │ │ │ │ ├── Montevideo\n", + "│ │ │ │ │ ├── Montreal\n", + "│ │ │ │ │ ├── Montserrat\n", + "│ │ │ │ │ ├── Nassau\n", + "│ │ │ │ │ ├── New_York\n", + "│ │ │ │ │ ├── Nipigon\n", + "│ │ │ │ │ ├── Nome\n", + "│ │ │ │ │ ├── Noronha\n", + "│ │ │ │ │ ├── North_Dakota\n", + "│ │ │ │ │ │ ├── Beulah\n", + "│ │ │ │ │ │ ├── Center\n", + "│ │ │ │ │ │ └── New_Salem\n", + "│ │ │ │ │ ├── Nuuk\n", + "│ │ │ │ │ ├── Ojinaga\n", + "│ │ │ │ │ ├── Panama\n", + "│ │ │ │ │ ├── Pangnirtung\n", + "│ │ │ │ │ ├── Paramaribo\n", + "│ │ │ │ │ ├── Phoenix\n", + "│ │ │ │ │ ├── Port-au-Prince\n", + "│ │ │ │ │ ├── Port_of_Spain\n", + "│ │ │ │ │ ├── Porto_Acre\n", + "│ │ │ │ │ ├── Porto_Velho\n", + "│ │ │ │ │ ├── Puerto_Rico\n", + "│ │ │ │ │ ├── Punta_Arenas\n", + "│ │ │ │ │ ├── Rainy_River\n", + "│ │ │ │ │ ├── Rankin_Inlet\n", + "│ │ │ │ │ ├── Recife\n", + "│ │ │ │ │ ├── Regina\n", + "│ │ │ │ │ ├── Resolute\n", + "│ │ │ │ │ ├── Rio_Branco\n", + "│ │ │ │ │ ├── Rosario\n", + "│ │ │ │ │ ├── Santa_Isabel\n", + "│ │ │ │ │ ├── Santarem\n", + "│ │ │ │ │ ├── Santiago\n", + "│ │ │ │ │ ├── Santo_Domingo\n", + "│ │ │ │ │ ├── Sao_Paulo\n", + "│ │ │ │ │ ├── Scoresbysund\n", + "│ │ │ │ │ ├── Shiprock\n", + "│ │ │ │ │ ├── Sitka\n", + "│ │ │ │ │ ├── St_Barthelemy\n", + "│ │ │ │ │ ├── St_Johns\n", + "│ │ │ │ │ ├── St_Kitts\n", + "│ │ │ │ │ ├── St_Lucia\n", + "│ │ │ │ │ ├── St_Thomas\n", + "│ │ │ │ │ ├── St_Vincent\n", + "│ │ │ │ │ ├── Swift_Current\n", + "│ │ │ │ │ ├── Tegucigalpa\n", + "│ │ │ │ │ ├── Thule\n", + "│ │ │ │ │ ├── Thunder_Bay\n", + "│ │ │ │ │ ├── Tijuana\n", + "│ │ │ │ │ ├── Toronto\n", + "│ │ │ │ │ ├── Tortola\n", + "│ │ │ │ │ ├── Vancouver\n", + "│ │ │ │ │ ├── Virgin\n", + "│ │ │ │ │ ├── Whitehorse\n", + "│ │ │ │ │ ├── Winnipeg\n", + "│ │ │ │ │ ├── Yakutat\n", + "│ │ │ │ │ └── Yellowknife\n", + "│ │ │ │ ├── Antarctica\n", + "│ │ │ │ │ ├── Casey\n", + "│ │ │ │ │ ├── Davis\n", + "│ │ │ │ │ ├── DumontDUrville\n", + "│ │ │ │ │ ├── Macquarie\n", + "│ │ │ │ │ ├── Mawson\n", + "│ │ │ │ │ ├── McMurdo\n", + "│ │ │ │ │ ├── Palmer\n", + "│ │ │ │ │ ├── Rothera\n", + "│ │ │ │ │ ├── South_Pole\n", + "│ │ │ │ │ ├── Syowa\n", + "│ │ │ │ │ ├── Troll\n", + "│ │ │ │ │ └── Vostok\n", + "│ │ │ │ ├── Arctic\n", + "│ │ │ │ │ └── Longyearbyen\n", + "│ │ │ │ ├── Asia\n", + "│ │ │ │ │ ├── Aden\n", + "│ │ │ │ │ ├── Almaty\n", + "│ │ │ │ │ ├── Amman\n", + "│ │ │ │ │ ├── Anadyr\n", + "│ │ │ │ │ ├── Aqtau\n", + "│ │ │ │ │ ├── Aqtobe\n", + "│ │ │ │ │ ├── Ashgabat\n", + "│ │ │ │ │ ├── Ashkhabad\n", + "│ │ │ │ │ ├── Atyrau\n", + "│ │ │ │ │ ├── Baghdad\n", + "│ │ │ │ │ ├── Bahrain\n", + "│ │ │ │ │ ├── Baku\n", + "│ │ │ │ │ ├── Bangkok\n", + "│ │ │ │ │ ├── Barnaul\n", + "│ │ │ │ │ ├── Beirut\n", + "│ │ │ │ │ ├── Bishkek\n", + "│ │ │ │ │ ├── Brunei\n", + "│ │ │ │ │ ├── Calcutta\n", + "│ │ │ │ │ ├── Chita\n", + "│ │ │ │ │ ├── Choibalsan\n", + "│ │ │ │ │ ├── Chongqing\n", + "│ │ │ │ │ ├── Chungking\n", + "│ │ │ │ │ ├── Colombo\n", + "│ │ │ │ │ ├── Dacca\n", + "│ │ │ │ │ ├── Damascus\n", + "│ │ │ │ │ ├── Dhaka\n", + "│ │ │ │ │ ├── Dili\n", + "│ │ │ │ │ ├── Dubai\n", + "│ │ │ │ │ ├── Dushanbe\n", + "│ │ │ │ │ ├── Famagusta\n", + "│ │ │ │ │ ├── Gaza\n", + "│ │ │ │ │ ├── Harbin\n", + "│ │ │ │ │ ├── Hebron\n", + "│ │ │ │ │ ├── Ho_Chi_Minh\n", + "│ │ │ │ │ ├── Hong_Kong\n", + "│ │ │ │ │ ├── Hovd\n", + "│ │ │ │ │ ├── Irkutsk\n", + "│ │ │ │ │ ├── Istanbul\n", + "│ │ │ │ │ ├── Jakarta\n", + "│ │ │ │ │ ├── Jayapura\n", + "│ │ │ │ │ ├── Jerusalem\n", + "│ │ │ │ │ ├── Kabul\n", + "│ │ │ │ │ ├── Kamchatka\n", + "│ │ │ │ │ ├── Karachi\n", + "│ │ │ │ │ ├── Kashgar\n", + "│ │ │ │ │ ├── Kathmandu\n", + "│ │ │ │ │ ├── Katmandu\n", + "│ │ │ │ │ ├── Khandyga\n", + "│ │ │ │ │ ├── Kolkata\n", + "│ │ │ │ │ ├── Krasnoyarsk\n", + "│ │ │ │ │ ├── Kuala_Lumpur\n", + "│ │ │ │ │ ├── Kuching\n", + "│ │ │ │ │ ├── Kuwait\n", + "│ │ │ │ │ ├── Macao\n", + "│ │ │ │ │ ├── Macau\n", + "│ │ │ │ │ ├── Magadan\n", + "│ │ │ │ │ ├── Makassar\n", + "│ │ │ │ │ ├── Manila\n", + "│ │ │ │ │ ├── Muscat\n", + "│ │ │ │ │ ├── Nicosia\n", + "│ │ │ │ │ ├── Novokuznetsk\n", + "│ │ │ │ │ ├── Novosibirsk\n", + "│ │ │ │ │ ├── Omsk\n", + "│ │ │ │ │ ├── Oral\n", + "│ │ │ │ │ ├── Phnom_Penh\n", + "│ │ │ │ │ ├── Pontianak\n", + "│ │ │ │ │ ├── Pyongyang\n", + "│ │ │ │ │ ├── Qatar\n", + "│ │ │ │ │ ├── Qostanay\n", + "│ │ │ │ │ ├── Qyzylorda\n", + "│ │ │ │ │ ├── Rangoon\n", + "│ │ │ │ │ ├── Riyadh\n", + "│ │ │ │ │ ├── Saigon\n", + "│ │ │ │ │ ├── Sakhalin\n", + "│ │ │ │ │ ├── Samarkand\n", + "│ │ │ │ │ ├── Seoul\n", + "│ │ │ │ │ ├── Shanghai\n", + "│ │ │ │ │ ├── Singapore\n", + "│ │ │ │ │ ├── Srednekolymsk\n", + "│ │ │ │ │ ├── Taipei\n", + "│ │ │ │ │ ├── Tashkent\n", + "│ │ │ │ │ ├── Tbilisi\n", + "│ │ │ │ │ ├── Tehran\n", + "│ │ │ │ │ ├── Tel_Aviv\n", + "│ │ │ │ │ ├── Thimbu\n", + "│ │ │ │ │ ├── Thimphu\n", + "│ │ │ │ │ ├── Tokyo\n", + "│ │ │ │ │ ├── Tomsk\n", + "│ │ │ │ │ ├── Ujung_Pandang\n", + "│ │ │ │ │ ├── Ulaanbaatar\n", + "│ │ │ │ │ ├── Ulan_Bator\n", + "│ │ │ │ │ ├── Urumqi\n", + "│ │ │ │ │ ├── Ust-Nera\n", + "│ │ │ │ │ ├── Vientiane\n", + "│ │ │ │ │ ├── Vladivostok\n", + "│ │ │ │ │ ├── Yakutsk\n", + "│ │ │ │ │ ├── Yangon\n", + "│ │ │ │ │ ├── Yekaterinburg\n", + "│ │ │ │ │ └── Yerevan\n", + "│ │ │ │ ├── Atlantic\n", + "│ │ │ │ │ ├── Azores\n", + "│ │ │ │ │ ├── Bermuda\n", + "│ │ │ │ │ ├── Canary\n", + "│ │ │ │ │ ├── Cape_Verde\n", + "│ │ │ │ │ ├── Faeroe\n", + "│ │ │ │ │ ├── Faroe\n", + "│ │ │ │ │ ├── Jan_Mayen\n", + "│ │ │ │ │ ├── Madeira\n", + "│ │ │ │ │ ├── Reykjavik\n", + "│ │ │ │ │ ├── South_Georgia\n", + "│ │ │ │ │ ├── St_Helena\n", + "│ │ │ │ │ └── Stanley\n", + "│ │ │ │ ├── Australia\n", + "│ │ │ │ │ ├── ACT\n", + "│ │ │ │ │ ├── Adelaide\n", + "│ │ │ │ │ ├── Brisbane\n", + "│ │ │ │ │ ├── Broken_Hill\n", + "│ │ │ │ │ ├── Canberra\n", + "│ │ │ │ │ ├── Currie\n", + "│ │ │ │ │ ├── Darwin\n", + "│ │ │ │ │ ├── Eucla\n", + "│ │ │ │ │ ├── Hobart\n", + "│ │ │ │ │ ├── LHI\n", + "│ │ │ │ │ ├── Lindeman\n", + "│ │ │ │ │ ├── Lord_Howe\n", + "│ │ │ │ │ ├── Melbourne\n", + "│ │ │ │ │ ├── NSW\n", + "│ │ │ │ │ ├── North\n", + "│ │ │ │ │ ├── Perth\n", + "│ │ │ │ │ ├── Queensland\n", + "│ │ │ │ │ ├── South\n", + "│ │ │ │ │ ├── Sydney\n", + "│ │ │ │ │ ├── Tasmania\n", + "│ │ │ │ │ ├── Victoria\n", + "│ │ │ │ │ ├── West\n", + "│ │ │ │ │ └── Yancowinna\n", + "│ │ │ │ ├── Brazil\n", + "│ │ │ │ │ ├── Acre\n", + "│ │ │ │ │ ├── DeNoronha\n", + "│ │ │ │ │ ├── East\n", + "│ │ │ │ │ └── West\n", + "│ │ │ │ ├── CET\n", + "│ │ │ │ ├── CST6CDT\n", + "│ │ │ │ ├── Canada\n", + "│ │ │ │ │ ├── Atlantic\n", + "│ │ │ │ │ ├── Central\n", + "│ │ │ │ │ ├── Eastern\n", + "│ │ │ │ │ ├── Mountain\n", + "│ │ │ │ │ ├── Newfoundland\n", + "│ │ │ │ │ ├── Pacific\n", + "│ │ │ │ │ ├── Saskatchewan\n", + "│ │ │ │ │ └── Yukon\n", + "│ │ │ │ ├── Chile\n", + "│ │ │ │ │ ├── Continental\n", + "│ │ │ │ │ └── EasterIsland\n", + "│ │ │ │ ├── Cuba\n", + "│ │ │ │ ├── EET\n", + "│ │ │ │ ├── EST\n", + "│ │ │ │ ├── EST5EDT\n", + "│ │ │ │ ├── Egypt\n", + "│ │ │ │ ├── Eire\n", + "│ │ │ │ ├── Etc\n", + "│ │ │ │ │ ├── GMT\n", + "│ │ │ │ │ ├── GMT+0\n", + "│ │ │ │ │ ├── GMT+1\n", + "│ │ │ │ │ ├── GMT+10\n", + "│ │ │ │ │ ├── GMT+11\n", + "│ │ │ │ │ ├── GMT+12\n", + "│ │ │ │ │ ├── GMT+2\n", + "│ │ │ │ │ ├── GMT+3\n", + "│ │ │ │ │ ├── GMT+4\n", + "│ │ │ │ │ ├── GMT+5\n", + "│ │ │ │ │ ├── GMT+6\n", + "│ │ │ │ │ ├── GMT+7\n", + "│ │ │ │ │ ├── GMT+8\n", + "│ │ │ │ │ ├── GMT+9\n", + "│ │ │ │ │ ├── GMT-0\n", + "│ │ │ │ │ ├── GMT-1\n", + "│ │ │ │ │ ├── GMT-10\n", + "│ │ │ │ │ ├── GMT-11\n", + "│ │ │ │ │ ├── GMT-12\n", + "│ │ │ │ │ ├── GMT-13\n", + "│ │ │ │ │ ├── GMT-14\n", + "│ │ │ │ │ ├── GMT-2\n", + "│ │ │ │ │ ├── GMT-3\n", + "│ │ │ │ │ ├── GMT-4\n", + "│ │ │ │ │ ├── GMT-5\n", + "│ │ │ │ │ ├── GMT-6\n", + "│ │ │ │ │ ├── GMT-7\n", + "│ │ │ │ │ ├── GMT-8\n", + "│ │ │ │ │ ├── GMT-9\n", + "│ │ │ │ │ ├── GMT0\n", + "│ │ │ │ │ ├── Greenwich\n", + "│ │ │ │ │ ├── UCT\n", + "│ │ │ │ │ ├── UTC\n", + "│ │ │ │ │ ├── Universal\n", + "│ │ │ │ │ └── Zulu\n", + "│ │ │ │ ├── Europe\n", + "│ │ │ │ │ ├── Amsterdam\n", + "│ │ │ │ │ ├── Andorra\n", + "│ │ │ │ │ ├── Astrakhan\n", + "│ │ │ │ │ ├── Athens\n", + "│ │ │ │ │ ├── Belfast\n", + "│ │ │ │ │ ├── Belgrade\n", + "│ │ │ │ │ ├── Berlin\n", + "│ │ │ │ │ ├── Bratislava\n", + "│ │ │ │ │ ├── Brussels\n", + "│ │ │ │ │ ├── Bucharest\n", + "│ │ │ │ │ ├── Budapest\n", + "│ │ │ │ │ ├── Busingen\n", + "│ │ │ │ │ ├── Chisinau\n", + "│ │ │ │ │ ├── Copenhagen\n", + "│ │ │ │ │ ├── Dublin\n", + "│ │ │ │ │ ├── Gibraltar\n", + "│ │ │ │ │ ├── Guernsey\n", + "│ │ │ │ │ ├── Helsinki\n", + "│ │ │ │ │ ├── Isle_of_Man\n", + "│ │ │ │ │ ├── Istanbul\n", + "│ │ │ │ │ ├── Jersey\n", + "│ │ │ │ │ ├── Kaliningrad\n", + "│ │ │ │ │ ├── Kiev\n", + "│ │ │ │ │ ├── Kirov\n", + "│ │ │ │ │ ├── Kyiv\n", + "│ │ │ │ │ ├── Lisbon\n", + "│ │ │ │ │ ├── Ljubljana\n", + "│ │ │ │ │ ├── London\n", + "│ │ │ │ │ ├── Luxembourg\n", + "│ │ │ │ │ ├── Madrid\n", + "│ │ │ │ │ ├── Malta\n", + "│ │ │ │ │ ├── Mariehamn\n", + "│ │ │ │ │ ├── Minsk\n", + "│ │ │ │ │ ├── Monaco\n", + "│ │ │ │ │ ├── Moscow\n", + "│ │ │ │ │ ├── Nicosia\n", + "│ │ │ │ │ ├── Oslo\n", + "│ │ │ │ │ ├── Paris\n", + "│ │ │ │ │ ├── Podgorica\n", + "│ │ │ │ │ ├── Prague\n", + "│ │ │ │ │ ├── Riga\n", + "│ │ │ │ │ ├── Rome\n", + "│ │ │ │ │ ├── Samara\n", + "│ │ │ │ │ ├── San_Marino\n", + "│ │ │ │ │ ├── Sarajevo\n", + "│ │ │ │ │ ├── Saratov\n", + "│ │ │ │ │ ├── Simferopol\n", + "│ │ │ │ │ ├── Skopje\n", + "│ │ │ │ │ ├── Sofia\n", + "│ │ │ │ │ ├── Stockholm\n", + "│ │ │ │ │ ├── Tallinn\n", + "│ │ │ │ │ ├── Tirane\n", + "│ │ │ │ │ ├── Tiraspol\n", + "│ │ │ │ │ ├── Ulyanovsk\n", + "│ │ │ │ │ ├── Uzhgorod\n", + "│ │ │ │ │ ├── Vaduz\n", + "│ │ │ │ │ ├── Vatican\n", + "│ │ │ │ │ ├── Vienna\n", + "│ │ │ │ │ ├── Vilnius\n", + "│ │ │ │ │ ├── Volgograd\n", + "│ │ │ │ │ ├── Warsaw\n", + "│ │ │ │ │ ├── Zagreb\n", + "│ │ │ │ │ ├── Zaporozhye\n", + "│ │ │ │ │ └── Zurich\n", + "│ │ │ │ ├── Factory\n", + "│ │ │ │ ├── GB\n", + "│ │ │ │ ├── GB-Eire\n", + "│ │ │ │ ├── GMT\n", + "│ │ │ │ ├── GMT+0\n", + "│ │ │ │ ├── GMT-0\n", + "│ │ │ │ ├── GMT0\n", + "│ │ │ │ ├── Greenwich\n", + "│ │ │ │ ├── HST\n", + "│ │ │ │ ├── Hongkong\n", + "│ │ │ │ ├── Iceland\n", + "│ │ │ │ ├── Indian\n", + "│ │ │ │ │ ├── Antananarivo\n", + "│ │ │ │ │ ├── Chagos\n", + "│ │ │ │ │ ├── Christmas\n", + "│ │ │ │ │ ├── Cocos\n", + "│ │ │ │ │ ├── Comoro\n", + "│ │ │ │ │ ├── Kerguelen\n", + "│ │ │ │ │ ├── Mahe\n", + "│ │ │ │ │ ├── Maldives\n", + "│ │ │ │ │ ├── Mauritius\n", + "│ │ │ │ │ ├── Mayotte\n", + "│ │ │ │ │ └── Reunion\n", + "│ │ │ │ ├── Iran\n", + "│ │ │ │ ├── Israel\n", + "│ │ │ │ ├── Jamaica\n", + "│ │ │ │ ├── Japan\n", + "│ │ │ │ ├── Kwajalein\n", + "│ │ │ │ ├── Libya\n", + "│ │ │ │ ├── MET\n", + "│ │ │ │ ├── MST\n", + "│ │ │ │ ├── MST7MDT\n", + "│ │ │ │ ├── Mexico\n", + "│ │ │ │ │ ├── BajaNorte\n", + "│ │ │ │ │ ├── BajaSur\n", + "│ │ │ │ │ └── General\n", + "│ │ │ │ ├── NZ\n", + "│ │ │ │ ├── NZ-CHAT\n", + "│ │ │ │ ├── Navajo\n", + "│ │ │ │ ├── PRC\n", + "│ │ │ │ ├── PST8PDT\n", + "│ │ │ │ ├── Pacific\n", + "│ │ │ │ │ ├── Apia\n", + "│ │ │ │ │ ├── Auckland\n", + "│ │ │ │ │ ├── Bougainville\n", + "│ │ │ │ │ ├── Chatham\n", + "│ │ │ │ │ ├── Chuuk\n", + "│ │ │ │ │ ├── Easter\n", + "│ │ │ │ │ ├── Efate\n", + "│ │ │ │ │ ├── Enderbury\n", + "│ │ │ │ │ ├── Fakaofo\n", + "│ │ │ │ │ ├── Fiji\n", + "│ │ │ │ │ ├── Funafuti\n", + "│ │ │ │ │ ├── Galapagos\n", + "│ │ │ │ │ ├── Gambier\n", + "│ │ │ │ │ ├── Guadalcanal\n", + "│ │ │ │ │ ├── Guam\n", + "│ │ │ │ │ ├── Honolulu\n", + "│ │ │ │ │ ├── Johnston\n", + "│ │ │ │ │ ├── Kanton\n", + "│ │ │ │ │ ├── Kiritimati\n", + "│ │ │ │ │ ├── Kosrae\n", + "│ │ │ │ │ ├── Kwajalein\n", + "│ │ │ │ │ ├── Majuro\n", + "│ │ │ │ │ ├── Marquesas\n", + "│ │ │ │ │ ├── Midway\n", + "│ │ │ │ │ ├── Nauru\n", + "│ │ │ │ │ ├── Niue\n", + "│ │ │ │ │ ├── Norfolk\n", + "│ │ │ │ │ ├── Noumea\n", + "│ │ │ │ │ ├── Pago_Pago\n", + "│ │ │ │ │ ├── Palau\n", + "│ │ │ │ │ ├── Pitcairn\n", + "│ │ │ │ │ ├── Pohnpei\n", + "│ │ │ │ │ ├── Ponape\n", + "│ │ │ │ │ ├── Port_Moresby\n", + "│ │ │ │ │ ├── Rarotonga\n", + "│ │ │ │ │ ├── Saipan\n", + "│ │ │ │ │ ├── Samoa\n", + "│ │ │ │ │ ├── Tahiti\n", + "│ │ │ │ │ ├── Tarawa\n", + "│ │ │ │ │ ├── Tongatapu\n", + "│ │ │ │ │ ├── Truk\n", + "│ │ │ │ │ ├── Wake\n", + "│ │ │ │ │ ├── Wallis\n", + "│ │ │ │ │ └── Yap\n", + "│ │ │ │ ├── Poland\n", + "│ │ │ │ ├── Portugal\n", + "│ │ │ │ ├── ROC\n", + "│ │ │ │ ├── ROK\n", + "│ │ │ │ ├── Singapore\n", + "│ │ │ │ ├── Turkey\n", + "│ │ │ │ ├── UCT\n", + "│ │ │ │ ├── US\n", + "│ │ │ │ │ ├── Alaska\n", + "│ │ │ │ │ ├── Aleutian\n", + "│ │ │ │ │ ├── Arizona\n", + "│ │ │ │ │ ├── Central\n", + "│ │ │ │ │ ├── East-Indiana\n", + "│ │ │ │ │ ├── Eastern\n", + "│ │ │ │ │ ├── Hawaii\n", + "│ │ │ │ │ ├── Indiana-Starke\n", + "│ │ │ │ │ ├── Michigan\n", + "│ │ │ │ │ ├── Mountain\n", + "│ │ │ │ │ ├── Pacific\n", + "│ │ │ │ │ └── Samoa\n", + "│ │ │ │ ├── UTC\n", + "│ │ │ │ ├── Universal\n", + "│ │ │ │ ├── W-SU\n", + "│ │ │ │ ├── WET\n", + "│ │ │ │ ├── Zulu\n", + "│ │ │ │ ├── iso3166.tab\n", + "│ │ │ │ ├── leapseconds\n", + "│ │ │ │ ├── tzdata.zi\n", + "│ │ │ │ ├── zone.tab\n", + "│ │ │ │ ├── zone1970.tab\n", + "│ │ │ │ └── zonenow.tab\n", + "│ │ │ ├── pytz-2024.1.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE.txt\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ ├── top_level.txt\n", + "│ │ │ │ └── zip-safe\n", + "│ │ │ ├── pyzmq-26.0.2.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ ├── entry_points.txt\n", + "│ │ │ │ └── licenses\n", + "│ │ │ │ ├── LICENSE.md\n", + "│ │ │ │ └── licenses\n", + "│ │ │ │ ├── LICENSE.libsodium.txt\n", + "│ │ │ │ └── LICENSE.zeromq.txt\n", + "│ │ │ ├── pyzmq.libs\n", + "│ │ │ │ ├── libsodium-b135f62c.so.26.1.0\n", + "│ │ │ │ └── libzmq-5dd2f677.so.5.2.5\n", + "│ │ │ ├── setuptools\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _deprecation_warning.cpython-310.pyc\n", + "│ │ │ │ │ ├── _imp.cpython-310.pyc\n", + "│ │ │ │ │ ├── archive_util.cpython-310.pyc\n", + "│ │ │ │ │ ├── build_meta.cpython-310.pyc\n", + "│ │ │ │ │ ├── config.cpython-310.pyc\n", + "│ │ │ │ │ ├── dep_util.cpython-310.pyc\n", + "│ │ │ │ │ ├── depends.cpython-310.pyc\n", + "│ │ │ │ │ ├── dist.cpython-310.pyc\n", + "│ │ │ │ │ ├── errors.cpython-310.pyc\n", + "│ │ │ │ │ ├── extension.cpython-310.pyc\n", + "│ │ │ │ │ ├── glob.cpython-310.pyc\n", + "│ │ │ │ │ ├── installer.cpython-310.pyc\n", + "│ │ │ │ │ ├── launch.cpython-310.pyc\n", + "│ │ │ │ │ ├── monkey.cpython-310.pyc\n", + "│ │ │ │ │ ├── msvc.cpython-310.pyc\n", + "│ │ │ │ │ ├── namespaces.cpython-310.pyc\n", + "│ │ │ │ │ ├── package_index.cpython-310.pyc\n", + "│ │ │ │ │ ├── py34compat.cpython-310.pyc\n", + "│ │ │ │ │ ├── sandbox.cpython-310.pyc\n", + "│ │ │ │ │ ├── unicode_utils.cpython-310.pyc\n", + "│ │ │ │ │ ├── version.cpython-310.pyc\n", + "│ │ │ │ │ ├── wheel.cpython-310.pyc\n", + "│ │ │ │ │ └── windows_support.cpython-310.pyc\n", + "│ │ │ │ ├── _deprecation_warning.py\n", + "│ │ │ │ ├── _distutils\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _msvccompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── archive_util.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── bcppcompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ccompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cmd.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── config.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── core.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── cygwinccompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── debug.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dep_util.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dir_util.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dist.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── errors.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── extension.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── fancy_getopt.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── file_util.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── filelist.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── log.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── msvc9compiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── msvccompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── py35compat.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── py38compat.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── spawn.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── sysconfig.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── text_file.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── unixccompiler.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── util.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── version.cpython-310.pyc\n", + "│ │ │ │ │ │ └── versionpredicate.cpython-310.pyc\n", + "│ │ │ │ │ ├── _msvccompiler.py\n", + "│ │ │ │ │ ├── archive_util.py\n", + "│ │ │ │ │ ├── bcppcompiler.py\n", + "│ │ │ │ │ ├── ccompiler.py\n", + "│ │ │ │ │ ├── cmd.py\n", + "│ │ │ │ │ ├── command\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── bdist.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── bdist_dumb.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── bdist_msi.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── bdist_rpm.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── bdist_wininst.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── build.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── build_clib.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── build_ext.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── build_py.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── build_scripts.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── check.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── clean.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── config.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── install.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── install_data.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── install_egg_info.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── install_headers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── install_lib.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── install_scripts.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── py37compat.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── register.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── sdist.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── upload.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── bdist.py\n", + "│ │ │ │ │ │ ├── bdist_dumb.py\n", + "│ │ │ │ │ │ ├── bdist_msi.py\n", + "│ │ │ │ │ │ ├── bdist_rpm.py\n", + "│ │ │ │ │ │ ├── bdist_wininst.py\n", + "│ │ │ │ │ │ ├── build.py\n", + "│ │ │ │ │ │ ├── build_clib.py\n", + "│ │ │ │ │ │ ├── build_ext.py\n", + "│ │ │ │ │ │ ├── build_py.py\n", + "│ │ │ │ │ │ ├── build_scripts.py\n", + "│ │ │ │ │ │ ├── check.py\n", + "│ │ │ │ │ │ ├── clean.py\n", + "│ │ │ │ │ │ ├── config.py\n", + "│ │ │ │ │ │ ├── install.py\n", + "│ │ │ │ │ │ ├── install_data.py\n", + "│ │ │ │ │ │ ├── install_egg_info.py\n", + "│ │ │ │ │ │ ├── install_headers.py\n", + "│ │ │ │ │ │ ├── install_lib.py\n", + "│ │ │ │ │ │ ├── install_scripts.py\n", + "│ │ │ │ │ │ ├── py37compat.py\n", + "│ │ │ │ │ │ ├── register.py\n", + "│ │ │ │ │ │ ├── sdist.py\n", + "│ │ │ │ │ │ └── upload.py\n", + "│ │ │ │ │ ├── config.py\n", + "│ │ │ │ │ ├── core.py\n", + "│ │ │ │ │ ├── cygwinccompiler.py\n", + "│ │ │ │ │ ├── debug.py\n", + "│ │ │ │ │ ├── dep_util.py\n", + "│ │ │ │ │ ├── dir_util.py\n", + "│ │ │ │ │ ├── dist.py\n", + "│ │ │ │ │ ├── errors.py\n", + "│ │ │ │ │ ├── extension.py\n", + "│ │ │ │ │ ├── fancy_getopt.py\n", + "│ │ │ │ │ ├── file_util.py\n", + "│ │ │ │ │ ├── filelist.py\n", + "│ │ │ │ │ ├── log.py\n", + "│ │ │ │ │ ├── msvc9compiler.py\n", + "│ │ │ │ │ ├── msvccompiler.py\n", + "│ │ │ │ │ ├── py35compat.py\n", + "│ │ │ │ │ ├── py38compat.py\n", + "│ │ │ │ │ ├── spawn.py\n", + "│ │ │ │ │ ├── sysconfig.py\n", + "│ │ │ │ │ ├── text_file.py\n", + "│ │ │ │ │ ├── unixccompiler.py\n", + "│ │ │ │ │ ├── util.py\n", + "│ │ │ │ │ ├── version.py\n", + "│ │ │ │ │ └── versionpredicate.py\n", + "│ │ │ │ ├── _imp.py\n", + "│ │ │ │ ├── _vendor\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ordered_set.cpython-310.pyc\n", + "│ │ │ │ │ │ └── pyparsing.cpython-310.pyc\n", + "│ │ │ │ │ ├── more_itertools\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── more.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── recipes.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── more.py\n", + "│ │ │ │ │ │ └── recipes.py\n", + "│ │ │ │ │ ├── ordered_set.py\n", + "│ │ │ │ │ ├── packaging\n", + "│ │ │ │ │ │ ├── __about__.py\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ │ ├── __about__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _manylinux.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _musllinux.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── _structures.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── markers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── requirements.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── specifiers.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── tags.cpython-310.pyc\n", + "│ │ │ │ │ │ │ ├── utils.cpython-310.pyc\n", + "│ │ │ │ │ │ │ └── version.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _manylinux.py\n", + "│ │ │ │ │ │ ├── _musllinux.py\n", + "│ │ │ │ │ │ ├── _structures.py\n", + "│ │ │ │ │ │ ├── markers.py\n", + "│ │ │ │ │ │ ├── requirements.py\n", + "│ │ │ │ │ │ ├── specifiers.py\n", + "│ │ │ │ │ │ ├── tags.py\n", + "│ │ │ │ │ │ ├── utils.py\n", + "│ │ │ │ │ │ └── version.py\n", + "│ │ │ │ │ └── pyparsing.py\n", + "│ │ │ │ ├── archive_util.py\n", + "│ │ │ │ ├── build_meta.py\n", + "│ │ │ │ ├── cli-32.exe\n", + "│ │ │ │ ├── cli-64.exe\n", + "│ │ │ │ ├── cli-arm64.exe\n", + "│ │ │ │ ├── cli.exe\n", + "│ │ │ │ ├── command\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── alias.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── bdist_egg.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── bdist_rpm.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── build_clib.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── build_ext.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── build_py.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── develop.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── dist_info.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── easy_install.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── egg_info.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── install.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── install_egg_info.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── install_lib.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── install_scripts.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── py36compat.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── register.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── rotate.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── saveopts.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── sdist.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── setopt.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── upload.cpython-310.pyc\n", + "│ │ │ │ │ │ └── upload_docs.cpython-310.pyc\n", + "│ │ │ │ │ ├── alias.py\n", + "│ │ │ │ │ ├── bdist_egg.py\n", + "│ │ │ │ │ ├── bdist_rpm.py\n", + "│ │ │ │ │ ├── build_clib.py\n", + "│ │ │ │ │ ├── build_ext.py\n", + "│ │ │ │ │ ├── build_py.py\n", + "│ │ │ │ │ ├── develop.py\n", + "│ │ │ │ │ ├── dist_info.py\n", + "│ │ │ │ │ ├── easy_install.py\n", + "│ │ │ │ │ ├── egg_info.py\n", + "│ │ │ │ │ ├── install.py\n", + "│ │ │ │ │ ├── install_egg_info.py\n", + "│ │ │ │ │ ├── install_lib.py\n", + "│ │ │ │ │ ├── install_scripts.py\n", + "│ │ │ │ │ ├── launcher manifest.xml\n", + "│ │ │ │ │ ├── py36compat.py\n", + "│ │ │ │ │ ├── register.py\n", + "│ │ │ │ │ ├── rotate.py\n", + "│ │ │ │ │ ├── saveopts.py\n", + "│ │ │ │ │ ├── sdist.py\n", + "│ │ │ │ │ ├── setopt.py\n", + "│ │ │ │ │ ├── test.py\n", + "│ │ │ │ │ ├── upload.py\n", + "│ │ │ │ │ └── upload_docs.py\n", + "│ │ │ │ ├── config.py\n", + "│ │ │ │ ├── dep_util.py\n", + "│ │ │ │ ├── depends.py\n", + "│ │ │ │ ├── dist.py\n", + "│ │ │ │ ├── errors.py\n", + "│ │ │ │ ├── extension.py\n", + "│ │ │ │ ├── extern\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ ├── glob.py\n", + "│ │ │ │ ├── gui-32.exe\n", + "│ │ │ │ ├── gui-64.exe\n", + "│ │ │ │ ├── gui-arm64.exe\n", + "│ │ │ │ ├── gui.exe\n", + "│ │ │ │ ├── installer.py\n", + "│ │ │ │ ├── launch.py\n", + "│ │ │ │ ├── monkey.py\n", + "│ │ │ │ ├── msvc.py\n", + "│ │ │ │ ├── namespaces.py\n", + "│ │ │ │ ├── package_index.py\n", + "│ │ │ │ ├── py34compat.py\n", + "│ │ │ │ ├── sandbox.py\n", + "│ │ │ │ ├── script (dev).tmpl\n", + "│ │ │ │ ├── script.tmpl\n", + "│ │ │ │ ├── unicode_utils.py\n", + "│ │ │ │ ├── version.py\n", + "│ │ │ │ ├── wheel.py\n", + "│ │ │ │ └── windows_support.py\n", + "│ │ │ ├── setuptools-59.6.0.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── REQUESTED\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ ├── entry_points.txt\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── six-1.16.0.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── six.py\n", + "│ │ │ ├── stack_data\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── core.cpython-310.pyc\n", + "│ │ │ │ │ ├── formatting.cpython-310.pyc\n", + "│ │ │ │ │ ├── serializing.cpython-310.pyc\n", + "│ │ │ │ │ ├── utils.cpython-310.pyc\n", + "│ │ │ │ │ └── version.cpython-310.pyc\n", + "│ │ │ │ ├── core.py\n", + "│ │ │ │ ├── formatting.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ ├── serializing.py\n", + "│ │ │ │ ├── utils.py\n", + "│ │ │ │ └── version.py\n", + "│ │ │ ├── stack_data-0.6.3.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE.txt\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── tornado\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _locale_data.cpython-310.pyc\n", + "│ │ │ │ │ ├── auth.cpython-310.pyc\n", + "│ │ │ │ │ ├── autoreload.cpython-310.pyc\n", + "│ │ │ │ │ ├── concurrent.cpython-310.pyc\n", + "│ │ │ │ │ ├── curl_httpclient.cpython-310.pyc\n", + "│ │ │ │ │ ├── escape.cpython-310.pyc\n", + "│ │ │ │ │ ├── gen.cpython-310.pyc\n", + "│ │ │ │ │ ├── http1connection.cpython-310.pyc\n", + "│ │ │ │ │ ├── httpclient.cpython-310.pyc\n", + "│ │ │ │ │ ├── httpserver.cpython-310.pyc\n", + "│ │ │ │ │ ├── httputil.cpython-310.pyc\n", + "│ │ │ │ │ ├── ioloop.cpython-310.pyc\n", + "│ │ │ │ │ ├── iostream.cpython-310.pyc\n", + "│ │ │ │ │ ├── locale.cpython-310.pyc\n", + "│ │ │ │ │ ├── locks.cpython-310.pyc\n", + "│ │ │ │ │ ├── log.cpython-310.pyc\n", + "│ │ │ │ │ ├── netutil.cpython-310.pyc\n", + "│ │ │ │ │ ├── options.cpython-310.pyc\n", + "│ │ │ │ │ ├── process.cpython-310.pyc\n", + "│ │ │ │ │ ├── queues.cpython-310.pyc\n", + "│ │ │ │ │ ├── routing.cpython-310.pyc\n", + "│ │ │ │ │ ├── simple_httpclient.cpython-310.pyc\n", + "│ │ │ │ │ ├── tcpclient.cpython-310.pyc\n", + "│ │ │ │ │ ├── tcpserver.cpython-310.pyc\n", + "│ │ │ │ │ ├── template.cpython-310.pyc\n", + "│ │ │ │ │ ├── testing.cpython-310.pyc\n", + "│ │ │ │ │ ├── util.cpython-310.pyc\n", + "│ │ │ │ │ ├── web.cpython-310.pyc\n", + "│ │ │ │ │ ├── websocket.cpython-310.pyc\n", + "│ │ │ │ │ └── wsgi.cpython-310.pyc\n", + "│ │ │ │ ├── _locale_data.py\n", + "│ │ │ │ ├── auth.py\n", + "│ │ │ │ ├── autoreload.py\n", + "│ │ │ │ ├── concurrent.py\n", + "│ │ │ │ ├── curl_httpclient.py\n", + "│ │ │ │ ├── escape.py\n", + "│ │ │ │ ├── gen.py\n", + "│ │ │ │ ├── http1connection.py\n", + "│ │ │ │ ├── httpclient.py\n", + "│ │ │ │ ├── httpserver.py\n", + "│ │ │ │ ├── httputil.py\n", + "│ │ │ │ ├── ioloop.py\n", + "│ │ │ │ ├── iostream.py\n", + "│ │ │ │ ├── locale.py\n", + "│ │ │ │ ├── locks.py\n", + "│ │ │ │ ├── log.py\n", + "│ │ │ │ ├── netutil.py\n", + "│ │ │ │ ├── options.py\n", + "│ │ │ │ ├── platform\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── asyncio.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── caresresolver.cpython-310.pyc\n", + "│ │ │ │ │ │ └── twisted.cpython-310.pyc\n", + "│ │ │ │ │ ├── asyncio.py\n", + "│ │ │ │ │ ├── caresresolver.py\n", + "│ │ │ │ │ └── twisted.py\n", + "│ │ │ │ ├── process.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ ├── queues.py\n", + "│ │ │ │ ├── routing.py\n", + "│ │ │ │ ├── simple_httpclient.py\n", + "│ │ │ │ ├── speedups.abi3.so\n", + "│ │ │ │ ├── speedups.pyi\n", + "│ │ │ │ ├── tcpclient.py\n", + "│ │ │ │ ├── tcpserver.py\n", + "│ │ │ │ ├── template.py\n", + "│ │ │ │ ├── test\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __main__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── asyncio_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── auth_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── autoreload_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── circlerefs_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── concurrent_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── curl_httpclient_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── escape_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── gen_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── http1connection_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── httpclient_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── httpserver_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── httputil_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── import_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ioloop_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── iostream_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── locale_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── locks_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── log_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── netutil_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── options_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── process_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── queues_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── resolve_test_helper.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── routing_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── runtests.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── simple_httpclient_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── tcpclient_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── tcpserver_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── template_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── testing_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── twisted_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── util.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── util_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── web_test.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── websocket_test.cpython-310.pyc\n", + "│ │ │ │ │ │ └── wsgi_test.cpython-310.pyc\n", + "│ │ │ │ │ ├── asyncio_test.py\n", + "│ │ │ │ │ ├── auth_test.py\n", + "│ │ │ │ │ ├── autoreload_test.py\n", + "│ │ │ │ │ ├── circlerefs_test.py\n", + "│ │ │ │ │ ├── concurrent_test.py\n", + "│ │ │ │ │ ├── csv_translations\n", + "│ │ │ │ │ │ └── fr_FR.csv\n", + "│ │ │ │ │ ├── curl_httpclient_test.py\n", + "│ │ │ │ │ ├── escape_test.py\n", + "│ │ │ │ │ ├── gen_test.py\n", + "│ │ │ │ │ ├── gettext_translations\n", + "│ │ │ │ │ │ └── fr_FR\n", + "│ │ │ │ │ │ └── LC_MESSAGES\n", + "│ │ │ │ │ │ ├── tornado_test.mo\n", + "│ │ │ │ │ │ └── tornado_test.po\n", + "│ │ │ │ │ ├── http1connection_test.py\n", + "│ │ │ │ │ ├── httpclient_test.py\n", + "│ │ │ │ │ ├── httpserver_test.py\n", + "│ │ │ │ │ ├── httputil_test.py\n", + "│ │ │ │ │ ├── import_test.py\n", + "│ │ │ │ │ ├── ioloop_test.py\n", + "│ │ │ │ │ ├── iostream_test.py\n", + "│ │ │ │ │ ├── locale_test.py\n", + "│ │ │ │ │ ├── locks_test.py\n", + "│ │ │ │ │ ├── log_test.py\n", + "│ │ │ │ │ ├── netutil_test.py\n", + "│ │ │ │ │ ├── options_test.cfg\n", + "│ │ │ │ │ ├── options_test.py\n", + "│ │ │ │ │ ├── options_test_types.cfg\n", + "│ │ │ │ │ ├── options_test_types_str.cfg\n", + "│ │ │ │ │ ├── process_test.py\n", + "│ │ │ │ │ ├── queues_test.py\n", + "│ │ │ │ │ ├── resolve_test_helper.py\n", + "│ │ │ │ │ ├── routing_test.py\n", + "│ │ │ │ │ ├── runtests.py\n", + "│ │ │ │ │ ├── simple_httpclient_test.py\n", + "│ │ │ │ │ ├── static\n", + "│ │ │ │ │ │ ├── dir\n", + "│ │ │ │ │ │ │ └── index.html\n", + "│ │ │ │ │ │ ├── robots.txt\n", + "│ │ │ │ │ │ ├── sample.xml\n", + "│ │ │ │ │ │ ├── sample.xml.bz2\n", + "│ │ │ │ │ │ └── sample.xml.gz\n", + "│ │ │ │ │ ├── static_foo.txt\n", + "│ │ │ │ │ ├── tcpclient_test.py\n", + "│ │ │ │ │ ├── tcpserver_test.py\n", + "│ │ │ │ │ ├── template_test.py\n", + "│ │ │ │ │ ├── templates\n", + "│ │ │ │ │ │ └── utf8.html\n", + "│ │ │ │ │ ├── test.crt\n", + "│ │ │ │ │ ├── test.key\n", + "│ │ │ │ │ ├── testing_test.py\n", + "│ │ │ │ │ ├── twisted_test.py\n", + "│ │ │ │ │ ├── util.py\n", + "│ │ │ │ │ ├── util_test.py\n", + "│ │ │ │ │ ├── web_test.py\n", + "│ │ │ │ │ ├── websocket_test.py\n", + "│ │ │ │ │ └── wsgi_test.py\n", + "│ │ │ │ ├── testing.py\n", + "│ │ │ │ ├── util.py\n", + "│ │ │ │ ├── web.py\n", + "│ │ │ │ ├── websocket.py\n", + "│ │ │ │ └── wsgi.py\n", + "│ │ │ ├── tornado-6.4.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── traitlets\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _version.cpython-310.pyc\n", + "│ │ │ │ │ ├── log.cpython-310.pyc\n", + "│ │ │ │ │ └── traitlets.cpython-310.pyc\n", + "│ │ │ │ ├── _version.py\n", + "│ │ │ │ ├── config\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── application.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── argcomplete_config.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── configurable.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── loader.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── manager.cpython-310.pyc\n", + "│ │ │ │ │ │ └── sphinxdoc.cpython-310.pyc\n", + "│ │ │ │ │ ├── application.py\n", + "│ │ │ │ │ ├── argcomplete_config.py\n", + "│ │ │ │ │ ├── configurable.py\n", + "│ │ │ │ │ ├── loader.py\n", + "│ │ │ │ │ ├── manager.py\n", + "│ │ │ │ │ └── sphinxdoc.py\n", + "│ │ │ │ ├── log.py\n", + "│ │ │ │ ├── py.typed\n", + "│ │ │ │ ├── tests\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── test_traitlets.cpython-310.pyc\n", + "│ │ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_traitlets.py\n", + "│ │ │ │ │ └── utils.py\n", + "│ │ │ │ ├── traitlets.py\n", + "│ │ │ │ └── utils\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── bunch.cpython-310.pyc\n", + "│ │ │ │ │ ├── decorators.cpython-310.pyc\n", + "│ │ │ │ │ ├── descriptions.cpython-310.pyc\n", + "│ │ │ │ │ ├── getargspec.cpython-310.pyc\n", + "│ │ │ │ │ ├── importstring.cpython-310.pyc\n", + "│ │ │ │ │ ├── nested_update.cpython-310.pyc\n", + "│ │ │ │ │ ├── sentinel.cpython-310.pyc\n", + "│ │ │ │ │ ├── text.cpython-310.pyc\n", + "│ │ │ │ │ └── warnings.cpython-310.pyc\n", + "│ │ │ │ ├── bunch.py\n", + "│ │ │ │ ├── decorators.py\n", + "│ │ │ │ ├── descriptions.py\n", + "│ │ │ │ ├── getargspec.py\n", + "│ │ │ │ ├── importstring.py\n", + "│ │ │ │ ├── nested_update.py\n", + "│ │ │ │ ├── sentinel.py\n", + "│ │ │ │ ├── text.py\n", + "│ │ │ │ └── warnings.py\n", + "│ │ │ ├── traitlets-5.14.3.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── licenses\n", + "│ │ │ │ └── LICENSE\n", + "│ │ │ ├── typing_extensions-4.11.0.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ └── WHEEL\n", + "│ │ │ ├── typing_extensions.py\n", + "│ │ │ ├── tzdata\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ ├── zoneinfo\n", + "│ │ │ │ │ ├── Africa\n", + "│ │ │ │ │ │ ├── Abidjan\n", + "│ │ │ │ │ │ ├── Accra\n", + "│ │ │ │ │ │ ├── Addis_Ababa\n", + "│ │ │ │ │ │ ├── Algiers\n", + "│ │ │ │ │ │ ├── Asmara\n", + "│ │ │ │ │ │ ├── Asmera\n", + "│ │ │ │ │ │ ├── Bamako\n", + "│ │ │ │ │ │ ├── Bangui\n", + "│ │ │ │ │ │ ├── Banjul\n", + "│ │ │ │ │ │ ├── Bissau\n", + "│ │ │ │ │ │ ├── Blantyre\n", + "│ │ │ │ │ │ ├── Brazzaville\n", + "│ │ │ │ │ │ ├── Bujumbura\n", + "│ │ │ │ │ │ ├── Cairo\n", + "│ │ │ │ │ │ ├── Casablanca\n", + "│ │ │ │ │ │ ├── Ceuta\n", + "│ │ │ │ │ │ ├── Conakry\n", + "│ │ │ │ │ │ ├── Dakar\n", + "│ │ │ │ │ │ ├── Dar_es_Salaam\n", + "│ │ │ │ │ │ ├── Djibouti\n", + "│ │ │ │ │ │ ├── Douala\n", + "│ │ │ │ │ │ ├── El_Aaiun\n", + "│ │ │ │ │ │ ├── Freetown\n", + "│ │ │ │ │ │ ├── Gaborone\n", + "│ │ │ │ │ │ ├── Harare\n", + "│ │ │ │ │ │ ├── Johannesburg\n", + "│ │ │ │ │ │ ├── Juba\n", + "│ │ │ │ │ │ ├── Kampala\n", + "│ │ │ │ │ │ ├── Khartoum\n", + "│ │ │ │ │ │ ├── Kigali\n", + "│ │ │ │ │ │ ├── Kinshasa\n", + "│ │ │ │ │ │ ├── Lagos\n", + "│ │ │ │ │ │ ├── Libreville\n", + "│ │ │ │ │ │ ├── Lome\n", + "│ │ │ │ │ │ ├── Luanda\n", + "│ │ │ │ │ │ ├── Lubumbashi\n", + "│ │ │ │ │ │ ├── Lusaka\n", + "│ │ │ │ │ │ ├── Malabo\n", + "│ │ │ │ │ │ ├── Maputo\n", + "│ │ │ │ │ │ ├── Maseru\n", + "│ │ │ │ │ │ ├── Mbabane\n", + "│ │ │ │ │ │ ├── Mogadishu\n", + "│ │ │ │ │ │ ├── Monrovia\n", + "│ │ │ │ │ │ ├── Nairobi\n", + "│ │ │ │ │ │ ├── Ndjamena\n", + "│ │ │ │ │ │ ├── Niamey\n", + "│ │ │ │ │ │ ├── Nouakchott\n", + "│ │ │ │ │ │ ├── Ouagadougou\n", + "│ │ │ │ │ │ ├── Porto-Novo\n", + "│ │ │ │ │ │ ├── Sao_Tome\n", + "│ │ │ │ │ │ ├── Timbuktu\n", + "│ │ │ │ │ │ ├── Tripoli\n", + "│ │ │ │ │ │ ├── Tunis\n", + "│ │ │ │ │ │ ├── Windhoek\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── America\n", + "│ │ │ │ │ │ ├── Adak\n", + "│ │ │ │ │ │ ├── Anchorage\n", + "│ │ │ │ │ │ ├── Anguilla\n", + "│ │ │ │ │ │ ├── Antigua\n", + "│ │ │ │ │ │ ├── Araguaina\n", + "│ │ │ │ │ │ ├── Argentina\n", + "│ │ │ │ │ │ │ ├── Buenos_Aires\n", + "│ │ │ │ │ │ │ ├── Catamarca\n", + "│ │ │ │ │ │ │ ├── ComodRivadavia\n", + "│ │ │ │ │ │ │ ├── Cordoba\n", + "│ │ │ │ │ │ │ ├── Jujuy\n", + "│ │ │ │ │ │ │ ├── La_Rioja\n", + "│ │ │ │ │ │ │ ├── Mendoza\n", + "│ │ │ │ │ │ │ ├── Rio_Gallegos\n", + "│ │ │ │ │ │ │ ├── Salta\n", + "│ │ │ │ │ │ │ ├── San_Juan\n", + "│ │ │ │ │ │ │ ├── San_Luis\n", + "│ │ │ │ │ │ │ ├── Tucuman\n", + "│ │ │ │ │ │ │ ├── Ushuaia\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── Aruba\n", + "│ │ │ │ │ │ ├── Asuncion\n", + "│ │ │ │ │ │ ├── Atikokan\n", + "│ │ │ │ │ │ ├── Atka\n", + "│ │ │ │ │ │ ├── Bahia\n", + "│ │ │ │ │ │ ├── Bahia_Banderas\n", + "│ │ │ │ │ │ ├── Barbados\n", + "│ │ │ │ │ │ ├── Belem\n", + "│ │ │ │ │ │ ├── Belize\n", + "│ │ │ │ │ │ ├── Blanc-Sablon\n", + "│ │ │ │ │ │ ├── Boa_Vista\n", + "│ │ │ │ │ │ ├── Bogota\n", + "│ │ │ │ │ │ ├── Boise\n", + "│ │ │ │ │ │ ├── Buenos_Aires\n", + "│ │ │ │ │ │ ├── Cambridge_Bay\n", + "│ │ │ │ │ │ ├── Campo_Grande\n", + "│ │ │ │ │ │ ├── Cancun\n", + "│ │ │ │ │ │ ├── Caracas\n", + "│ │ │ │ │ │ ├── Catamarca\n", + "│ │ │ │ │ │ ├── Cayenne\n", + "│ │ │ │ │ │ ├── Cayman\n", + "│ │ │ │ │ │ ├── Chicago\n", + "│ │ │ │ │ │ ├── Chihuahua\n", + "│ │ │ │ │ │ ├── Ciudad_Juarez\n", + "│ │ │ │ │ │ ├── Coral_Harbour\n", + "│ │ │ │ │ │ ├── Cordoba\n", + "│ │ │ │ │ │ ├── Costa_Rica\n", + "│ │ │ │ │ │ ├── Creston\n", + "│ │ │ │ │ │ ├── Cuiaba\n", + "│ │ │ │ │ │ ├── Curacao\n", + "│ │ │ │ │ │ ├── Danmarkshavn\n", + "│ │ │ │ │ │ ├── Dawson\n", + "│ │ │ │ │ │ ├── Dawson_Creek\n", + "│ │ │ │ │ │ ├── Denver\n", + "│ │ │ │ │ │ ├── Detroit\n", + "│ │ │ │ │ │ ├── Dominica\n", + "│ │ │ │ │ │ ├── Edmonton\n", + "│ │ │ │ │ │ ├── Eirunepe\n", + "│ │ │ │ │ │ ├── El_Salvador\n", + "│ │ │ │ │ │ ├── Ensenada\n", + "│ │ │ │ │ │ ├── Fort_Nelson\n", + "│ │ │ │ │ │ ├── Fort_Wayne\n", + "│ │ │ │ │ │ ├── Fortaleza\n", + "│ │ │ │ │ │ ├── Glace_Bay\n", + "│ │ │ │ │ │ ├── Godthab\n", + "│ │ │ │ │ │ ├── Goose_Bay\n", + "│ │ │ │ │ │ ├── Grand_Turk\n", + "│ │ │ │ │ │ ├── Grenada\n", + "│ │ │ │ │ │ ├── Guadeloupe\n", + "│ │ │ │ │ │ ├── Guatemala\n", + "│ │ │ │ │ │ ├── Guayaquil\n", + "│ │ │ │ │ │ ├── Guyana\n", + "│ │ │ │ │ │ ├── Halifax\n", + "│ │ │ │ │ │ ├── Havana\n", + "│ │ │ │ │ │ ├── Hermosillo\n", + "│ │ │ │ │ │ ├── Indiana\n", + "│ │ │ │ │ │ │ ├── Indianapolis\n", + "│ │ │ │ │ │ │ ├── Knox\n", + "│ │ │ │ │ │ │ ├── Marengo\n", + "│ │ │ │ │ │ │ ├── Petersburg\n", + "│ │ │ │ │ │ │ ├── Tell_City\n", + "│ │ │ │ │ │ │ ├── Vevay\n", + "│ │ │ │ │ │ │ ├── Vincennes\n", + "│ │ │ │ │ │ │ ├── Winamac\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── Indianapolis\n", + "│ │ │ │ │ │ ├── Inuvik\n", + "│ │ │ │ │ │ ├── Iqaluit\n", + "│ │ │ │ │ │ ├── Jamaica\n", + "│ │ │ │ │ │ ├── Jujuy\n", + "│ │ │ │ │ │ ├── Juneau\n", + "│ │ │ │ │ │ ├── Kentucky\n", + "│ │ │ │ │ │ │ ├── Louisville\n", + "│ │ │ │ │ │ │ ├── Monticello\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── Knox_IN\n", + "│ │ │ │ │ │ ├── Kralendijk\n", + "│ │ │ │ │ │ ├── La_Paz\n", + "│ │ │ │ │ │ ├── Lima\n", + "│ │ │ │ │ │ ├── Los_Angeles\n", + "│ │ │ │ │ │ ├── Louisville\n", + "│ │ │ │ │ │ ├── Lower_Princes\n", + "│ │ │ │ │ │ ├── Maceio\n", + "│ │ │ │ │ │ ├── Managua\n", + "│ │ │ │ │ │ ├── Manaus\n", + "│ │ │ │ │ │ ├── Marigot\n", + "│ │ │ │ │ │ ├── Martinique\n", + "│ │ │ │ │ │ ├── Matamoros\n", + "│ │ │ │ │ │ ├── Mazatlan\n", + "│ │ │ │ │ │ ├── Mendoza\n", + "│ │ │ │ │ │ ├── Menominee\n", + "│ │ │ │ │ │ ├── Merida\n", + "│ │ │ │ │ │ ├── Metlakatla\n", + "│ │ │ │ │ │ ├── Mexico_City\n", + "│ │ │ │ │ │ ├── Miquelon\n", + "│ │ │ │ │ │ ├── Moncton\n", + "│ │ │ │ │ │ ├── Monterrey\n", + "│ │ │ │ │ │ ├── Montevideo\n", + "│ │ │ │ │ │ ├── Montreal\n", + "│ │ │ │ │ │ ├── Montserrat\n", + "│ │ │ │ │ │ ├── Nassau\n", + "│ │ │ │ │ │ ├── New_York\n", + "│ │ │ │ │ │ ├── Nipigon\n", + "│ │ │ │ │ │ ├── Nome\n", + "│ │ │ │ │ │ ├── Noronha\n", + "│ │ │ │ │ │ ├── North_Dakota\n", + "│ │ │ │ │ │ │ ├── Beulah\n", + "│ │ │ │ │ │ │ ├── Center\n", + "│ │ │ │ │ │ │ ├── New_Salem\n", + "│ │ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── Nuuk\n", + "│ │ │ │ │ │ ├── Ojinaga\n", + "│ │ │ │ │ │ ├── Panama\n", + "│ │ │ │ │ │ ├── Pangnirtung\n", + "│ │ │ │ │ │ ├── Paramaribo\n", + "│ │ │ │ │ │ ├── Phoenix\n", + "│ │ │ │ │ │ ├── Port-au-Prince\n", + "│ │ │ │ │ │ ├── Port_of_Spain\n", + "│ │ │ │ │ │ ├── Porto_Acre\n", + "│ │ │ │ │ │ ├── Porto_Velho\n", + "│ │ │ │ │ │ ├── Puerto_Rico\n", + "│ │ │ │ │ │ ├── Punta_Arenas\n", + "│ │ │ │ │ │ ├── Rainy_River\n", + "│ │ │ │ │ │ ├── Rankin_Inlet\n", + "│ │ │ │ │ │ ├── Recife\n", + "│ │ │ │ │ │ ├── Regina\n", + "│ │ │ │ │ │ ├── Resolute\n", + "│ │ │ │ │ │ ├── Rio_Branco\n", + "│ │ │ │ │ │ ├── Rosario\n", + "│ │ │ │ │ │ ├── Santa_Isabel\n", + "│ │ │ │ │ │ ├── Santarem\n", + "│ │ │ │ │ │ ├── Santiago\n", + "│ │ │ │ │ │ ├── Santo_Domingo\n", + "│ │ │ │ │ │ ├── Sao_Paulo\n", + "│ │ │ │ │ │ ├── Scoresbysund\n", + "│ │ │ │ │ │ ├── Shiprock\n", + "│ │ │ │ │ │ ├── Sitka\n", + "│ │ │ │ │ │ ├── St_Barthelemy\n", + "│ │ │ │ │ │ ├── St_Johns\n", + "│ │ │ │ │ │ ├── St_Kitts\n", + "│ │ │ │ │ │ ├── St_Lucia\n", + "│ │ │ │ │ │ ├── St_Thomas\n", + "│ │ │ │ │ │ ├── St_Vincent\n", + "│ │ │ │ │ │ ├── Swift_Current\n", + "│ │ │ │ │ │ ├── Tegucigalpa\n", + "│ │ │ │ │ │ ├── Thule\n", + "│ │ │ │ │ │ ├── Thunder_Bay\n", + "│ │ │ │ │ │ ├── Tijuana\n", + "│ │ │ │ │ │ ├── Toronto\n", + "│ │ │ │ │ │ ├── Tortola\n", + "│ │ │ │ │ │ ├── Vancouver\n", + "│ │ │ │ │ │ ├── Virgin\n", + "│ │ │ │ │ │ ├── Whitehorse\n", + "│ │ │ │ │ │ ├── Winnipeg\n", + "│ │ │ │ │ │ ├── Yakutat\n", + "│ │ │ │ │ │ ├── Yellowknife\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── Antarctica\n", + "│ │ │ │ │ │ ├── Casey\n", + "│ │ │ │ │ │ ├── Davis\n", + "│ │ │ │ │ │ ├── DumontDUrville\n", + "│ │ │ │ │ │ ├── Macquarie\n", + "│ │ │ │ │ │ ├── Mawson\n", + "│ │ │ │ │ │ ├── McMurdo\n", + "│ │ │ │ │ │ ├── Palmer\n", + "│ │ │ │ │ │ ├── Rothera\n", + "│ │ │ │ │ │ ├── South_Pole\n", + "│ │ │ │ │ │ ├── Syowa\n", + "│ │ │ │ │ │ ├── Troll\n", + "│ │ │ │ │ │ ├── Vostok\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── Arctic\n", + "│ │ │ │ │ │ ├── Longyearbyen\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── Asia\n", + "│ │ │ │ │ │ ├── Aden\n", + "│ │ │ │ │ │ ├── Almaty\n", + "│ │ │ │ │ │ ├── Amman\n", + "│ │ │ │ │ │ ├── Anadyr\n", + "│ │ │ │ │ │ ├── Aqtau\n", + "│ │ │ │ │ │ ├── Aqtobe\n", + "│ │ │ │ │ │ ├── Ashgabat\n", + "│ │ │ │ │ │ ├── Ashkhabad\n", + "│ │ │ │ │ │ ├── Atyrau\n", + "│ │ │ │ │ │ ├── Baghdad\n", + "│ │ │ │ │ │ ├── Bahrain\n", + "│ │ │ │ │ │ ├── Baku\n", + "│ │ │ │ │ │ ├── Bangkok\n", + "│ │ │ │ │ │ ├── Barnaul\n", + "│ │ │ │ │ │ ├── Beirut\n", + "│ │ │ │ │ │ ├── Bishkek\n", + "│ │ │ │ │ │ ├── Brunei\n", + "│ │ │ │ │ │ ├── Calcutta\n", + "│ │ │ │ │ │ ├── Chita\n", + "│ │ │ │ │ │ ├── Choibalsan\n", + "│ │ │ │ │ │ ├── Chongqing\n", + "│ │ │ │ │ │ ├── Chungking\n", + "│ │ │ │ │ │ ├── Colombo\n", + "│ │ │ │ │ │ ├── Dacca\n", + "│ │ │ │ │ │ ├── Damascus\n", + "│ │ │ │ │ │ ├── Dhaka\n", + "│ │ │ │ │ │ ├── Dili\n", + "│ │ │ │ │ │ ├── Dubai\n", + "│ │ │ │ │ │ ├── Dushanbe\n", + "│ │ │ │ │ │ ├── Famagusta\n", + "│ │ │ │ │ │ ├── Gaza\n", + "│ │ │ │ │ │ ├── Harbin\n", + "│ │ │ │ │ │ ├── Hebron\n", + "│ │ │ │ │ │ ├── Ho_Chi_Minh\n", + "│ │ │ │ │ │ ├── Hong_Kong\n", + "│ │ │ │ │ │ ├── Hovd\n", + "│ │ │ │ │ │ ├── Irkutsk\n", + "│ │ │ │ │ │ ├── Istanbul\n", + "│ │ │ │ │ │ ├── Jakarta\n", + "│ │ │ │ │ │ ├── Jayapura\n", + "│ │ │ │ │ │ ├── Jerusalem\n", + "│ │ │ │ │ │ ├── Kabul\n", + "│ │ │ │ │ │ ├── Kamchatka\n", + "│ │ │ │ │ │ ├── Karachi\n", + "│ │ │ │ │ │ ├── Kashgar\n", + "│ │ │ │ │ │ ├── Kathmandu\n", + "│ │ │ │ │ │ ├── Katmandu\n", + "│ │ │ │ │ │ ├── Khandyga\n", + "│ │ │ │ │ │ ├── Kolkata\n", + "│ │ │ │ │ │ ├── Krasnoyarsk\n", + "│ │ │ │ │ │ ├── Kuala_Lumpur\n", + "│ │ │ │ │ │ ├── Kuching\n", + "│ │ │ │ │ │ ├── Kuwait\n", + "│ │ │ │ │ │ ├── Macao\n", + "│ │ │ │ │ │ ├── Macau\n", + "│ │ │ │ │ │ ├── Magadan\n", + "│ │ │ │ │ │ ├── Makassar\n", + "│ │ │ │ │ │ ├── Manila\n", + "│ │ │ │ │ │ ├── Muscat\n", + "│ │ │ │ │ │ ├── Nicosia\n", + "│ │ │ │ │ │ ├── Novokuznetsk\n", + "│ │ │ │ │ │ ├── Novosibirsk\n", + "│ │ │ │ │ │ ├── Omsk\n", + "│ │ │ │ │ │ ├── Oral\n", + "│ │ │ │ │ │ ├── Phnom_Penh\n", + "│ │ │ │ │ │ ├── Pontianak\n", + "│ │ │ │ │ │ ├── Pyongyang\n", + "│ │ │ │ │ │ ├── Qatar\n", + "│ │ │ │ │ │ ├── Qostanay\n", + "│ │ │ │ │ │ ├── Qyzylorda\n", + "│ │ │ │ │ │ ├── Rangoon\n", + "│ │ │ │ │ │ ├── Riyadh\n", + "│ │ │ │ │ │ ├── Saigon\n", + "│ │ │ │ │ │ ├── Sakhalin\n", + "│ │ │ │ │ │ ├── Samarkand\n", + "│ │ │ │ │ │ ├── Seoul\n", + "│ │ │ │ │ │ ├── Shanghai\n", + "│ │ │ │ │ │ ├── Singapore\n", + "│ │ │ │ │ │ ├── Srednekolymsk\n", + "│ │ │ │ │ │ ├── Taipei\n", + "│ │ │ │ │ │ ├── Tashkent\n", + "│ │ │ │ │ │ ├── Tbilisi\n", + "│ │ │ │ │ │ ├── Tehran\n", + "│ │ │ │ │ │ ├── Tel_Aviv\n", + "│ │ │ │ │ │ ├── Thimbu\n", + "│ │ │ │ │ │ ├── Thimphu\n", + "│ │ │ │ │ │ ├── Tokyo\n", + "│ │ │ │ │ │ ├── Tomsk\n", + "│ │ │ │ │ │ ├── Ujung_Pandang\n", + "│ │ │ │ │ │ ├── Ulaanbaatar\n", + "│ │ │ │ │ │ ├── Ulan_Bator\n", + "│ │ │ │ │ │ ├── Urumqi\n", + "│ │ │ │ │ │ ├── Ust-Nera\n", + "│ │ │ │ │ │ ├── Vientiane\n", + "│ │ │ │ │ │ ├── Vladivostok\n", + "│ │ │ │ │ │ ├── Yakutsk\n", + "│ │ │ │ │ │ ├── Yangon\n", + "│ │ │ │ │ │ ├── Yekaterinburg\n", + "│ │ │ │ │ │ ├── Yerevan\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── Atlantic\n", + "│ │ │ │ │ │ ├── Azores\n", + "│ │ │ │ │ │ ├── Bermuda\n", + "│ │ │ │ │ │ ├── Canary\n", + "│ │ │ │ │ │ ├── Cape_Verde\n", + "│ │ │ │ │ │ ├── Faeroe\n", + "│ │ │ │ │ │ ├── Faroe\n", + "│ │ │ │ │ │ ├── Jan_Mayen\n", + "│ │ │ │ │ │ ├── Madeira\n", + "│ │ │ │ │ │ ├── Reykjavik\n", + "│ │ │ │ │ │ ├── South_Georgia\n", + "│ │ │ │ │ │ ├── St_Helena\n", + "│ │ │ │ │ │ ├── Stanley\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── Australia\n", + "│ │ │ │ │ │ ├── ACT\n", + "│ │ │ │ │ │ ├── Adelaide\n", + "│ │ │ │ │ │ ├── Brisbane\n", + "│ │ │ │ │ │ ├── Broken_Hill\n", + "│ │ │ │ │ │ ├── Canberra\n", + "│ │ │ │ │ │ ├── Currie\n", + "│ │ │ │ │ │ ├── Darwin\n", + "│ │ │ │ │ │ ├── Eucla\n", + "│ │ │ │ │ │ ├── Hobart\n", + "│ │ │ │ │ │ ├── LHI\n", + "│ │ │ │ │ │ ├── Lindeman\n", + "│ │ │ │ │ │ ├── Lord_Howe\n", + "│ │ │ │ │ │ ├── Melbourne\n", + "│ │ │ │ │ │ ├── NSW\n", + "│ │ │ │ │ │ ├── North\n", + "│ │ │ │ │ │ ├── Perth\n", + "│ │ │ │ │ │ ├── Queensland\n", + "│ │ │ │ │ │ ├── South\n", + "│ │ │ │ │ │ ├── Sydney\n", + "│ │ │ │ │ │ ├── Tasmania\n", + "│ │ │ │ │ │ ├── Victoria\n", + "│ │ │ │ │ │ ├── West\n", + "│ │ │ │ │ │ ├── Yancowinna\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── Brazil\n", + "│ │ │ │ │ │ ├── Acre\n", + "│ │ │ │ │ │ ├── DeNoronha\n", + "│ │ │ │ │ │ ├── East\n", + "│ │ │ │ │ │ ├── West\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── CET\n", + "│ │ │ │ │ ├── CST6CDT\n", + "│ │ │ │ │ ├── Canada\n", + "│ │ │ │ │ │ ├── Atlantic\n", + "│ │ │ │ │ │ ├── Central\n", + "│ │ │ │ │ │ ├── Eastern\n", + "│ │ │ │ │ │ ├── Mountain\n", + "│ │ │ │ │ │ ├── Newfoundland\n", + "│ │ │ │ │ │ ├── Pacific\n", + "│ │ │ │ │ │ ├── Saskatchewan\n", + "│ │ │ │ │ │ ├── Yukon\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── Chile\n", + "│ │ │ │ │ │ ├── Continental\n", + "│ │ │ │ │ │ ├── EasterIsland\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── Cuba\n", + "│ │ │ │ │ ├── EET\n", + "│ │ │ │ │ ├── EST\n", + "│ │ │ │ │ ├── EST5EDT\n", + "│ │ │ │ │ ├── Egypt\n", + "│ │ │ │ │ ├── Eire\n", + "│ │ │ │ │ ├── Etc\n", + "│ │ │ │ │ │ ├── GMT\n", + "│ │ │ │ │ │ ├── GMT+0\n", + "│ │ │ │ │ │ ├── GMT+1\n", + "│ │ │ │ │ │ ├── GMT+10\n", + "│ │ │ │ │ │ ├── GMT+11\n", + "│ │ │ │ │ │ ├── GMT+12\n", + "│ │ │ │ │ │ ├── GMT+2\n", + "│ │ │ │ │ │ ├── GMT+3\n", + "│ │ │ │ │ │ ├── GMT+4\n", + "│ │ │ │ │ │ ├── GMT+5\n", + "│ │ │ │ │ │ ├── GMT+6\n", + "│ │ │ │ │ │ ├── GMT+7\n", + "│ │ │ │ │ │ ├── GMT+8\n", + "│ │ │ │ │ │ ├── GMT+9\n", + "│ │ │ │ │ │ ├── GMT-0\n", + "│ │ │ │ │ │ ├── GMT-1\n", + "│ │ │ │ │ │ ├── GMT-10\n", + "│ │ │ │ │ │ ├── GMT-11\n", + "│ │ │ │ │ │ ├── GMT-12\n", + "│ │ │ │ │ │ ├── GMT-13\n", + "│ │ │ │ │ │ ├── GMT-14\n", + "│ │ │ │ │ │ ├── GMT-2\n", + "│ │ │ │ │ │ ├── GMT-3\n", + "│ │ │ │ │ │ ├── GMT-4\n", + "│ │ │ │ │ │ ├── GMT-5\n", + "│ │ │ │ │ │ ├── GMT-6\n", + "│ │ │ │ │ │ ├── GMT-7\n", + "│ │ │ │ │ │ ├── GMT-8\n", + "│ │ │ │ │ │ ├── GMT-9\n", + "│ │ │ │ │ │ ├── GMT0\n", + "│ │ │ │ │ │ ├── Greenwich\n", + "│ │ │ │ │ │ ├── UCT\n", + "│ │ │ │ │ │ ├── UTC\n", + "│ │ │ │ │ │ ├── Universal\n", + "│ │ │ │ │ │ ├── Zulu\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── Europe\n", + "│ │ │ │ │ │ ├── Amsterdam\n", + "│ │ │ │ │ │ ├── Andorra\n", + "│ │ │ │ │ │ ├── Astrakhan\n", + "│ │ │ │ │ │ ├── Athens\n", + "│ │ │ │ │ │ ├── Belfast\n", + "│ │ │ │ │ │ ├── Belgrade\n", + "│ │ │ │ │ │ ├── Berlin\n", + "│ │ │ │ │ │ ├── Bratislava\n", + "│ │ │ │ │ │ ├── Brussels\n", + "│ │ │ │ │ │ ├── Bucharest\n", + "│ │ │ │ │ │ ├── Budapest\n", + "│ │ │ │ │ │ ├── Busingen\n", + "│ │ │ │ │ │ ├── Chisinau\n", + "│ │ │ │ │ │ ├── Copenhagen\n", + "│ │ │ │ │ │ ├── Dublin\n", + "│ │ │ │ │ │ ├── Gibraltar\n", + "│ │ │ │ │ │ ├── Guernsey\n", + "│ │ │ │ │ │ ├── Helsinki\n", + "│ │ │ │ │ │ ├── Isle_of_Man\n", + "│ │ │ │ │ │ ├── Istanbul\n", + "│ │ │ │ │ │ ├── Jersey\n", + "│ │ │ │ │ │ ├── Kaliningrad\n", + "│ │ │ │ │ │ ├── Kiev\n", + "│ │ │ │ │ │ ├── Kirov\n", + "│ │ │ │ │ │ ├── Kyiv\n", + "│ │ │ │ │ │ ├── Lisbon\n", + "│ │ │ │ │ │ ├── Ljubljana\n", + "│ │ │ │ │ │ ├── London\n", + "│ │ │ │ │ │ ├── Luxembourg\n", + "│ │ │ │ │ │ ├── Madrid\n", + "│ │ │ │ │ │ ├── Malta\n", + "│ │ │ │ │ │ ├── Mariehamn\n", + "│ │ │ │ │ │ ├── Minsk\n", + "│ │ │ │ │ │ ├── Monaco\n", + "│ │ │ │ │ │ ├── Moscow\n", + "│ │ │ │ │ │ ├── Nicosia\n", + "│ │ │ │ │ │ ├── Oslo\n", + "│ │ │ │ │ │ ├── Paris\n", + "│ │ │ │ │ │ ├── Podgorica\n", + "│ │ │ │ │ │ ├── Prague\n", + "│ │ │ │ │ │ ├── Riga\n", + "│ │ │ │ │ │ ├── Rome\n", + "│ │ │ │ │ │ ├── Samara\n", + "│ │ │ │ │ │ ├── San_Marino\n", + "│ │ │ │ │ │ ├── Sarajevo\n", + "│ │ │ │ │ │ ├── Saratov\n", + "│ │ │ │ │ │ ├── Simferopol\n", + "│ │ │ │ │ │ ├── Skopje\n", + "│ │ │ │ │ │ ├── Sofia\n", + "│ │ │ │ │ │ ├── Stockholm\n", + "│ │ │ │ │ │ ├── Tallinn\n", + "│ │ │ │ │ │ ├── Tirane\n", + "│ │ │ │ │ │ ├── Tiraspol\n", + "│ │ │ │ │ │ ├── Ulyanovsk\n", + "│ │ │ │ │ │ ├── Uzhgorod\n", + "│ │ │ │ │ │ ├── Vaduz\n", + "│ │ │ │ │ │ ├── Vatican\n", + "│ │ │ │ │ │ ├── Vienna\n", + "│ │ │ │ │ │ ├── Vilnius\n", + "│ │ │ │ │ │ ├── Volgograd\n", + "│ │ │ │ │ │ ├── Warsaw\n", + "│ │ │ │ │ │ ├── Zagreb\n", + "│ │ │ │ │ │ ├── Zaporozhye\n", + "│ │ │ │ │ │ ├── Zurich\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── Factory\n", + "│ │ │ │ │ ├── GB\n", + "│ │ │ │ │ ├── GB-Eire\n", + "│ │ │ │ │ ├── GMT\n", + "│ │ │ │ │ ├── GMT+0\n", + "│ │ │ │ │ ├── GMT-0\n", + "│ │ │ │ │ ├── GMT0\n", + "│ │ │ │ │ ├── Greenwich\n", + "│ │ │ │ │ ├── HST\n", + "│ │ │ │ │ ├── Hongkong\n", + "│ │ │ │ │ ├── Iceland\n", + "│ │ │ │ │ ├── Indian\n", + "│ │ │ │ │ │ ├── Antananarivo\n", + "│ │ │ │ │ │ ├── Chagos\n", + "│ │ │ │ │ │ ├── Christmas\n", + "│ │ │ │ │ │ ├── Cocos\n", + "│ │ │ │ │ │ ├── Comoro\n", + "│ │ │ │ │ │ ├── Kerguelen\n", + "│ │ │ │ │ │ ├── Mahe\n", + "│ │ │ │ │ │ ├── Maldives\n", + "│ │ │ │ │ │ ├── Mauritius\n", + "│ │ │ │ │ │ ├── Mayotte\n", + "│ │ │ │ │ │ ├── Reunion\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── Iran\n", + "│ │ │ │ │ ├── Israel\n", + "│ │ │ │ │ ├── Jamaica\n", + "│ │ │ │ │ ├── Japan\n", + "│ │ │ │ │ ├── Kwajalein\n", + "│ │ │ │ │ ├── Libya\n", + "│ │ │ │ │ ├── MET\n", + "│ │ │ │ │ ├── MST\n", + "│ │ │ │ │ ├── MST7MDT\n", + "│ │ │ │ │ ├── Mexico\n", + "│ │ │ │ │ │ ├── BajaNorte\n", + "│ │ │ │ │ │ ├── BajaSur\n", + "│ │ │ │ │ │ ├── General\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── NZ\n", + "│ │ │ │ │ ├── NZ-CHAT\n", + "│ │ │ │ │ ├── Navajo\n", + "│ │ │ │ │ ├── PRC\n", + "│ │ │ │ │ ├── PST8PDT\n", + "│ │ │ │ │ ├── Pacific\n", + "│ │ │ │ │ │ ├── Apia\n", + "│ │ │ │ │ │ ├── Auckland\n", + "│ │ │ │ │ │ ├── Bougainville\n", + "│ │ │ │ │ │ ├── Chatham\n", + "│ │ │ │ │ │ ├── Chuuk\n", + "│ │ │ │ │ │ ├── Easter\n", + "│ │ │ │ │ │ ├── Efate\n", + "│ │ │ │ │ │ ├── Enderbury\n", + "│ │ │ │ │ │ ├── Fakaofo\n", + "│ │ │ │ │ │ ├── Fiji\n", + "│ │ │ │ │ │ ├── Funafuti\n", + "│ │ │ │ │ │ ├── Galapagos\n", + "│ │ │ │ │ │ ├── Gambier\n", + "│ │ │ │ │ │ ├── Guadalcanal\n", + "│ │ │ │ │ │ ├── Guam\n", + "│ │ │ │ │ │ ├── Honolulu\n", + "│ │ │ │ │ │ ├── Johnston\n", + "│ │ │ │ │ │ ├── Kanton\n", + "│ │ │ │ │ │ ├── Kiritimati\n", + "│ │ │ │ │ │ ├── Kosrae\n", + "│ │ │ │ │ │ ├── Kwajalein\n", + "│ │ │ │ │ │ ├── Majuro\n", + "│ │ │ │ │ │ ├── Marquesas\n", + "│ │ │ │ │ │ ├── Midway\n", + "│ │ │ │ │ │ ├── Nauru\n", + "│ │ │ │ │ │ ├── Niue\n", + "│ │ │ │ │ │ ├── Norfolk\n", + "│ │ │ │ │ │ ├── Noumea\n", + "│ │ │ │ │ │ ├── Pago_Pago\n", + "│ │ │ │ │ │ ├── Palau\n", + "│ │ │ │ │ │ ├── Pitcairn\n", + "│ │ │ │ │ │ ├── Pohnpei\n", + "│ │ │ │ │ │ ├── Ponape\n", + "│ │ │ │ │ │ ├── Port_Moresby\n", + "│ │ │ │ │ │ ├── Rarotonga\n", + "│ │ │ │ │ │ ├── Saipan\n", + "│ │ │ │ │ │ ├── Samoa\n", + "│ │ │ │ │ │ ├── Tahiti\n", + "│ │ │ │ │ │ ├── Tarawa\n", + "│ │ │ │ │ │ ├── Tongatapu\n", + "│ │ │ │ │ │ ├── Truk\n", + "│ │ │ │ │ │ ├── Wake\n", + "│ │ │ │ │ │ ├── Wallis\n", + "│ │ │ │ │ │ ├── Yap\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── Poland\n", + "│ │ │ │ │ ├── Portugal\n", + "│ │ │ │ │ ├── ROC\n", + "│ │ │ │ │ ├── ROK\n", + "│ │ │ │ │ ├── Singapore\n", + "│ │ │ │ │ ├── Turkey\n", + "│ │ │ │ │ ├── UCT\n", + "│ │ │ │ │ ├── US\n", + "│ │ │ │ │ │ ├── Alaska\n", + "│ │ │ │ │ │ ├── Aleutian\n", + "│ │ │ │ │ │ ├── Arizona\n", + "│ │ │ │ │ │ ├── Central\n", + "│ │ │ │ │ │ ├── East-Indiana\n", + "│ │ │ │ │ │ ├── Eastern\n", + "│ │ │ │ │ │ ├── Hawaii\n", + "│ │ │ │ │ │ ├── Indiana-Starke\n", + "│ │ │ │ │ │ ├── Michigan\n", + "│ │ │ │ │ │ ├── Mountain\n", + "│ │ │ │ │ │ ├── Pacific\n", + "│ │ │ │ │ │ ├── Samoa\n", + "│ │ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ │ └── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── UTC\n", + "│ │ │ │ │ ├── Universal\n", + "│ │ │ │ │ ├── W-SU\n", + "│ │ │ │ │ ├── WET\n", + "│ │ │ │ │ ├── Zulu\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ └── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── iso3166.tab\n", + "│ │ │ │ │ ├── leapseconds\n", + "│ │ │ │ │ ├── tzdata.zi\n", + "│ │ │ │ │ ├── zone.tab\n", + "│ │ │ │ │ ├── zone1970.tab\n", + "│ │ │ │ │ └── zonenow.tab\n", + "│ │ │ │ └── zones\n", + "│ │ │ ├── tzdata-2024.1.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── LICENSE_APACHE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ └── top_level.txt\n", + "│ │ │ ├── wcwidth\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── table_vs16.cpython-310.pyc\n", + "│ │ │ │ │ ├── table_wide.cpython-310.pyc\n", + "│ │ │ │ │ ├── table_zero.cpython-310.pyc\n", + "│ │ │ │ │ ├── unicode_versions.cpython-310.pyc\n", + "│ │ │ │ │ └── wcwidth.cpython-310.pyc\n", + "│ │ │ │ ├── table_vs16.py\n", + "│ │ │ │ ├── table_wide.py\n", + "│ │ │ │ ├── table_zero.py\n", + "│ │ │ │ ├── unicode_versions.py\n", + "│ │ │ │ └── wcwidth.py\n", + "│ │ │ ├── wcwidth-0.2.13.dist-info\n", + "│ │ │ │ ├── INSTALLER\n", + "│ │ │ │ ├── LICENSE\n", + "│ │ │ │ ├── METADATA\n", + "│ │ │ │ ├── RECORD\n", + "│ │ │ │ ├── WHEEL\n", + "│ │ │ │ ├── top_level.txt\n", + "│ │ │ │ └── zip-safe\n", + "│ │ │ └── zmq\n", + "│ │ │ ├── __init__.pxd\n", + "│ │ │ ├── __init__.py\n", + "│ │ │ ├── __init__.pyi\n", + "│ │ │ ├── __pycache__\n", + "│ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ ├── _future.cpython-310.pyc\n", + "│ │ │ │ ├── _typing.cpython-310.pyc\n", + "│ │ │ │ ├── asyncio.cpython-310.pyc\n", + "│ │ │ │ ├── constants.cpython-310.pyc\n", + "│ │ │ │ ├── decorators.cpython-310.pyc\n", + "│ │ │ │ └── error.cpython-310.pyc\n", + "│ │ │ ├── _future.py\n", + "│ │ │ ├── _typing.py\n", + "│ │ │ ├── asyncio.py\n", + "│ │ │ ├── auth\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── asyncio.cpython-310.pyc\n", + "│ │ │ │ │ ├── base.cpython-310.pyc\n", + "│ │ │ │ │ ├── certs.cpython-310.pyc\n", + "│ │ │ │ │ ├── ioloop.cpython-310.pyc\n", + "│ │ │ │ │ └── thread.cpython-310.pyc\n", + "│ │ │ │ ├── asyncio.py\n", + "│ │ │ │ ├── base.py\n", + "│ │ │ │ ├── certs.py\n", + "│ │ │ │ ├── ioloop.py\n", + "│ │ │ │ └── thread.py\n", + "│ │ │ ├── backend\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ └── select.cpython-310.pyc\n", + "│ │ │ │ ├── cffi\n", + "│ │ │ │ │ ├── README.md\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── _poll.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── context.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── devices.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── error.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── message.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── socket.cpython-310.pyc\n", + "│ │ │ │ │ │ └── utils.cpython-310.pyc\n", + "│ │ │ │ │ ├── _cdefs.h\n", + "│ │ │ │ │ ├── _cffi_src.c\n", + "│ │ │ │ │ ├── _poll.py\n", + "│ │ │ │ │ ├── context.py\n", + "│ │ │ │ │ ├── devices.py\n", + "│ │ │ │ │ ├── error.py\n", + "│ │ │ │ │ ├── message.py\n", + "│ │ │ │ │ ├── socket.py\n", + "│ │ │ │ │ └── utils.py\n", + "│ │ │ │ ├── cython\n", + "│ │ │ │ │ ├── __init__.pxd\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ └── _zmq.cpython-310.pyc\n", + "│ │ │ │ │ ├── _externs.pxd\n", + "│ │ │ │ │ ├── _zmq.cpython-310-x86_64-linux-gnu.so\n", + "│ │ │ │ │ ├── _zmq.pxd\n", + "│ │ │ │ │ ├── _zmq.py\n", + "│ │ │ │ │ ├── constant_enums.pxi\n", + "│ │ │ │ │ └── libzmq.pxd\n", + "│ │ │ │ └── select.py\n", + "│ │ │ ├── constants.py\n", + "│ │ │ ├── decorators.py\n", + "│ │ │ ├── devices\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── basedevice.cpython-310.pyc\n", + "│ │ │ │ │ ├── monitoredqueue.cpython-310.pyc\n", + "│ │ │ │ │ ├── monitoredqueuedevice.cpython-310.pyc\n", + "│ │ │ │ │ ├── proxydevice.cpython-310.pyc\n", + "│ │ │ │ │ └── proxysteerabledevice.cpython-310.pyc\n", + "│ │ │ │ ├── basedevice.py\n", + "│ │ │ │ ├── monitoredqueue.py\n", + "│ │ │ │ ├── monitoredqueuedevice.py\n", + "│ │ │ │ ├── proxydevice.py\n", + "│ │ │ │ └── proxysteerabledevice.py\n", + "│ │ │ ├── error.py\n", + "│ │ │ ├── eventloop\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── _deprecated.cpython-310.pyc\n", + "│ │ │ │ │ ├── future.cpython-310.pyc\n", + "│ │ │ │ │ ├── ioloop.cpython-310.pyc\n", + "│ │ │ │ │ └── zmqstream.cpython-310.pyc\n", + "│ │ │ │ ├── _deprecated.py\n", + "│ │ │ │ ├── future.py\n", + "│ │ │ │ ├── ioloop.py\n", + "│ │ │ │ └── zmqstream.py\n", + "│ │ │ ├── green\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── core.cpython-310.pyc\n", + "│ │ │ │ │ ├── device.cpython-310.pyc\n", + "│ │ │ │ │ └── poll.cpython-310.pyc\n", + "│ │ │ │ ├── core.py\n", + "│ │ │ │ ├── device.py\n", + "│ │ │ │ ├── eventloop\n", + "│ │ │ │ │ ├── __init__.py\n", + "│ │ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ │ ├── ioloop.cpython-310.pyc\n", + "│ │ │ │ │ │ └── zmqstream.cpython-310.pyc\n", + "│ │ │ │ │ ├── ioloop.py\n", + "│ │ │ │ │ └── zmqstream.py\n", + "│ │ │ │ └── poll.py\n", + "│ │ │ ├── log\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __main__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── __main__.cpython-310.pyc\n", + "│ │ │ │ │ └── handlers.cpython-310.pyc\n", + "│ │ │ │ └── handlers.py\n", + "│ │ │ ├── py.typed\n", + "│ │ │ ├── ssh\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── forward.cpython-310.pyc\n", + "│ │ │ │ │ └── tunnel.cpython-310.pyc\n", + "│ │ │ │ ├── forward.py\n", + "│ │ │ │ └── tunnel.py\n", + "│ │ │ ├── sugar\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __init__.pyi\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── attrsettr.cpython-310.pyc\n", + "│ │ │ │ │ ├── context.cpython-310.pyc\n", + "│ │ │ │ │ ├── frame.cpython-310.pyc\n", + "│ │ │ │ │ ├── poll.cpython-310.pyc\n", + "│ │ │ │ │ ├── socket.cpython-310.pyc\n", + "│ │ │ │ │ ├── stopwatch.cpython-310.pyc\n", + "│ │ │ │ │ ├── tracker.cpython-310.pyc\n", + "│ │ │ │ │ └── version.cpython-310.pyc\n", + "│ │ │ │ ├── attrsettr.py\n", + "│ │ │ │ ├── context.py\n", + "│ │ │ │ ├── frame.py\n", + "│ │ │ │ ├── poll.py\n", + "│ │ │ │ ├── socket.py\n", + "│ │ │ │ ├── stopwatch.py\n", + "│ │ │ │ ├── tracker.py\n", + "│ │ │ │ └── version.py\n", + "│ │ │ ├── tests\n", + "│ │ │ │ ├── __init__.py\n", + "│ │ │ │ ├── __pycache__\n", + "│ │ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ │ ├── conftest.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_asyncio.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_auth.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_cffi_backend.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_constants.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_context.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_cython.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_decorators.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_device.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_draft.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_error.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_etc.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_ext.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_future.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_imports.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_includes.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_ioloop.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_log.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_message.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_monitor.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_monqueue.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_multipart.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_mypy.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_pair.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_poll.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_proxy_steerable.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_pubsub.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_reqrep.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_retry_eintr.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_security.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_socket.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_ssh.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_version.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_win32_shim.cpython-310.pyc\n", + "│ │ │ │ │ ├── test_z85.cpython-310.pyc\n", + "│ │ │ │ │ └── test_zmqstream.cpython-310.pyc\n", + "│ │ │ │ ├── conftest.py\n", + "│ │ │ │ ├── cython_ext.pyx\n", + "│ │ │ │ ├── test_asyncio.py\n", + "│ │ │ │ ├── test_auth.py\n", + "│ │ │ │ ├── test_cffi_backend.py\n", + "│ │ │ │ ├── test_constants.py\n", + "│ │ │ │ ├── test_context.py\n", + "│ │ │ │ ├── test_cython.py\n", + "│ │ │ │ ├── test_decorators.py\n", + "│ │ │ │ ├── test_device.py\n", + "│ │ │ │ ├── test_draft.py\n", + "│ │ │ │ ├── test_error.py\n", + "│ │ │ │ ├── test_etc.py\n", + "│ │ │ │ ├── test_ext.py\n", + "│ │ │ │ ├── test_future.py\n", + "│ │ │ │ ├── test_imports.py\n", + "│ │ │ │ ├── test_includes.py\n", + "│ │ │ │ ├── test_ioloop.py\n", + "│ │ │ │ ├── test_log.py\n", + "│ │ │ │ ├── test_message.py\n", + "│ │ │ │ ├── test_monitor.py\n", + "│ │ │ │ ├── test_monqueue.py\n", + "│ │ │ │ ├── test_multipart.py\n", + "│ │ │ │ ├── test_mypy.py\n", + "│ │ │ │ ├── test_pair.py\n", + "│ │ │ │ ├── test_poll.py\n", + "│ │ │ │ ├── test_proxy_steerable.py\n", + "│ │ │ │ ├── test_pubsub.py\n", + "│ │ │ │ ├── test_reqrep.py\n", + "│ │ │ │ ├── test_retry_eintr.py\n", + "│ │ │ │ ├── test_security.py\n", + "│ │ │ │ ├── test_socket.py\n", + "│ │ │ │ ├── test_ssh.py\n", + "│ │ │ │ ├── test_version.py\n", + "│ │ │ │ ├── test_win32_shim.py\n", + "│ │ │ │ ├── test_z85.py\n", + "│ │ │ │ └── test_zmqstream.py\n", + "│ │ │ └── utils\n", + "│ │ │ ├── __init__.py\n", + "│ │ │ ├── __pycache__\n", + "│ │ │ │ ├── __init__.cpython-310.pyc\n", + "│ │ │ │ ├── garbage.cpython-310.pyc\n", + "│ │ │ │ ├── interop.cpython-310.pyc\n", + "│ │ │ │ ├── jsonapi.cpython-310.pyc\n", + "│ │ │ │ ├── monitor.cpython-310.pyc\n", + "│ │ │ │ ├── strtypes.cpython-310.pyc\n", + "│ │ │ │ ├── win32.cpython-310.pyc\n", + "│ │ │ │ └── z85.cpython-310.pyc\n", + "│ │ │ ├── buffers.pxd\n", + "│ │ │ ├── garbage.py\n", + "│ │ │ ├── getpid_compat.h\n", + "│ │ │ ├── interop.py\n", + "│ │ │ ├── ipcmaxlen.h\n", + "│ │ │ ├── jsonapi.py\n", + "│ │ │ ├── monitor.py\n", + "│ │ │ ├── mutex.h\n", + "│ │ │ ├── pyversion_compat.h\n", + "│ │ │ ├── strtypes.py\n", + "│ │ │ ├── win32.py\n", + "│ │ │ ├── z85.py\n", + "│ │ │ └── zmq_compat.h\n", + "│ │ ├── pyvenv.cfg\n", + "│ │ └── share\n", + "│ │ ├── jupyter\n", + "│ │ │ └── kernels\n", + "│ │ │ └── python3\n", + "│ │ │ ├── kernel.json\n", + "│ │ │ ├── logo-32x32.png\n", + "│ │ │ ├── logo-64x64.png\n", + "│ │ │ └── logo-svg.svg\n", + "│ │ └── man\n", + "│ │ └── man1\n", + "│ │ └── ipython.1\n", + "│ ├── contig_alignment_info.csv\n", + "│ ├── contig_alignment_info.ods\n", + "│ ├── distance_chewbbaca\n", + "│ │ ├── allele_matrix_fil.csv\n", + "│ │ ├── distance_matrix.csv\n", + "│ │ └── distance_matrix_core.csv\n", + "│ ├── distance_matrix_symmetric.tsv\n", + "│ ├── distance_seqsphere\n", + "│ │ └── distance_matrix.csv\n", + "│ ├── distance_taranis\n", + "│ │ ├── allele_matrix_fil.csv\n", + "│ │ ├── distance_matrix.csv\n", + "│ │ └── distance_matrix_core.csv\n", + "│ ├── results_alleles_chewbacca_masked.tsv\n", + "│ ├── results_alleles_chewbbaca.csv\n", + "│ ├── results_alleles_chewbbaca.ods\n", + "│ ├── results_alleles_chewbbaca.tsv\n", + "│ ├── results_alleles_taranis.csv\n", + "│ ├── results_alleles_taranis.ods\n", + "│ ├── summary_chewbbaca.csv\n", + "│ ├── summary_chewbbaca.tsv\n", + "│ ├── summary_taranis.csv\n", + "│ └── test.ipynb\n", + "├── summary_comparison.csv\n", + "└── summary_comparison.png\n" + ] + } + ], + "source": [ + "# Imports\n", + "import os\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "\n", + "def print_tree(directory, prefix=''):\n", + " \"\"\"Recursively prints a tree view of the directory structure.\"\"\"\n", + " files = sorted(os.listdir(directory))\n", + " for index, file in enumerate(files):\n", + " path = os.path.join(directory, file)\n", + " is_last = index == len(files) - 1\n", + " print(prefix + '└── ' if is_last else prefix + '├── ', file)\n", + " if os.path.isdir(path):\n", + " extension = ' ' if is_last else '│ '\n", + " print_tree(path, prefix=prefix+extension)\n", + "\n", + "# move to working directory\n", + "os.chdir(\"/home/smonzon/temp/comparison\")\n", + "print_tree(os.getcwd())" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Graph reference alleles per locus\n", + "datasets = [\"bmelitensis\", \"lmonocytogenes\", \"mtuberculosis\"]\n", + "thresholds = [\"80\", \"85\", \"90\"]\n", + "df_num_ref = pd.DataFrame()\n", + "for dataset in datasets:\n", + " for thr in thresholds:\n", + " df = pd.read_csv(f\"./{dataset}/cluster_per_locus_{thr}.csv\")\n", + " df[\"Dataset\"] = dataset\n", + " df[\"Threshold\"] = thr\n", + " df_num_ref = pd.concat([df_num_ref, df], ignore_index=True)\n", + "\n", + "# Set up the figure for a 3x3 grid of plots\n", + "fig, axes = plt.subplots(3, 3, figsize=(18, 12), sharex=False, sharey=True)\n", + "fig.suptitle('Reference alleles per Locus distribution across datasets', fontsize = 20)\n", + "\n", + "# Iterate over each dataset and threshold to create a subplot\n", + "for i, dataset in enumerate(datasets):\n", + " for j, thr in enumerate(thresholds):\n", + " ax = axes[i, j]\n", + " # Filter the dataframe for the current dataset and threshold\n", + " subset = df_num_ref[(df_num_ref['Dataset'] == dataset) & (df_num_ref['Threshold'] == thr)]\n", + " # Plotting the distribution of the number of locus per number of clusters\n", + " sns.barplot(data=subset, x='number of clusters', y=' number of locus', ax=ax, color='skyblue')\n", + " \n", + " # Set subplot title\n", + " ax.set_title(f'Threshold {thr}')\n", + " \n", + " # Set labels\n", + " ax.set_xlabel('Number of Clusters' if i == 2 else '') # Only label x-axis for the bottom row\n", + " ax.set_ylabel('Number of Locus' if j == 0 else '') # Only label y-axis for the first column\n", + "\n", + "for i, dataset in enumerate(datasets):\n", + " fig.text(0.01, 0.8 - i * 0.3, dataset.capitalize(), va='center', rotation='vertical', fontsize=20)\n", + "\n", + "# Adjust layout\n", + "plt.tight_layout(rect=[0.05, 0.05, 1, 0.95]) # Adjust the rect to prevent overlap and to leave space for the suptitle\n", + "\n", + "# Save and show the figure\n", + "plt.savefig(\"locus_distribution.png\", dpi=300)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Type % of Total Software Dataset\n", + "0 NIPHEM 0.00 taranis bmelitensis\n", + "1 NIPH 0.07 taranis bmelitensis\n", + "2 EXC 95.12 taranis bmelitensis\n", + "3 PLOT 0.03 taranis bmelitensis\n", + "4 ASM 0.00 taranis bmelitensis\n", + "5 ALM 0.00 taranis bmelitensis\n", + "6 INF 0.74 taranis bmelitensis\n", + "7 LNF 0.81 taranis bmelitensis\n", + "8 TPR 3.23 taranis bmelitensis\n", + "9 EXC 89.83 chewbbaca bmelitensis\n", + "10 INF 0.49 chewbbaca bmelitensis\n", + "11 PLOT3 0.00 chewbbaca bmelitensis\n", + "12 PLOT5 0.03 chewbbaca bmelitensis\n", + "13 LOTSC 0.00 chewbbaca bmelitensis\n", + "14 NIPH 0.01 chewbbaca bmelitensis\n", + "15 NIPHEM 0.09 chewbbaca bmelitensis\n", + "16 ALM 1.52 chewbbaca bmelitensis\n", + "17 ASM 0.87 chewbbaca bmelitensis\n", + "18 PAMA 0.00 chewbbaca bmelitensis\n", + "19 LNF 7.16 chewbbaca bmelitensis\n", + "20 NIPHEM 0.00 taranis lmonocytogenes\n", + "21 NIPH 0.00 taranis lmonocytogenes\n", + "22 EXC 95.74 taranis lmonocytogenes\n", + "23 PLOT 0.04 taranis lmonocytogenes\n", + "24 ASM 0.00 taranis lmonocytogenes\n", + "25 ALM 0.00 taranis lmonocytogenes\n", + "26 INF 0.81 taranis lmonocytogenes\n", + "27 LNF 0.12 taranis lmonocytogenes\n", + "28 TPR 3.29 taranis lmonocytogenes\n", + "29 EXC 98.59 chewbbaca lmonocytogenes\n", + "30 INF 0.14 chewbbaca lmonocytogenes\n", + "31 PLOT3 0.00 chewbbaca lmonocytogenes\n", + "32 PLOT5 0.06 chewbbaca lmonocytogenes\n", + "33 LOTSC 0.00 chewbbaca lmonocytogenes\n", + "34 NIPH 0.01 chewbbaca lmonocytogenes\n", + "35 NIPHEM 0.05 chewbbaca lmonocytogenes\n", + "36 ALM 0.18 chewbbaca lmonocytogenes\n", + "37 ASM 0.23 chewbbaca lmonocytogenes\n", + "38 PAMA 0.00 chewbbaca lmonocytogenes\n", + "39 LNF 0.74 chewbbaca lmonocytogenes\n", + "40 NIPHEM 0.00 taranis mtuberculosis\n", + "41 NIPH 1.56 taranis mtuberculosis\n", + "42 EXC 88.65 taranis mtuberculosis\n", + "43 PLOT 0.29 taranis mtuberculosis\n", + "44 ASM 0.00 taranis mtuberculosis\n", + "45 ALM 0.00 taranis mtuberculosis\n", + "46 INF 0.63 taranis mtuberculosis\n", + "47 LNF 7.19 taranis mtuberculosis\n", + "48 TPR 1.69 taranis mtuberculosis\n", + "49 EXC 77.24 chewbbaca mtuberculosis\n", + "50 INF 8.61 chewbbaca mtuberculosis\n", + "51 PLOT3 0.00 chewbbaca mtuberculosis\n", + "52 PLOT5 0.19 chewbbaca mtuberculosis\n", + "53 LOTSC 0.00 chewbbaca mtuberculosis\n", + "54 NIPH 6.13 chewbbaca mtuberculosis\n", + "55 NIPHEM 0.90 chewbbaca mtuberculosis\n", + "56 ALM 0.78 chewbbaca mtuberculosis\n", + "57 ASM 0.93 chewbbaca mtuberculosis\n", + "58 PAMA 0.03 chewbbaca mtuberculosis\n", + "59 LNF 5.18 chewbbaca mtuberculosis\n" + ] + } + ], + "source": [ + "# Check summary results\n", + "datasets = [\"bmelitensis\", \"lmonocytogenes\", \"mtuberculosis\"]\n", + "software = [\"taranis\", \"chewbbaca\"]\n", + "final_summary_table = pd.DataFrame()\n", + "\n", + "for dataset in datasets:\n", + " for soft in software:\n", + " summary_table = pd.read_csv(f\"./{dataset}/summary_{soft}.csv\", delimiter=\",\")\n", + " column_totals = summary_table.drop(columns=['Sample']).sum()\n", + " # Calculate the grand total of these totals\n", + " grand_total = column_totals.sum()\n", + " # Calculate the percentage of each column total relative to the grand total\n", + " percentage_totals = (column_totals / grand_total * 100).round(2)\n", + " # Convert to DataFrame for better presentation\n", + " percentage_summary_table = percentage_totals.reset_index()\n", + " percentage_summary_table.columns = ['Type', '% of Total']\n", + " # Add columns for 'Software' and 'Dataset'\n", + " percentage_summary_table['Software'] = soft\n", + " percentage_summary_table['Dataset'] = dataset\n", + " \n", + " # Append the result to the final DataFrame\n", + " final_summary_table = pd.concat([final_summary_table, percentage_summary_table], ignore_index=True)\n", + "\n", + "final_summary_table.to_csv(\"summary_comparison.csv\")\n", + "print(final_summary_table)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot summary data\n", + "\n", + "# Sort the types alphabetically and get the unique sorted list for ordering in the plot\n", + "type_order = sorted(final_summary_table['Type'].unique())\n", + "\n", + "# Create a faceted plot: each software/dataset combination gets a subplot\n", + "g = sns.catplot(\n", + " data=final_summary_table,\n", + " kind=\"bar\",\n", + " x=\"Type\",\n", + " y=\"% of Total\",\n", + " hue=\"Software\",\n", + " col=\"Dataset\",\n", + " palette=\"viridis\",\n", + " aspect=0.7,\n", + " height=4,\n", + " order=type_order # Order the x-axis categories alphabetically\n", + ")\n", + "\n", + "# Improve the readability of the plot\n", + "g.set_xticklabels(rotation=90) # Rotate x labels for better visibility\n", + "g.set_titles(\"{col_name}\") # Set titles to be just the dataset names\n", + "g.set_axis_labels(\"\", \"% of Total\") # Set axis labels\n", + "\n", + "for ax, title in zip(g.axes.flat, g.col_names):\n", + " ax.set_title(title.capitalize()) # Capitalize each subplot title\n", + " \n", + "g.tight_layout() # Adjust subplots to fit into figure area.\n", + "\n", + "# Show plot\n", + "plt.savefig(\"summary_comparison.png\", dpi=300, bbox_inches='tight') # Save as PNG with high resolution\n", + "plt.show()\n", + "plt.close() # Close the plot to free up memory" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Datasets and software tools\n", + "datasets = [\"bmelitensis\", \"lmonocytogenes\", \"mtuberculosis\"]\n", + "software = [\"taranis\", \"chewbbaca\", \"seqsphere\"]\n", + "\n", + "# Function to extract upper triangle of the matrix without the diagonal\n", + "def get_upper_triangle(matrix):\n", + " triu_indices = np.triu_indices_from(matrix, k=1)\n", + " return matrix.values[triu_indices]\n", + "\n", + "# Setup the figure and axes for the subplots\n", + "fig, axes = plt.subplots(len(datasets), len(list(combinations(software, 2))), figsize=(18, 18))\n", + "fig.suptitle('1-vs-1 Distance Correlation Across Datasets', fontsize=24)\n", + "\n", + "# Iterate over each dataset and software combination\n", + "for row, dataset in enumerate(datasets):\n", + " for col, (soft1, soft2) in enumerate(combinations(software, 2)):\n", + " matrix1 = pd.read_csv(f\"./{dataset}/distance_{soft1}/distance_matrix.csv\", index_col=0)\n", + " matrix2 = pd.read_csv(f\"./{dataset}/distance_{soft2}/distance_matrix.csv\", index_col=0)\n", + " \n", + " matrix2 = matrix2.reindex(index=matrix1.index, columns=matrix1.columns)\n", + " flat_matrix1 = get_upper_triangle(matrix1)\n", + " flat_matrix2 = get_upper_triangle(matrix2)\n", + " correlation = np.corrcoef(flat_matrix1, flat_matrix2)[0, 1]\n", + " \n", + " ax = axes[row][col]\n", + " ax.scatter(flat_matrix1, flat_matrix2, edgecolors='k', alpha=0.75, s=50)\n", + " ax.set_title(f\"{soft1} vs. {soft2} (r={correlation:.2f})\", fontsize=20)\n", + " ax.set_xlabel(f\"{soft1} Distances\", fontsize=18)\n", + " ax.set_ylabel(f\"{soft2} Distances\", fontsize=18)\n", + " ax.grid(True)\n", + " ax.tick_params(axis='both', which='major', labelsize=15)\n", + "\n", + "# Adding dataset-specific titles along the left side\n", + "for i, dataset in enumerate(datasets):\n", + " fig.text(0.01, 0.8 - i * 0.3, dataset.capitalize(), va='center', rotation='vertical', fontsize=24)\n", + "\n", + "# Adjust layout to prevent overlap\n", + "plt.tight_layout(rect=[0.05, 0.05, 1, 0.95]) # Adjust the left margin to make space for the dataset label\n", + "\n", + "\n", + "# save fig\n", + "plt.savefig(\"distance_comparison.png\", dpi=300, bbox_inches='tight') # Save as PNG with high resolution\n", + "# Show plot\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From bd2f4956bf5cd8a7fe4c1b3fe4a0f2ec75f2b726 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Wed, 1 May 2024 21:57:17 +0200 Subject: [PATCH 195/214] added masking after filtering per row in df_filter --- taranis/utils.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/taranis/utils.py b/taranis/utils.py index 86918f0..0875833 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -343,8 +343,10 @@ def filter_df( rows_to_drop = mask.sum(axis=1) / len(df.columns) > row_thr filtered_df = df.loc[~rows_to_drop, :] + mask_fil = filtered_df.applymap(lambda x: bool(re.search(regex_pattern, str(x)))) + # Filter columns: Drop columns where the count of true in mask / total rows >= column_thr - cols_to_drop = mask.sum(axis=0) / len(df) > column_thr + cols_to_drop = mask_fil.sum(axis=0) / len(df) > column_thr filtered_df = filtered_df.loc[:, ~cols_to_drop] return filtered_df From 7e381ca5f71f75cd44ebf29e9cb7e77772e91382 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Wed, 1 May 2024 22:00:34 +0200 Subject: [PATCH 196/214] black linting --- assets/mash_blast_correlation.py | 4 +++- taranis/distance.py | 16 ++++++++++------ taranis/utils.py | 2 +- 3 files changed, 14 insertions(+), 8 deletions(-) diff --git a/assets/mash_blast_correlation.py b/assets/mash_blast_correlation.py index 8663805..a6e7485 100644 --- a/assets/mash_blast_correlation.py +++ b/assets/mash_blast_correlation.py @@ -138,7 +138,9 @@ def mantel_tester(blast_paths, mash_paths, pval=0.01): plt.suptitle("") # Elimina el título por defecto plt.xlabel("Dataset") # Etiqueta para el eje x plt.ylabel("Mantel correlation value") # Etiqueta para el eje y -ax.set_xticklabels([ticklabel.get_text().capitalize() for ticklabel in ax.get_xticklabels()]) +ax.set_xticklabels( + [ticklabel.get_text().capitalize() for ticklabel in ax.get_xticklabels()] +) # Guarda el boxplot como PNG plt.savefig( diff --git a/taranis/distance.py b/taranis/distance.py index 87c3b31..b42ee34 100644 --- a/taranis/distance.py +++ b/taranis/distance.py @@ -108,14 +108,14 @@ def create_matrix(self, mask_values: list) -> pd.DataFrame: pd.DataFrame: Hamming distance matrix as panda DataFrame """ # Mask unwanted values directly in the DataFrame - regex_pattern = '|'.join([f".*{value}.*" for value in mask_values]) + regex_pattern = "|".join([f".*{value}.*" for value in mask_values]) self.allele_matrix.replace(regex_pattern, np.nan, regex=True, inplace=True) # Get unique values excluding NaN - unique_values = pd.unique( - self.allele_matrix.values.ravel("K") - ) - unique_values = unique_values[~pd.isna(unique_values)] # Exclude NaNs from unique values + unique_values = pd.unique(self.allele_matrix.values.ravel("K")) + unique_values = unique_values[ + ~pd.isna(unique_values) + ] # Exclude NaNs from unique values # Create binary matrix ('1' or '0' ) matching the input matrix vs the unique_values[0] # astype(int) is used to transform the boolean matrix into integer @@ -141,4 +141,8 @@ def create_matrix(self, mask_values: list) -> pd.DataFrame: pairwise_valid_counts = pairwise_valid.sum(axis=2) distance_matrix = pairwise_valid_counts - H - return pd.DataFrame(distance_matrix, index=self.allele_matrix.index, columns=self.allele_matrix.index) + return pd.DataFrame( + distance_matrix, + index=self.allele_matrix.index, + columns=self.allele_matrix.index, + ) diff --git a/taranis/utils.py b/taranis/utils.py index 0875833..77f6748 100644 --- a/taranis/utils.py +++ b/taranis/utils.py @@ -334,7 +334,7 @@ def filter_df( row_thr /= 100 # Identify filter values and create a mask for the DataFrame - regex_pattern = '|'.join(filter_values) # This creates 'ASM|LNF|EXC' + regex_pattern = "|".join(filter_values) # This creates 'ASM|LNF|EXC' # Apply regex across the DataFrame to create a mask mask = df.applymap(lambda x: bool(re.search(regex_pattern, str(x)))) From df4e0f63b760244c3245bde599aa59de9fd82575 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Fri, 3 May 2024 21:13:54 +0200 Subject: [PATCH 197/214] fix snp output printing --- taranis/allele_calling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index 8fab7ec..b27b836 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -901,7 +901,7 @@ def read_reference_alleles(ref_alleles: list) -> dict[dict]: + "," + ref_allele + "," - + ",".join(snp_info) + + ",".join([str(value) for value in snp_info]) + "\n" ) # create alignment files From 7b6729ac71e280f7bcdb2f927dd5a93d397d507d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Tue, 7 May 2024 11:23:30 +0200 Subject: [PATCH 198/214] minor modifications adding seqsphere for notebook, added blast_id_list.py --- assets/benchmark.ipynb | 72 +++++++++++++----- assets/blast_id_dist.py | 163 ++++++++++++++++++++++++++++++++++++++++ 2 files changed, 217 insertions(+), 18 deletions(-) create mode 100644 assets/blast_id_dist.py diff --git a/assets/benchmark.ipynb b/assets/benchmark.ipynb index 921a4f9..78a9e0c 100644 --- a/assets/benchmark.ipynb +++ b/assets/benchmark.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -26,13 +26,13 @@ "Requirement already satisfied: numpy in /home/smonzon/.local/lib/python3.10/site-packages (1.25.2)\n", "Requirement already satisfied: pytz>=2020.1 in /home/smonzon/.local/lib/python3.10/site-packages (from pandas) (2022.6)\n", "Requirement already satisfied: python-dateutil>=2.8.1 in /home/smonzon/.local/lib/python3.10/site-packages (from pandas) (2.8.2)\n", - "Requirement already satisfied: pyparsing>=2.3.1 in /usr/lib/python3/dist-packages (from matplotlib) (2.4.7)\n", - "Requirement already satisfied: kiwisolver>=1.3.1 in /home/smonzon/.local/lib/python3.10/site-packages (from matplotlib) (1.4.5)\n", - "Requirement already satisfied: pillow>=8 in /usr/lib/python3/dist-packages (from matplotlib) (9.0.1)\n", - "Requirement already satisfied: cycler>=0.10 in /home/smonzon/.local/lib/python3.10/site-packages (from matplotlib) (0.12.1)\n", "Requirement already satisfied: contourpy>=1.0.1 in /home/smonzon/.local/lib/python3.10/site-packages (from matplotlib) (1.2.1)\n", + "Requirement already satisfied: kiwisolver>=1.3.1 in /home/smonzon/.local/lib/python3.10/site-packages (from matplotlib) (1.4.5)\n", "Requirement already satisfied: fonttools>=4.22.0 in /home/smonzon/.local/lib/python3.10/site-packages (from matplotlib) (4.51.0)\n", + "Requirement already satisfied: cycler>=0.10 in /home/smonzon/.local/lib/python3.10/site-packages (from matplotlib) (0.12.1)\n", + "Requirement already satisfied: pillow>=8 in /usr/lib/python3/dist-packages (from matplotlib) (9.0.1)\n", "Requirement already satisfied: packaging>=20.0 in /home/smonzon/.local/lib/python3.10/site-packages (from matplotlib) (23.2)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/lib/python3/dist-packages (from matplotlib) (2.4.7)\n", "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n" ] } @@ -43,55 +43,80 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "├── .~lock.summary_comparison.csv#\n", "├── bmelitensis\n", "│ ├── .~lock.results_alleles_chewbbaca.tsv#\n", "│ ├── .~lock.summary_taranis.csv#\n", + "│ ├── cluster_per_locus_80.csv\n", + "│ ├── cluster_per_locus_85.csv\n", + "│ ├── cluster_per_locus_90.csv\n", "│ ├── distance_chewbbaca\n", "│ │ ├── allele_matrix_fil.csv\n", + "│ │ ├── allele_matrix_fil.tsv\n", "│ │ ├── distance_matrix.csv\n", "│ │ └── distance_matrix_core.csv\n", "│ ├── distance_seqsphere\n", + "│ │ ├── allele_matrix_fil.tsv\n", "│ │ └── distance_matrix.csv\n", "│ ├── distance_taranis\n", "│ │ ├── allele_matrix_fil.csv\n", + "│ │ ├── allele_matrix_fil.tsv\n", "│ │ ├── distance_matrix.csv\n", "│ │ └── distance_matrix_core.csv\n", "│ ├── results_alleles_chewbbaca.csv\n", "│ ├── results_alleles_chewbbaca.tsv\n", "│ ├── results_alleles_chewbbaca_masked.tsv\n", + "│ ├── results_alleles_seqsphere.csv\n", "│ ├── results_alleles_taranis.csv\n", "│ ├── summary_chewbbaca.csv\n", "│ ├── summary_chewbbaca.tsv\n", "│ └── summary_taranis.csv\n", + "├── boxplot_mantel_test.png\n", + "├── comprobaciones.txt\n", "├── datasets.txt\n", "├── distance_comparison.png\n", "├── lablog\n", "├── lmonocytogenes\n", + "│ ├── cluster_per_locus_80.csv\n", + "│ ├── cluster_per_locus_85.csv\n", + "│ ├── cluster_per_locus_90.csv\n", "│ ├── distance_chewbbaca\n", "│ │ ├── allele_matrix_fil.csv\n", + "│ │ ├── allele_matrix_fil.tsv\n", "│ │ ├── distance_matrix.csv\n", "│ │ └── distance_matrix_core.csv\n", "│ ├── distance_seqsphere\n", - "│ │ └── distance_matrix.csv\n", + "│ │ ├── allele_matrix_fil.csv\n", + "│ │ ├── allele_matrix_fil.tsv\n", + "│ │ ├── distance_matrix.csv\n", + "│ │ └── distance_matrix_core.csv\n", "│ ├── distance_taranis\n", "│ │ ├── allele_matrix_fil.csv\n", + "│ │ ├── allele_matrix_fil.tsv\n", "│ │ ├── distance_matrix.csv\n", "│ │ └── distance_matrix_core.csv\n", "│ ├── results_alleles_chewbbaca.csv\n", "│ ├── results_alleles_chewbbaca.tsv\n", "│ ├── results_alleles_chewbbaca_masked.tsv\n", + "│ ├── results_alleles_seqsphere.ccsv\n", + "│ ├── results_alleles_seqsphere.csv\n", "│ ├── results_alleles_taranis.csv\n", "│ ├── summary_chewbbaca.csv\n", "│ ├── summary_chewbbaca.tsv\n", "│ └── summary_taranis.csv\n", + "├── locus_distribution.png\n", + "├── mst_bmelitensis_chewbbaca.svg\n", + "├── mst_bmelitensis_taranis.svg\n", + "├── mst_lmonocytogenes_chewbbaca.svg\n", + "├── mst_lmonocytogenes_taranis.svg\n", + "├── mst_mtuberculosis_chewbbaca.svg\n", + "├── mst_mtuberculosis_taranis.svg\n", "├── mtuberculosis\n", "│ ├── .venv\n", "│ │ ├── .gitignore\n", @@ -26687,23 +26712,32 @@ "│ │ └── man\n", "│ │ └── man1\n", "│ │ └── ipython.1\n", + "│ ├── cluster_per_locus_80.csv\n", + "│ ├── cluster_per_locus_85.csv\n", + "│ ├── cluster_per_locus_90.csv\n", "│ ├── contig_alignment_info.csv\n", "│ ├── contig_alignment_info.ods\n", "│ ├── distance_chewbbaca\n", "│ │ ├── allele_matrix_fil.csv\n", + "│ │ ├── allele_matrix_fil.tsv\n", "│ │ ├── distance_matrix.csv\n", "│ │ └── distance_matrix_core.csv\n", - "│ ├── distance_matrix_symmetric.tsv\n", "│ ├── distance_seqsphere\n", - "│ │ └── distance_matrix.csv\n", + "│ │ ├── allele_matrix_fil.csv\n", + "│ │ ├── allele_matrix_fil.tsv\n", + "│ │ ├── distance_matrix.csv\n", + "│ │ └── distance_matrix_core.csv\n", "│ ├── distance_taranis\n", "│ │ ├── allele_matrix_fil.csv\n", + "│ │ ├── allele_matrix_fil.tsv\n", "│ │ ├── distance_matrix.csv\n", "│ │ └── distance_matrix_core.csv\n", "│ ├── results_alleles_chewbacca_masked.tsv\n", "│ ├── results_alleles_chewbbaca.csv\n", "│ ├── results_alleles_chewbbaca.ods\n", "│ ├── results_alleles_chewbbaca.tsv\n", + "│ ├── results_alleles_seqsphere.ccsv\n", + "│ ├── results_alleles_seqsphere.csv\n", "│ ├── results_alleles_taranis.csv\n", "│ ├── results_alleles_taranis.ods\n", "│ ├── summary_chewbbaca.csv\n", @@ -26722,6 +26756,7 @@ "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", + "from itertools import combinations\n", "\n", "\n", "def print_tree(directory, prefix=''):\n", @@ -26742,12 +26777,12 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -26770,7 +26805,7 @@ "\n", "# Set up the figure for a 3x3 grid of plots\n", "fig, axes = plt.subplots(3, 3, figsize=(18, 12), sharex=False, sharey=True)\n", - "fig.suptitle('Reference alleles per Locus distribution across datasets', fontsize = 20)\n", + "fig.suptitle('Reference alleles per Locus distribution across datasets', fontsize = 24)\n", "\n", "# Iterate over each dataset and threshold to create a subplot\n", "for i, dataset in enumerate(datasets):\n", @@ -26782,11 +26817,12 @@ " sns.barplot(data=subset, x='number of clusters', y=' number of locus', ax=ax, color='skyblue')\n", " \n", " # Set subplot title\n", - " ax.set_title(f'Threshold {thr}')\n", + " ax.set_title(f'Threshold {thr}', fontsize=20)\n", " \n", " # Set labels\n", - " ax.set_xlabel('Number of Clusters' if i == 2 else '') # Only label x-axis for the bottom row\n", - " ax.set_ylabel('Number of Locus' if j == 0 else '') # Only label y-axis for the first column\n", + " ax.set_xlabel('Number of Clusters' if i == 2 else '', fontsize=18) # Only label x-axis for the bottom row\n", + " ax.set_ylabel('Number of Locus' if j == 0 else '', fontsize=18) # Only label y-axis for the first column\n", + " ax.tick_params(axis='both', which='major', labelsize=15)\n", "\n", "for i, dataset in enumerate(datasets):\n", " fig.text(0.01, 0.8 - i * 0.3, dataset.capitalize(), va='center', rotation='vertical', fontsize=20)\n", @@ -26954,12 +26990,12 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] diff --git a/assets/blast_id_dist.py b/assets/blast_id_dist.py new file mode 100644 index 0000000..7e273fb --- /dev/null +++ b/assets/blast_id_dist.py @@ -0,0 +1,163 @@ +import argparse +import glob +import os +import subprocess + +import pandas as pd +from Bio import SeqIO + +# Set up argument parsing +parser = argparse.ArgumentParser( + description="Cluster sequences based on identity threshold." +) +parser.add_argument( + "fasta_file", help="The path to the input FASTA file containing the sequences." +) +parser.add_argument( + "db_name", help="The name (path) for the BLAST database to be created or used." +) +parser.add_argument( + "output_file", help="Path to the file where clusters will be saved." +) + +# Parse arguments +args = parser.parse_args() +# Step 1: Read sequences from a multi-FASTA file +fasta_file = args.fasta_file +sequences = list(SeqIO.parse(fasta_file, "fasta")) + +# Initialize a pandas DataFrame to store percentage of identical matches (pident) +pident_matrix = pd.DataFrame( + index=[seq.id for seq in sequences], + columns=[seq.id for seq in sequences], + data=None, +) + +# BLAST parameters +db_name = args.db_name +makeblastdb_command = [ + "makeblastdb", + "-in", + fasta_file, + "-dbtype", + "nucl", + "-out", + db_name, +] + +subprocess.run(makeblastdb_command) +blast_parameters = [ + "blastn", + "-task", + "blastn", + "-db", + db_name, + "-outfmt", + "6", + "-max_target_seqs", + "10000", + "-max_hsps", + "1", + "-evalue", + "10", + "-reward", + "1", + "-penalty", + "-2", + "-gapopen", + "1", + "-gapextend", + "1", +] + +for i, query_seq in enumerate(sequences): + # Save the query sequence to a temporary file + query_file = f"temp_query_{db_name}_{i}.fasta" + SeqIO.write(query_seq, query_file, "fasta") + + # Run BLASTn using subprocess and capture output + blast_command = blast_parameters + ["-query", query_file] + result = subprocess.run(blast_command, capture_output=True, text=True) + # Process the BLAST output directly from memory + for line in result.stdout.strip().split("\n"): + parts = line.strip().split() + query_id, subject_id, pident, align_length = ( + parts[0], + parts[1], + float(parts[2]), + int(parts[3]), + ) + query_len = len(query_seq.seq) + + # Ensure alignment length is greater than 80% of the query length + if align_length >= 0.8 * query_len: + pident_matrix.at[query_id, subject_id] = pident + else: + pident_matrix.at[query_id, subject_id] = ( + None # Fill with None if not meeting criteria + ) + + # Cleanup: remove temporary query file + os.remove(query_file) + +# Write the matrix to a CSV file +output_matrix_file = f"pident_matrix_{db_name}.csv" +pident_matrix.to_csv(output_matrix_file) + +# Create the pattern to match all files starting with db_name +pattern = f"{db_name}*" + +# Find all files matching the pattern +files_to_delete = glob.glob(pattern) + +# Loop through the files and delete them +for file_path in files_to_delete: + try: + os.remove(file_path) + print(f"Deleted {file_path}") + except Exception as e: + print(f"Error deleting {file_path}: {e}") + + +print(f"Pairwise identity matrix saved to '{output_matrix_file}'.") + +# Prepare a dictionary to hold the clusters +clusters = {} + + +# Function to find the cluster for a sequence +def find_cluster(seq_id): + for cluster_id, members in clusters.items(): + if seq_id in members: + return cluster_id + return None + + +# Iterate over the matrix to cluster sequences +for seq_id in pident_matrix.columns: + # Skip if the sequence is already in a cluster + if find_cluster(seq_id) is not None: + continue + + # Create a new cluster for this sequence + cluster_id = len(clusters) + 1 + clusters[cluster_id] = [seq_id] + + # Check against all other sequences + for other_seq_id in pident_matrix.columns: + # Skip comparison with itself + if seq_id == other_seq_id: + continue + + # Check if the identity is above 90% + if pident_matrix.at[seq_id, other_seq_id] > 90: + # Add to the same cluster if not already in another cluster + if find_cluster(other_seq_id) is None: + clusters[cluster_id].append(other_seq_id) + +# Output the clusters +with open(args.output_file, "w") as f: + for cluster_id, members in clusters.items(): + f.write(f"Cluster {cluster_id}: {', '.join(members)}\n") + +print(f"Clusters saved to '{args.output_file}'.") From 455e7dcda4b3a92dfb840fa66a2310c64e5c1bf5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Mon, 30 Dec 2024 14:15:10 +0100 Subject: [PATCH 199/214] modifications in jup notebook --- assets/benchmark.ipynb | 332 ++++++++++++++++++++++++++++++----------- 1 file changed, 246 insertions(+), 86 deletions(-) diff --git a/assets/benchmark.ipynb b/assets/benchmark.ipynb index 78a9e0c..d151227 100644 --- a/assets/benchmark.ipynb +++ b/assets/benchmark.ipynb @@ -12,28 +12,28 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Defaulting to user installation because normal site-packages is not writeable\n", - "Requirement already satisfied: pandas in /home/smonzon/.local/lib/python3.10/site-packages (1.5.3)\n", - "Requirement already satisfied: matplotlib in /home/smonzon/.local/lib/python3.10/site-packages (3.8.4)\n", - "Requirement already satisfied: seaborn in /home/smonzon/.local/lib/python3.10/site-packages (0.13.2)\n", - "Requirement already satisfied: numpy in /home/smonzon/.local/lib/python3.10/site-packages (1.25.2)\n", - "Requirement already satisfied: pytz>=2020.1 in /home/smonzon/.local/lib/python3.10/site-packages (from pandas) (2022.6)\n", - "Requirement already satisfied: python-dateutil>=2.8.1 in /home/smonzon/.local/lib/python3.10/site-packages (from pandas) (2.8.2)\n", - "Requirement already satisfied: contourpy>=1.0.1 in /home/smonzon/.local/lib/python3.10/site-packages (from matplotlib) (1.2.1)\n", - "Requirement already satisfied: kiwisolver>=1.3.1 in /home/smonzon/.local/lib/python3.10/site-packages (from matplotlib) (1.4.5)\n", - "Requirement already satisfied: fonttools>=4.22.0 in /home/smonzon/.local/lib/python3.10/site-packages (from matplotlib) (4.51.0)\n", - "Requirement already satisfied: cycler>=0.10 in /home/smonzon/.local/lib/python3.10/site-packages (from matplotlib) (0.12.1)\n", - "Requirement already satisfied: pillow>=8 in /usr/lib/python3/dist-packages (from matplotlib) (9.0.1)\n", - "Requirement already satisfied: packaging>=20.0 in /home/smonzon/.local/lib/python3.10/site-packages (from matplotlib) (23.2)\n", - "Requirement already satisfied: pyparsing>=2.3.1 in /usr/lib/python3/dist-packages (from matplotlib) (2.4.7)\n", - "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n" + "Requirement already satisfied: pandas in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (2.2.2)\n", + "Requirement already satisfied: matplotlib in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (3.8.4)\n", + "Requirement already satisfied: seaborn in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (0.13.2)\n", + "Requirement already satisfied: numpy in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (1.26.4)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (from pandas) (2.9.0.post0)\n", + "Requirement already satisfied: pytz>=2020.1 in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (from pandas) (2024.1)\n", + "Requirement already satisfied: tzdata>=2022.7 in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (from pandas) (2024.1)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (from matplotlib) (1.2.1)\n", + "Requirement already satisfied: cycler>=0.10 in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (from matplotlib) (0.12.1)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (from matplotlib) (4.51.0)\n", + "Requirement already satisfied: kiwisolver>=1.3.1 in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (from matplotlib) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (from matplotlib) (24.0)\n", + "Requirement already satisfied: pillow>=8 in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (from matplotlib) (10.3.0)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (from matplotlib) (3.1.2)\n", + "Requirement already satisfied: six>=1.5 in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n" ] } ], @@ -43,7 +43,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -62,8 +62,10 @@ "│ │ ├── distance_matrix.csv\n", "│ │ └── distance_matrix_core.csv\n", "│ ├── distance_seqsphere\n", + "│ │ ├── allele_matrix_fil.csv\n", "│ │ ├── allele_matrix_fil.tsv\n", - "│ │ └── distance_matrix.csv\n", + "│ │ ├── distance_matrix.csv\n", + "│ │ └── distance_matrix_core.csv\n", "│ ├── distance_taranis\n", "│ │ ├── allele_matrix_fil.csv\n", "│ │ ├── allele_matrix_fil.tsv\n", @@ -72,10 +74,12 @@ "│ ├── results_alleles_chewbbaca.csv\n", "│ ├── results_alleles_chewbbaca.tsv\n", "│ ├── results_alleles_chewbbaca_masked.tsv\n", + "│ ├── results_alleles_seqsphere.ccsv\n", "│ ├── results_alleles_seqsphere.csv\n", "│ ├── results_alleles_taranis.csv\n", "│ ├── summary_chewbbaca.csv\n", "│ ├── summary_chewbbaca.tsv\n", + "│ ├── summary_seqsphere.csv\n", "│ └── summary_taranis.csv\n", "├── boxplot_mantel_test.png\n", "├── comprobaciones.txt\n", @@ -109,13 +113,16 @@ "│ ├── results_alleles_taranis.csv\n", "│ ├── summary_chewbbaca.csv\n", "│ ├── summary_chewbbaca.tsv\n", + "│ ├── summary_seqsphere.csv\n", "│ └── summary_taranis.csv\n", "├── locus_distribution.png\n", "├── mst_bmelitensis_chewbbaca.svg\n", "├── mst_bmelitensis_taranis.svg\n", "├── mst_lmonocytogenes_chewbbaca.svg\n", + "├── mst_lmonocytogenes_seqsphere.svg\n", "├── mst_lmonocytogenes_taranis.svg\n", "├── mst_mtuberculosis_chewbbaca.svg\n", + "├── mst_mtuberculosis_seqsphere.svg\n", "├── mst_mtuberculosis_taranis.svg\n", "├── mtuberculosis\n", "│ ├── .venv\n", @@ -26742,6 +26749,7 @@ "│ ├── results_alleles_taranis.ods\n", "│ ├── summary_chewbbaca.csv\n", "│ ├── summary_chewbbaca.tsv\n", + "│ ├── summary_seqsphere.csv\n", "│ ├── summary_taranis.csv\n", "│ └── test.ipynb\n", "├── summary_comparison.csv\n", @@ -26837,81 +26845,34 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - " Type % of Total Software Dataset\n", - "0 NIPHEM 0.00 taranis bmelitensis\n", - "1 NIPH 0.07 taranis bmelitensis\n", - "2 EXC 95.12 taranis bmelitensis\n", - "3 PLOT 0.03 taranis bmelitensis\n", - "4 ASM 0.00 taranis bmelitensis\n", - "5 ALM 0.00 taranis bmelitensis\n", - "6 INF 0.74 taranis bmelitensis\n", - "7 LNF 0.81 taranis bmelitensis\n", - "8 TPR 3.23 taranis bmelitensis\n", - "9 EXC 89.83 chewbbaca bmelitensis\n", - "10 INF 0.49 chewbbaca bmelitensis\n", - "11 PLOT3 0.00 chewbbaca bmelitensis\n", - "12 PLOT5 0.03 chewbbaca bmelitensis\n", - "13 LOTSC 0.00 chewbbaca bmelitensis\n", - "14 NIPH 0.01 chewbbaca bmelitensis\n", - "15 NIPHEM 0.09 chewbbaca bmelitensis\n", - "16 ALM 1.52 chewbbaca bmelitensis\n", - "17 ASM 0.87 chewbbaca bmelitensis\n", - "18 PAMA 0.00 chewbbaca bmelitensis\n", - "19 LNF 7.16 chewbbaca bmelitensis\n", - "20 NIPHEM 0.00 taranis lmonocytogenes\n", - "21 NIPH 0.00 taranis lmonocytogenes\n", - "22 EXC 95.74 taranis lmonocytogenes\n", - "23 PLOT 0.04 taranis lmonocytogenes\n", - "24 ASM 0.00 taranis lmonocytogenes\n", - "25 ALM 0.00 taranis lmonocytogenes\n", - "26 INF 0.81 taranis lmonocytogenes\n", - "27 LNF 0.12 taranis lmonocytogenes\n", - "28 TPR 3.29 taranis lmonocytogenes\n", - "29 EXC 98.59 chewbbaca lmonocytogenes\n", - "30 INF 0.14 chewbbaca lmonocytogenes\n", - "31 PLOT3 0.00 chewbbaca lmonocytogenes\n", - "32 PLOT5 0.06 chewbbaca lmonocytogenes\n", - "33 LOTSC 0.00 chewbbaca lmonocytogenes\n", - "34 NIPH 0.01 chewbbaca lmonocytogenes\n", - "35 NIPHEM 0.05 chewbbaca lmonocytogenes\n", - "36 ALM 0.18 chewbbaca lmonocytogenes\n", - "37 ASM 0.23 chewbbaca lmonocytogenes\n", - "38 PAMA 0.00 chewbbaca lmonocytogenes\n", - "39 LNF 0.74 chewbbaca lmonocytogenes\n", - "40 NIPHEM 0.00 taranis mtuberculosis\n", - "41 NIPH 1.56 taranis mtuberculosis\n", - "42 EXC 88.65 taranis mtuberculosis\n", - "43 PLOT 0.29 taranis mtuberculosis\n", - "44 ASM 0.00 taranis mtuberculosis\n", - "45 ALM 0.00 taranis mtuberculosis\n", - "46 INF 0.63 taranis mtuberculosis\n", - "47 LNF 7.19 taranis mtuberculosis\n", - "48 TPR 1.69 taranis mtuberculosis\n", - "49 EXC 77.24 chewbbaca mtuberculosis\n", - "50 INF 8.61 chewbbaca mtuberculosis\n", - "51 PLOT3 0.00 chewbbaca mtuberculosis\n", - "52 PLOT5 0.19 chewbbaca mtuberculosis\n", - "53 LOTSC 0.00 chewbbaca mtuberculosis\n", - "54 NIPH 6.13 chewbbaca mtuberculosis\n", - "55 NIPHEM 0.90 chewbbaca mtuberculosis\n", - "56 ALM 0.78 chewbbaca mtuberculosis\n", - "57 ASM 0.93 chewbbaca mtuberculosis\n", - "58 PAMA 0.03 chewbbaca mtuberculosis\n", - "59 LNF 5.18 chewbbaca mtuberculosis\n" + " Type % of Total Software Dataset\n", + "0 NIPHEM 0.00 taranis bmelitensis\n", + "1 NIPH 0.07 taranis bmelitensis\n", + "2 EXC 95.12 taranis bmelitensis\n", + "3 PLOT 0.03 taranis bmelitensis\n", + "4 ASM 0.00 taranis bmelitensis\n", + ".. ... ... ... ...\n", + "61 ASM 0.93 chewbbaca mtuberculosis\n", + "62 PAMA 0.03 chewbbaca mtuberculosis\n", + "63 LNF 5.18 chewbbaca mtuberculosis\n", + "64 EXC 90.13 seqsphere mtuberculosis\n", + "65 LNF 9.87 seqsphere mtuberculosis\n", + "\n", + "[66 rows x 4 columns]\n" ] } ], "source": [ "# Check summary results\n", "datasets = [\"bmelitensis\", \"lmonocytogenes\", \"mtuberculosis\"]\n", - "software = [\"taranis\", \"chewbbaca\"]\n", + "software = [\"taranis\", \"chewbbaca\", \"seqsphere\"]\n", "final_summary_table = pd.DataFrame()\n", "\n", "for dataset in datasets:\n", @@ -26938,12 +26899,12 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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Ll04wrEyZMrbHKh49elTGGI0cOTLB3/rrr78uSQnqp3jx4nbvjx07JkmqWLFiGqxB0t8Z5cqV0z///GM7pXvKlCn6/fffFRwcrNq1a2v06NF2O7wSQw0DGYdrjJFqVqtVjzzyiKZPn64jR47Y7gR5557bO91ruDFGkmw3kPn4448VFBSUYLrkfhw7KqXLuVfedwoMDNSePXv03Xffac2aNVqzZo0WLFigLl26aNGiRfec76233lK3bt20cuVKrVu3TgMGDNDEiRO1bds2u5uIAPcrtXV5m6NHWhyN50wdOnTQoEGDtHjxYv3vf//TJ598opo1ayb6wzU9xMfHKzAw0O4o9p3u3tHmyGcWHx+vxo0ba9iwYYlOW6ZMGUmp/y6Ca+rUqZN69eqlc+fOqUmTJrbr6R1lsVgSre27b07pqNvb5yFDhigsLCzRae7eEeXp6ZmqZd3rOy0uLs6h7b4j2rVrp4YNG2rFihVat26d3nzzTU2ePFlffvml7druu1HDQMahMcZ9uX265eXLl+/7EQklS5aUdGujcPsoyP2410bP2cu5zc3NTS1atFCLFi0UHx+vfv366b333tPIkSMTPcp0W6VKlVSpUiW99tpr+vnnn1W/fn3NnTtXb7zxhtNyA1KraNGiio+P15EjR2xHdKVbN9CKjIxU0aJFUxXz0KFDCYYfPHjQNl66VasRERG6ceNGko80yZs3r5o1a6bFixerc+fO2rJli+1mVLfd6/vg9rIOHTqkRx991G7coUOHbONv//fo0aMJYtw9rGTJktqwYYPq16+f6h/tdytZsqQuX77s0HdWar+L4Hqeeuop9enTR9u2bdNnn32W6DRJ7RTLkydPokdA7z6T5LbbZ2zd6fDhw7abVZUoUULSrUcYpXb7fHsb//vvvyf5954nTx5FRkYmGH7q1ClbHom58zvjbgcPHlT+/Pntfg8VLFhQ/fr1U79+/XThwgVVr15d48ePv2djLFHDQEbhVGqk2o0bN7Ru3Tq5ubnZ/WBOrbCwMPn6+mrChAm6ceNGgvF///13iuLd3jDdveFz9nIk6eLFi3bvrVar7a6ddz/S5rbo6GjbjoXbKlWqJKvVes95gPTWtGlTSUrQaL799tuSbl2Lm5qY27dv19atW23Drly5ovfff1/FihVT+fLlJUlt2rTRP//8o1mzZiWIcfdRqmeffVb79+/X0KFDlSNHDnXo0MFu/L2+D2rWrKnAwEDNnTvXru7WrFmjAwcO2NavUKFCqlixoj766CNdvnzZNt2mTZv022+/2cVs166d4uLiNG7cuAR537x5M9Ef48lp166dtm7dqu+++y7BuMjISNt3SWq+i+C6vL29NWfOHI0ePVotWrRIdJrcuXNLSlg70q0m9ODBg3bbzb1792rLli2Jxvrqq6/srhHevn27IiIibE1iYGCgGjVqpPfee09nz55NML8j2+fHH39cPj4+mjhxoq5du2Y37s7vjZIlS2rbtm12l4J9/fXXOnPmTJLxCxYsqKpVq2rRokV2n8nvv/+udevW2b4z4+LiElwaFRgYqEKFCiVZi9QwkHE4YgyHrVmzxnZE58KFC1qyZImOHDmi4cOHy9fXN9FnEqeEr6+v5syZo2effVbVq1dXhw4dFBAQoNOnT+ubb75R/fr1E/2BfC9Vq1ZVjhw5NHnyZEVFRcnd3V2PPvqoAgMDnboc6dbjbC5duqRHH31UDzzwgE6dOqWZM2eqatWq99xp8P3336t///56+umnVaZMGd28eVMff/yxcuTIoTZt2qRo+cD8+fMTvQ6tSpUq9xW3SpUq6tq1q95//31FRkYqJCRE27dv16JFi9SqVSs98sgjKY45fPhw22NiBgwYoLx582rRokU6ceKEvvjiC1mtt/bZdunSRR999JEGDx6s7du3q2HDhrpy5Yo2bNigfv362T2/uFmzZsqXL5+WLVumJk2aKDAw0G6ZJUuWlL+/v+bOnSsfHx95eXmpTp06Kl68uCZPnqzu3bsrJCREHTt2tD2uqVixYho0aJAtxoQJE9SyZUvVr19f3bt317///qtZs2apYsWKds1ySEiI+vTpo4kTJ2rPnj16/PHHlStXLh05ckTLli3T9OnTU3wt59ChQ7Vq1So1b97c9iinK1eu6LffftPy5ct18uRJ5c+fP1XfRXBtXbt2TXK8p6enypcvr88++0xlypRR3rx5VbFiRVWsWFE9evTQ22+/rbCwMPXs2VMXLlzQ3LlzVaFCBUVHRyeIVapUKTVo0EDPP/+8YmNjNW3aNOXLl8/uEoHZs2erQYMGqlSpknr16qUSJUro/Pnz2rp1q/744w/t3bs3yXx9fX31zjvv6LnnnlOtWrXUqVMn5cmTR3v37tXVq1dtpyM/99xzWr58uZ544gm1a9dOx44d0yeffGI74pyUN998U02aNFHdunXVs2dP2+Oa/Pz8bM94jomJ0QMPPKC2bduqSpUq8vb21oYNG7Rjxw699dZb94xNDQMZKMPuh40sI7HHNXl4eJiqVauaOXPm2B5/cPuRBm+++abd/LcfF3P3Y4nu9ZiZH374wYSFhRk/Pz/j4eFhSpYsabp162Z27txpm8aRxzUZY8wHH3xgSpQoYXLkyJHg0U2OLKdr167Gy8srwWdy9/KXL19uHn/8cRMYGGjc3NxMkSJFTJ8+fczZs2cTfA63czh+/Ljp0aOHKVmypPHw8DB58+Y1jzzyiNmwYUOC5QH3cq/Hqd1+ffzxxymqv9t/23///bdt2I0bN8yYMWNM8eLFTa5cuUxwcLAZMWKE3eNUjLF/xMudEnuky7Fjx0zbtm2Nv7+/8fDwMLVr1zZff/11gnmvXr1qXn31Vduyg4KCTNu2bc2xY8cSTNuvXz8jySxZsiTRz2rlypWmfPnyJmfOnAkev/LZZ5+ZatWqGXd3d5M3b17TuXNnu8fK3Pbpp5+asmXLGnd3d1OxYkWzatUq06ZNG1O2bNkE077//vumRo0axtPT0/j4+JhKlSqZYcOGmb/++itVn1lMTIwZMWKEKVWqlHFzczP58+c39erVM1OnTrU9isuR7yK4ruQe73bb3X+XP//8s6lRo4Zxc3NL8OimTz75xJQoUcK4ubmZqlWrmu++++6ej2t68803zVtvvWWCg4ONu7u7adiwodm7d2+C5R87dsx06dLFBAUFmVy5cpnChQub5s2bm+XLlzu8LqtWrTL16tUznp6extfX19SuXdssXbrUbpq33nrLFC5c2Li7u5v69eubnTt3OvS4JmOM2bBhg6lfv74tfosWLcz+/ftt42NjY83QoUNNlSpVjI+Pj/Hy8jJVqlQx7777rl2cuz8rahjIOBZj0vCOKAAApJNBgwZp3rx5OnfunO30z/RQtWpVBQQE8HgVAACyMK4xBgBkedeuXdMnn3yiNm3apFlTfOPGjQT3BQgPD9fevXvVqFGjNFkmAABIH1xjDADIsi5cuKANGzZo+fLlunjxol566aU0W9aff/6p0NBQPfPMMypUqJAOHjyouXPnKigoSH379k2z5QIAgLRHYwwAyLL279+vzp07KzAwUDNmzFDVqlXTbFl58uRRjRo19OGHH+rvv/+Wl5eXmjVrpkmTJilfvnxptlwAAJD2uMYYAAAAAODSuMYYAAAAAODSaIwBAAAAAC6NxliSMUbR0dHirHLAdVD3gGui9gEAiaExlhQTEyM/Pz/FxMRkdCoA0gl1D7gmah8AkBgaYwAAAACAS6MxBgAAAAC4NBpjAAAAAIBLozEGAAAAALg0GmMAAAAAgEvL0Mb4xx9/VIsWLVSoUCFZLBZ99dVXduONMRo1apQKFiwoT09PhYaG6siRI3bTXLp0SZ07d5avr6/8/f3Vs2dPXb58OR3XAgAAAACQlWVoY3zlyhVVqVJFs2fPTnT8lClTNGPGDM2dO1cRERHy8vJSWFiYrl27Zpumc+fO2rdvn9avX6+vv/5aP/74o3r37p1eqwAAAAAAyOIsJpM84d5isWjFihVq1aqVpFtHiwsVKqSXX35ZQ4YMkSRFRUWpQIECWrhwoTp06KADBw6ofPny2rFjh2rWrClJWrt2rZo2bao//vhDhQoVcmjZ0dHR8vPzU1RUlHx9fdNk/QBkLtQ94JqofQBAYjLtNcYnTpzQuXPnFBoaahvm5+enOnXqaOvWrZKkrVu3yt/f39YUS1JoaKisVqsiIiLuGTs2NlbR0dF2LwDZG3UPuCZqHwDgiEzbGJ87d06SVKBAAbvhBQoUsI07d+6cAgMD7cbnzJlTefPmtU2TmIkTJ8rPz8/2Cg4OdnL2ADIb6h5wTdQ+AMARmbYxTksjRoxQVFSU7XXmzJmMTglAGqPuAddE7QMAHJEzoxO4l6CgIEnS+fPnVbBgQdvw8+fPq2rVqrZpLly4YDffzZs3denSJdv8iXF3d5e7u7vzkwaQaVH3gGui9gEAjsi0R4yLFy+uoKAgbdy40TYsOjpaERERqlu3riSpbt26ioyM1K5du2zTfP/994qPj1edOnXSPWcAAAAAQNaToUeML1++rKNHj9renzhxQnv27FHevHlVpEgRDRw4UG+88YZKly6t4sWLa+TIkSpUqJDtztXlypXTE088oV69emnu3Lm6ceOG+vfvrw4dOjh8R2oAyAqqvjE6wbCGdf9NMGzmY9PTIRsAAIDsJUMb4507d+qRRx6xvR88eLAkqWvXrlq4cKGGDRumK1euqHfv3oqMjFSDBg20du1aeXh42OZZvHix+vfvr8cee0xWq1Vt2rTRjBkz0n1dAAAAAABZU6Z5jnFG4pmGgOvJanXPEWPAObJa7QMA0kemvcYYAAAAAID0QGMMAAAAAHBpNMYAAAAAAJdGYwwAAAAAcGk0xgAAAAAAl5ahj2sCACT0ePuxCQdWSv88AAAAXAVHjAEAAAAALo0jxpnc3UeO1n02KsE0L258KcEwnmUKAAAAAI7hiDEAAAAAwKXRGAMAAAAAXBqNMQAAAADApdEYAwAAAABcGo0xAAAAAMCl0RgDAAAAAFwajTEAAAAAwKXRGAMAAAAAXBqNMQAAAADApdEYAwAAAABcGo0xAAAAAMCl0RgDAAAAAFxazoxOAAAAAI57ceNLCYbNfGx6BmQCANkHjXEWU/WN0QmGNayb/nkAAAAAQHbBqdQAAAAAAJdGYwwAAAAAcGmcSg0AAJAJPN5+bIJh6z4blQGZAIDr4YgxAAAAAMCl0RgDAAAAAFwajTEAAAAAwKXRGAMAAAAAXBqNMQAAAADApdEYAwAAAABcGo0xAAAAAMCl0RgDAAAAAFwajTEAAAAAwKXRGAMAAAAAXBqNMQAAAADApdEYAwAAAABcGo0xAAAAAMCl0RgDAAAAAFwajTEAAAAAwKXRGAMAAAAAXBqNMQAAAADApdEYAwAAAABcGo0xAAAAAMClZerGOC4uTiNHjlTx4sXl6empkiVLaty4cTLG2KYxxmjUqFEqWLCgPD09FRoaqiNHjmRg1gAAAACArCRTN8aTJ0/WnDlzNGvWLB04cECTJ0/WlClTNHPmTNs0U6ZM0YwZMzR37lxFRETIy8tLYWFhunbtWgZmDgAAAADIKnJmdAJJ+fnnn9WyZUs1a9ZMklSsWDEtXbpU27dvl3TraPG0adP02muvqWXLlpKkjz76SAUKFNBXX32lDh06ZFjuAAAAAICsIVMfMa5Xr542btyow4cPS5L27t2rzZs3q0mTJpKkEydO6Ny5cwoNDbXN4+fnpzp16mjr1q33jBsbG6vo6Gi7F4DsjboHXBO1DwBwRKZujIcPH64OHTqobNmyypUrl6pVq6aBAweqc+fOkqRz585JkgoUKGA3X4ECBWzjEjNx4kT5+fnZXsHBwWm3EgAyBeoecE3UPgDAEZm6Mf7888+1ePFiLVmyRL/88osWLVqkqVOnatGiRfcVd8SIEYqKirK9zpw546SMAWRW1D3gmqh9AIAjMvU1xkOHDrUdNZakSpUq6dSpU5o4caK6du2qoKAgSdL58+dVsGBB23znz59X1apV7xnX3d1d7u7uaZo7gMyFugdcE7UPAHBEpj5ifPXqVVmt9inmyJFD8fHxkqTixYsrKChIGzdutI2Pjo5WRESE6tatm665AgAAAACypkx9xLhFixYaP368ihQpogoVKmj37t16++231aNHD0mSxWLRwIED9cYbb6h06dIqXry4Ro4cqUKFCqlVq1YZmzwAAAAAIEvI1I3xzJkzNXLkSPXr108XLlxQoUKF1KdPH40aNco2zbBhw3TlyhX17t1bkZGRatCggdauXSsPD48MzBwAAAAAkFVk6sbYx8dH06ZN07Rp0+45jcVi0dixYzV27Nj0SwwAAAAAkG1k6muMAQAAAABIa5n6iDEAAIArq/rG6ATDGnJ/UQBwOo4YAwAAAABcGo0xAAAAAMCl0RgDAAAAAFwajTEAAAAAwKXRGAMAAAAAXBqNMQAAAADApdEYAwAAAABcGo0xAAAAAMCl0RgDAAAAAFwajTEAAAAAwKXRGAMAAAAAXBqNMQAAAADApdEYAwAAAABcGo0xAAAAAMCl0RgDAAAAAFwajTEAAAAAwKXRGAMAAAAAXBqNMQAAAADApdEYAwAAAABcGo0xAAAAAMCl0RgDAAAAAFwajTEAAAAAwKXRGAMAAAAAXBqNMQAAAADApdEYAwAAAABcGo0xAAAAAMCl0RgDAAAAAFwajTEAAAAAwKXRGAMAAAAAXBqNMQAAAADApdEYAwAAAABcGo0xAAAAAMCl0RgDAAAAAFwajTEAAAAAwKXRGAMAAAAAXBqNMQAAAADApeV0ZKLWrVs7HPDLL79MdTIAAAAAAKQ3hxpjPz+/tM4DAAAAAIAM4VBjvGDBgrTOAwAAAACADME1xgAAAAAAl+bQEeO7LV++XJ9//rlOnz6t69ev24375ZdfnJIYAAAAAADpIcVHjGfMmKHu3burQIEC2r17t2rXrq18+fLp+PHjatKkidMT/PPPP/XMM88oX7588vT0VKVKlbRz507beGOMRo0apYIFC8rT01OhoaE6cuSI0/MAAAAAAGRPKW6M3333Xb3//vuaOXOm3NzcNGzYMK1fv14DBgxQVFSUU5P7999/Vb9+feXKlUtr1qzR/v379dZbbylPnjy2aaZMmaIZM2Zo7ty5ioiIkJeXl8LCwnTt2jWn5gIAAAAAyJ5SfCr16dOnVa9ePUmSp6enYmJiJEnPPvusHnroIc2aNctpyU2ePFnBwcF2N/8qXry47d/GGE2bNk2vvfaaWrZsKUn66KOPVKBAAX311Vfq0KGD03IBAAAAAGRPKT5iHBQUpEuXLkmSihQpom3btkmSTpw4IWOMU5NbtWqVatasqaefflqBgYGqVq2aPvjgA9v4EydO6Ny5cwoNDbUN8/PzU506dbR161an5gIAAAAAyJ5S3Bg/+uijWrVqlSSpe/fuGjRokBo3bqz27dvrqaeecmpyx48f15w5c1S6dGl99913ev755zVgwAAtWrRIknTu3DlJUoECBezmK1CggG1cYmJjYxUdHW33ApC9UfeAa6L2AQCOSPGp1O+//77i4+MlSS+88ILy5cunn3/+WU8++aT69Onj1OTi4+NVs2ZNTZgwQZJUrVo1/f7775o7d666du2a6rgTJ07UmDFjnJUmgCyAugdcE7UPAHBEio8Y//HHH8qRI4ftfYcOHTRjxgz1798/yaO0qVGwYEGVL1/ebli5cuV0+vRpSbdO65ak8+fP201z/vx527jEjBgxQlFRUbbXmTNnnJo3gMyHugdcE7UPAHBEio8YFy9eXGfPnlVgYKDd8EuXLql48eKKi4tzWnL169fXoUOH7IYdPnxYRYsWteUSFBSkjRs3qmrVqpKk6OhoRURE6Pnnn79nXHd3d7m7uzstTwCZH3UPuCZqHwDgiBQ3xsYYWSyWBMMvX74sDw8PpyR126BBg1SvXj1NmDBB7dq10/bt2/X+++/r/ffflyRZLBYNHDhQb7zxhkqXLq3ixYtr5MiRKlSokFq1auXUXAAAAAAA2ZPDjfHgwYMl3WpGR44cqdy5c9vGxcXFKSIiwnbU1llq1aqlFStWaMSIERo7dqyKFy+uadOmqXPnzrZphg0bpitXrqh3796KjIxUgwYNtHbtWqc36QAAAACA7Mnhxnj37t2Sbh0x/u233+Tm5mYb5+bmpipVqmjIkCFOT7B58+Zq3rz5PcdbLBaNHTtWY8eOdfqyAQAAACAze//99zVu3Dj9+eefevvttzVw4MCMTilLcrgx/uGHHyTdekTT9OnT5evrm2ZJAQAAAEB29/fff2vUqFH65ptvdP78eeXJk0dVqlTRqFGjVL9+/WTnj46OVv/+/fX222+rTZs28vPzU6NGjVS1alVNmzYt7VcgG0nxNcYLFiyw/fuPP/6QJD3wwAPOywgAAAAAXECbNm10/fp1LVq0SCVKlND58+e1ceNGXbx40aH5T58+rRs3bqhZs2YqWLBgGmd7b8YYxcXFKWfOFLeXmUaKH9cUHx+vsWPHys/PT0WLFlXRokXl7++vcePG2Z5vDAAAAAC4t8jISP3000+aPHmyHnnkERUtWlS1a9fWiBEj9OSTT0q61fi2bNlS3t7e8vX1Vbt27WyPql24cKEqVaokSSpRooQsFou6deumTZs2afr06bJYLLJYLDp58qRq1qypqVOn2pbdqlUr5cqVS5cvX5Z064CnxWLR0aNHJUkff/yxatasKR8fHwUFBalTp066cOGCbf7w8HBZLBatWbNGNWrUkLu7uzZv3qz4+HhNnDhRxYsXl6enp6pUqaLly5eny+d5v1LcGL/66quaNWuWJk2apN27d2v37t2aMGGCZs6cqZEjR6ZFjgAAAACQrXh7e8vb21tfffWVYmNjE4yPj49Xy5YtdenSJW3atEnr16/X8ePH1b59e0lS+/bttWHDBknS9u3bdfbsWU2fPl1169ZVr169dPbsWZ09e1bBwcEKCQlReHi4pFtHd3/66Sf5+/tr8+bNkqRNmzapcOHCKlWqlCTpxo0bGjdunPbu3auvvvpKJ0+eVLdu3RLkOHz4cE2aNEkHDhxQ5cqVNXHiRH300UeaO3eu9u3bp0GDBumZZ57Rpk2b0uATdK4UH+tetGiRPvzwQ9teDEmqXLmyChcurH79+mn8+PFOTRAAAAAAspucOXNq4cKF6tWrl+bOnavq1asrJCREHTp0UOXKlbVx40b99ttvOnHihIKDgyVJH330kSpUqKAdO3aoVq1aypcvnyQpICBAQUFBkm7dGDl37ty295LUqFEjzZs3T3Fxcfr999/l5uam9u3bKzw8XE888YTCw8MVEhJim75Hjx62f5coUUIzZsxQrVq1dPnyZXl7e9vGjR07Vo0bN5YkxcbGasKECdqwYYPq1q1rm3fz5s1677337OJnRik+Ynzp0iWVLVs2wfCyZcvq0qVLTkkKAAAAALK7Nm3a6K+//tKqVatsDWr16tW1cOFCHThwQMHBwbamWJLKly8vf39/HThwIEXLadiwoWJiYrR7925t2rRJISEhatSoke0o8qZNm9SoUSPb9Lt27VKLFi1UpEgR+fj42Jra06dP28WtWbOm7d9Hjx7V1atX1bhxY9vRcG9vb3300Uc6duxYCj+Z9JfixrhKlSqaNWtWguGzZs1SlSpVnJIUAAAAALgCDw8PNW7cWCNHjtTPP/+sbt266fXXX3fqMvz9/VWlShWFh4fbmuCHH35Yu3fv1uHDh3XkyBFb83vlyhWFhYXJ19dXixcv1o4dO7RixQpJ0vXr1+3ienl52f59+3rlb775Rnv27LG99u/fnyWuM3b4VOoSJUpox44dmjJlipo1a2Z3iHzr1q06c+aMvv322zRLFAAAAACyu/Lly+urr75SuXLldObMGZ05c8Z21Hj//v2KjIxU+fLl7zm/m5ub4uLiEgwPCQnRDz/8oO3bt2v8+PHKmzevypUrp/Hjx6tgwYIqU6aMJOngwYO6ePGiJk2aZFvuzp07Hcrb3d1dp0+fzvSnTSfG4SPGJ0+eVFxcnEJCQnTo0CE99dRTioyMVGRkpFq3bq1Dhw6pYcOGaZkrAAAAAGQLFy9e1KOPPqpPPvlEv/76q06cOKFly5ZpypQpatmypUJDQ1WpUiV17txZv/zyi7Zv364uXbooJCTE7hTmuxUrVkwRERE6efKk/vnnH9uTgxo1aqTvvvtOOXPmtF0a26hRIy1evNiukS1SpIjc3Nw0c+ZMHT9+XKtWrdK4ceOSXR8fHx8NGTJEgwYN0qJFi3Ts2DH98ssvmjlzphYtWnSfn1baS9WDpgoXLsxNtgAAAAAglby9vVWnTh298847OnbsmG7cuKHg4GD16tVL//vf/2SxWLRy5Uq9+OKLevjhh2W1WvXEE09o5syZScYdMmSIunbtqvLly+u///7TiRMnVKxYMTVs2FDx8fF2TXCjRo00ffp0u+uLAwICtHDhQv3vf//TjBkzVL16dU2dOtXu5sv3Mm7cOAUEBGjixIk6fvy4/P39Vb16df3vf/9L9eeUXizGGOPIhFarVYsWLZKfn1+S0znygWU20dHR8vPzU1RUlHx9fTM6HTuPtx9r9/5CpYTPim5Y998Ew2Y+Nj3NcgKyg6xU9xK1DzgLtQ8ASEyKjhh37do1yfEWiyXR89kBAAAAAMisUnRX6nPnzik+Pv6eL5piAAAAAEBW43BjbLFY0jIPAAAAAAAyhMONsYOXIgMAAAAAkKU43Bh37dpVnp6eaZkLAAAAAADpzuGbby1YsCAt8wAAAAAAIEOk6OZbAAAAAABkNzTGAAAAAACX5lBj/Ouvvyo+PuED5gEAAAAAyOocaoyrVaumf/75R5JUokQJXbx4MU2TAgAAAABkTqNHj1bVqlUzOg2ncujmW/7+/jpx4oQCAwN18uRJjh4DAAAAyDYebz823Za17rNRKZ6nUaNGqlq1qqZNm+b8hFJhyJAhevHFFzM6DadyqDFu06aNQkJCVLBgQVksFtWsWVM5cuRIdNrjx487NUEAAAAAwP25fv263NzcnBLL29tb3t7eTomVWTh0KvX777+vr776Si+//LKMMerVq5deeumlRF8AAAAAAOfo1q2bNm3apOnTp8tischisejYsWPq2bOnihcvLk9PTz344IOaPn16gvlatWql8ePHq1ChQnrwwQclSR9//LFq1qwpHx8fBQUFqVOnTrpw4YJtvvDwcFksFm3cuFE1a9ZU7ty5Va9ePR06dMg2zd2nUoeHh6t27dry8vKSv7+/6tevr1OnTqXtB+NkDj/H+IknnpAk7dq1Sy+99JJ8fHzSLCkAAAAAgDR9+nQdPnxYFStW1Nixt075zpMnjx544AEtW7ZM+fLl088//6zevXurYMGCateunW3ejRs3ytfXV+vXr7cNu3HjhsaNG6cHH3xQFy5c0ODBg9WtWzd9++23dst99dVX9dZbbykgIEB9+/ZVjx49tGXLlgT53bx5U61atVKvXr20dOlSXb9+Xdu3b5fFYkmjTyRtONwY37ZgwQLbv//44w9J0gMPPOC8jAAAAAAAkiQ/Pz+5ubkpd+7cCgoKsg0fM2aM7d/FixfX1q1b9fnnn9s1xl5eXvrwww/tTqHu0aOH7d8lSpTQjBkzVKtWLV2+fNnu9Ojx48crJCREkjR8+HA1a9ZM165dk4eHh11+0dHRioqKUvPmzVWyZElJUrly5Zy09uknxc8xjo+P19ixY+Xn56eiRYuqaNGi8vf317hx47gpFwAAAACkg9mzZ6tGjRoKCAiQt7e33n//fZ0+fdpumkqVKiW4rnjXrl1q0aKFihQpIh8fH1vze/e8lStXtv27YMGCkmR3yvVtefPmVbdu3RQWFqYWLVpo+vTpOnv2rFPWMT2luDF+9dVXNWvWLE2aNEm7d+/W7t27NWHCBM2cOVMjR45MixwBAAAAAP/fp59+qiFDhqhnz55at26d9uzZo+7du+v69et203l5edm9v3LlisLCwuTr66vFixdrx44dWrFihSQlmDdXrly2f98+LfpeB0IXLFigrVu3ql69evrss89UpkwZbdu27b7XMz2l+FTqRYsW6cMPP9STTz5pG1a5cmUVLlxY/fr10/jx452aIAAAAAC4Mjc3N8XFxdneb9myRfXq1VO/fv1sw44dO5ZsnIMHD+rixYuaNGmSgoODJUk7d+50So7VqlVTtWrVNGLECNWtW1dLlizRQw895JTY6SHFR4wvXbqksmXLJhhetmxZXbp0ySlJAQAAAABuKVasmCIiInTy5En9888/Kl26tHbu3KnvvvtOhw8f1siRI7Vjx45k4xQpUkRubm6aOXOmjh8/rlWrVmncuHH3lduJEyc0YsQIbd26VadOndK6det05MiRLHedcYob4ypVqmjWrFkJhs+aNUtVqlRxSlIAAAAAgFuGDBmiHDlyqHz58goICFBYWJhat26t9u3bq06dOrp48aLd0eN7CQgI0MKFC7Vs2TKVL19ekyZN0tSpU+8rt9y5c+vgwYNq06aNypQpo969e+uFF15Qnz597ituerMYY0xKZti0aZOaNWumIkWKqG7dupKkrVu36syZM/r222/VsGHDNEk0LUVHR8vPz09RUVHy9fXN6HTsPN5+rN37C5USntffsO6/CYbNfGx6gmEA/k9WqnuJ2gechdoHACQmxUeMQ0JCdPjwYT311FOKjIxUZGSkWrdurUOHDmXJphgAAAAA4NpSfPMtSSpUqBA32QIAAAAAZAspPmIMAAAAAEB2QmMMAAAAAHBpNMYAAAAAAJdGYwwAAAAAcGmpuvnWbf/8848iIiIUFxenWrVqqWDBgs7KCwAAAACAdJHqxviLL75Qz549VaZMGd24cUOHDh3S7Nmz1b17d2fmBwAAAABAmnL4VOrLly/bvR8zZoy2b9+u7du3a/fu3Vq2bJleffVVpycIAAAAAEjo5MmTslgs2rNnT7otc/To0apatWqS03Tr1k2tWrVKl3ycxeHGuEaNGlq5cqXtfc6cOXXhwgXb+/Pnz8vNzc252QEAAAAAkMYcPpX6u+++0wsvvKCFCxdq9uzZmj59utq3b6+4uDjdvHlTVqtVCxcuTMNUAQAAAMD5qr4xOt2Wtee19FsWHOfwEeNixYrpm2++Ubt27RQSEqI9e/bo6NGjWr9+vTZs2KDTp0+radOmaZkrAAAAALic+Ph4TZkyRaVKlZK7u7uKFCmi8ePH28YfP35cjzzyiHLnzq0qVapo69atdvNv3rxZDRs2lKenp4KDgzVgwABduXJFkjRr1ixVrFjRNu1XX30li8WiuXPn2oaFhobqtddes4v53nvvKTg4WLlz51a7du0UFRWVIO8xY8YoICBAvr6+6tu3r65fv24bt3btWjVo0ED+/v7Kly+fmjdvrmPHjtnN/8cff6hjx47KmzevvLy8VLNmTUVEREiSjh07ppYtW6pAgQLy9vZWrVq1tGHDhpR+tDYpflxTx44dtWPHDu3du1eNGjVSfHy8qlatKg8Pj1Qn4ahJkybJYrFo4MCBtmHXrl3TCy+8oHz58snb21tt2rTR+fPn0zwXAAAAAEgPI0aM0KRJkzRy5Ejt379fS5YsUYECBWzjX331VQ0ZMkR79uxRmTJl1LFjR928eVPSrQbyiSeeUJs2bfTrr7/qs88+0+bNm9W/f39JUkhIiPbv36+///5bkrRp0yblz59f4eHhkqQbN25o69atatSokW15R48e1eeff67Vq1dr7dq12r17t/r162eX88aNG3XgwAGFh4dr6dKl+vLLLzVmzBjb+CtXrmjw4MHauXOnNm7cKKvVqqeeekrx8fGSbt3jKiQkRH/++adWrVqlvXv3atiwYXbjmzZtqo0bN2r37t164okn1KJFC50+fTpVn3GK7kr97bff6sCBA6pSpYo+/PBDbdq0SZ07d1aTJk00duxYeXp6pioJR+zYsUPvvfeeKleubDd80KBB+uabb7Rs2TL5+fmpf//+at26tbZs2ZJmuQAAAABAeoiJidH06dM1a9Ysde3aVZJUsmRJNWjQQCdPnpQkDRkyRM2aNZN06yhthQoVdPToUZUtW1YTJ05U586dbQcXS5curRkzZigkJERz5sxRxYoVlTdvXm3atElt27ZVeHi4Xn75ZU2fPl2StH37dt24cUP16tWz5XTt2jV99NFHKly4sCRp5syZatasmd566y0FBQVJktzc3DR//nzlzp1bFSpU0NixYzV06FCNGzdOVqtVbdq0sVvP+fPnKyAgQPv371fFihW1ZMkS/f3339qxY4fy5s0rSSpVqpRt+ipVqqhKlSq29+PGjdOKFSu0atUqW9OfEg4fMX755ZfVvXt37dixQ3369NG4ceMUEhKiX375RR4eHqpWrZrWrFmT4gQccfnyZXXu3FkffPCB8uTJYxseFRWlefPm6e2339ajjz6qGjVqaMGCBfr555+1bdu2NMkFAAAAANLLgQMHFBsbq8cee+ye09x58LBgwYKSZLtR8t69e7Vw4UJ5e3vbXmFhYYqPj9eJEydksVj08MMPKzw8XJGRkdq/f7/69eun2NhYHTx4UJs2bVKtWrWUO3du2zKKFClia4olqW7duoqPj9ehQ4dsw6pUqWI3T926dXX58mWdOXNGknTkyBF17NhRJUqUkK+vr4oVKyZJtiO+e/bsUbVq1WxN8d0uX76sIUOGqFy5cvL395e3t7cOHDiQ6iPGDjfGCxcu1LfffqtPP/1UO3bs0Mcffyzp1p6AcePG6csvv9SECRNSlURyXnjhBTVr1kyhoaF2w3ft2qUbN27YDS9btqyKFCmS4Lx6AAAAAMhqHDkrN1euXLZ/WywWSbI75bhPnz7as2eP7bV3714dOXJEJUuWlCQ1atRI4eHh+umnn1StWjX5+vramuVNmzYpJCTE6evVokULXbp0SR988IEiIiJs1w7fvg45ufUeMmSIVqxYoQkTJuinn37Snj17VKlSJbvrmFPC4VOpvby8dOLECdWoUUNnzpxJcE1x+fLl9dNPP6UqiaR8+umn+uWXX7Rjx44E486dOyc3Nzf5+/vbDS9QoIDOnTt3z5ixsbGKjY21vY+OjnZavgAyJ+oecE3UPoCsrnTp0vL09NTGjRv13HPPpXj+6tWra//+/XanId8tJCREAwcO1LJly2zXEjdq1EgbNmzQli1b9PLLL9tNf/r0af31118qVKiQJGnbtm2yWq168MEHbdPs3btX//33n63B3bZtm7y9vRUcHKyLFy/q0KFD+uCDD9SwYUNJt24QdqfKlSvrww8/1KVLlxI9arxlyxZ169ZNTz31lKRbOwBun1qeGg4fMZ44caK6dOmiQoUKKSQkROPGjUv1Qh115swZvfTSS1q8eLFTb+41ceJE+fn52V7BwcFOiw0gc6LuAddE7QPI6jw8PPTKK69o2LBh+uijj3Ts2DFt27ZN8+bNc2j+V155RT///LP69++vPXv26MiRI1q5cqXddbiVK1dWnjx5tGTJErvG+KuvvlJsbKzq16+fIKeuXbtq7969+umnnzRgwAC1a9fOdn2xdOvIb8+ePbV//359++23ev3119W/f39ZrVblyZNH+fLl0/vvv6+jR4/q+++/1+DBg+2W0bFjRwUFBalVq1basmWLjh8/ri+++MJ2ZnDp0qX15Zdf2o6Ad+rUyXaUPDUcbow7d+6sM2fOaOXKlTp58qRatmyZ6oU6ateuXbpw4YKqV6+unDlzKmfOnNq0aZNmzJihnDlzqkCBArp+/boiIyPt5jt//rzd/5S7jRgxQlFRUbbX7fPcAWRf1D3gmqh9ANnByJEj9fLLL2vUqFEqV66c2rdvb7uGODmVK1fWpk2bdPjwYTVs2FDVqlXTqFGjbEd7pVunXzds2FAWi0UNGjSwzefr66uaNWvKy8vLLmapUqXUunVrNW3aVI8//rgqV66sd999126axx57TKVLl9bDDz+s9u3b68knn9To0aMlSVarVZ9++ql27dqlihUratCgQXrzzTft5ndzc9O6desUGBiopk2bqlKlSpo0aZJy5MghSXr77beVJ08e1atXTy1atFBYWJiqV6+eos/1ThZjjEn13GksJiZGp06dshvWvXt3lS1bVq+88oqCg4MVEBCgpUuX2u5qdujQIZUtW1Zbt27VQw895NByoqOj5efnp6ioKPn6+jp9Pe7H4+3H2r2/UCnhXpCGdf9NMGzmY9PTLCcgO8hKdS9R+4CzUPsAgMSk6HFN6c3Hx8fuYdPSrWud8+XLZxves2dPDR48WHnz5pWvr69efPFF1a1b1+GmGAAAAADg2jJ1Y+yId955x/YcrNjYWIWFhSU4jA8AAAAAwL1kucY4PDzc7r2Hh4dmz56t2bNnZ0xCAAAAAIAszeGbbwEAAAAAkB3RGAMAAAAAXBqNMQAAAADApdEYAwAAAABcGo0xAAAAAMCl0RgDAAAAAFwajTEAAAAAIFXCw8NlsVgUGRmZ0ancFxpjAAAAAIBLy5nRCQAAAABARnpx40vptqyZj01Pt2VlZTdu3FCuXLnSbXkcMQYAAACATGz58uWqVKmSPD09lS9fPoWGhurKlSuSpA8//FDlypWTh4eHypYtq3fffddu3u3bt6tatWry8PBQzZo1tWLFClksFu3Zs0eS9O+//6pz584KCAiQp6enSpcurQULFkiSTp48KYvFok8//VT16tWTh4eHKlasqE2bNiXIcdeuXapZs6Zy586tevXq6dChQ3bjV65cqerVq8vDw0MlSpTQmDFjdPPmTdt4i8WiOXPm6Mknn5SXl5fGjx/v0HzOwhFjAAAAAMikzp49q44dO2rKlCl66qmnFBMTo59++knGGC1evFijRo3SrFmzVK1aNe3evVu9evWSl5eXunbtqsuXL6t58+Zq3LixPvnkE504cUIvvWR/dHzkyJHav3+/1qxZo/z58+vo0aP677//7KYZOnSopk2bpvLly+vtt99WixYtdOLECeXLl882zauvvqq33npLAQEB6tu3r3r06KEtW7ZIkn766Sd16dJFM2bMUMOGDXXs2DH17t1bkvT666/bYowePVqTJk3StGnTlDNnTofncwYaYwAAAADIpM6ePaubN2+qdevWKlq0qCSpUqVKkm41h2+99ZZat24tSSpevLj279+v9957T127dtWSJUsUHx+vefPmycPDQxUqVNAff/yh559/3hb/9OnTqlatmmrWrClJKlasWIIc+vfvrzZt2kiS5syZo7Vr12revHkaNmyYbZrx48crJCREkjR8+HA1a9ZM165dk4eHh8aMGaPhw4era9eukqQSJUpo3LhxGjZsmF2D26lTJ3Xv3t32vkePHg7N5ww0xgAAAACQSVWpUkWPPfaYKlWqpLCwMD3++ONq27at3NzcdOzYMfXs2VO9evWyTX/z5k35+flJkg4cOKDKlSvLw8PDNr5u3bp28Z9//nm1adNGv/zyix5//HG1atVK9erVs5vmznly5sypmjVr6sCBA3bTVK5c2fbvggULSpIuXLigIkWKaO/evdqyZYvt9GhJiouL07Vr13T16lXlzp1bkmzN+W2OzucMNMYAAAAAkEnlyJFD69ev188//6x169Zp5syZevXVV7V69WpJ0gcffKA6deokmMdRTZo00alTp/Ttt99q/fr1euyxx/TCCy9o6tSpKcrzzhtlWSwWSVJ8fLwk6fLlyxozZoztyPad7mzavby87MY5Op8z0BgDAAAAQCZmsVhUv3591a9fX6NGjVLRokW1ZcsWFSpUSMePH1fnzp0Tna9cuXL6+OOPbac0S9K2bdsSTBcQEKCuXbuqa9euatiwoYYOHWrXGG/btk0PP/ywpFtHpHft2qX+/fs7nH/16tV16NAhlSpVKiWrner5UoPGGAAAAAAyqYiICG3cuFGPP/64AgMDFRERob///lvlypXTmDFjNGDAAPn5+emJJ55QbGysdu7cqX///VeDBw9Wp06d9Oqrr6pXr14aMWKETp48meBI8KhRo1SjRg1VqFBBsbGx+vrrr1WuXDm7aWbPnq3SpUurXLlyeuedd/Tvv/+qR48eDq/DqFGj1Lx5cxUpUkRt27aV1WrV3r179fvvv+uNN95w+nypQWMMAAAAAJmUr6+vfvzxR02bNk3R0dEqWrSo3nrrLTVp0kSSlDt3br355psaOnSovLy8VKlSJQ0cOFCS5O3trdWrV6tv376qVq2aypcvr8mTJ9tupCVJbm5utqbZ09NTDRs21KeffmqXw6RJkzRp0iTt2bNHpUqV0qpVq5Q/f36H1yEsLExff/21xo4dq8mTJytXrlwqW7asnnvuuTSZLzVojAEAAAC4tJmPTc/oFO6pXLlyWrt27T3Hd+rUSZ06dbrn+Iceesj2zGLp1rOJ7/Taa6/ptddeSzaHiIiIRMc1atRIxhi7YVWrVk0wLCwsTGFhYfdcxt3TOzqfs1jTfAkAAAAAAGRiNMYAAAAAAJfGqdQAAAAA4CKKFSt2z9OW72farI4jxgAAAAAAl0ZjDAAAAABwaTTGAAAAAACXRmMMAAAAAHBpNMYAAAAAAJdGYwwAAAAAcGk0xgAAAAAAl0ZjDAAAAABwaTTGAAAAAACXRmMMAAAAAHBpNMYAAAAAAJdGYwwAAAAAcGk0xgAAAAAAl0ZjDAAAAABwaTTGAAAAAACXRmMMAAAAAHBpNMYAAAAAAJdGYwwAAAAAcGk0xgAAAAAAl0ZjDAAAAABwaTTGAAAAAACXRmMMAAAAAHBpNMYAAAAAAJeWqRvjiRMnqlatWvLx8VFgYKBatWqlQ4cO2U1z7do1vfDCC8qXL5+8vb3Vpk0bnT9/PoMyBgAAAABkNZm6Md60aZNeeOEFbdu2TevXr9eNGzf0+OOP68qVK7ZpBg0apNWrV2vZsmXatGmT/vrrL7Vu3ToDswYAAAAAZCU5MzqBpKxdu9bu/cKFCxUYGKhdu3bp4YcfVlRUlObNm6clS5bo0UcflSQtWLBA5cqV07Zt2/TQQw9lRNoAAAAAgCwkUx8xvltUVJQkKW/evJKkXbt26caNGwoNDbVNU7ZsWRUpUkRbt27NkBwBAAAAAFlLpj5ifKf4+HgNHDhQ9evXV8WKFSVJ586dk5ubm/z9/e2mLVCggM6dO3fPWLGxsYqNjbW9j46OTpOcAWQe1D3gmqh9AIAjsswR4xdeeEG///67Pv300/uONXHiRPn5+dlewcHBTsgQQGZG3QOuidoHADgiSzTG/fv319dff60ffvhBDzzwgG14UFCQrl+/rsjISLvpz58/r6CgoHvGGzFihKKiomyvM2fOpFXqADIJ6h5wTdQ+AMARmfpUamOMXnzxRa1YsULh4eEqXry43fgaNWooV65c2rhxo9q0aSNJOnTokE6fPq26deveM667u7vc3d3TNHcAmQt1D7gmah8A4IhM3Ri/8MILWrJkiVauXCkfHx/bdcN+fn7y9PSUn5+fevbsqcGDBytv3rzy9fXViy++qLp163JHagAAAACAQzJ1YzxnzhxJUqNGjeyGL1iwQN26dZMkvfPOO7JarWrTpo1iY2MVFhamd999N50zBQAAAABkVZm6MTbGJDuNh4eHZs+erdmzZ6dDRgAAAACA7CZL3HwLAAAAAIC0QmMMAAAAAHBpNMYAAAAAAJdGYwwAAAAAcGk0xgAAAAAAl0ZjDAAAAABwaTTGAAAAAACXRmMMAAAAAHBpNMYAAAAAAJdGYwwAAAAAcGk0xgAAAAAAl0ZjDAAAAABwaTTGAAAAAACXRmMMAAAAAHBpNMYAAAAAAJdGYwwAAAAAcGk0xgAAAAAAl0ZjDAAAAABwaTTGAAAAAACXRmMMAAAAAHBpNMYAAAAAAJeWM6MTAAAAQNb14saXEgyb+dj0DMgEAFKPI8YAAAAAAJdGYwwAAAAAcGk0xgAAAAAAl8Y1xgAAANlc1TdGJxi257WEwwDAVXHEGAAAAADg0miMAQAAAAAujcYYAAAAAODSuMYY9+3u65a4ZgkAgKzv8fZjEwxb99moDMgEANIeR4wBAAAAAC6NxhgAAAAA4NI4lRoAAABZEo+hAuAsHDEGAAAAALg0jhgDAADAIYkdoW1YN/3zAABnozGG07248aUEw2Y+Nj0DMgEAAACA5HEqNQAAAADApdEYAwAAAABcGo0xAAAAAMClcY0xAABIU4+3H5tg2LrPRmVAJgAAJI7GGEC64ccxAAAAMiNOpQYAAAAAuDQaYwAAAACAS6MxBgAAAAC4tGxzjfHs2bP15ptv6ty5c6pSpYpmzpyp2rVr33fcqm+MTjBsz2sJhwEAACBtJbhXRaWMyQOO4d4iyEqyRWP82WefafDgwZo7d67q1KmjadOmKSwsTIcOHVJgYGBGpwcALuHFjS8lGDbzsekZkAkAAEDKZIvG+O2331avXr3UvXt3SdLcuXP1zTffaP78+Ro+fHgGZ3dLRh95dtYeu8TisLcWyLzurlln7alP7DutYV2nhAYylLO21xm93UfK8P8LQJZvjK9fv65du3ZpxIgRtmFWq1WhoaHaunVrovPExsYqNjbW9j4qKkqSFB0dnWDauGuxCYYlNl1yUhvn5o1rd8UxCaa5fiX52HfHkaRKr42we79l2IgE0ySXT2I5OZIPXFNifz+p+dvw8fGRxWJJ0TwpqfuM5kidSamrfWetb2LfadQ+7sUZtZ+aupdSXvsZud13Vu236jYpYZzyCeOkVz7O5MjvovT8nYakZWTtAylmsrg///zTSDI///yz3fChQ4ea2rVrJzrP66+/biTx4sUri76ioqJS/F1B3fPilbVfqal7ap8Xr6z/Sm3tAyllMcYYZWF//fWXChcurJ9//ll16/7feXzDhg3Tpk2bFBERkWCeu/cex8fH69KlS8qXL98990hFR0crODhYZ86cka+vb6rzJQ5xslOcjMrJGUeMHan7lOaVFOIQhzj3F8dZR4yz4jY/M+ZEHOJk9toHUirLn0qdP39+5ciRQ+fPn7cbfv78eQUFBSU6j7u7u9zd3e2G+fv7O7Q8X1/f+964EYc42S2OM2M5M6c73U/dS5lv/YhDHOI4Jjtt850ZizjEyU5xAGfI8s8xdnNzU40aNbRx40bbsPj4eG3cuNHuCDIAAAAAAInJ8keMJWnw4MHq2rWratasqdq1a2vatGm6cuWK7S7VAAAAAADcS7ZojNu3b6+///5bo0aN0rlz51S1alWtXbtWBQoUcNoy3N3d9frrryc4HYs4xHHlOJk1J2fKbOtHHOIQJ+1lxvXLbDkRhziZIQ7gTFn+5lsAAAAAANyPLH+NMQAAAAAA94PGGAAAAADg0miMAQAAAAAujcYYAAAAAODSaIwBAAAAAC6NxhhApvLll1+qcuXKGZ0GgHRE3QOuidpHZpItnmPsTCVKlHBouuPHj6dLHEmKi4vTvn37VLp0aXl6etqNu3r1qo4ePaqKFSvKak2f/Rzff/+9Hn74YeXMeX9/Pv/995/Wr1+vRx55RD4+PnbjoqOjFR4errCwsGz7jLvff/9dFStWTHKaHDly6OzZswoMDEynrNLHe++9p/Xr18vNzU0vvfSS6tSpo++//14vv/yyDh8+rC5duqR7TtR+8qh953DV2s/Ode/sWNR+9qt9V617KXPWPpAYGuO7nDx5UkWLFlWnTp3u64vJWXEk6eOPP9asWbMUERGRYJybm5t69OihgQMH6plnnkkyzqhRozR8+HDlzp1bkvTvv/8qT548Kc6ncePGdl/cDz30kL744gsVLlw4RXHef/99rVq1Sk8++WSCcb6+vpoxY4bOnDmjF154IdlY1apVk8ViSXa6X375JcnxY8eOTTaGdOuzTI2YmBgtXbpUH374oXbt2qW4uLgkp3fmY8YPHDigbdu2qW7duipbtqwOHjyo6dOnKzY2Vs8884weffTRFMe8cuWKPv/8cx09elQFCxZUx44dlS9fviTnmTRpkkaNGqXKlSvr4MGDWrlypV599VXNnDlTL730kvr06ZOqv8v7Re0nL7PVflape4naz+517+xY1H72qP3sVvdS9ql9IFEGdj7//HPzxBNPGA8PD/PUU0+Z1atXm7i4uAyLY4wxDRo0MEuXLr3n+M8++8w0bNgw2ThWq9WcP3/e9t7Hx8ccO3YsxflYLBa7ON7e3qmKU6tWLbNq1ap7jl+9erWpVauWQ7FGjx5te73++uvGzc3NDBgwwG746NGjk41jsVhM4cKFTbVq1UzVqlUTfVWrVs3hdbxt06ZNpkuXLsbLy8uULl3avPLKK2b79u0O5XPnZ51aa9asMW5ubiZv3rzGw8PDrFmzxgQEBJjQ0FDz6KOPmhw5cpiNGzcmG6dcuXLm4sWLxhhjTp8+bYoVK2b8/PxMrVq1TN68eU1gYKA5fvx4kjHKlCljFi5caIwx5scffzQWi8U0a9bMXL58+b7X835Q+8nLbLWf2eveGGr/tuxe986ORe0nLbPXfnape2Oyb+0DiaExvoc//vjDvPHGG6ZUqVKmUKFC5pVXXjGHDx/OkDgBAQHmxIkT9xx//Phxkz9//mTjOGvD5qw4/v7+5tSpU/ccf+rUKePv75/iuPeTU9OmTY2Hh4dp2bKlWblyZap/1BhjzNmzZ83EiRNNqVKlTGBgoOnfv7/JmTOn2bdvn8MxLBaLGT9+vJk+fXqSr+TUrVvXvPrqq8YYY5YuXWry5Mlj/ve//9nGDx8+3DRu3NihfG7/v+/cubOpV6+eiYyMNMYYExMTY0JDQ03Hjh2TjOHh4WFOnz5te+/m5mZ27tyZ7LLTC7Wf9nHSqvYzQ90bQ+0nxlXq3lmxqP2UyQy1nx3r/nZO2bn2gTvRGDsgPDzcNGrUyFitVnPp0qV0j5M7d26zd+/ee47fu3evyZ07d7JxnLVhs1qt5sKFC7b3Pj4+yR4lTIy3t3eSX447d+403t7eKY57O3Zq1s0YY/78808zYcIEU6ZMGRMUFGSGDRtmDh48mKIYzZs3N76+vqZjx47m66+/Njdv3jTGmFRtJIODg02xYsXu+SpevHiycXx9fc2RI0eMMcbExcWZnDlzml9++cU2/rfffjMFChRwKJ/bf0MlSpQw69atsxu/ZcsWExwcnGyMO/9+vL29U/X3kx6ofXuZvfYzuu6NofaTmt/V6v5+YlH7KY/LNv//OKvub+fkKrUPcI1xEq5du6bly5dr/vz5ioiI0NNPP227Tic945QuXVo///zzPe/at3nzZpUuXTrZOBaLRTExMfLw8JAxRhaLRZcvX1Z0dLTddL6+vknGMcboscces92E4+rVq2rRooXc3Nzspkvu2p4KFSpow4YNqlGjRqLj161bpwoVKiS3Wk5XqFAhjRgxQiNGjNCPP/6oBQsWqFatWqpUqZI2bNiQ4EYoiVmzZo0GDBig559/3qH/N0nZuXOnU27Ecft6LKvVKg8PD/n5+dnG+fj4KCoqKkVxrl27poIFC9qNK1y4sP7+++9kY4wcOdJWA9evX9cbb7xhl48kvf322w7lkxao/cRl59p3Rt1L1H5SXKXunRGL2k8/bPMdj5Vdax+4jcY4EREREZo3b54+//xzlShRQj169NAXX3yR4psDOCtOp06d9Nprr6levXoJNpJ79+7VqFGjNGzYsGTjGGNUpkwZu/fVqlWze2+xWJK9OcTrr79u975ly5aOrEYCPXr00ODBg1WhQgU1b97cbtzq1as1fvz4DP+irFWrlk6ePKn9+/dr9+7dunHjhkMbyc2bN2vevHmqUaOGypUrp2effVYdOnRI8fIdubmII4oVK6YjR46oZMmSkqStW7eqSJEitvGnT59OsLG7l9s/jqKjo3Xo0CG7u2yeOnUq2ZtvPfzwwzp06JDtfb169RLcpdVZ651S1D61L6W+7iVq/15coe6dGYvazxhs8xOXXWsfuBuN8V0qVKigCxcuqFOnTtq0aZOqVKmSoXEkadCgQVqzZo1q1Kih0NBQlS1bVpJ08OBBbdiwQfXr19egQYOSjfPDDz+kOoc73b2BTK3evXvrxx9/1JNPPqmyZcvqwQcflHRrvQ4fPqx27dqpd+/eDsWaMWOG3fubN29q4cKFyp8/v93wAQMGOBRv69atmj9/vj7//HOVKVNG3bt3V6dOnZLdq37bQw89pIceekjTpk3TZ599pvnz52vw4MGKj4/X+vXrFRwcnOBRFYkxTrpD5fPPP2/3w+fuR0asWbPGoTtUjho1ym4D5u3tbTd+9erVatiwYZIxwsPDHcg4/VH7yctstZ/Z6l6i9u8lu9e9s2NR+0nLbLWfXeteyr61DyTGYpxVhdmE1WqVl5eXcubMmeQerEuXLqVLnNtu3Lihd955R0uWLNGRI0dse4E7deqkgQMHJjidKSv5/PPPE12vdu3aORyjePHiyU5jsViSfX7klClTtHDhQv3zzz/q3Lmzunfv7rQHzx86dEjz5s3Txx9/rMjISDVu3FirVq1Kcp4xY8Zo6NChqT6dL7OKjo5WRESErl+/rtq1aysgICCjU6L2M8D91n5WqHuJ2r8tO9e9s2NJ1H5SskLtU/f/JzPWPpAYGuO7LFq0yKHpunbtmi5xJGnDhg0KDQ295/j4+HhNmDBBr732mkPLvF+PPPJIsqe9WCwWbdy4MV3ycSar1aoiRYqoefPmSf7ouJ9TveLi4rR69WrNnz8/2Y3kbTt27NDSpUt1+PBhSbL9iKhZs6ZD8+fIkcPuGZSp5Yw4e/bsUdOmTXXu3DlJt651+vzzzxUWFnZfud0vaj952bX206PuJdeu/exe986ORe2nD7b5aR8rs9Y+kBga4xS6efOmLly4oEKFCqXbMt3c3NS7d29NmTIlwV7E33//XV27dtW5c+f0559/JhnHarU6tGG7efNmktMkdfpWTEyMlixZotjY2GSvWbp586bi4uLk7u5uG3b+/HnNnTtXV65cUYsWLZI9LfdOxhgdPXpU169f14MPPmi7SUhKNGrUyKHP6Pvvv09x7NQaNmyYpk6dKm9vb5UoUUKSdOzYMV29elVDhgzR5MmTk41htVp17ty5+95IOiNOWFiYLl++rKlTp8rDw0Pjxo3Tb7/9piNHjtxXbmmN2s+ctZ9d617KXrVP3acMtZ+87Fr7manunRErq9Y+XBONcQrt3btX1atXT/bLPzHXrl3TZ599pitXrqhx48YO37kwIiJC3bp1s11DU79+fdve4nHjxqlNmzaaPXt2sjf3WLly5T3Hbd26VTNmzFB8fLyuXbuWovWSbm3sZs+erfHjx8vPz0/jxo1L9sYT3bt3l5ubm9577z1JtzauFSpUsN31cP/+/Vq5cqWaNm2a7PJPnDihJ598Uvv375d06y6JX3zxhWrVqpXidXGGHj16JDuNxWLRvHnzkpxm0aJF6tu3r95880316dNHuXLlknTrFLs5c+bolVde0XvvvacuXbokGSez/DiWpPz582vdunWqXr26JCkyMlJ58+ZVZGRkiq7lTG/UfuIysvYzW91L1P69uGLdS9T+3bJr7WfXundGrKxa+3BNNMYp5OhGcvDgwbpx44Zmzpwp6dbt6evUqaN9+/Ypd+7cunnzptavX6+6des6tNxr165p+PDhevfdd9W7d29t27ZNZ86c0Zw5c9S6detUr8+hQ4c0fPhwrV69Wp07d9bYsWNVtGjRFMVYvHixRo0apf/++0+vvfaaevfu7dCe2zJlymjWrFl6/PHHJUmzZ8/WhAkTtH//fvn5+emVV17R9u3bHbp5SNu2bbVv3z6NGjVKHh4emjp1qq5du6Zdu3alaF2cxWq1qmjRoqpWrVqSN9NYsWJFknFq166tjh073nNv/dtvv61PP/1U27dvTzafN954I8FNM+6W3I1KnBEnsY2sj4+Pfv31V4euG8so1H5CGV37ma3uJWr/XnGye91L1L4r1352rXtnxMqqtQ8XlXaPSM6e9uzZY6xWa7LTVahQwaxcudL2fv78+SZPnjzm5MmTJj4+3nTr1s00bdo0RcuOj483HTt2NBaLxXh7e6f4AfR3+vPPP81zzz1ncuXKZZo3b25+++23FMdYs2aNqVKlivH19TVjx441ly9fTtH8uXPntnvI+1NPPWVefPFF2/t9+/aZgIAAh2IVKFDA/PTTT7b3f/31l7FarSnOqVy5cubixYu2988//7z5+++/be/Pnz9vPD09k43Tr18/kydPHlO1alUzffp0u5gpkTt3bnPs2LF7jj927JjJnTt3snEsFosJDg42xYoVu+erePHi6RLHYrGYH374wezdu9f28vLyMt98843dsMyG2v8/maX2M1vdG0PtJzV/dq57Y6h9R2TX2s+ude+MWFm19uGaaIxTyNGNpI+Pjzly5IjtfYcOHUyvXr1s73fv3m0KFizo8HKPHj1qGjRoYAoUKGDee+8989BDD5mgoCDz1VdfpSj/yMhIM2zYMOPp6Wnq1q1rfvzxxxTNb4wxERERplGjRsbDw8MMHDjQbiOSEnnz5jX79u2zvS9YsKD55JNPbO+PHTvm8I9Ri8Vizp07ZzfMy8vLbgPsaJzz58/b3vv4+NhtpM6dO2csFotDsa5du2aWLFliQkNDTe7cuc3TTz9t1q5da+Lj4x3Ox8fHxxw4cOCe4w8ePGh8fHySjXP3eqWWM+JYLBZjtVqNxWJJ8Lo93NEfoumJ2s98tZ8Z694Yav9e82fnujeG2ndEdq797Fj3zoiVVWsfronnGN/l119/TXL8nQ8pT4rVarU7nWbbtm0aOXKk7b2/v7/+/fdfh2LNmjVLw4cPV1hYmL788ksFBAToueee05tvvqkOHTqobdu2mjlzpvz9/ZOMM2XKFE2ePFlBQUFaunSpWrZs6dDy7/bQQw/J09NTffv2VfHixbVkyZJEp0vuFJ2qVavq448/1sSJE/XTTz/p/Pnzds/VO3bsmMM3PLFYLLp8+bI8PT1tw6xWq2JiYhQdHW0bltLrWUwip0Q5+iB6d3d3dezYUR07dtSpU6e0cOFC9evXTzdv3tS+ffuSPS1JkqpXr67Fixdr3LhxiY7/+OOPbdftJMXRnNMjzokTJ5yQifNR+8nLbLWfGeteovYTk93rXqL2Jdeu/exY986IlVlrH0hURnblmVFSe7bu3MOVnIceesi89dZbxhhjfv/9d2O1Wu32ZIaHh5uiRYs6lFOePHns9qje6ffffzc1atQwhQoVcmjdcufObZ588knz1FNP3fOVnKJFiyZ5So2jp+iEh4cbT09PU6JECePp6Wl69OhhN/755583Xbp0STbO7XWzWq12rzuHOfr/7e49o97e3gn2Hqdmz+bp06fNmDFjTPHixU3hwoVNTEyMQ/OtXr3a5MiRwwwdOtRu7/jZs2fNkCFDTM6cOc3q1auTjZNZjhoZY8yYMWPMlStX7jsXZ6P2s17tZ/a6N4bavy27170x1D61/3+yS907I1ZmrX0gMRwxvosje7ZiYmKSnWbYsGHq0KGDvvnmG+3bt09Nmza1u8nAt99+q9q1azuU0759+1SwYMFEx1WoUEERERGaMGFCsnG6dOnilL2IJ0+eTHL8H3/8obFjxyYbJyQkRLt27dK6desUFBSkp59+2m581apVHf6MHLlBlyMsFkuCzyi1n1lsbKy+/PJLzZ8/X5s3b1bz5s01a9YsPfHEE7JarQ7FaN68ud555x0NGTJEb731lvz8/CRJUVFRypkzp6ZOnarmzZsnG+f11193aG91esQZM2aM+vbtm+ARJBmN2k9eZqv9zFj3ErWfmOxe9xK17+q1nx3r3hmxMmvtA4nhrtQOiomJ0dKlSzVv3jzt3LnToTtUbty4UV9//bWCgoL04osv2n0pjBkzRnnz5tWLL76YbJymTZtq6dKlti/ISZMmqW/fvrZTqC5evKiGDRvaHluQ0Ry9i2ePHj00ffp0+fj4pFNmybNarapYsaLt7pq//vqrypYtKzc3N0mynRKV3Lr169dPn376qYKDg9WjRw917txZ+fPnT3Vef/zxh5YtW2Z77l+ZMmXUpk0bBQcHpyjOjh07tHTpUh0+fNgWp1OnTqpZs2a6xXHmYyTSA7XvuKxa+86qe4navxdXqHuJ2nfV2s/udX8/sbJa7cO10Rgn48cff9S8efP0xRdfqFChQmrdurXatGmT6mfl3d7Yfvjhh9q1a5dDG9scOXLo7Nmzti8VX19f7dmzx/bg9/Pnz6tQoULJxnLk8Q4Wi0VffPGFA2tyb45uIO9er/tx5zVFSUnueqMxY8Y4FOf1119PcrzValWRIkVUrVq1JPc+f/nllw4t715+/fVX1axZU9evX0922mHDhmnq1Kny9va2/e0cO3ZMV69e1ZAhQzR58mSHlnm/caxWq86fP6+AgACHlpdRqP2US+/az2x1L1H79+KqdS9R+3fKrrWfnev+fmNlldoHJIlTqRNx7tw5LVy4UPPmzVN0dLTatWun2NhYffXVVypfvnyqYia2sZ09e7ZD89697yK1+zJu73nOLJy5T8bf3z/JjZExRhaLJdmNtiM/fB3hrNPXkmOMcehH1qJFizRz5kzNmDFDffr0Ua5cuSRJN27c0Jw5c/TKK6+oQoUK6tKlS7rEKVOmTLKfz6VLl5JdL2ej9tOHs2o/s9W9RO0nFceV6l6i9hOTXWs/u9a9s2Jl1toH7kZjfJcWLVroxx9/VLNmzTRt2jQ98cQTypEjh+bOnZviWGm1sU2tBQsWpPsykxMTEyMPD48kp3HkrpLff/99umyUHLVw4cKMTsHO7NmzNWHCBPXv399ueK5cuTRgwADdvHlTs2bNSnYj6aw4Y8aMyXQ/2Kj99OWM2s9sdS9R+0nFye51L1H7jsiOtZ9d695ZsTJj7QOJ4VTqu+TMmVMDBgzQ888/r9KlS9uG58qVS3v37nV4w3bnxrZz5862jW1K40i3Tj06d+6c7TQUHx8f/frrr7abejh6SpWzJHdqVmRkpDZt2pRsPlar1Sl7fJ0pudOgbvvll19SvQxjjNauXat58+Zp+fLlqY4jOX76mpeXl3777TfbKVB3O378uCpVqqQrV66keZzMer0RtZ+87Fr76VH3kmvXfnave4nap/YTl5Xr3hmxMmvtA4nhiPFdNm/erHnz5qlGjRoqV66cnn32WXXo0CHFcdasWZPoxjY1jDHq1q2b3N3dJUnXrl1T37595eXlJenWnRDTU3J7/fz8/BzaCylJy5cvV968ee87p+Q2ttKt66hu3ryZ5DStWrWy/dsYo4kTJ6pv375OyfHEiROaP3++Fi5cqL///luhoaHJzpPcdVSO3i01R44cSV6TdOPGDeXIkSNd4mSmvfx3ovaTl9lqPyvUvUTtS9m/7iVqn9q3lx3q3hmxMmvtA4nhiPE9XLlyRZ999pnmz5+v7du3Ky4uTm+//bZ69Ojh0N0Ut23bpnnz5umzzz6z29gWLFgwxXuOu3fv7tB0mfGUqaQ4cy/iypUr7zlu69atmjFjhuLj43Xt2rUUxfXx8dHevXvvuac0ObGxsVq+fLnmzZunzZs3Ky4uTlOnTlXPnj0dOkXcWXvXGzVqpIYNG2rcuHGJjn/ttde0efNmhYeHp3mczL73mNpPe876G8isdS9R+3fL7nUvUfuOyO61n93q3hmxMnvtA3bS8iHJ2cXBgwfN0KFDTVBQkPHw8DAtWrRweN7Lly+befPmmfr165tcuXIZq9Vqpk2bZqKjo9Mw46zBmQ+gT8zBgwdNq1atTI4cOUyXLl3MyZMnUxzD29vbHDt2LMXz7dy50zz//PPG39/f1KxZ00yfPt2cO3fO5MyZ0+zbt8/hOOHh4Q69krN69WqTI0cOM3ToUHPu3Dnb8LNnz5ohQ4aYnDlzmtWrV6dbnKyC2k8baVn7GVn3xlD72cH91L0x1H5SsmvtZ9e6d3YsILOjMU6BmzdvmhUrVqR4I3nb/W5ss5tixYqZf/75x+lx//zzT/Pcc8+ZXLlymebNm5vffvst1bFSu5HMkSOHGThwoDl48KDd8JRuJOPi4sykSZNMvXr1TM2aNc0rr7xirl69muJ8jDFmxowZxs3NzVitVpMnTx6TJ08eY7VajZubm5k2bVq6x8lKqH3nSovazwx1bwy1n53cb90bQ+3fLbvWfnaue2fHAjIzTqXOAHFxcVq9erXmz5+vVatWZXQ6mcKvv/5q99D4ypUrpzhGVFSUJkyYoJkzZ6pq1aqaPHmyGjZseF95pfa0qrCwMG3dulUtWrTQs88+q7CwMFkslhTfhGXcuHEaPXq0QkND5enpqe+++04dO3bU/PnzU7M6+uOPP7Rs2TIdOXJE0q3Puk2bNgoODs6QOK6G2k/ofms/M9W9RO0jcdR+Qtmp9rN73Ts7FpBZ0RgjQ23fvl09e/bU/v37bc83tFgsqlChgubNm6datWo5FGfKlCmaPHmygoKCNGHCBLVs2TJV+cyYMcPu/SuvvKKhQ4cqf/78dsMHDBiQbKwzZ87Ybrzx33//qX379nr33Xf166+/qly5cg7lU7p0aQ0ZMkR9+vSRJG3YsEHNmjXTf//9J6vV6uBaJe/XX39VzZo1k7zBRnrGQfbnjNrPjHUvUftAUrJr7bti3Ts7FpDRaIyRYfbv3686deqoXLlyGjRokG3DsX//fr3zzjs6dOiQtm3b5tCeVqvVKk9PT4WGhiZ5d8Qvv/wyyTi3H4WRFIvFouPHjyc73Z02bNig+fPna8WKFQoODlbbtm3Vtm1bVa9ePcn53N3ddfToUbs9sh4eHjp69KgeeOCBFOWQFEcfAZFecZC9Oav2M3vdS9Q+cCdXqX1XqXtnxwIyGo0xMky7du108+ZNffHFFwnuwmiMUevWrZUrVy59/vnnycbq1q2bQ48ESK87eN6+E+WqVat0/fp1PfbYY3r99dd17do1ffLJJ5o/f75+/fXXZDckdz/LUkr4PEtn4Mcx0pOzaj+z1b1E7VP7SEp2rX1XrXtnxwIyGo0xMkxAQIDWrFmjmjVrJjp+x44datq0qf7+++90zcsYo6NHj+r69et68MEHlTNnyh/37ch1Qr/88kuye4+tVquaNGlie5alJK1evVqPPvqo7XmWUvJ7xZPDj2Okp8xY+86oe4nap/aRlOxa+65a986OBWS01G35ASeIiYlRgQIF7jk+KCjI4YfZO8uJEyf05JNPav/+/ZKkwoUL64svvnD4WufbPvroI7377rsJrhP68MMPbdcJJbeBlKSuXbsmGPbMM8+kKBdJio6OTnK8o5+zs+LAtWW22ndW3UvUPpCU7Fr72bXunR0LyOxojJFhihYtqu3bt9/zjoYREREqWrRouuY0dOhQ3bx5U5988ok8PDw0depU9e3bV7t27UpRnNOnT6tp06a296GhobJYLPrrr79SdJ2Qs04D8/f3T/K0M2OMQ6elOSsOXFtmq31n1b1E7QNJya61n13r3tmxgMyOxhgZpkOHDho8eLAefPBBVaxY0W7cb7/9piFDhqhLly7pmtPmzZu1fPlyNWjQQJL00EMP6YEHHtCVK1fsTmNKzs2bN+Xh4WE3LFeuXLpx44ZT83XUDz/8kKniwLVlttp3Vt1L1D6QlOxa+9m17p0dC8jsuMYYGebatWt67LHHFBERocaNG6tcuXIyxujAgQPasGGDateure+//z7BxiYtWa1WnT171u5UL29vb/32228puvFFel0n5Kj4+Hi9+eabCW4M4unpmSFx4NoyW+07q+5vx6L2gcRl19rPrnXv7FhAZscRY2QYDw8P/fDDD3rnnXe0dOlSbdq0SdKth8a/8cYbeuaZZzRgwAC9//776ZaTxWLR5cuX7b7wrVarYmJi7K6z8fX1TTKOs64Tcpbx48fb3Rhk+vTpunDhgt2NQdIzDlxbZqt9Z9W9RO0DScmutZ9d697ZsYBMzwCZ1J49e4zVak3XZVosFmO1Wu1edw67/e+splSpUmbu3Lm29+vXrzdubm4mLi4uQ+IASUnv2s+udW8MtY+shdp3DmfWK7UPV8Kp1Mi0MuIRALf3XicnJCQkjTNxLnd3dx09etTuhiceHh46evRoim4M4qw4QFLSu/aza91L1D6yFmrfOZxZr9Q+XAmnUgN3yGobP0c568Ygme0GI4AzZNe6l6h9ICnZtfadWa/UPlwJjTFwh+Se13ebI9caZibGGHXr1s3uxiDXrl1T3759U3RjEGfFATKT7Fr3ErUPJCW71r4z65XahyuhMUaGad26dZLjIyMj0yeROzj6vL70PL3bGZx1Y5DMdoMRZE2Zrfaza91L1D4yF2o/fTizXql9uBKuMUaG6d69u0PTOeuB944IDw936EH12fX0KyA9ZLbap+6B9EHtA8jMaIwBAAAAAC6NU6mBO1it1mT3HlssFt28eTOdMgKQ1qh7wDVR+wDuRGMM3GHFihX3HLd161bNmDFD8fHx6ZgRgLRG3QOuidoHcCdOpQaScejQIQ0fPlyrV69W586dNXbsWBUtWjSj0wKQhqh7wDVR+4DrsmZ0AkBm9ddff6lXr16qVKmSbt68qT179mjRokVsIIFsjLoHXBO1D4DGGLhLVFSUXnnlFZUqVUr79u3Txo0btXr1alWsWDGjUwOQRqh7wDVR+wBu4xpj4A5TpkzR5MmTFRQUpKVLl6ply5YZnRKANEbdA66J2gdwJ64xBu5gtVrl6emp0NBQ5ciR457Tffnll+mYFYC0RN0DronaB3AnjhgDd+jSpUuyj24AkL1Q94BrovYB3IkjxgAAAAAAl8bNtwAAAAAALo3GGAAAAADg0miMAQAAAAAujcYYAAAAAODSaIwBAAAAAC6NxhgAAAAA4NJojAEAAAAALo3GGAAAAADg0v4fi83qxkia/10AAAAASUVORK5CYII=", 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" ] @@ -26990,12 +26951,206 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 12, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bmelitensis-seqsphere\n", + "[1766 1816 1817 ... 1885 1793 1922]\n", + "[1745 1778 1778 ... 1837 1750 1856]\n", + "[[1. 0.99974054]\n", + " [0.99974054 1. ]]\n", + "bmelitensis-seqsphere-0.9997405443579204\n", + "bmelitensis-seqsphere\n", + "[1766 1816 1817 ... 1885 1793 1922]\n", + "[ nan nan nan ... 685. 656. 693.]\n", + "[[ 1. nan]\n", + " [nan nan]]\n", + "bmelitensis-seqsphere-nan\n", + "bmelitensis-seqsphere\n", + "[ 221 1778 276 ... 1837 1750 1856]\n", + "[ nan nan nan ... 685. 656. 693.]\n", + "[[ 1. nan]\n", + " [nan nan]]\n", + "bmelitensis-seqsphere-nan\n", + "lmonocytogenes-seqsphere\n", + "[24 55 31 46 89 29 24 29 29 30 32 35 32 30 29 33 31 30 31 32 55 30 30 34\n", + " 31 29 33 29 34 30 49 29 40 85 24 22 24 24 25 27 30 27 25 24 27 26 25 26\n", + " 27 49 25 26 29 26 24 28 24 29 25 61 48 86 57 54 57 56 58 59 61 60 58 56\n", + " 59 58 57 58 60 0 58 56 60 58 56 59 57 59 57 51 94 34 25 34 34 35 37 40\n", + " 36 34 34 38 35 35 36 37 61 35 36 39 36 34 38 34 39 35 83 45 44 45 46 47\n", + " 48 51 49 46 45 50 48 47 46 48 48 46 44 50 47 46 48 46 49 46 89 89 90 90\n", + " 90 92 95 92 90 89 94 92 91 92 93 86 91 88 93 91 90 92 90 94 91 28 0 0\n", + " 1 4 6 3 1 0 33 2 1 2 3 57 1 3 5 2 0 4 0 5 1 28 28 29\n", + " 31 34 31 29 28 31 30 29 30 31 54 29 29 33 30 28 32 28 33 29 0 1 4 6\n", + " 3 1 0 33 2 1 2 3 57 1 3 5 2 0 4 0 5 1 1 4 6 3 1 0\n", + " 33 2 1 2 3 56 1 3 5 2 0 4 0 5 1 5 7 4 2 1 34 3 2 3\n", + " 4 58 2 4 6 3 1 5 1 6 2 10 7 5 4 35 6 5 6 7 59 5 7 9\n", + " 6 4 8 4 9 5 9 7 6 39 8 7 8 9 61 7 9 3 8 6 2 6 3 7\n", + " 2 3 36 3 4 5 6 60 4 5 8 5 3 7 3 8 4 1 34 1 2 3 4 58\n", + " 2 4 6 3 1 5 1 6 2 33 2 1 2 3 56 1 3 5 2 0 4 0 5 1\n", + " 34 34 35 36 59 34 34 38 35 33 37 33 38 34 3 4 5 58 3 5 7 4 2 6\n", + " 2 7 3 3 4 57 2 4 6 3 1 5 1 6 2 5 58 3 5 7 4 2 6 2\n", + " 7 3 60 4 6 8 5 3 7 3 8 4 58 56 60 58 56 59 57 59 57 4 6 3\n", + " 1 5 1 6 2 8 5 3 7 3 8 4 7 5 1 5 2 6 2 6 2 7 3 4\n", + " 0 5 1 4 1 5 5 1 6]\n", + "[25 57 32 48 92 29 24 29 29 29 32 36 32 30 29 34 31 30 31 32 57 31 36 34\n", + " 30 28 33 29 34 30 52 31 43 87 23 23 23 23 25 26 30 26 24 23 27 25 24 25\n", + " 26 52 25 32 28 24 24 27 23 28 24 66 53 90 58 56 58 58 60 61 63 61 59 58\n", + " 62 60 59 60 61 0 60 64 61 59 57 60 58 61 59 55 98 35 26 35 35 35 38 42\n", + " 37 35 35 40 36 36 37 38 66 37 43 40 36 36 39 35 40 36 88 47 47 47 47 49\n", + " 50 53 50 48 47 52 49 48 48 50 53 49 51 51 48 47 50 47 51 48 90 91 90 90\n", + " 92 93 96 92 91 90 94 92 91 92 93 90 92 94 94 91 91 93 90 94 91 28 0 0\n", + " 2 4 7 3 1 0 32 2 1 2 3 58 2 10 5 1 1 4 0 5 1 28 28 30\n", + " 31 35 31 29 28 32 30 29 30 31 56 30 36 33 29 27 32 28 33 29 0 2 4 7\n", + " 3 1 0 32 2 1 2 3 58 2 10 5 1 1 4 0 5 1 2 4 7 3 1 0\n", + " 32 2 1 2 3 58 2 10 5 1 1 4 0 5 1 6 9 5 3 2 34 4 3 4\n", + " 5 60 4 11 7 3 3 6 2 7 3 11 7 5 4 34 6 5 6 7 61 6 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45 46 47\n", + " 48 51 49 46 45 50 48 47 46 48 48 46 44 50 47 46 48 46 49 46 89 89 90 90\n", + " 90 92 95 92 90 89 94 92 91 92 93 86 91 88 93 91 90 92 90 94 91 28 0 0\n", + " 1 4 6 3 1 0 33 2 1 2 3 57 1 3 5 2 0 4 0 5 1 28 28 29\n", + " 31 34 31 29 28 31 30 29 30 31 54 29 29 33 30 28 32 28 33 29 0 1 4 6\n", + " 3 1 0 33 2 1 2 3 57 1 3 5 2 0 4 0 5 1 1 4 6 3 1 0\n", + " 33 2 1 2 3 56 1 3 5 2 0 4 0 5 1 5 7 4 2 1 34 3 2 3\n", + " 4 58 2 4 6 3 1 5 1 6 2 10 7 5 4 35 6 5 6 7 59 5 7 9\n", + " 6 4 8 4 9 5 9 7 6 39 8 7 8 9 61 7 9 3 8 6 2 6 3 7\n", + " 2 3 36 3 4 5 6 60 4 5 8 5 3 7 3 8 4 1 34 1 2 3 4 58\n", + " 2 4 6 3 1 5 1 6 2 33 2 1 2 3 56 1 3 5 2 0 4 0 5 1\n", + " 34 34 35 36 59 34 34 38 35 33 37 33 38 34 3 4 5 58 3 5 7 4 2 6\n", + " 2 7 3 3 4 57 2 4 6 3 1 5 1 6 2 5 58 3 5 7 4 2 6 2\n", + " 7 3 60 4 6 8 5 3 7 3 8 4 58 56 60 58 56 59 57 59 57 4 6 3\n", + " 1 5 1 6 2 8 5 3 7 3 8 4 7 5 1 5 2 6 2 6 2 7 3 4\n", + " 0 5 1 4 1 5 5 1 6]\n", + "[17 37 26 31 59 21 19 21 21 21 23 26 22 22 21 21 23 22 22 23 37 22 22 25\n", + " 22 21 24 21 25 21 31 21 25 53 15 14 15 15 15 17 20 16 16 15 14 17 16 16\n", + " 17 31 16 17 19 16 15 18 15 19 15 42 32 54 36 35 36 36 36 38 39 37 37 36\n", + " 35 38 37 37 38 0 37 37 38 37 36 37 36 38 36 36 63 25 19 25 25 25 27 30\n", + " 25 25 25 25 26 26 26 27 42 26 27 29 26 25 28 25 29 25 55 29 29 29 29 29\n", + " 31 33 30 30 29 29 31 30 29 31 32 30 30 32 30 29 31 29 32 29 56 57 56 56\n", + " 56 58 60 57 57 56 57 58 57 57 58 54 57 57 59 57 56 58 56 59 56 19 0 0\n", + " 0 3 5 1 1 0 19 2 1 1 2 36 1 3 4 1 0 3 0 4 0 19 19 19\n", + " 21 24 20 20 19 18 21 20 20 21 35 20 20 23 20 19 22 19 23 19 0 0 3 5\n", + " 1 1 0 19 2 1 1 2 36 1 3 4 1 0 3 0 4 0 0 3 5 1 1 0\n", + " 19 2 1 1 2 36 1 3 4 1 0 3 0 4 0 3 5 1 1 0 19 2 1 1\n", + " 2 36 1 3 4 1 0 3 0 4 0 8 4 4 3 20 5 4 4 5 38 4 6 7\n", + " 4 3 6 3 7 3 6 6 5 24 7 6 6 7 39 6 8 3 6 5 2 5 3 5\n", + " 0 1 20 1 2 2 3 37 2 4 5 2 1 4 1 5 1 1 20 1 2 2 3 37\n", + " 2 4 5 2 1 4 1 5 1 19 2 1 1 2 36 1 3 4 1 0 3 0 4 0\n", + " 20 20 20 21 35 20 20 23 20 19 22 19 23 19 3 3 4 38 3 5 6 3 2 5\n", + " 2 6 2 2 3 37 2 4 5 2 1 4 1 5 1 3 37 2 4 5 2 1 4 1\n", + " 5 1 38 3 5 6 3 2 5 2 6 2 37 37 38 37 36 37 36 38 36 4 5 2\n", + " 1 4 1 5 1 7 4 3 6 3 7 3 5 4 1 4 2 4 1 4 1 5 1 3\n", + " 0 4 0 3 1 3 4 0 4]\n", + "[[1. 0.99663036]\n", + " [0.99663036 1. ]]\n", + "lmonocytogenes-seqsphere-0.996630356375772\n", + "lmonocytogenes-seqsphere\n", + "[25 32 57 92 48 24 29 29 29 32 29 36 32 29 30 34 31 31 30 32 57 36 31 34\n", + " 30 28 33 29 34 30 31 52 87 43 23 23 23 23 26 25 30 26 23 24 27 25 25 24\n", + " 26 52 32 25 28 24 24 27 23 28 24 66 98 55 26 35 35 35 38 35 42 37 35 35\n", + " 40 36 37 36 38 66 43 37 40 36 36 39 35 40 36 90 53 56 58 58 58 61 60 63\n", + " 61 58 59 62 60 60 59 61 0 64 60 61 59 57 60 58 61 59 88 91 90 90 90 93\n", + " 92 96 92 90 91 94 92 92 91 93 90 94 92 94 91 91 93 90 94 91 47 47 47 47\n", + " 50 49 53 50 47 48 52 49 48 48 50 53 51 49 51 48 47 50 47 51 48 28 28 28\n", + " 31 30 35 31 28 29 32 30 30 29 31 56 36 30 33 29 27 32 28 33 29 0 0 4\n", + " 2 7 3 0 1 32 2 2 1 3 58 10 2 5 1 1 4 0 5 1 0 4 2 7\n", + " 3 0 1 32 2 2 1 3 58 10 2 5 1 1 4 0 5 1 4 2 7 3 0 1\n", + " 32 2 2 1 3 58 10 2 5 1 1 4 0 5 1 6 11 7 4 5 34 6 6 5\n", + " 7 61 14 6 9 5 5 8 4 9 5 9 5 2 3 34 4 4 3 5 60 11 4 7\n", + " 3 3 6 2 7 3 10 7 8 39 9 9 8 10 63 17 9 4 8 8 3 7 4 8\n", + " 3 2 35 3 5 4 6 61 12 5 8 4 4 7 3 8 4 1 32 2 2 1 3 58\n", + " 10 2 5 1 1 4 0 5 1 33 1 3 2 4 59 11 3 6 2 2 5 1 6 2\n", + " 33 34 33 35 62 39 34 37 33 33 36 32 37 33 4 3 5 60 12 4 7 3 3 6\n", + " 2 7 3 3 5 60 12 4 7 3 3 6 2 7 3 4 59 11 3 6 2 2 5 1\n", + " 6 2 61 13 5 8 4 4 7 3 8 4 64 60 61 59 57 60 58 61 59 12 15 11\n", + " 11 14 10 15 11 7 3 3 6 2 7 3 6 6 1 5 2 6 2 5 1 6 2 5\n", + " 1 6 2 4 1 5 5 1 6]\n", + "[17 26 37 59 31 19 21 21 21 23 21 26 22 21 22 21 23 22 22 23 37 22 22 25\n", + " 22 21 24 21 25 21 21 31 53 25 14 15 15 15 17 15 20 16 15 16 14 17 16 16\n", + " 17 31 17 16 19 16 15 18 15 19 15 42 63 36 19 25 25 25 27 25 30 25 25 25\n", + " 25 26 26 26 27 42 27 26 29 26 25 28 25 29 25 54 32 35 36 36 36 38 36 39\n", + " 37 36 37 35 38 37 37 38 0 37 37 38 37 36 37 36 38 36 55 57 56 56 56 58\n", + " 56 60 57 56 57 57 58 57 57 58 54 57 57 59 57 56 58 56 59 56 29 29 29 29\n", + " 31 29 33 30 29 30 29 31 29 30 31 32 30 30 32 30 29 31 29 32 29 19 19 19\n", + " 21 19 24 20 19 20 18 21 20 20 21 35 20 20 23 20 19 22 19 23 19 0 0 3\n", + " 0 5 1 0 1 19 2 1 1 2 36 3 1 4 1 0 3 0 4 0 0 3 0 5\n", + " 1 0 1 19 2 1 1 2 36 3 1 4 1 0 3 0 4 0 3 0 5 1 0 1\n", + " 19 2 1 1 2 36 3 1 4 1 0 3 0 4 0 3 8 4 3 4 20 5 4 4\n", + " 5 38 6 4 7 4 3 6 3 7 3 5 1 0 1 19 2 1 1 2 36 3 1 4\n", + " 1 0 3 0 4 0 6 5 6 24 7 6 6 7 39 8 6 3 6 5 2 5 3 5\n", + " 1 0 20 1 2 2 3 37 4 2 5 2 1 4 1 5 1 1 19 2 1 1 2 36\n", + " 3 1 4 1 0 3 0 4 0 20 1 2 2 3 37 4 2 5 2 1 4 1 5 1\n", + " 20 20 20 21 35 20 20 23 20 19 22 19 23 19 3 3 4 38 5 3 6 3 2 5\n", + " 2 6 2 2 3 37 4 2 5 2 1 4 1 5 1 3 37 4 2 5 2 1 4 1\n", + " 5 1 38 5 3 6 3 2 5 2 6 2 37 37 38 37 36 37 36 38 36 4 7 4\n", + " 3 6 3 7 3 5 2 1 4 1 5 1 5 4 1 4 2 4 1 4 1 5 1 3\n", + " 0 4 0 3 1 3 4 0 4]\n", + "[[1. 0.99571042]\n", + " [0.99571042 1. ]]\n", + "lmonocytogenes-seqsphere-0.9957104166314541\n", + "mtuberculosis-seqsphere\n", + "[555 234 268 2 318 1 3 2 832 18 16 230 492 559 584 554 561 558\n", + " 873 562 561 1 235 232 232 235 234 235 231 231 274 2 269 273 270 682\n", + " 272 271 324 3 1 3 838 20 18 319 323 320 848 320 319 2 1 830\n", + " 16 14 4 841 19 17 837 17 15 831 830 2]\n", + "[ 556 1985 85 20 323 23 30 26 853 48 52 1974 144 560\n", + " 582 559 566 563 889 565 571 28 2001 2000 1990 2010 2002 2008\n", + " 1979 1979 85 20 81 88 84 218 85 88 320 27 20 24\n", + " 854 48 52 324 322 319 861 323 328 25 19 854 43 51\n", + " 13 856 46 46 853 40 46 852 851 20]\n", + "[[1. 0.3064135]\n", + " [0.3064135 1. ]]\n", + "mtuberculosis-seqsphere-0.3064135017194518\n", + "mtuberculosis-seqsphere\n", + "[555 234 268 2 318 1 3 2 832 18 16 230 492 559 584 554 561 558\n", + " 873 562 561 1 235 232 232 235 234 235 231 231 274 2 269 273 270 682\n", + " 272 271 324 3 1 3 838 20 18 319 323 320 848 320 319 2 1 830\n", + " 16 14 4 841 19 17 837 17 15 831 830 2]\n", + "[193 17 99 2 100 0 2 0 295 7 6 17 192 194 193 192 194 193\n", + " 298 194 194 15 17 17 17 17 17 17 17 17 101 0 99 101 99 280\n", + " 99 99 102 2 0 2 294 9 8 100 102 100 282 100 100 2 0 292\n", + " 7 6 2 297 9 8 295 7 6 295 295 1]\n", + "[[1. 0.97263193]\n", + " [0.97263193 1. ]]\n", + "mtuberculosis-seqsphere-0.9726319342605187\n", + "mtuberculosis-seqsphere\n", + "[ 85 1985 556 323 20 23 30 26 853 48 52 28 144 20\n", + " 85 81 88 84 218 85 88 1974 2000 2001 1990 2010 2002 2008\n", + " 1979 1979 582 560 559 566 563 889 565 571 320 324 322 319\n", + " 861 323 328 27 20 24 854 48 52 25 19 854 43 51\n", + " 13 856 46 46 853 40 46 852 851 20]\n", + "[ 99 17 193 100 2 0 2 0 295 7 6 15 192 0 101 99 101 99\n", + " 280 99 99 17 17 17 17 17 17 17 17 17 193 194 192 194 193 298\n", + " 194 194 102 100 102 100 282 100 100 2 0 2 294 9 8 2 0 292\n", + " 7 6 2 297 9 8 295 7 6 295 295 1]\n", + "[[1. 0.08371285]\n", + " [0.08371285 1. ]]\n", + "mtuberculosis-seqsphere-0.0837128488775461\n" + ] + }, { "data": { - "image/png": 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", + "image/png": 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" ] @@ -27023,12 +27178,17 @@ " for col, (soft1, soft2) in enumerate(combinations(software, 2)):\n", " matrix1 = pd.read_csv(f\"./{dataset}/distance_{soft1}/distance_matrix.csv\", index_col=0)\n", " matrix2 = pd.read_csv(f\"./{dataset}/distance_{soft2}/distance_matrix.csv\", index_col=0)\n", + " print(matrix1)\n", + " print(matrix2)\n", " \n", " matrix2 = matrix2.reindex(index=matrix1.index, columns=matrix1.columns)\n", " flat_matrix1 = get_upper_triangle(matrix1)\n", " flat_matrix2 = get_upper_triangle(matrix2)\n", + " print(flat_matrix1)\n", + " print(flat_matrix2)\n", + " print(np.corrcoef(flat_matrix1, flat_matrix2))\n", " correlation = np.corrcoef(flat_matrix1, flat_matrix2)[0, 1]\n", - " \n", + " print(f\"{dataset}-{soft}-{correlation}\")\n", " ax = axes[row][col]\n", " ax.scatter(flat_matrix1, flat_matrix2, edgecolors='k', alpha=0.75, s=50)\n", " ax.set_title(f\"{soft1} vs. {soft2} (r={correlation:.2f})\", fontsize=20)\n", From ae500026b0e9d0c448379fc70a061ef5c79c76a7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Thu, 2 Jan 2025 10:22:03 +0100 Subject: [PATCH 200/214] change name from taranis to taranys --- .github/workflows/tests.yml | 12 ++--- Dockerfile | 26 +++++------ README.md | 36 +++++++-------- assets/benchmark.ipynb | 74 +++++++++++++++---------------- environment.yml | 2 +- logging_config.ini | 4 +- pyproject.toml | 4 +- setup.py | 6 +-- taranis/__init__.py | 2 +- taranis/__main__.py | 82 +++++++++++++++++------------------ taranis/allele_calling.old_py | 10 ++--- taranis/allele_calling.py | 34 +++++++-------- taranis/analyze_schema.py | 32 +++++++------- taranis/blast.py | 4 +- taranis/clustering.py | 8 ++-- taranis/distance.py | 4 +- taranis/eval_cluster.py | 12 ++--- taranis/pruebas.py | 14 +++--- taranis/reference_alleles.py | 26 +++++------ test/test.sh | 14 +++--- 20 files changed, 203 insertions(+), 203 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index db64b1c..6342cdb 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -16,22 +16,22 @@ jobs: - name: Set up Miniconda uses: conda-incubator/setup-miniconda@v3 with: - activate-environment: taranis_env + activate-environment: taranys_env environment-file: environment.yml - name: Verify Conda environment run: conda env list - - name: Activate env and install taranis + - name: Activate env and install taranys run: | source $CONDA/etc/profile.d/conda.sh - conda activate taranis_env + conda activate taranys_env python -m pip install . - taranis analyze-schema -i test/MLST_listeria/analyze_schema -o analyze_schema_test --cpus 1 --output-allele-annot --remove-no-cds --remove-duplicated --remove-subset + taranys analyze-schema -i test/MLST_listeria/analyze_schema -o analyze_schema_test --cpus 1 --output-allele-annot --remove-no-cds --remove-duplicated --remove-subset - name: Testing Reference allele run: | source $CONDA/etc/profile.d/conda.sh - conda activate taranis_env - taranis reference-alleles -s test/MLST_listeria/reference_allele -o reference_allele_test --cpus 1 + conda activate taranys_env + taranys reference-alleles -s test/MLST_listeria/reference_allele -o reference_allele_test --cpus 1 \ No newline at end of file diff --git a/Dockerfile b/Dockerfile index 28f3c5c..0852013 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,17 +1,17 @@ FROM continuumio/miniconda3:latest -RUN mkdir /opt/taranis/ -ADD utils /opt/taranis/utils -ADD test /opt/taranis/test -ADD *.py /opt/taranis/ -ADD environment.yml /opt/taranis/ -ADD logging_config.ini /opt/taranis/ -ADD README.md /opt/taranis/ -ADD LICENSE /opt/taranis/ +RUN mkdir /opt/taranys/ +ADD utils /opt/taranys/utils +ADD test /opt/taranys/test +ADD *.py /opt/taranys/ +ADD environment.yml /opt/taranys/ +ADD logging_config.ini /opt/taranys/ +ADD README.md /opt/taranys/ +ADD LICENSE /opt/taranys/ SHELL ["/bin/bash", "-c"] -RUN cd /opt/taranis -RUN /opt/conda/bin/conda env create -f /opt/taranis/environment.yml && /opt/conda/bin/conda clean -a -RUN /opt/conda/bin/conda env export --name taranis > taranis.yml -RUN echo "conda activate taranis" > ~/.bashrc -ENV PATH /opt/conda/envs/taranis:/opt/conda/envs/taranis/utils:$PATH +RUN cd /opt/taranys +RUN /opt/conda/bin/conda env create -f /opt/taranys/environment.yml && /opt/conda/bin/conda clean -a +RUN /opt/conda/bin/conda env export --name taranys > taranys.yml +RUN echo "conda activate taranys" > ~/.bashrc +ENV PATH /opt/conda/envs/taranys:/opt/conda/envs/taranys/utils:$PATH diff --git a/README.md b/README.md index 4ddebf7..8676463 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -# Taranis +# taranys - [Introduction](#introduction) - [Dependencies](#dependencies) @@ -12,9 +12,9 @@ ## Introduction -**Taranis** is a computational stand-alone pipeline for **gene-by-gene allele calling analysis** based on BLASTn using whole genome (wg) and core genome (cg) multilocus sequence typing (MLST) schemas on complete or draft genomes resulting from de novo assemblers, while tracking helpful and informative data among the process. +**taranys** is a computational stand-alone pipeline for **gene-by-gene allele calling analysis** based on BLASTn using whole genome (wg) and core genome (cg) multilocus sequence typing (MLST) schemas on complete or draft genomes resulting from de novo assemblers, while tracking helpful and informative data among the process. -Taranis includes four main functionalities: MLST **schema analysis**, gene-by-gene **allele calling**, **reference alleles** obtainment for allele calling analysis and the final **distance matrix** construction. +taranys includes four main functionalities: MLST **schema analysis**, gene-by-gene **allele calling**, **reference alleles** obtainment for allele calling analysis and the final **distance matrix** construction. ## Dependencies @@ -34,9 +34,9 @@ Taranis includes four main functionalities: MLST **schema analysis**, gene-by-ge Install all dependencies and add them to $PATH. -`git clone https://github.com/BU-ISCIII/taranis.git` +`git clone https://github.com/BU-ISCIII/taranys.git` -Add taranis and ./bin to $PATH. +Add taranys and ./bin to $PATH. #### Install using conda @@ -44,7 +44,7 @@ This option is recomended. Install Anaconda3. -`conda install -c conda-forge -c bioconda -c defaults taranis` +`conda install -c conda-forge -c bioconda -c defaults taranys` Wait for the environment to solve.
Ignore warnings/errors. @@ -56,7 +56,7 @@ Ignore warnings/errors. Schema analysis: ``` -taranis analyze_schema \ +taranys analyze_schema \ -inputdir schema_dir \ -output output_analyze_schema_dir --ouput-allele-annotation annotation_dir @@ -65,7 +65,7 @@ taranis analyze_schema \ Schema analysis for removing duplicated, subsequences and no CDS alleles: ``` -taranis analyze_schema \ +taranys analyze_schema \ -inputdir schema_dir \ -output output_analyze_schema_dir \ --remove-subsets \ @@ -84,7 +84,7 @@ taranis analyze_schema \ Get reference alleles: ``` -taranis reference_alleles \ +taranys reference_alleles \ -s schema_dir \ -o output_reference_alleles_dir \ --eval-cluster \ @@ -95,7 +95,7 @@ taranis reference_alleles \ Reference alleles with clustering settings: ``` -taranis reference_alleles \ +taranys reference_alleles \ -s schema_dir \ -o output_reference_alleles_dir \ --eval-cluster \ @@ -111,7 +111,7 @@ taranis reference_alleles \ Run allele calling: ``` -taranis allele_calling \ +taranys allele_calling \ -s schema_dir \ -a annotation_file \ -r reference_alleles_dir \ @@ -129,7 +129,7 @@ samples_dir Allele calling for blast and threshold settings: ``` -taranis allele_calling \ +taranys allele_calling \ -s schema_dir \ -a annotation_file \ -r reference_alleles_dir \ @@ -149,7 +149,7 @@ samples_dir Get distance matrix: ``` -taranis distance_matrix \ +taranys distance_matrix \ -a allele_calling_match.csv file \ -o distance_matrix_dir --force overwrite output folder @@ -158,7 +158,7 @@ taranis distance_matrix \ Distance matrix with threshold settings: ``` -taranis distance_matrix \ +taranys distance_matrix \ -a allele_calling_match.csv file \ -o distance_matrix_dir -l threshold for missing locus \ @@ -174,7 +174,7 @@ taranis distance_matrix \ - **analyze_schema mode:** ``` -Usage: taranis analyze-schema [OPTIONS] +Usage: taranys analyze-schema [OPTIONS] Options: -i, --inputdir PATH Directory where the schema with the core @@ -205,7 +205,7 @@ Options: - **reference_alleles mode:** ``` -Usage: taranis reference-alleles [OPTIONS] +Usage: taranys reference-alleles [OPTIONS] Options: -s, --schema PATH Directory where the schema with the core @@ -227,7 +227,7 @@ Options: - **allele_calling mode:** ``` -Usage: taranis allele-calling [OPTIONS] ASSEMBLIES... +Usage: taranys allele-calling [OPTIONS] ASSEMBLIES... Options: -s, --schema PATH Directory where the schema with the core @@ -258,7 +258,7 @@ Options: - **distance_matrix mode:** ``` -Usage: taranis distance-matrix [OPTIONS] +Usage: taranys distance-matrix [OPTIONS] Options: -a, --alleles PATH Alleles matrix file from which to obtain diff --git a/assets/benchmark.ipynb b/assets/benchmark.ipynb index d151227..af761de 100644 --- a/assets/benchmark.ipynb +++ b/assets/benchmark.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Here we are going to benchmark chewbbaca, taranis and seqshpere using three different datasets:\n", + "Here we are going to benchmark chewbbaca, taranys and seqshpere using three different datasets:\n", "- ECDC EQA - mtuberculosis\n", "- Halbedel et al. 2019 - lmonocytogenes\n", "- UNSGM PT3 - bmelitensis" @@ -19,21 +19,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "Requirement already satisfied: pandas in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (2.2.2)\n", - "Requirement already satisfied: matplotlib in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (3.8.4)\n", - "Requirement already satisfied: seaborn in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (0.13.2)\n", - "Requirement already satisfied: numpy in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (1.26.4)\n", - "Requirement already satisfied: python-dateutil>=2.8.2 in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (from pandas) (2.9.0.post0)\n", - "Requirement already satisfied: pytz>=2020.1 in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (from pandas) (2024.1)\n", - "Requirement already satisfied: tzdata>=2022.7 in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (from pandas) (2024.1)\n", - "Requirement already satisfied: contourpy>=1.0.1 in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (from matplotlib) (1.2.1)\n", - "Requirement already satisfied: cycler>=0.10 in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (from matplotlib) (0.12.1)\n", - "Requirement already satisfied: fonttools>=4.22.0 in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (from matplotlib) (4.51.0)\n", - "Requirement already satisfied: kiwisolver>=1.3.1 in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (from matplotlib) (1.4.5)\n", - "Requirement already satisfied: packaging>=20.0 in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (from matplotlib) (24.0)\n", - "Requirement already satisfied: pillow>=8 in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (from matplotlib) (10.3.0)\n", - "Requirement already satisfied: pyparsing>=2.3.1 in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (from matplotlib) (3.1.2)\n", - "Requirement already satisfied: six>=1.5 in /home/smonzon/Documents/desarrollo/taranis/.venv/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n" + "Requirement already satisfied: pandas in /home/smonzon/Documents/desarrollo/taranys/.venv/lib/python3.10/site-packages (2.2.2)\n", + "Requirement already satisfied: matplotlib in /home/smonzon/Documents/desarrollo/taranys/.venv/lib/python3.10/site-packages (3.8.4)\n", + "Requirement already satisfied: seaborn in /home/smonzon/Documents/desarrollo/taranys/.venv/lib/python3.10/site-packages (0.13.2)\n", + "Requirement already satisfied: numpy in /home/smonzon/Documents/desarrollo/taranys/.venv/lib/python3.10/site-packages (1.26.4)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /home/smonzon/Documents/desarrollo/taranys/.venv/lib/python3.10/site-packages (from pandas) (2.9.0.post0)\n", + "Requirement already satisfied: pytz>=2020.1 in /home/smonzon/Documents/desarrollo/taranys/.venv/lib/python3.10/site-packages (from pandas) (2024.1)\n", + "Requirement already satisfied: tzdata>=2022.7 in /home/smonzon/Documents/desarrollo/taranys/.venv/lib/python3.10/site-packages (from pandas) (2024.1)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /home/smonzon/Documents/desarrollo/taranys/.venv/lib/python3.10/site-packages (from matplotlib) (1.2.1)\n", + "Requirement already satisfied: cycler>=0.10 in /home/smonzon/Documents/desarrollo/taranys/.venv/lib/python3.10/site-packages (from matplotlib) (0.12.1)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /home/smonzon/Documents/desarrollo/taranys/.venv/lib/python3.10/site-packages (from matplotlib) (4.51.0)\n", + "Requirement already satisfied: kiwisolver>=1.3.1 in /home/smonzon/Documents/desarrollo/taranys/.venv/lib/python3.10/site-packages (from matplotlib) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /home/smonzon/Documents/desarrollo/taranys/.venv/lib/python3.10/site-packages (from matplotlib) (24.0)\n", + "Requirement already satisfied: pillow>=8 in /home/smonzon/Documents/desarrollo/taranys/.venv/lib/python3.10/site-packages (from matplotlib) (10.3.0)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /home/smonzon/Documents/desarrollo/taranys/.venv/lib/python3.10/site-packages (from matplotlib) (3.1.2)\n", + "Requirement already satisfied: six>=1.5 in /home/smonzon/Documents/desarrollo/taranys/.venv/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n" ] } ], @@ -52,7 +52,7 @@ "text": [ "├── bmelitensis\n", "│ ├── .~lock.results_alleles_chewbbaca.tsv#\n", - "│ ├── .~lock.summary_taranis.csv#\n", + "│ ├── .~lock.summary_taranys.csv#\n", "│ ├── cluster_per_locus_80.csv\n", "│ ├── cluster_per_locus_85.csv\n", "│ ├── cluster_per_locus_90.csv\n", @@ -66,7 +66,7 @@ "│ │ ├── allele_matrix_fil.tsv\n", "│ │ ├── distance_matrix.csv\n", "│ │ └── distance_matrix_core.csv\n", - "│ ├── distance_taranis\n", + "│ ├── distance_taranys\n", "│ │ ├── allele_matrix_fil.csv\n", "│ │ ├── allele_matrix_fil.tsv\n", "│ │ ├── distance_matrix.csv\n", @@ -76,11 +76,11 @@ "│ ├── results_alleles_chewbbaca_masked.tsv\n", "│ ├── results_alleles_seqsphere.ccsv\n", "│ ├── results_alleles_seqsphere.csv\n", - "│ ├── results_alleles_taranis.csv\n", + "│ ├── results_alleles_taranys.csv\n", "│ ├── summary_chewbbaca.csv\n", "│ ├── summary_chewbbaca.tsv\n", "│ ├── summary_seqsphere.csv\n", - "│ └── summary_taranis.csv\n", + "│ └── summary_taranys.csv\n", "├── boxplot_mantel_test.png\n", "├── comprobaciones.txt\n", "├── datasets.txt\n", @@ -100,7 +100,7 @@ "│ │ ├── allele_matrix_fil.tsv\n", "│ │ ├── distance_matrix.csv\n", "│ │ └── distance_matrix_core.csv\n", - "│ ├── distance_taranis\n", + "│ ├── distance_taranys\n", "│ │ ├── allele_matrix_fil.csv\n", "│ │ ├── allele_matrix_fil.tsv\n", "│ │ ├── distance_matrix.csv\n", @@ -110,20 +110,20 @@ "│ ├── results_alleles_chewbbaca_masked.tsv\n", "│ ├── results_alleles_seqsphere.ccsv\n", "│ ├── results_alleles_seqsphere.csv\n", - "│ ├── results_alleles_taranis.csv\n", + "│ ├── results_alleles_taranys.csv\n", "│ ├── summary_chewbbaca.csv\n", "│ ├── summary_chewbbaca.tsv\n", "│ ├── summary_seqsphere.csv\n", - "│ └── summary_taranis.csv\n", + "│ └── summary_taranys.csv\n", "├── locus_distribution.png\n", "├── mst_bmelitensis_chewbbaca.svg\n", - "├── mst_bmelitensis_taranis.svg\n", + "├── mst_bmelitensis_taranys.svg\n", "├── mst_lmonocytogenes_chewbbaca.svg\n", "├── mst_lmonocytogenes_seqsphere.svg\n", - "├── mst_lmonocytogenes_taranis.svg\n", + "├── mst_lmonocytogenes_taranys.svg\n", "├── mst_mtuberculosis_chewbbaca.svg\n", "├── mst_mtuberculosis_seqsphere.svg\n", - "├── mst_mtuberculosis_taranis.svg\n", + "├── mst_mtuberculosis_taranys.svg\n", "├── mtuberculosis\n", "│ ├── .venv\n", "│ │ ├── .gitignore\n", @@ -26734,7 +26734,7 @@ "│ │ ├── allele_matrix_fil.tsv\n", "│ │ ├── distance_matrix.csv\n", "│ │ └── distance_matrix_core.csv\n", - "│ ├── distance_taranis\n", + "│ ├── distance_taranys\n", "│ │ ├── allele_matrix_fil.csv\n", "│ │ ├── allele_matrix_fil.tsv\n", "│ │ ├── distance_matrix.csv\n", @@ -26745,12 +26745,12 @@ "│ ├── results_alleles_chewbbaca.tsv\n", "│ ├── results_alleles_seqsphere.ccsv\n", "│ ├── results_alleles_seqsphere.csv\n", - "│ ├── results_alleles_taranis.csv\n", - "│ ├── results_alleles_taranis.ods\n", + "│ ├── results_alleles_taranys.csv\n", + "│ ├── results_alleles_taranys.ods\n", "│ ├── summary_chewbbaca.csv\n", "│ ├── summary_chewbbaca.tsv\n", "│ ├── summary_seqsphere.csv\n", - "│ ├── summary_taranis.csv\n", + "│ ├── summary_taranys.csv\n", "│ └── test.ipynb\n", "├── summary_comparison.csv\n", "└── summary_comparison.png\n" @@ -26853,11 +26853,11 @@ "output_type": "stream", "text": [ " Type % of Total Software Dataset\n", - "0 NIPHEM 0.00 taranis bmelitensis\n", - "1 NIPH 0.07 taranis bmelitensis\n", - "2 EXC 95.12 taranis bmelitensis\n", - "3 PLOT 0.03 taranis bmelitensis\n", - "4 ASM 0.00 taranis bmelitensis\n", + "0 NIPHEM 0.00 taranys bmelitensis\n", + "1 NIPH 0.07 taranys bmelitensis\n", + "2 EXC 95.12 taranys bmelitensis\n", + "3 PLOT 0.03 taranys bmelitensis\n", + "4 ASM 0.00 taranys bmelitensis\n", ".. ... ... ... ...\n", "61 ASM 0.93 chewbbaca mtuberculosis\n", "62 PAMA 0.03 chewbbaca mtuberculosis\n", @@ -26872,7 +26872,7 @@ "source": [ "# Check summary results\n", "datasets = [\"bmelitensis\", \"lmonocytogenes\", \"mtuberculosis\"]\n", - "software = [\"taranis\", \"chewbbaca\", \"seqsphere\"]\n", + "software = [\"taranys\", \"chewbbaca\", \"seqsphere\"]\n", "final_summary_table = pd.DataFrame()\n", "\n", "for dataset in datasets:\n", @@ -27162,7 +27162,7 @@ "source": [ "# Datasets and software tools\n", "datasets = [\"bmelitensis\", \"lmonocytogenes\", \"mtuberculosis\"]\n", - "software = [\"taranis\", \"chewbbaca\", \"seqsphere\"]\n", + "software = [\"taranys\", \"chewbbaca\", \"seqsphere\"]\n", "\n", "# Function to extract upper triangle of the matrix without the diagonal\n", "def get_upper_triangle(matrix):\n", diff --git a/environment.yml b/environment.yml index 1d45d06..4203d9f 100644 --- a/environment.yml +++ b/environment.yml @@ -1,4 +1,4 @@ -name: taranis_env +name: taranys_env channels: - conda-forge - bioconda diff --git a/logging_config.ini b/logging_config.ini index 8d725f2..2c44c20 100644 --- a/logging_config.ini +++ b/logging_config.ini @@ -18,6 +18,6 @@ format=%(asctime)s %(funcName)-12s %(levelname)-8s %(lineno)s %(message)s class=handlers.RotatingFileHandler level=NOTSET ## args(log_file_name, 'a', maxBytes , backupCount) -#args=('Programas/taranis_b/logs/taranis.log','a',500000,5) -args=("taranis.log",'a',500000,5) +#args=('Programas/taranys_b/logs/taranys.log','a',500000,5) +args=("taranys.log",'a',500000,5) formatter=logfileformatter diff --git a/pyproject.toml b/pyproject.toml index c2e6af9..7cdfdfe 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ requires = ["setuptools", "wheel"] build-backend = "setuptools.build_meta" [project] -name = "taranis" +name = "taranys" version = "3.0.0" dynamic = ["dependencies"] requires-python = ">=3.10" @@ -24,7 +24,7 @@ license = {file = "LICENSE"} dependencies = {file = ["requirements.txt"]} [project.scripts] -taranis = "taranis.__main__:run_taranis" +taranys = "taranys.__main__:run_taranys" [tool.setuptools.packages.find] exclude = ["img", "virtualenv"] diff --git a/setup.py b/setup.py index d6ecd35..5f6c85e 100644 --- a/setup.py +++ b/setup.py @@ -11,7 +11,7 @@ required = f.read().splitlines() setup( - name="taranis", + name="taranys", version=version, description="Tools for gene-by-gene allele calling analysis", long_description=readme, @@ -26,9 +26,9 @@ ], author="Sara Monzon", author_email="smonzon@isciii.es", - url="https://github.com/BU-ISCIII/taranis", + url="https://github.com/BU-ISCIII/taranys", license="GNU GENERAL PUBLIC LICENSE v.3", - entry_points={"console_scripts": ["taranis=taranis.__main__:run_taranis"]}, + entry_points={"console_scripts": ["taranys=taranys.__main__:run_taranys"]}, python_requires=">=3.9, <4", install_requires=required, packages=find_packages(exclude=("docs")), diff --git a/taranis/__init__.py b/taranis/__init__.py index caed400..87e41d8 100644 --- a/taranis/__init__.py +++ b/taranis/__init__.py @@ -1,3 +1,3 @@ import pkg_resources -__version__ = pkg_resources.get_distribution("taranis").version +__version__ = pkg_resources.get_distribution("taranys").version diff --git a/taranis/__main__.py b/taranis/__main__.py index ce7dcb5..a864ff8 100644 --- a/taranis/__main__.py +++ b/taranis/__main__.py @@ -10,19 +10,19 @@ import sys import time -import taranis.distance -import taranis.utils -import taranis.analyze_schema -import taranis.reference_alleles -import taranis.allele_calling +import taranys.distance +import taranys.utils +import taranys.analyze_schema +import taranys.reference_alleles +import taranys.allele_calling -import taranis.inferred_alleles +import taranys.inferred_alleles log = logging.getLogger() # Set up rich stderr console stderr = rich.console.Console( - stderr=True, force_terminal=taranis.utils.rich_force_colors() + stderr=True, force_terminal=taranys.utils.rich_force_colors() ) @@ -40,11 +40,11 @@ def expand_wildcards(ctx, param, value): return None -def run_taranis(): +def run_taranys(): # Set up the rich traceback rich.traceback.install(console=stderr, width=200, word_wrap=True, extra_lines=1) - # Print taranis header + # Print taranys header stderr.print( "[blue] ______ ___ ___ ", highlight=False, @@ -69,10 +69,10 @@ def run_taranis(): # stderr.print("[green] `._,._,'\n", highlight=False) __version__ = "3.0.0" stderr.print( - "\n" "[grey39] Taranis version {}".format(__version__), highlight=False + "\n" "[grey39] taranys version {}".format(__version__), highlight=False ) # Lanch the click cli - taranis_cli() + taranys_cli() # Customise the order of subcommands for --help @@ -112,7 +112,7 @@ def decorator(f): @click.group(cls=CustomHelpOrder) -@click.version_option(taranis.__version__) +@click.version_option(taranys.__version__) @click.option( "-v", "--verbose", @@ -123,7 +123,7 @@ def decorator(f): @click.option( "-l", "--log-file", help="Save a verbose log to a file.", metavar="filename" ) -def taranis_cli(verbose, log_file): +def taranys_cli(verbose, log_file): # Set the base logger to output DEBUG log.setLevel(logging.DEBUG) @@ -139,7 +139,7 @@ def taranis_cli(verbose, log_file): log.addHandler(log_fh) -@taranis_cli.command(help_priority=1) +@taranys_cli.command(help_priority=1) @click.option( "-i", "--inputdir", @@ -218,11 +218,11 @@ def analyze_schema( usegenus: str, cpus: int, ): - _ = taranis.utils.check_additional_programs_installed([["prokka", "--version"]]) - schema_files = taranis.utils.get_files_in_folder(inputdir, "fasta") + _ = taranys.utils.check_additional_programs_installed([["prokka", "--version"]]) + schema_files = taranys.utils.get_files_in_folder(inputdir, "fasta") results = [] - max_cpus = taranis.utils.cpus_available() + max_cpus = taranys.utils.cpus_available() if cpus > max_cpus: stderr.print("[red] Number of CPUs bigger than the CPUs available") stderr.print("Running code with ", max_cpus) @@ -233,7 +233,7 @@ def analyze_schema( with concurrent.futures.ThreadPoolExecutor(max_workers=using_cpus) as executor: futures = [ executor.submit( - taranis.analyze_schema.parallel_execution, + taranys.analyze_schema.parallel_execution, schema_file, output, remove_subset, @@ -249,14 +249,14 @@ def analyze_schema( # Collect results as they complete for future in concurrent.futures.as_completed(futures): results.append(future.result()) - _ = taranis.analyze_schema.collect_statistics(results, output, output_allele_annot) + _ = taranys.analyze_schema.collect_statistics(results, output, output_allele_annot) finish = time.perf_counter() print(f"Schema analyze finish in {round((finish-start)/60, 2)} minutes") # Reference alleles -@taranis_cli.command(help_priority=2) +@taranys_cli.command(help_priority=2) @click.option( "-s", "--schema", @@ -344,26 +344,26 @@ def reference_alleles( cpus: int, force: bool, ): - _ = taranis.utils.check_additional_programs_installed( + _ = taranys.utils.check_additional_programs_installed( [["mash", "--version"], ["makeblastdb", "-version"], ["blastn", "-version"]] ) start = time.perf_counter() - max_cpus = taranis.utils.cpus_available() + max_cpus = taranys.utils.cpus_available() if cpus > max_cpus: stderr.print("[red] Number of CPUs bigger than the CPUs available") stderr.print("Running code with ", max_cpus) cpus = max_cpus - schema_files = taranis.utils.get_files_in_folder(schema, "fasta") + schema_files = taranys.utils.get_files_in_folder(schema, "fasta") # Check if output folder exists if not force: - _ = taranis.utils.prompt_user_if_folder_exists(output) + _ = taranys.utils.prompt_user_if_folder_exists(output) """Create the reference alleles from the schema """ results = [] with concurrent.futures.ThreadPoolExecutor(max_workers=cpus) as executor: futures = [ executor.submit( - taranis.reference_alleles.parallel_execution, + taranys.reference_alleles.parallel_execution, f_file, output, eval_cluster, @@ -381,12 +381,12 @@ def reference_alleles( except Exception as e: print(e) continue - _ = taranis.reference_alleles.collect_statistics(results, eval_cluster, output) + _ = taranys.reference_alleles.collect_statistics(results, eval_cluster, output) finish = time.perf_counter() print(f"Reference alleles finish in {round((finish-start)/60, 2)} minutes") -@taranis_cli.command(help_priority=3) +@taranys_cli.command(help_priority=3) @click.option( "-s", "--schema", @@ -503,10 +503,10 @@ def allele_calling( increase_sequence: int, cpus: int, ): - _ = taranis.utils.check_additional_programs_installed( + _ = taranys.utils.check_additional_programs_installed( [["blastn", "-version"], ["makeblastdb", "-version"], ["mafft", "--version"]] ) - schema_ref_files = taranis.utils.get_files_in_folder(reference, "fasta") + schema_ref_files = taranys.utils.get_files_in_folder(reference, "fasta") if len(schema_ref_files) == 0: log.error("Referenc allele folder %s does not have any fasta file", schema) stderr.print("[red] reference allele folder does not have any fasta file") @@ -514,9 +514,9 @@ def allele_calling( # Check if output folder exists if not force: - _ = taranis.utils.prompt_user_if_folder_exists(output) + _ = taranys.utils.prompt_user_if_folder_exists(output) # Filter fasta files from reference folder - max_cpus = taranis.utils.cpus_available() + max_cpus = taranys.utils.cpus_available() if cpus > max_cpus: stderr.print("[red] Number of CPUs bigger than the CPUs available") stderr.print("Running code with ", max_cpus) @@ -525,11 +525,11 @@ def allele_calling( stderr.print("[green] Reading annotation file") log.info("Reading annotation file") map_pred = [["gene", 7], ["product", 8], ["allele_quality", 9]] - prediction_data = taranis.utils.read_compressed_file( + prediction_data = taranys.utils.read_compressed_file( annotation, separator=",", index_key=1, mapping=map_pred ) # Create the instanace for inference alleles - inf_allele_obj = taranis.inferred_alleles.InferredAllele() + inf_allele_obj = taranys.inferred_alleles.InferredAllele() """Analyze the sample file against schema to identify alleles """ @@ -539,7 +539,7 @@ def allele_calling( with concurrent.futures.ThreadPoolExecutor(max_workers=cpus) as executor: futures = [ executor.submit( - taranis.allele_calling.parallel_execution, + taranys.allele_calling.parallel_execution, assembly_file, schema, prediction_data, @@ -562,7 +562,7 @@ def allele_calling( print(e) continue - _ = taranis.allele_calling.collect_data( + _ = taranys.allele_calling.collect_data( results, output, snp, alignment, schema_ref_files, cpus ) finish = time.perf_counter() @@ -570,7 +570,7 @@ def allele_calling( log.info("Allele calling finish in %s minutes", round((finish - start) / 60, 2)) -@taranis_cli.command(help_priority=3) +@taranys_cli.command(help_priority=3) @click.option( "-a", "--alleles", @@ -646,13 +646,13 @@ def distance_matrix( plot_filter: bool, ): # Check if file exists - if not taranis.utils.file_exists(alleles): + if not taranys.utils.file_exists(alleles): log.error("Alleles matrix file %s does not exist", alleles) stderr.print("[red] Alleles matrix file does not exist") sys.exit(1) # Check if output folder exists if not force: - _ = taranis.utils.prompt_user_if_folder_exists(output) + _ = taranys.utils.prompt_user_if_folder_exists(output) start = time.perf_counter() # filter the alleles matrix according to the thresholds and filters allele_matrix = pd.read_csv(alleles, sep=",", index_col=0, header=0, dtype=str) @@ -665,7 +665,7 @@ def distance_matrix( if plot_filter: to_mask.append("PLOT") - allele_matrix_fil = taranis.utils.filter_df( + allele_matrix_fil = taranys.utils.filter_df( allele_matrix, locus_missing_threshold, sample_missing_threshold, @@ -674,12 +674,12 @@ def distance_matrix( allele_matrix_fil.to_csv(f"{output}/allele_matrix_fil.csv") # Create the distance matrix - d_matrix_obj = taranis.distance.HammingDistance(allele_matrix) + d_matrix_obj = taranys.distance.HammingDistance(allele_matrix) distance_matrix = d_matrix_obj.create_matrix(to_mask) distance_matrix.to_csv(f"{output}/distance_matrix.csv") # Create the filtered distance matrix - d_matrix_core_obj = taranis.distance.HammingDistance(allele_matrix_fil) + d_matrix_core_obj = taranys.distance.HammingDistance(allele_matrix_fil) distance_matrix_core = d_matrix_core_obj.create_matrix(to_mask) distance_matrix_core.to_csv(f"{output}/distance_matrix_core.csv") diff --git a/taranis/allele_calling.old_py b/taranis/allele_calling.old_py index e8be72f..0030736 100644 --- a/taranis/allele_calling.old_py +++ b/taranis/allele_calling.old_py @@ -22,7 +22,7 @@ from Bio.Blast import NCBIXML import pandas as pd import shutil from progressbar import ProgressBar -from utils.taranis_utils import * +from utils.taranys_utils import * import math import csv import plotly.graph_objects as go @@ -2781,7 +2781,7 @@ def update_schema( + ">" + str(allele_number) + " # " - + "INF by Taranis" + + "INF by taranys" + "\n" + inf + "\n" @@ -2803,7 +2803,7 @@ def update_schema( + ">" + complete_inf_id + " # " - + "INF by Taranis" + + "INF by taranys" + "\n" + inf + "\n" @@ -2838,7 +2838,7 @@ def update_schema (updateschema, schemadir, storedir, core_gene_list_files, infe with open (locus_schema_file, 'a') as core_fh: for inf in inf_list: allele_number += 1 - core_fh.write('\n' + '>' + str(allele_number) + ' # ' + 'INF by Taranis' + '\n' + inf + '\n') + core_fh.write('\n' + '>' + str(allele_number) + ' # ' + 'INF by taranys' + '\n' + inf + '\n') return True """ @@ -4148,7 +4148,7 @@ def processing_allele_calling(arguments): print("Start the execution at :", start_time) # Open log file - logger = open_log("taranis_wgMLST.log") + logger = open_log("taranys_wgMLST.log") # print('Checking the pre-requisites.') ############################################################ diff --git a/taranis/allele_calling.py b/taranis/allele_calling.py index b27b836..fcbed24 100644 --- a/taranis/allele_calling.py +++ b/taranis/allele_calling.py @@ -4,8 +4,8 @@ import os import rich.console -import taranis.utils -import taranis.blast +import taranys.utils +import taranys.blast from collections import OrderedDict from pathlib import Path @@ -18,7 +18,7 @@ stderr=True, style="dim", highlight=False, - force_terminal=taranis.utils.rich_force_colors(), + force_terminal=taranys.utils.rich_force_colors(), ) @@ -55,7 +55,7 @@ def __init__( """ self.prediction_data = annotation # store prediction annotation self.sample_file = sample_file - self.sample_contigs = taranis.utils.read_fasta_file( + self.sample_contigs = taranys.utils.read_fasta_file( self.sample_file, convert_to_dict=True ) self.schema = schema @@ -66,7 +66,7 @@ def __init__( self.s_name = Path(sample_file).stem self.blast_dir = os.path.join(out_folder, "blastdb") # create blast for sample file - self.blast_obj = taranis.blast.Blast("nucl") + self.blast_obj = taranys.blast.Blast("nucl") _ = self.blast_obj.create_blastdb(sample_file, self.blast_dir) # store inferred allele object self.inf_alle_obj = inf_alle_obj @@ -156,7 +156,7 @@ def _extend_seq_find_start_stop_codon( extended_end = min(len(contig_seq), end + i) extended_seq = contig_seq[extended_start:extended_end] - _, protein, error, error_details = taranis.utils.convert_to_protein( + _, protein, error, error_details = taranys.utils.convert_to_protein( extended_seq, force_coding=True ) i += 3 @@ -229,7 +229,7 @@ def _get_allele_details( match_sequence = split_blast_result[13].replace("-", "") # check if the sequence is coding direction, protein, prot_error, prot_error_details = ( - taranis.utils.convert_to_protein(match_sequence, force_coding=True) + taranys.utils.convert_to_protein(match_sequence, force_coding=True) ) # get blast details allele_details = OrderedDict( @@ -343,11 +343,11 @@ def fix_protein(sample_allele_data): "is not a multiple of three" in sample_allele_data["prot_error_details"] ): - if taranis.utils.has_start_codon( + if taranys.utils.has_start_codon( sample_allele_data["sample_allele_seq"] ): search = "3_prime" - elif taranis.utils.has_stop_codon( + elif taranys.utils.has_stop_codon( sample_allele_data["sample_allele_seq"] ): search = "5_prime" @@ -544,7 +544,7 @@ def search_allele(self): f"Processing allele {ref_allele}: {count} of {len(self.ref_alleles)}" ) - alleles = taranis.utils.read_fasta_file(ref_allele, convert_to_dict=True) + alleles = taranys.utils.read_fasta_file(ref_allele, convert_to_dict=True) match_found = False count_2 = 0 for r_id, r_seq in alleles.items(): @@ -617,19 +617,19 @@ def search_allele(self): if self.snp_request and result["allele_type"][locus_name] != "LNF": # run snp analysis - result["snp_data"][locus_name] = taranis.utils.get_snp_information( + result["snp_data"][locus_name] = taranys.utils.get_snp_information( ref_allele_seq, allele_seq, ref_allele_name ) if self.aligment_request and result["allele_type"][locus_name] != "LNF": # run alignment analysis result["alignment_data"][locus_name] = ( - taranis.utils.get_alignment_data( + taranys.utils.get_alignment_data( ref_allele_seq, allele_seq, ref_allele_name ) ) # delete blast folder - _ = taranis.utils.delete_folder(os.path.join(self.blast_dir, self.s_name)) + _ = taranys.utils.delete_folder(os.path.join(self.blast_dir, self.s_name)) return result @@ -712,7 +712,7 @@ def create_multiple_alignment( input_buffer.seek(0) allele_multiple_align.append( - taranis.utils.get_multiple_alignment(input_buffer, mafft_cpus) + taranys.utils.get_multiple_alignment(input_buffer, mafft_cpus) ) # release memory input_buffer.close() @@ -765,7 +765,7 @@ def stats_graphics(stats_folder: str, summary_result: dict) -> None: classif_data[allele_type] = [] graphic_folder = os.path.join(stats_folder, "graphics") - _ = taranis.utils.create_new_folder(graphic_folder) + _ = taranys.utils.create_new_folder(graphic_folder) s_list = [] # collecting data to create graphics for sample, classif_counts in summary_result.items(): @@ -774,7 +774,7 @@ def stats_graphics(stats_folder: str, summary_result: dict) -> None: classif_data[classif].append(int(count)) # create graphics per each classification type for allele_type, counts in classif_data.items(): - _ = taranis.utils.create_graphic( + _ = taranys.utils.create_graphic( graphic_folder, str(allele_type + "_graphic.png"), "bar", @@ -907,7 +907,7 @@ def read_reference_alleles(ref_alleles: list) -> dict[dict]: # create alignment files if aligment_request: alignment_folder = os.path.join(output, "alignments") - _ = taranis.utils.create_new_folder(alignment_folder) + _ = taranys.utils.create_new_folder(alignment_folder) align_collection = {} for result in results: for sample, values in result.items(): diff --git a/taranis/analyze_schema.py b/taranis/analyze_schema.py index 15c1f71..ec1f626 100644 --- a/taranis/analyze_schema.py +++ b/taranis/analyze_schema.py @@ -10,7 +10,7 @@ from collections import OrderedDict, defaultdict -import taranis.utils +import taranys.utils log = logging.getLogger(__name__) @@ -18,7 +18,7 @@ stderr=True, style="dim", highlight=False, - force_terminal=taranis.utils.rich_force_colors(), + force_terminal=taranys.utils.rich_force_colors(), ) @@ -104,7 +104,7 @@ def check_allele_quality(self, prokka_annotation: dict) -> OrderedDict: allele_seq[record.id] = str(record.seq) a_quality[record.id]["length"] = str(len(str(record.seq))) a_quality[record.id]["dna_seq"] = str(record.seq) - sequence_direction = taranis.utils.get_seq_direction(str(record.seq)) + sequence_direction = taranys.utils.get_seq_direction(str(record.seq)) if sequence_direction == "reverse": record.seq = record.seq.reverse_complement() @@ -169,7 +169,7 @@ def check_allele_quality(self, prokka_annotation: dict) -> OrderedDict: bad_quality_record.append(rec_id) new_schema_folder = os.path.join(self.output, "new_schema") - _ = taranis.utils.create_new_folder(new_schema_folder) + _ = taranys.utils.create_new_folder(new_schema_folder) new_schema_file = os.path.join(new_schema_folder, self.allele_name + ".fasta") with open(self.schema_allele, "r") as _: with open(new_schema_file, "w") as fo: @@ -205,7 +205,7 @@ def fetch_statistics_from_alleles(self, a_quality: dict) -> dict: a_length.append(int(a_quality[record_id]["length"])) if a_quality[record_id]["quality"] == "Bad quality": bad_quality_counter += 1 - for reason in taranis.utils.POSIBLE_BAD_QUALITY: + for reason in taranys.utils.POSIBLE_BAD_QUALITY: if reason in a_quality[record_id]["reason"]: bad_quality_reason[reason] = ( bad_quality_reason.get(reason, 0) + 1 @@ -218,7 +218,7 @@ def fetch_statistics_from_alleles(self, a_quality: dict) -> dict: record_data["good_percent"] = round( 100 * (total_alleles - bad_quality_counter) / total_alleles, 2 ) - for item in taranis.utils.POSIBLE_BAD_QUALITY: + for item in taranys.utils.POSIBLE_BAD_QUALITY: record_data[item] = ( bad_quality_reason[item] if item in bad_quality_reason else 0 ) @@ -242,11 +242,11 @@ def analyze_allele_in_schema(self) -> list[dict, dict]: log.info("Analizing allele %s", self.allele_name) # run annotations prokka_folder = os.path.join(self.output, "prokka", self.allele_name) - anotation_files = taranis.utils.create_annotation_files( + anotation_files = taranys.utils.create_annotation_files( self.schema_allele, prokka_folder, self.allele_name, cpus=self.prokka_cpus ) log.info("Fetching anotation information for %s", self.allele_name) - prokka_annotation = taranis.utils.read_annotation_file(anotation_files + ".gff") + prokka_annotation = taranys.utils.read_annotation_file(anotation_files + ".gff") # Perform quality a_quality = self.check_allele_quality(prokka_annotation) @@ -307,13 +307,13 @@ def stats_graphics(stats_folder: str) -> None: log.info("Creating graphics") allele_range = [0, 300, 600, 1000, 1500] graphic_folder = os.path.join(stats_folder, "graphics") - _ = taranis.utils.create_new_folder(graphic_folder) + _ = taranys.utils.create_new_folder(graphic_folder) # create graphic for alleles/number of genes group_alleles_df = stats_df.groupby( pd.cut(stats_df["num_alleles"], allele_range), observed=False ).count() - _ = taranis.utils.create_graphic( + _ = taranys.utils.create_graphic( graphic_folder, "num_genes_per_allele.png", "bar", @@ -325,12 +325,12 @@ def stats_graphics(stats_folder: str) -> None: sum_all_alleles = stats_df["num_alleles"].sum() - labels = taranis.utils.POSIBLE_BAD_QUALITY + labels = taranys.utils.POSIBLE_BAD_QUALITY values = [stats_df[item].sum() for item in labels] labels.append("Good quality") values.append(sum_all_alleles - sum(values)) - _ = taranis.utils.create_graphic( + _ = taranys.utils.create_graphic( graphic_folder, "quality_percent.png", "pie", @@ -340,7 +340,7 @@ def stats_graphics(stats_folder: str) -> None: "Quality percent", ) # create box plot for allele length variability - _ = taranis.utils.create_graphic( + _ = taranys.utils.create_graphic( graphic_folder, "allele_variability.png", "box", @@ -358,8 +358,8 @@ def stats_graphics(stats_folder: str) -> None: stats_df = pd.DataFrame(summary_data) stats_folder = os.path.join(out_folder, "statistics") - _ = taranis.utils.create_new_folder(stats_folder) - _ = taranis.utils.write_data_to_file(stats_folder, "statistics.csv", stats_df) + _ = taranys.utils.create_new_folder(stats_folder) + _ = taranys.utils.write_data_to_file(stats_folder, "statistics.csv", stats_df) stats_graphics(stats_folder) if output_allele_annot: @@ -401,7 +401,7 @@ def stats_graphics(stats_folder: str) -> None: + ",".join(data_field) + "\n" ) - _ = taranis.utils.write_data_to_compress_filed( + _ = taranys.utils.write_data_to_compress_filed( out_folder, "allele_annotation.csv", ann_data ) return diff --git a/taranis/blast.py b/taranis/blast.py index 74ad732..33dceeb 100644 --- a/taranis/blast.py +++ b/taranis/blast.py @@ -2,7 +2,7 @@ import os import rich import subprocess -import taranis.utils +import taranys.utils from pathlib import Path from Bio.Blast.Applications import NcbiblastnCommandline @@ -12,7 +12,7 @@ stderr=True, style="dim", highlight=False, - force_terminal=taranis.utils.rich_force_colors(), + force_terminal=taranys.utils.rich_force_colors(), ) diff --git a/taranis/clustering.py b/taranis/clustering.py index a1adeaa..b19f5de 100644 --- a/taranis/clustering.py +++ b/taranis/clustering.py @@ -3,15 +3,15 @@ import numpy as np import rich.console -import taranis.seq_cluster -import taranis.utils +import taranys.seq_cluster +import taranys.utils log = logging.getLogger(__name__) stderr = rich.console.Console( stderr=True, style="dim", highlight=False, - force_terminal=taranis.utils.rich_force_colors(), + force_terminal=taranys.utils.rich_force_colors(), ) @@ -144,7 +144,7 @@ def create_clusters(self, resolution) -> list[dict]: matrix indexes adn second the statistics data for each cluster """ self.resolution = resolution - seq_cluster_obj = taranis.seq_cluster.SeqCluster(self.resolution, self.seed) + seq_cluster_obj = taranys.seq_cluster.SeqCluster(self.resolution, self.seed) cluster_ptrs = seq_cluster_obj.cluster_seqs(self.dist_matrix) clusters_data = self.collect_data_cluster(cluster_ptrs) return [cluster_ptrs, clusters_data] diff --git a/taranis/distance.py b/taranis/distance.py index b42ee34..937b258 100644 --- a/taranis/distance.py +++ b/taranis/distance.py @@ -6,14 +6,14 @@ import rich import sys from pathlib import Path -import taranis.utils +import taranys.utils log = logging.getLogger(__name__) stderr = rich.console.Console( stderr=True, style="dim", highlight=False, - force_terminal=taranis.utils.rich_force_colors(), + force_terminal=taranys.utils.rich_force_colors(), ) diff --git a/taranis/eval_cluster.py b/taranis/eval_cluster.py index 90f1e62..1c954d8 100644 --- a/taranis/eval_cluster.py +++ b/taranis/eval_cluster.py @@ -3,8 +3,8 @@ import numpy as np import rich.console import os -import taranis.utils -import taranis.blast +import taranys.utils +import taranys.blast from Bio import SeqIO log = logging.getLogger(__name__) @@ -12,7 +12,7 @@ stderr=True, style="dim", highlight=False, - force_terminal=taranis.utils.rich_force_colors(), + force_terminal=taranys.utils.rich_force_colors(), ) @@ -30,15 +30,15 @@ def __init__(self, locus_path: str, locus_name: str, eval_id: float, output: str self.eval_id = eval_id self.output = os.path.join(output, "evaluate_cluster") - taranis.utils.create_new_folder(self.output) + taranys.utils.create_new_folder(self.output) # locus_blast_dir = os.path.join(self.output, locus_name) - self.blast_obj = taranis.blast.Blast("nucl") + self.blast_obj = taranys.blast.Blast("nucl") _ = self.blast_obj.create_blastdb(locus_path, self.output) return def delete_blast_db_folder(self): """Delete blast db folder""" - taranis.utils.delete_folder(os.path.join(self.output, self.locus_name)) + taranys.utils.delete_folder(os.path.join(self.output, self.locus_name)) def find_cluster_from_ref_allele(self, cluster_ref_alleles: dict) -> dict: """Create a dictionary to map de cluster belongs to the reference allele diff --git a/taranis/pruebas.py b/taranis/pruebas.py index cac4ea4..c38e2b8 100644 --- a/taranis/pruebas.py +++ b/taranis/pruebas.py @@ -4,7 +4,7 @@ from Bio.Blast.Applications import NcbiblastnCommandline import subprocess -# import taranis.utils +# import taranys.utils import random @@ -12,14 +12,14 @@ Para hacer las pruebas con alfaclust activo el entorno de conda alfatclust_env despues me voy a la carpeta donde me he descargado, de git, alfatclust y ejecuto : - ./alfatclust.py -i /media/lchapado/Reference_data/proyectos_isciii/taranis/taranis_testing_data/listeria_testing_schema/lmo0003.fasta -o /media/lchapado/Reference_data/proyectos_isciii/taranis/test/alfacluster_test/resultado_alfaclust_lmo003 -l 0.9 + ./alfatclust.py -i /media/lchapado/Reference_data/proyectos_isciii/taranys/taranys_testing_data/listeria_testing_schema/lmo0003.fasta -o /media/lchapado/Reference_data/proyectos_isciii/taranys/test/alfacluster_test/resultado_alfaclust_lmo003 -l 0.9 despues ejecuto este programa de prueba cambiando los ficheros de resultados """ # read result of alfatclust -alfa_clust_file = "/media/lchapado/Reference_data/proyectos_isciii/taranis/test/resultado_alfatclust-090" +alfa_clust_file = "/media/lchapado/Reference_data/proyectos_isciii/taranys/test/resultado_alfatclust-090" with open(alfa_clust_file, "r") as fh: lines = fh.readlines() alleles_found = False @@ -36,11 +36,11 @@ locus_list.append(line) rand_locus = random.choice(locus_list) -schema_file = "/media/lchapado/Reference_data/proyectos_isciii/taranis/taranis_testing_data/listeria_testing_schema/lmo0002.fasta" +schema_file = "/media/lchapado/Reference_data/proyectos_isciii/taranys/taranys_testing_data/listeria_testing_schema/lmo0002.fasta" new_schema_file = ( - "/media/lchapado/Reference_data/proyectos_isciii/taranis/test/cluster_lmo0002.fasta" + "/media/lchapado/Reference_data/proyectos_isciii/taranys/test/cluster_lmo0002.fasta" ) -q_file = "/media/lchapado/Reference_data/proyectos_isciii/taranis/test/q_file.fasta" +q_file = "/media/lchapado/Reference_data/proyectos_isciii/taranys/test/q_file.fasta" with open(schema_file) as fh: with open(new_schema_file, "w") as fo: for record in SeqIO.parse(schema_file, "fasta"): @@ -55,7 +55,7 @@ SeqIO.write(record, fo, "fasta") break print("Selected locus: ", rand_locus) -db_name = "/media/lchapado/Reference_data/proyectos_isciii/taranis/test/testing_clster/lmo0002" +db_name = "/media/lchapado/Reference_data/proyectos_isciii/taranys/test/testing_clster/lmo0002" blast_command = [ "makeblastdb", "-in", diff --git a/taranis/reference_alleles.py b/taranis/reference_alleles.py index 27b3c89..6cf640b 100644 --- a/taranis/reference_alleles.py +++ b/taranis/reference_alleles.py @@ -4,10 +4,10 @@ from pathlib import Path import os -import taranis.utils -import taranis.distance -import taranis.clustering -import taranis.eval_cluster +import taranys.utils +import taranys.distance +import taranys.clustering +import taranys.eval_cluster from Bio import SeqIO log = logging.getLogger(__name__) @@ -15,7 +15,7 @@ stderr=True, style="dim", highlight=False, - force_terminal=taranis.utils.rich_force_colors(), + force_terminal=taranys.utils.rich_force_colors(), ) @@ -62,7 +62,7 @@ def create_distance_matrix(self) -> list: dict: position to allele name """ log.debug("Processing distance matrix for $s", self.fasta_file) - distance_obj = taranis.distance.DistanceMatrix( + distance_obj = taranys.distance.DistanceMatrix( self.fasta_file, self.kmer_size, self.sketch_size ) mash_distance_df = distance_obj.create_matrix() @@ -91,7 +91,7 @@ def processing_cluster_data( cluster_ptrs, position_to_allele ) cluster_folder = os.path.join(self.output, "Clusters") - _ = taranis.utils.create_new_folder(cluster_folder) + _ = taranys.utils.create_new_folder(cluster_folder) cluster_file = os.path.join( cluster_folder, "cluster_alleles_" + self.locus_name + ".txt" ) @@ -137,9 +137,9 @@ def create_ref_alleles(self) -> dict: dict: containg statistics information for each cluster, and optionally a list of evaluation cluster results """ - self.records = taranis.utils.read_fasta_file(self.fasta_file) + self.records = taranys.utils.read_fasta_file(self.fasta_file) dist_matrix_np, position_to_allele = self.create_distance_matrix() - self.cluster_obj = taranis.clustering.ClusterDistance( + self.cluster_obj = taranys.clustering.ClusterDistance( dist_matrix=dist_matrix_np, ref_seq_name=self.locus_name, dist_value=self.eval_id / 100, @@ -159,7 +159,7 @@ def create_ref_alleles(self) -> dict: # evaluate clusters aginst blast results stderr.print(f"Evaluating clusters for {self.locus_name}") - evaluation_obj = taranis.eval_cluster.EvaluateCluster( + evaluation_obj = taranys.eval_cluster.EvaluateCluster( self.fasta_file, self.locus_name, self.eval_id, self.output ) evaluation_result = evaluation_obj.evaluate_clusters( @@ -207,7 +207,7 @@ def parallel_execution( cluster_resolution (float): resolution for clustering seed (int): seed for random number generator """ - ref_alleles_obj = taranis.reference_alleles.ReferenceAlleles( + ref_alleles_obj = taranys.reference_alleles.ReferenceAlleles( fasta_file, output, eval_cluster, @@ -242,9 +242,9 @@ def stats_graphics(stats_folder: str, cluster_alleles: dict) -> None: stderr.print("Creating graphics") log.info("Creating graphics") graphic_folder = os.path.join(stats_folder, "graphics") - _ = taranis.utils.create_new_folder(graphic_folder) + _ = taranys.utils.create_new_folder(graphic_folder) cluster, alleles = zip(*cluster_alleles.items()) - _ = taranis.utils.create_graphic( + _ = taranys.utils.create_graphic( graphic_folder, "num_clusters_per_locus.png", "bar", diff --git a/test/test.sh b/test/test.sh index f80a206..a6e573c 100755 --- a/test/test.sh +++ b/test/test.sh @@ -17,7 +17,7 @@ set -e # #ACKNOLEDGE: longops2getops.sh: https://gist.github.com/adamhotep/895cebf290e95e613c006afbffef09d7 # -#DESCRIPTION: test.sh uses test data for testing taranis installation. +#DESCRIPTION: test.sh uses test data for testing taranys installation. # # #================================================================ @@ -124,18 +124,18 @@ done shift $((OPTIND-1)) ## Execute plasmidID with test data. -echo "Executing:../taranis.py allele_calling -coregenedir $schema -inputdir $assemblies -refgenome $refgenome -outputdir allele_calling_test -percentlength 20 -refalleles $refallele -profile $profile" +echo "Executing:../taranys.py allele_calling -coregenedir $schema -inputdir $assemblies -refgenome $refgenome -outputdir allele_calling_test -percentlength 20 -refalleles $refallele -profile $profile" echo "Assemblies: $assemblies" echo "Schema: $schema" echo "$PWD" cd -echo "Executing taranis analyze_schema" -$script_dir/../taranis.py analyze_schema -i $script_dir/MLST_listeria -o analyze_schema_test --output-allele-annot --cpus 1 +echo "Executing taranys analyze_schema" +$script_dir/../taranys.py analyze_schema -i $script_dir/MLST_listeria -o analyze_schema_test --output-allele-annot --cpus 1 -# $script_dir/../taranis.py reference_alleles -coregenedir $script_dir/MLST_listeria -outputdir reference_alleles_test +# $script_dir/../taranys.py reference_alleles -coregenedir $script_dir/MLST_listeria -outputdir reference_alleles_test -# $script_dir/../taranis.py allele_calling -coregenedir $script_dir/$schema -inputdir $script_dir/$assemblies -refgenome $script_dir/$refgenome -outputdir allele_calling_test -percentlength 20 -refalleles reference_alleles_test -profile $script_dir/$profile +# $script_dir/../taranys.py allele_calling -coregenedir $script_dir/$schema -inputdir $script_dir/$assemblies -refgenome $script_dir/$refgenome -outputdir allele_calling_test -percentlength 20 -refalleles reference_alleles_test -profile $script_dir/$profile -# $script_dir/../taranis.py distance_matrix -alleles_matrix allele_calling_test/result.tsv -outputdir distance_matrix_test +# $script_dir/../taranys.py distance_matrix -alleles_matrix allele_calling_test/result.tsv -outputdir distance_matrix_test echo "ALL DONE. TEST COMPLETED SUCCESSFULLY." From c55e33e861c2285f117c387cf3d0ee246097f99f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Thu, 2 Jan 2025 10:31:36 +0100 Subject: [PATCH 201/214] rename folder and delete unused files --- taranis/allele_calling.old_py | 4413 --------------------- taranis/pruebas.py | 96 - {taranis => taranys}/__init__.py | 0 {taranis => taranys}/__main__.py | 0 {taranis => taranys}/allele_calling.py | 0 {taranis => taranys}/analyze_schema.py | 0 {taranis => taranys}/blast.py | 0 {taranis => taranys}/clustering.py | 0 {taranis => taranys}/distance.py | 0 {taranis => taranys}/eval_cluster.py | 0 {taranis => taranys}/inferred_alleles.py | 0 {taranis => taranys}/reference_alleles.py | 0 {taranis => taranys}/seq_cluster.py | 0 {taranis => taranys}/utils.py | 0 14 files changed, 4509 deletions(-) delete mode 100644 taranis/allele_calling.old_py delete mode 100644 taranis/pruebas.py rename {taranis => taranys}/__init__.py (100%) rename {taranis => taranys}/__main__.py (100%) rename {taranis => taranys}/allele_calling.py (100%) rename {taranis => taranys}/analyze_schema.py (100%) rename {taranis => taranys}/blast.py (100%) rename {taranis => taranys}/clustering.py (100%) rename {taranis => taranys}/distance.py (100%) rename {taranis => taranys}/eval_cluster.py (100%) rename {taranis => taranys}/inferred_alleles.py (100%) rename {taranis => taranys}/reference_alleles.py (100%) rename {taranis => taranys}/seq_cluster.py (100%) rename {taranis => taranys}/utils.py (100%) diff --git a/taranis/allele_calling.old_py b/taranis/allele_calling.old_py deleted file mode 100644 index 0030736..0000000 --- a/taranis/allele_calling.old_py +++ /dev/null @@ -1,4413 +0,0 @@ -#!/usr/bin/env python3 - -import argparse -import sys -import io -import os -import re -import statistics -import logging -from logging.handlers import RotatingFileHandler -from datetime import datetime -import glob -import pickle -from Bio import SeqIO -from Bio.SeqRecord import SeqRecord -from Bio import Seq -from Bio import pairwise2 -from Bio.pairwise2 import format_alignment -from Bio.Blast.Applications import NcbiblastnCommandline -from io import StringIO -from Bio.Blast import NCBIXML -import pandas as pd -import shutil -from progressbar import ProgressBar -from utils.taranys_utils import * -import math -import csv -import plotly.graph_objects as go - - -def check_blast(reference_allele, sample_files, db_name, logger): ## N - for s_file in sample_files: - f_name = os.path.basename(s_file).split(".") - dir_name = os.path.dirname(s_file) - blast_dir = os.path.join(dir_name, db_name, f_name[0]) - blast_db = os.path.join(blast_dir, f_name[0]) - if not os.path.exists(blast_dir): - logger.error("Blast db folder for sample %s does not exist", f_name) - return False - cline = NcbiblastnCommandline( - db=blast_db, - evalue=0.001, - outfmt=5, - max_target_seqs=10, - max_hsps=10, - num_threads=1, - query=reference_allele, - ) - out, err = cline() - - psiblast_xml = StringIO(out) - blast_records = NCBIXML.parse(psiblast_xml) - - for blast_record in blast_records: - locationcontigs = [] - for alignment in blast_record.alignments: - # select the best match - for match in alignment.hsps: - alleleMatchid = int((blast_record.query_id.split("_"))[-1]) - return True - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # -# Parse samples and core genes schema fasta files to dictionary # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # - - -def parsing_fasta_file_to_dict(fasta_file, logger): - fasta_dict = {} - fasta_dict_ordered = {} - for contig in SeqIO.parse(fasta_file, "fasta"): - fasta_dict[str(contig.id)] = str(contig.seq.upper()) - logger.debug("file %s parsed to dictionary", fasta_file) - - for key in sorted(list(fasta_dict.keys())): - fasta_dict_ordered[key] = fasta_dict[key] - return fasta_dict_ordered - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # -# Get core genes schema info before allele calling analysis # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # - - -# def prepare_core_gene (core_gene_file_list, store_dir, ref_alleles_dir, logger): -def prepare_core_gene( - core_gene_file_list, store_dir, ref_alleles_dir, genus, species, usegenus, logger -): - ## Initialize dict for keeping id-allele, quality, length variability, length statistics and annotation info for each schema core gene - alleles_in_locus_dict = {} - schema_quality = {} - annotation_core_dict = {} - schema_variability = {} - schema_statistics = {} - - ## Process each schema core gene - blast_dir = os.path.join(store_dir, "blastdb") - logger.info("start preparation of core genes files") - for fasta_file in core_gene_file_list: - f_name = os.path.basename(fasta_file).split(".") - - # Parse core gene fasta file and keep id-sequence info in dictionary - fasta_file_parsed_dict = parsing_fasta_file_to_dict(fasta_file, logger) - if f_name[0] not in alleles_in_locus_dict.keys(): - alleles_in_locus_dict[f_name[0]] = {} - alleles_in_locus_dict[f_name[0]] = fasta_file_parsed_dict - - # dump fasta file into pickle file - # with open (file_list[-1],'wb') as f: - # pickle.dump(fasta_file_parsed_dict, f) - - # Get core gene alleles quality - locus_quality = check_core_gene_quality(fasta_file, logger) - if f_name[0] not in schema_quality.keys(): - schema_quality[f_name[0]] = {} - schema_quality[f_name[0]] = locus_quality - - # Get gene and product annotation for core gene using reference allele(s) - ref_allele = os.path.join(ref_alleles_dir, f_name[0] + ".fasta") - - gene_annot, product_annot = get_gene_annotation( - ref_allele, store_dir, genus, species, usegenus, logger - ) - # gene_annot, product_annot = get_gene_annotation (ref_allele, store_dir, logger) - if f_name[0] not in annotation_core_dict.keys(): - annotation_core_dict[f_name[0]] = {} - annotation_core_dict[f_name[0]] = [gene_annot, product_annot] - - # Get core gene alleles length to keep length variability and statistics info - alleles_len = [] - for allele in fasta_file_parsed_dict: - alleles_len.append(len(fasta_file_parsed_dict[allele])) - - # alleles_in_locus = list (SeqIO.parse(fasta_file, "fasta")) ## parse - # for allele in alleles_in_locus : ## parse - # alleles_len.append(len(str(allele.seq))) ## parse - - schema_variability[f_name[0]] = list(set(alleles_len)) - - if len(alleles_len) == 1: - stdev = 0 - else: - stdev = statistics.stdev(alleles_len) - schema_statistics[f_name[0]] = [ - statistics.mean(alleles_len), - stdev, - min(alleles_len), - max(alleles_len), - ] - - return ( - alleles_in_locus_dict, - annotation_core_dict, - schema_variability, - schema_statistics, - schema_quality, - ) - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # -# Get Prodigal training file from reference genome for samples gene prediction # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # - - -def prodigal_training(reference_genome_file, prodigal_dir, logger): - f_name = os.path.basename(reference_genome_file).split(".")[0] - prodigal_train_dir = os.path.join(prodigal_dir, "training") - - output_prodigal_train_dir = os.path.join(prodigal_train_dir, f_name + ".trn") - - if not os.path.exists(prodigal_train_dir): - try: - os.makedirs(prodigal_train_dir) - logger.debug("Created prodigal directory for training file %s", f_name) - except: - logger.info("Cannot create prodigal directory for training file %s", f_name) - print( - "Error when creating the directory %s for training file", - prodigal_train_dir, - ) - exit(0) - - prodigal_command = [ - "prodigal", - "-i", - reference_genome_file, - "-t", - output_prodigal_train_dir, - ] - prodigal_result = subprocess.run( - prodigal_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE - ) - - # if prodigal_result.stderr: - # logger.error('cannot create training file for %s', f_name) - # logger.error('prodigal returning error code %s', prodigal_result.stderr) - # return False - else: - logger.info( - "Skeeping prodigal training file creation for %s, as it has already been created", - f_name, - ) - - return output_prodigal_train_dir - - -# · * · * · * · * · * · * · * · * · * # -# Get Prodigal sample gene prediction # -# · * · * · * · * · * · * · * · * · * # - - -def prodigal_prediction(file_name, prodigal_dir, prodigal_train_dir, logger): - f_name = ".".join(os.path.basename(file_name).split(".")[:-1]) - prodigal_dir_sample = os.path.join(prodigal_dir, f_name) - - output_prodigal_coord = os.path.join( - prodigal_dir_sample, f_name + "_coord.gff" - ) ## no - output_prodigal_prot = os.path.join( - prodigal_dir_sample, f_name + "_prot.faa" - ) ## no - output_prodigal_dna = os.path.join(prodigal_dir_sample, f_name + "_dna.faa") - - if not os.path.exists(prodigal_dir_sample): - try: - os.makedirs(prodigal_dir_sample) - logger.debug("Created prodigal directory for Core Gene %s", f_name) - except: - logger.info("Cannot create prodigal directory for Core Gene %s", f_name) - print( - "Error when creating the directory %s for prodigal genes prediction", - prodigal_dir_sample, - ) - exit(0) - - prodigal_command = [ - "prodigal", - "-i", - file_name, - "-t", - prodigal_train_dir, - "-f", - "gff", - "-o", - output_prodigal_coord, - "-a", - output_prodigal_prot, - "-d", - output_prodigal_dna, - ] - prodigal_result = subprocess.run( - prodigal_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE - ) - - # if prodigal_result.stderr: - # logger.error('cannot predict genes for %s ', f_name) - # logger.error('prodigal returning error code %s', prodigal_result.stderr) - # return False - else: - logger.info( - "Skeeping prodigal genes prediction for %s, as it has already been made", - f_name, - ) - - return True - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # -# Get Prodigal predicted gene sequence equivalent to BLAST result matching bad quality allele or to no Exact Match BLAST result in allele calling analysis # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # - - -def get_prodigal_sequence( - blast_sseq, - contig_blast_id, - prodigal_directory, - sample_name, - blast_parameters, - logger, -): - prodigal_directory_sample = os.path.join(prodigal_directory, sample_name) - genes_file = os.path.join(prodigal_directory_sample, sample_name + "_dna.faa") - - ## Create directory for storing prodigal genes prediction per contig BLAST databases - blastdb_per_contig_directory = "blastdb_per_contig" - full_path_blastdb_per_contig = os.path.join( - prodigal_directory_sample, blastdb_per_contig_directory - ) - if not os.path.exists(full_path_blastdb_per_contig): - try: - os.makedirs(full_path_blastdb_per_contig) - logger.info("Directory %s has been created", full_path_blastdb_per_contig) - except: - print("Cannot create the directory ", full_path_blastdb_per_contig) - logger.info("Directory %s cannot be created", full_path_blastdb_per_contig) - exit(0) - - ## Create directory for storing prodigal genes prediction sequences per contig - prodigal_genes_per_contig_directory = "prodigal_genes_per_contig" - full_path_prodigal_genes_per_contig = os.path.join( - prodigal_directory_sample, prodigal_genes_per_contig_directory - ) - if not os.path.exists(full_path_prodigal_genes_per_contig): - try: - os.makedirs(full_path_prodigal_genes_per_contig) - logger.info( - "Directory %s has been created", full_path_prodigal_genes_per_contig - ) - except: - print("Cannot create the directory ", full_path_prodigal_genes_per_contig) - logger.info( - "Directory %s cannot be created", full_path_prodigal_genes_per_contig - ) - exit(0) - - ## Parse prodigal genes prediction fasta file - predicted_genes = SeqIO.parse(genes_file, "fasta") - - ## Create fasta file containing Prodigal predicted genes sequences for X contig in sample - contig_genes_path = os.path.join( - full_path_prodigal_genes_per_contig, contig_blast_id + ".fasta" - ) - with open(contig_genes_path, "w") as out_fh: - for rec in predicted_genes: - contig_prodigal_id = "_".join((rec.id).split("_")[:-1]) - if contig_prodigal_id == contig_blast_id: - out_fh.write(">" + str(rec.description) + "\n" + str(rec.seq) + "\n") - - ## Create local BLAST database for Prodigal predicted genes sequences for X contig in sample - if not create_blastdb( - contig_genes_path, full_path_blastdb_per_contig, "nucl", logger - ): - print( - "Error when creating the blastdb for samples files. Check log file for more information. \n " - ) - return False - - ## Local BLAST Prodigal predicted genes sequences database VS BLAST sequence obtained in sample in allele calling analysis - blast_db_name = os.path.join( - full_path_blastdb_per_contig, contig_blast_id, contig_blast_id - ) - - cline = NcbiblastnCommandline( - db=blast_db_name, - evalue=0.001, - perc_identity=90, - outfmt=blast_parameters, - max_target_seqs=10, - max_hsps=10, - num_threads=1, - ) - out, err = cline(stdin=blast_sseq) - out_lines = out.splitlines() - - bigger_bitscore = 0 - if len(out_lines) > 0: - for line in out_lines: - values = line.split("\t") - if float(values[8]) > bigger_bitscore: - ( - qseqid, - sseqid, - pident, - qlen, - s_length, - mismatch, - r_gapopen, - r_evalue, - bitscore, - sstart, - send, - qstart, - qend, - sseq, - qseq, - ) = values - bigger_bitscore = float(bitscore) - - ## Get complete Prodigal sequence matching allele calling BLAST sequence using ID - predicted_genes_in_contig = SeqIO.parse(contig_genes_path, "fasta") - - for rec in predicted_genes_in_contig: - if rec.id == sseqid: - predicted_gene_sequence = str(rec.seq) - start_prodigal = str(rec.description.split("#")[1]) - end_prodigal = str(rec.description.split("#")[2]) - break - - ## Sequence not found by Prodigal when there are no BLAST results matching allele calling BLAST sequence - if len(out_lines) == 0: - predicted_gene_sequence = "Sequence not found by Prodigal" - start_prodigal = "-" - end_prodigal = "-" - - return ( - predicted_gene_sequence, - start_prodigal, - end_prodigal, - ) ### start_prodigal y end_prodigal para report prodigal - - -# · * · * · * · * · * · * · * · * · * · * · * · * # -# Get samples info before allele calling analysis # -# · * · * · * · * · * · * · * · * · * · * · * · * # - - -def prepare_samples(sample_file_list, store_dir, reference_genome_file, logger): - ## Initialize dictionary for keeping id-contig - contigs_in_sample_dict = {} - - ## Paths for samples blastdb, Prodigal genes prediction and BLAST results - blast_dir = os.path.join(store_dir, "blastdb") - prodigal_dir = os.path.join(store_dir, "prodigal") - blast_results_seq_dir = os.path.join( - store_dir, "blast_results", "blast_results_seq" - ) - - ## Get training file for Prodigal genes prediction - output_prodigal_train_dir = prodigal_training( - reference_genome_file, prodigal_dir, logger - ) - if not output_prodigal_train_dir: - print( - "Error when creating training file for genes prediction. Check log file for more information. \n " - ) - return False - - for fasta_file in sample_file_list: - f_name = ".".join(os.path.basename(fasta_file).split(".")[:-1]) - - # Get samples id-contig dictionary - fasta_file_parsed_dict = parsing_fasta_file_to_dict(fasta_file, logger) - if f_name not in contigs_in_sample_dict.keys(): - contigs_in_sample_dict[f_name] = {} - contigs_in_sample_dict[f_name] = fasta_file_parsed_dict - - # dump fasta file into pickle file - # with open (file_list[-1],'wb') as f: # generación de diccionarios de contigs para cada muestra - # pickle.dump(fasta_file_parsed_dict, f) - - # Create directory for storing BLAST results using reference allele(s) - blast_results_seq_per_sample_dir = os.path.join(blast_results_seq_dir, f_name) - - if not os.path.exists(blast_results_seq_per_sample_dir): - try: - os.makedirs(blast_results_seq_per_sample_dir) - logger.debug("Created blast results directory for sample %s", f_name) - except: - logger.info( - "Cannot create blast results directory for sample %s", f_name - ) - print( - "Error when creating the directory for blast results", - blast_results_seq_per_sample_dir, - ) - exit(0) - - # Prodigal genes prediction for each sample - if not prodigal_prediction( - fasta_file, prodigal_dir, output_prodigal_train_dir, logger - ): - print( - "Error when predicting genes for samples files. Check log file for more information. \n " - ) - return False - - # Create local BLAST db for each sample fasta file - if not create_blastdb(fasta_file, blast_dir, "nucl", logger): - print( - "Error when creating the blastdb for samples files. Check log file for more information. \n " - ) - return False - - return contigs_in_sample_dict - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # -# Get established length thresholds for allele tagging in allele calling analysis # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # - - -def length_thresholds(core_name, schema_statistics, percent): ### logger - locus_mean = int(schema_statistics[core_name][0]) - - if percent != "SD": - max_length_threshold = math.ceil( - locus_mean + ((locus_mean * float(percent)) / 100) - ) - min_length_threshold = math.floor( - locus_mean - ((locus_mean * float(percent)) / 100) - ) - else: - percent = float(schema_statistics[core_name][1]) - - max_length_threshold = math.ceil(locus_mean + (locus_mean * percent)) - min_length_threshold = math.floor(locus_mean - (locus_mean * percent)) - - return max_length_threshold, min_length_threshold - - -# · * · * · * · * · * · * · * · * · * · * · # -# Convert dna sequence to protein sequence # -# · * · * · * · * · * · * · * · * · * · * · # - - -def convert_to_protein(sequence): - seq = Seq.Seq(sequence) - protein = str(seq.translate()) - - return protein - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * # -# Get SNPs between BLAST sequence and matching allele # -# · * · * · * · * · * · * · * · * · * · * · * · * · * # - - -def get_snp(sample, query): - prot_annotation = { - "S": "polar", - "T": "polar", - "Y": "polar", - "Q": "polar", - "N": "polar", - "C": "polar", - "S": "polar", - "F": "nonpolar", - "L": "nonpolar", - "I": "nonpolar", - "M": "nonpolar", - "P": "nonpolar", - "V": "nonpolar", - "A": "nonpolar", - "W": "nonpolar", - "G": "nonpolar", - "D": "acidic", - "E": "acidic", - "H": "basic", - "K": "basic", - "R": "basic", - "-": "-----", - "*": "Stop codon", - } - snp_list = [] - sample = sample.replace("-", "") - # length = max(len(sample), len(query)) - length = len(query) - # normalize the length of the sample for the iteration - if len(sample) < length: - need_to_add = length - len(sample) - sample = sample + need_to_add * "-" - - # convert to Seq class to translate to protein - seq_sample = Seq.Seq(sample) - seq_query = Seq.Seq(query) - - for index in range(length): - if seq_query[index] != seq_sample[index]: - triple_index = index - (index % 3) - codon_seq = seq_sample[triple_index : triple_index + 3] - codon_que = seq_query[triple_index : triple_index + 3] - if not "-" in str(codon_seq): - prot_seq = str(codon_seq.translate()) - prot_que = str(codon_que.translate()) - else: - prot_seq = "-" - prot_que = str(seq_query[triple_index:].translate()) - if prot_annotation[prot_que[0]] == prot_annotation[prot_seq[0]]: - missense_synonym = "Synonymous" - elif prot_seq == "*": - missense_synonym = "Nonsense" - else: - missense_synonym = "Missense" - # snp_list.append([str(index+1),str(seq_sample[index]) + '/' + str(seq_query[index]), str(codon_seq) + '/'+ str(codon_que), - snp_list.append( - [ - str(index + 1), - str(seq_query[index]) + "/" + str(seq_sample[index]), - str(codon_que) + "/" + str(codon_seq), - # when one of the sequence ends but not the other we will translate the remain sequence to proteins - # in that case we will only annotate the first protein. Using [0] as key of the dictionary annotation - prot_que + "/" + prot_seq, - missense_synonym, - prot_annotation[prot_que[0]] + " / " + prot_annotation[prot_seq[0]], - ] - ) - if "-" in str(codon_seq): - break - return snp_list - - -def nucleotide_to_protein_alignment( - sample_seq, query_seq -): ### Sustituido por get_alignment - aligment = [] - sample_prot = convert_to_protein(sample_seq) - query_prot = convert_to_protein(query_seq) - minimun_length = min(len(sample_prot), len(query_prot)) - for i in range(minimun_length): - if sample_prot[i] == query_prot[i]: - aligment.append("|") - else: - aligment.append(" ") - protein_alignment = [ - ["sample", sample_prot], - ["match", "".join(aligment)], - ["schema", query_prot], - ] - - return protein_alignment - - -def get_alignment_for_indels(blast_db_name, qseq): ### Sustituido por get_alignment - # match_alignment =[] - cline = NcbiblastnCommandline( - db=blast_db_name, - evalue=0.001, - perc_identity=80, - outfmt=5, - max_target_seqs=10, - max_hsps=10, - num_threads=1, - ) - out, err = cline(stdin=qseq) - psiblast_xml = StringIO(out) - blast_records = NCBIXML.parse(psiblast_xml) - for blast_record in blast_records: - for alignment in blast_record.alignments: - for match in alignment.hsps: - match_alignment = [ - ["sample", match.sbjct], - ["match", match.match], - ["schema", match.query], - ] - return match_alignment - - -def get_alignment_for_deletions( - sample_seq, query_seq -): ### Sustituido por get_alignment - index_found = False - alignments = pairwise2.align.globalxx(sample_seq, query_seq) - for index in range(len(alignments)): - if alignments[index][4] == len(query_seq): - index_found = True - break - if not index_found: - index = 0 - values = format_alignment(*alignments[index]).split("\n") - match_alignment = [ - ["sample", values[0]], - ["match", values[1]], - ["schema", values[2]], - ] - return match_alignment - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # -# Get DNA and protein alignment between the final sequence found in the sample and the matching allele # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # - - -def get_alignment( - sample_seq, query_seq, reward, penalty, gapopen, gapextend, seq_type="dna" -): - ## If sequences alignment type desired is "protein" convert dna sequences to protein - if seq_type == "protein": - sample_seq = convert_to_protein(sample_seq) - query_seq = convert_to_protein(query_seq) - - ## Get dna/protein alignment between final sequence found and matching allele - # arguments pairwise2.align.globalms: match, mismatch, gap opening, gap extending - alignments = pairwise2.align.localms( - sample_seq, query_seq, reward, penalty, -gapopen, -gapextend - ) - values = format_alignment(*alignments[0]).split("\n") - match_alignment = [ - ["sample", values[0]], - ["match", values[1]], - ["schema", values[2]], - ] - - return match_alignment - - -# · * · * · * · * · * · * · * · * # -# Tag LNF cases and keep LNF info # -# · * · * · * · * · * · * · * · * # - - -def lnf_tpr_tag( - core_name, - sample_name, - alleles_in_locus_dict, - samples_matrix_dict, - lnf_tpr_dict, - schema_statistics, - locus_alleles_path, - qseqid, - pident, - s_length, - new_sequence_length, - perc_identity_ref, - coverage, - schema_quality, - annotation_core_dict, - count_dict, - logger, -): - gene_annot, product_annot = annotation_core_dict[core_name] - - if qseqid == "-": - samples_matrix_dict[sample_name].append("LNF") - tag_report = "LNF" - matching_allele_length = "-" - - else: - if new_sequence_length == "-": - samples_matrix_dict[sample_name].append("LNF_" + str(qseqid)) - tag_report = "LNF" - else: - samples_matrix_dict[sample_name].append("TPR_" + str(qseqid)) - tag_report = "TPR" - - matching_allele_seq = alleles_in_locus_dict[core_name][qseqid] - matching_allele_length = len(matching_allele_seq) - - # alleles_in_locus = list (SeqIO.parse(locus_alleles_path, "fasta")) ## parse - # for allele in alleles_in_locus : ## parse - # if allele.id == qseqid : ## parse - # break ## parse - # matching_allele_seq = str(allele.seq) ## parse - # matching_allele_length = len(matching_allele_seq) ## parse - - if pident == "-": - # (los dos BLAST sin resultado) - coverage_blast = "-" - coverage_new_sequence = "-" - add_info = "Locus not found" - logger.info("Locus not found at sample %s, for gene %s", sample_name, core_name) - - # Get allele quality - allele_quality = "-" - - # (recuento tags para plot) - count_dict[sample_name]["not_found"] += 1 - count_dict[sample_name]["total"] += 1 - - elif 90 > float(pident): - # (BLAST 90 sin resultado y BLAST 70 con resultado) - coverage_blast = "-" - coverage_new_sequence = "-" - add_info = "BLAST sequence ID under threshold: {}%".format(perc_identity_ref) - logger.info( - "BLAST sequence ID %s under threshold at sample %s, for gene %s", - pident, - sample_name, - core_name, - ) - - # Get allele quality - allele_quality = "-" - - # (recuento tags para plot) - count_dict[sample_name]["low_id"] += 1 - count_dict[sample_name]["total"] += 1 - - elif 90 <= float(pident) and new_sequence_length == "-": - # (BLAST 90 con resultado, bajo coverage BLAST) - locus_mean = int(schema_statistics[core_name][0]) - coverage_blast = int(s_length) / locus_mean - # coverage_blast = int(s_length) / matching_allele_length - coverage_new_sequence = "-" - if coverage_blast < 1: - add_info = "BLAST sequence coverage under threshold: {}%".format(coverage) - else: - add_info = "BLAST sequence coverage above threshold: {}%".format(coverage) - logger.info( - "BLAST sequence coverage %s under threshold at sample %s, for gene %s", - coverage_blast, - sample_name, - core_name, - ) - - # Get allele quality - allele_quality = "-" - - # (recuento tags para plot) - count_dict[sample_name]["low_coverage"] += 1 - count_dict[sample_name]["total"] += 1 - - elif 90 <= float(pident) and new_sequence_length != "-": - # (BLAST 90 con resultado, buen coverage BLAST, bajo coverage new_sseq) - locus_mean = int(schema_statistics[core_name][0]) - coverage_blast = int(s_length) / locus_mean * 100 - # coverage_blast = int(s_length) / matching_allele_length - coverage_new_sequence = new_sequence_length / matching_allele_length * 100 - if coverage_new_sequence < 1: - add_info = "New sequence coverage under threshold: {}%".format(coverage) - else: - add_info = "New sequence coverage above threshold: {}%".format(coverage) - logger.info( - "New sequence coverage %s under threshold at sample %s, for gene %s", - coverage_new_sequence, - sample_name, - core_name, - ) - - # Get allele quality - allele_quality = schema_quality[core_name][qseqid] - - # (recuento tags para plot) - count_dict[sample_name]["total"] += 1 - for count_class in count_dict[sample_name]: - if count_class in allele_quality: - count_dict[sample_name][count_class] += 1 - # if "bad_quality" in allele_quality: - # count_dict[sample_name]['bad_quality'] += 1 - - ## Keeping LNF and TPR report info - if not core_name in lnf_tpr_dict: - lnf_tpr_dict[core_name] = {} - if not sample_name in lnf_tpr_dict[core_name]: - lnf_tpr_dict[core_name][sample_name] = [] - - lnf_tpr_dict[core_name][sample_name].append( - [ - gene_annot, - product_annot, - tag_report, - qseqid, - allele_quality, - pident, - str(coverage_blast), - str(coverage_new_sequence), - str(matching_allele_length), - str(s_length), - str(new_sequence_length), - add_info, - ] - ) ### Meter secuencias alelo, blast y new_sseq (si las hay)? - - return True - - -# · * · * · * · * · * · * · * · * · * · * · * · * # -# Tag paralog and exact match cases and keep info # -# · * · * · * · * · * · * · * · * · * · * · * · * # - - -def paralog_exact_tag( - sample_name, - core_name, - tag, - schema_quality, - matching_genes_dict, - samples_matrix_dict, - allele_found, - tag_dict, - prodigal_report, - prodigal_directory, - blast_parameters, - annotation_core_dict, - count_dict, - logger, -): - logger.info("Found %s at sample %s for core gene %s ", tag, sample_name, core_name) - - paralog_quality_count = ( - [] - ) # (lista para contabilizar parálogos debido a bad o good quality) - - gene_annot, product_annot = annotation_core_dict[core_name] - - if not sample_name in tag_dict: - tag_dict[sample_name] = {} - if not core_name in tag_dict[sample_name]: - tag_dict[sample_name][core_name] = [] - - if tag == "EXACT": - allele = list(allele_found.keys())[0] - qseqid = allele_found[allele][0] - tag = qseqid - - samples_matrix_dict[sample_name].append(tag) - - for sequence in allele_found: - ( - qseqid, - sseqid, - pident, - qlen, - s_length, - mismatch, - r_gapopen, - r_evalue, - bitscore, - sstart, - send, - qstart, - qend, - sseq, - qseq, - ) = allele_found[sequence] - sseq = sseq.replace("-", "") - - # Get allele quality - allele_quality = schema_quality[core_name][qseqid] - - if len(allele_found) > 1: - paralog_quality_count.append(allele_quality) - - # Get prodigal gene prediction if allele quality is 'bad_quality' - if "bad_quality" in allele_quality: - ( - complete_predicted_seq, - start_prodigal, - end_prodigal, - ) = get_prodigal_sequence( - sseq, sseqid, prodigal_directory, sample_name, blast_parameters, logger - ) - - ##### informe prodigal ##### - prodigal_report.append( - [ - core_name, - sample_name, - qseqid, - tag, - sstart, - send, - start_prodigal, - end_prodigal, - sseq, - complete_predicted_seq, - ] - ) - - else: - complete_predicted_seq = "-" - - if not sseqid in matching_genes_dict[sample_name]: - matching_genes_dict[sample_name][sseqid] = [] - if sstart > send: - # matching_genes_dict[sample_name][sseqid].append([core_name, sstart, send,'-', tag]) - matching_genes_dict[sample_name][sseqid].append( - [core_name, qseqid, sstart, send, "-", tag] - ) - else: - # matching_genes_dict[sample_name][sseqid].append([core_name, sstart, send,'+', tag]) - matching_genes_dict[sample_name][sseqid].append( - [core_name, qseqid, sstart, send, "+", tag] - ) - - ## Keeping paralog NIPH/NIPHEM report info - if tag == "NIPH" or tag == "NIPHEM": - tag_dict[sample_name][core_name].append( - [ - gene_annot, - product_annot, - tag, - pident, - qseqid, - allele_quality, - sseqid, - bitscore, - sstart, - send, - sseq, - complete_predicted_seq, - ] - ) - else: - tag_dict[sample_name][core_name] = [ - gene_annot, - product_annot, - qseqid, - allele_quality, - sseqid, - s_length, - sstart, - send, - sseq, - complete_predicted_seq, - ] - - # (recuento tags para plot) - count_dict[sample_name]["total"] += 1 - for count_class in count_dict[sample_name]: - if count_class in allele_quality: - if ( - "no_start_stop" not in count_class - and "no_start_stop" in allele_quality - ): - if count_class == "bad_quality": - count_dict[sample_name][count_class] += 1 - else: - count_dict[sample_name][count_class] += 1 - - # (recuento tags para plot (parálogos)) - if len(allele_found) > 0: - count = 0 - for paralog_quality in paralog_quality_count: - count += 1 - if "bad_quality" in paralog_quality: - count_dict[sample_name]["total"] += 1 - for count_class in count_dict[sample_name]: - if count_class in paralog_quality: - if ( - "no_start_stop" not in count_class - and "no_start_stop" in paralog_quality - ): - if count_class == "bad_quality": - count_dict[sample_name][count_class] += 1 - else: - next - else: - count_dict[sample_name][count_class] += 1 - break - - else: - if count == len(paralog_quality_count): - count_dict[sample_name]["total"] += 1 - count_dict[sample_name]["good_quality"] += 1 - - return True - - -# · * · * · * · * · * · * · * · * · * · * # -# Tag INF/ASM/ALM/PLOT cases and keep info # -# · * · * · * · * · * · * · * · * · * · * # - - -def inf_asm_alm_tag( - core_name, - sample_name, - tag, - blast_values, - allele_quality, - new_sseq, - matching_allele_length, - tag_dict, - list_tag, - samples_matrix_dict, - matching_genes_dict, - prodigal_report, - start_prodigal, - end_prodigal, - complete_predicted_seq, - annotation_core_dict, - count_dict, - logger, -): - gene_annot, product_annot = annotation_core_dict[core_name] - - ( - qseqid, - sseqid, - pident, - qlen, - s_length, - mismatch, - r_gapopen, - r_evalue, - bitscore, - sstart, - send, - qstart, - qend, - sseq, - qseq, - ) = blast_values - - sseq = sseq.replace("-", "") - s_length = len(sseq) - new_sequence_length = len(new_sseq) - - logger.info("Found %s at sample %s for core gene %s ", tag, sample_name, core_name) - - if tag == "PLOT": - tag_allele = tag + "_" + str(qseqid) - else: - # Adding ASM/ALM/INF allele to the allele_matrix if it is not already include - if not core_name in tag_dict: - tag_dict[core_name] = [] - if not new_sseq in tag_dict[core_name]: - tag_dict[core_name].append(new_sseq) - # Find the index of ASM/ALM/INF to include it in the sample matrix dict - index_tag = tag_dict[core_name].index(new_sseq) - - tag_allele = tag + "_" + core_name + "_" + str(qseqid) + "_" + str(index_tag) - - samples_matrix_dict[sample_name].append(tag_allele) - - # Keeping INF/ASM/ALM/PLOT report info - if not core_name in list_tag: - list_tag[core_name] = {} - if not sample_name in list_tag[core_name]: - list_tag[core_name][sample_name] = {} - - if tag == "INF": - list_tag[core_name][sample_name][tag_allele] = [ - gene_annot, - product_annot, - qseqid, - allele_quality, - sseqid, - bitscore, - str(matching_allele_length), - str(s_length), - str(new_sequence_length), - mismatch, - r_gapopen, - sstart, - send, - new_sseq, - complete_predicted_seq, - ] - - # (recuento tags para plots) - count_dict[sample_name]["total"] += 1 - for count_class in count_dict[sample_name]: - if count_class in allele_quality: - count_dict[sample_name][count_class] += 1 - # if "bad_quality" in allele_quality: - # count_dict[sample_name]['bad_quality'] += 1 - - elif tag == "PLOT": - list_tag[core_name][sample_name] = [ - gene_annot, - product_annot, - qseqid, - allele_quality, - sseqid, - bitscore, - sstart, - send, - sseq, - new_sseq, - ] - - # (recuento tags para plots) - count_dict[sample_name]["total"] += 1 - - else: - if tag == "ASM": - newsseq_vs_blastseq = "shorter" - elif tag == "ALM": - newsseq_vs_blastseq = "longer" - - if len(sseq) < matching_allele_length: - add_info = ( - "Global effect: DELETION. BLAST sequence length shorter than matching allele sequence length / Net result: " - + tag - + ". Final gene sequence length " - + newsseq_vs_blastseq - + " than matching allele sequence length" - ) - - elif len(sseq) == matching_allele_length: - add_info = ( - "Global effect: BASE SUBSTITUTION. BLAST sequence length equal to matching allele sequence length / Net result: " - + tag - + ". Final gene sequence length " - + newsseq_vs_blastseq - + " than matching allele sequence length" - ) - - elif len(sseq) > matching_allele_length: - add_info = ( - "Global effect: INSERTION. BLAST sequence length longer than matching allele sequence length / Net result: " - + tag - + ". Final gene sequence length " - + newsseq_vs_blastseq - + " than matching allele sequence length" - ) - - list_tag[core_name][sample_name][tag_allele] = [ - gene_annot, - product_annot, - qseqid, - allele_quality, - sseqid, - bitscore, - str(matching_allele_length), - str(s_length), - str(new_sequence_length), - mismatch, - r_gapopen, - sstart, - send, - new_sseq, - add_info, - complete_predicted_seq, - ] - - # (recuento tags para plots) - if tag == "ASM": - count_dict[sample_name]["total"] += 1 - for mut_type in count_dict[sample_name]: - if mut_type in add_info.lower(): - count_dict[sample_name][mut_type] += 1 - - elif tag == "ALM": - count_dict[sample_name]["total"] += 1 - for mut_type in count_dict[sample_name]: - if mut_type in add_info.lower(): - count_dict[sample_name][mut_type] += 1 - - if not sseqid in matching_genes_dict[sample_name]: - matching_genes_dict[sample_name][sseqid] = [] - if sstart > send: - # matching_genes_dict[sample_name][sseqid].append([core_name, str(int(sstart)-new_sequence_length -1), sstart,'-', tag_allele]) - matching_genes_dict[sample_name][sseqid].append( - [ - core_name, - qseqid, - str(int(sstart) - new_sequence_length - 1), - sstart, - "-", - tag_allele, - ] - ) - else: - # matching_genes_dict[sample_name][sseqid].append([core_name, sstart, str(int(sstart)+ new_sequence_length),'+', tag_allele]) - matching_genes_dict[sample_name][sseqid].append( - [ - core_name, - qseqid, - sstart, - str(int(sstart) + new_sequence_length), - "+", - tag_allele, - ] - ) - - ##### informe prodigal ##### - prodigal_report.append( - [ - core_name, - sample_name, - qseqid, - tag_allele, - sstart, - send, - start_prodigal, - end_prodigal, - sseq, - complete_predicted_seq, - ] - ) - - return True - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # -# Keep best results info after BLAST using results from previous reference allele BLAST as database VS ALL alleles in locus as query in allele calling analysis # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # - - -def get_blast_results( - sample_name, values, contigs_in_sample_dict, allele_found, logger -): - ( - qseqid, - sseqid, - pident, - qlen, - s_length, - mismatch, - r_gapopen, - r_evalue, - bitscore, - sstart, - send, - qstart, - qend, - sseq, - qseq, - ) = values - - ## Get contig ID and BLAST sequence - sseqid_blast = "_".join(sseqid.split("_")[1:]) - sseq_no_gaps = sseq.replace("-", "") - - ## Get start and end positions in contig searching for BLAST sequence index in contig sequence - - # Get contig sequence - accession_sequence = contigs_in_sample_dict[sample_name][sseqid_blast] - - # for record in sample_contigs: ## parse - # if record.id == sseqid_blast : ## parse - # break ## parse - # accession_sequence = str(record.seq) ## parse - - # Try to get BLAST sequence index in contig. If index -> error because different contig sequence and BLAST sequence - # direction, obtain reverse complement BLAST sequence and try again. - try: - sseq_index_1 = int(accession_sequence.index(sseq_no_gaps)) + 1 - - except: - sseq_no_gaps = str(Seq.Seq(sseq_no_gaps).reverse_complement()) - sseq_index_1 = int(accession_sequence.index(sseq_no_gaps)) + 1 - - sseq_index_2 = int(sseq_index_1) + len(sseq_no_gaps) - 1 - - # Assign found indexes to start and end possitions depending on BLAST sequence and allele sequence direction - if int(sstart) < int(send): - sstart_new = str(min(sseq_index_1, sseq_index_2)) - send_new = str(max(sseq_index_1, sseq_index_2)) - else: - sstart_new = str(max(sseq_index_1, sseq_index_2)) - send_new = str(min(sseq_index_1, sseq_index_2)) - - ## Keep BLAST results info discarding subsets - allele_is_subset = False - - if len(allele_found) > 0: - for allele_id in allele_found: - min_index = min( - int(allele_found[allele_id][9]), int(allele_found[allele_id][10]) - ) - max_index = max( - int(allele_found[allele_id][9]), int(allele_found[allele_id][10]) - ) - if int(sstart_new) in range(min_index, max_index + 1) or int( - send_new - ) in range( - min_index, max_index + 1 - ): # if both genome locations overlap - if ( - sseqid_blast == allele_found[allele_id][1] - ): # if both sequences are in the same contig - logger.info( - "Found allele %s that starts or ends at the same position as %s ", - qseqid, - allele_id, - ) - allele_is_subset = True - break - - if len(allele_found) == 0 or not allele_is_subset: - contig_id_start = str(sseqid_blast + "_" + sstart_new) - - # Skip the allele found in the 100% identity and 100% alignment - if not contig_id_start in allele_found: - allele_found[contig_id_start] = [ - qseqid, - sseqid_blast, - pident, - qlen, - s_length, - mismatch, - r_gapopen, - r_evalue, - bitscore, - sstart_new, - send_new, - "-", - "-", - sseq, - qseq, - ] - - return True - - -# · * · * · * · * · * · * · * · * · * · # -# Get SNPs and ADN and protein alignment # -# · * · * · * · * · * · * · * · * · * · # - - -def keep_snp_alignment_info( - sseq, - new_sseq, - matching_allele_seq, - qseqid, - query_direction, - core_name, - sample_name, - reward, - penalty, - gapopen, - gapextend, - snp_dict, - match_alignment_dict, - protein_dict, - logger, -): - ## Check allele sequence direction - if query_direction == "reverse": - matching_allele_seq = str(Seq.Seq(matching_allele_seq).reverse_complement()) - else: - matching_allele_seq = str(matching_allele_seq) - - ## Get the SNP information - snp_information = get_snp(sseq, matching_allele_seq) - if len(snp_information) > 0: - if not core_name in snp_dict: - snp_dict[core_name] = {} - if not sample_name in snp_dict[core_name]: - snp_dict[core_name][sample_name] = {} - snp_dict[core_name][sample_name][qseqid] = snp_information - - ## Get new sequence-allele sequence dna alignment - if not core_name in match_alignment_dict: - match_alignment_dict[core_name] = {} - if not sample_name in match_alignment_dict[core_name]: - match_alignment_dict[core_name][sample_name] = get_alignment( - new_sseq, matching_allele_seq, reward, penalty, gapopen, gapextend - ) - - ## Get new sequence-allele sequence protein alignment - if not core_name in protein_dict: - protein_dict[core_name] = {} - if not sample_name in protein_dict[core_name]: - protein_dict[core_name][sample_name] = [] - protein_dict[core_name][sample_name] = get_alignment( - new_sseq, matching_allele_seq, reward, penalty, gapopen, gapextend, "protein" - ) - - return True - - -# · * · * · * · * · * · * · * · * · * · * · # -# Create allele tag summary for each sample # -# · * · * · * · * · * · * · * · * · * · * · # - - -def create_summary(samples_matrix_dict, logger): - summary_dict = {} - summary_result_list = [] - summary_heading_list = [ - "Exact match", - "INF", - "ASM", - "ALM", - "LNF", - "TPR", - "NIPH", - "NIPHEM", - "PLOT", - "ERROR", - ] - summary_result_list.append("File\t" + "\t".join(summary_heading_list)) - for key in sorted(samples_matrix_dict): - summary_dict[key] = { - "Exact match": 0, - "INF": 0, - "ASM": 0, - "ALM": 0, - "LNF": 0, - "TPR": 0, - "NIPH": 0, - "NIPHEM": 0, - "PLOT": 0, - "ERROR": 0, - } - for values in samples_matrix_dict[key]: - if "INF_" in values: - summary_dict[key]["INF"] += 1 - elif "ASM_" in values: - summary_dict[key]["ASM"] += 1 - elif "ALM_" in values: - summary_dict[key]["ALM"] += 1 - elif "LNF" in values: - summary_dict[key]["LNF"] += 1 - elif "TPR" in values: - summary_dict[key]["TPR"] += 1 - elif "NIPH" == values: - summary_dict[key]["NIPH"] += 1 - elif "NIPHEM" == values: - summary_dict[key]["NIPHEM"] += 1 - elif "PLOT" in values: - summary_dict[key]["PLOT"] += 1 - elif "ERROR" in values: - summary_dict[key]["ERROR"] += 1 - else: - try: - number = int(values) - summary_dict[key]["Exact match"] += 1 - except: - if "_" in values: - tmp_value = values - try: - number = int(tmp_value[-1]) - summary_dict[key]["Exact match"] += 1 - except: - logger.debug( - "The value %s, was found when collecting summary information for the %s", - values, - summary_dict[key], - ) - else: - logger.debug( - "The value %s, was found when collecting summary information for the %s", - values, - summary_dict[key], - ) - summary_sample_list = [] - for item in summary_heading_list: - summary_sample_list.append(str(summary_dict[key][item])) - summary_result_list.append(key + "\t" + "\t".join(summary_sample_list)) - return summary_result_list - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # -# Get gene and product annotation for core gene using Prokka # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · # - - -### (tsv para algunos locus? Utils para analyze schema?) -def get_gene_annotation( - annotation_file, annotation_dir, genus, species, usegenus, logger -): - name_file = os.path.basename(annotation_file).split(".") - annotation_dir = os.path.join(annotation_dir, "annotation", name_file[0]) - - if usegenus == "true": - annotation_result = subprocess.run( - [ - "prokka", - annotation_file, - "--outdir", - annotation_dir, - "--genus", - genus, - "--species", - species, - "--usegenus", - "--gcode", - "11", - "--prefix", - name_file[0], - "--quiet", - ] - ) - - elif usegenus == "false": - annotation_result = subprocess.run( - [ - "prokka", - annotation_file, - "--outdir", - annotation_dir, - "--genus", - genus, - "--species", - species, - "--gcode", - "11", - "--prefix", - name_file[0], - "--quiet", - ] - ) - - annot_tsv = [] - tsv_path = os.path.join(annotation_dir, name_file[0] + ".tsv") - - try: - with open(tsv_path) as tsvfile: - tsvreader = csv.reader(tsvfile, delimiter="\t") - for line in tsvreader: - annot_tsv.append(line) - - if len(annot_tsv) > 1: - gene_index = annot_tsv[0].index("gene") - product_index = annot_tsv[0].index("product") - - try: - if "_" in annot_tsv[1][2]: - gene_annot = annot_tsv[1][gene_index].split("_")[0] - else: - gene_annot = annot_tsv[1][gene_index] - except: - gene_annot = "Not found by Prokka" - - try: - product_annot = annot_tsv[1][product_index] - except: - product_annot = "Not found by Prokka" - else: - gene_annot = "Not found by Prokka" - product_annot = "Not found by Prokka" - except: - gene_annot = "Not found by Prokka" - product_annot = "Not found by Prokka" - - return gene_annot, product_annot - - -""" -def get_gene_annotation (annotation_file, annotation_dir, logger) : - name_file = os.path.basename(annotation_file).split('.') - annotation_dir = os.path.join (annotation_dir, 'annotation', name_file[0]) - - annotation_result = subprocess.run (['prokka', annotation_file, '--outdir', annotation_dir, - '--prefix', name_file[0], '--quiet']) - - annot_tsv = [] - tsv_path = os.path.join (annotation_dir, name_file[0] + '.tsv') - - try: - with open(tsv_path) as tsvfile: - tsvreader = csv.reader(tsvfile, delimiter="\t") - for line in tsvreader: - annot_tsv.append(line) - - if len(annot_tsv) > 1: - try: - if '_' in annot_tsv[1][2]: - gene_annot = annot_tsv[1][2].split('_')[0] - else: - gene_annot = annot_tsv[1][2] - except: - gene_annot = 'Not found by Prokka' - - try: - product_annot = annot_tsv[1][4] - except: - product_annot = 'Not found by Prokka' - else: - gene_annot = 'Not found by Prokka' - product_annot = 'Not found by Prokka' - except: - gene_annot = 'Not found by Prokka' - product_annot = 'Not found by Prokka' - - return gene_annot, product_annot -""" - -""" -def get_gene_annotation (annotation_file, annotation_dir, logger) : - name_file = os.path.basename(annotation_file).split('.') - annotation_dir = os.path.join (annotation_dir, 'annotation', name_file[0]) - - annotation_result = subprocess.run (['prokka', annotation_file, '--outdir', annotation_dir, - '--prefix', name_file[0], '--quiet']) - - annot_tsv = [] - tsv_path = os.path.join (annotation_dir, name_file[0] + '.tsv') - with open(tsv_path) as tsvfile: - tsvreader = csv.reader(tsvfile, delimiter="\t") - for line in tsvreader: - annot_tsv.append(line) - - if len(annot_tsv) > 1: - try: - if '_' in annot_tsv[1][2]: - gene_annot = annot_tsv[1][2].split('_')[0] - else: - gene_annot = annot_tsv[1][2] - except: - gene_annot = 'Not found by Prokka' - - try: - product_annot = annot_tsv[1][4] - except: - product_annot = 'Not found by Prokka' - else: - gene_annot = 'Not found by Prokka' - product_annot = 'Not found by Prokka' - - return gene_annot, product_annot -""" - - -def analize_annotation_files(in_file, logger): ## N - examiner = GFF.GFFExaminer() - file_fh = open(in_file) - datos = examiner.available_limits(in_file) - file_fh.close() - return True - - -def get_inferred_allele_number(core_dict, logger): ## N - # This function will look for the highest locus number and it will return a safe high value - # that will be added to the schema database - logger.debug("running get_inferred_allele_number function") - int_keys = [] - for key in core_dict.keys(): - int_keys.append(key) - max_value = max(int_keys) - digit_length = len(str(max_value)) - return True # str 1 ( #'1'+ '0'*digit_length + 2) - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # -# Get ST profile for each samples based on allele calling results # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # - - -def get_ST_profile( - outputdir, - profile_csv_path, - exact_dict, - inf_dict, - core_gene_list_files, - sample_list_files, - logger, -): - ## logger - - csv_read = [] - ST_profiles_dict = {} - samples_profiles_dict = {} - analysis_profiles_dict = {} - inf_ST = {} - count_st = {} - - with open(profile_csv_path) as csvfile: - csvreader = csv.reader(csvfile, delimiter="\t") - for line in csvreader: - csv_read.append(line) - - profile_header = csv_read[0][1 : len(core_gene_list_files) + 1] - - for ST_index in range(1, len(csv_read)): - ST_profiles_dict[csv_read[ST_index][0]] = {} - for core_index in range(len(profile_header)): - ST_profiles_dict[csv_read[ST_index][0]][ - profile_header[core_index] - ] = csv_read[ST_index][core_index + 1] - - for sample_file in sample_list_files: - sample_name = ".".join(os.path.basename(sample_file).split(".")[:-1]) - - st_counter = 0 - for ST in ST_profiles_dict: - core_counter = 0 - - for core_name in profile_header: - allele_in_ST = ST_profiles_dict[ST][core_name] - exact_gene = True - - if sample_name in exact_dict: - if core_name in exact_dict[sample_name]: - allele_in_sample = exact_dict[sample_name][core_name][2] - - if not "_" in allele_in_ST: - if "_" in allele_in_sample: - allele_in_sample = allele_in_sample.split("_")[1] - - if st_counter == 0: - if sample_name not in analysis_profiles_dict: - analysis_profiles_dict[sample_name] = {} - analysis_profiles_dict[sample_name][ - core_name - ] = allele_in_sample - - if allele_in_sample == allele_in_ST: - core_counter += 1 - - else: - exact_gene = False - - else: - exact_gene = False - - if exact_gene == False: - if sample_name in inf_dict: - if core_name in inf_dict[sample_name]: - if st_counter == 0: - allele_in_sample = inf_dict[sample_name][core_name][2] - if sample_name not in analysis_profiles_dict: - analysis_profiles_dict[sample_name] = {} - analysis_profiles_dict[sample_name][ - core_name - ] = allele_in_sample - - else: - if st_counter == 0: - if sample_name not in analysis_profiles_dict: - analysis_profiles_dict[sample_name] = {} - - if allele_in_ST == "N" and "allele_in_sample" not in locals(): - core_counter += 1 - - st_counter += 1 - if core_counter == len(profile_header): - samples_profiles_dict[sample_name] = ST - - if "_INF" in ST: - if "New" not in count_st: - count_st["New"] = {} - if ST not in count_st["New"]: - count_st["New"][ST] = 0 - count_st["New"][ST] += 1 - - else: - if "Known" not in count_st: - count_st["Known"] = {} - if ST not in count_st["Known"]: - count_st["Known"][ST] = 0 - count_st["Known"][ST] += 1 - - break - - if sample_name not in samples_profiles_dict: - if sample_name in analysis_profiles_dict: - if len(analysis_profiles_dict[sample_name]) == len(profile_header): - new_st_id = str(len(ST_profiles_dict) + 1) - ST_profiles_dict[new_st_id + "_INF"] = analysis_profile_dict[ - sample_name - ] - inf_ST[new_st_id] = analysis_profile_dict[sample_name] - - samples_profiles_dict[sample_name] = new_st_id + "_INF" - - if "New" not in count_st: - count_st["New"] = {} - if new_st_id not in count_st["New"]: - count_st["New"][new_st_id] = 0 - count_st["New"][new_st_id] += 1 - - else: - samples_profiles_dict[sample_name] = "-" - - if "Unknown" not in count_st: - count_st["Unknown"] = 0 - count_st["Unknown"] += 1 - else: - samples_profiles_dict[sample_name] = "-" - - if "Unknown" not in count_st: - count_st["Unknown"] = 0 - count_st["Unknown"] += 1 - - ## Create ST profile results report - save_st_profile_results(outputdir, samples_profiles_dict, logger) - - ## Obtain interactive piechart - logger.info("Creating interactive ST results piechart") - create_sunburst_plot_st(outputdir, count_st, logger) - - return True, inf_ST - - -# · * · * · * · * · * · * # -# Create ST results report # -# · * · * · * · * · * · * # - - -def save_st_profile_results(outputdir, samples_profiles_dict, logger): - header_stprofile = ["Sample Name", "ST"] - - if samples_profiles_dict != "": - ## Saving ST profile to file - logger.info("Saving ST profile information to file..") - stprofile_file = os.path.join(outputdir, "stprofile.tsv") - with open(stprofile_file, "w") as st_fh: - st_fh.write("\t".join(header_stprofile) + "\n") - for sample in sorted(samples_profiles_dict): - st_fh.write(sample + "\t" + samples_profiles_dict[sample] + "\n") - - return True - - -def create_sunburst_plot_st(outputdir, count_st, logger): - ### logger? - counts = [] - st_ids = ["ST"] - st_labels = ["ST"] - st_parents = [""] - - total_samples = 0 - - for st_type in count_st: - if type(count_st[st_type]) == dict: - total_st_type_count = sum(count_st[st_type].values()) - else: - total_st_type_count = count_st[st_type] - - counts.append(total_st_type_count) - st_ids.append(st_type) - st_labels.append(st_type) - st_parents.append("ST") - - total_samples += total_st_type_count - - if type(count_st[st_type]) == dict: - for st in count_st[st_type]: - counts.append(count_st[st_type][st]) - st_ids.append(st + " - " + st_type) - st_labels.append(st) - st_parents.append(st_type) - - counts.insert(0, total_samples) - - fig = go.Figure( - go.Sunburst( - ids=st_ids, - labels=st_labels, - parents=st_parents, - values=counts, - branchvalues="total", - ) - ) - - fig.update_layout(margin=dict(t=0, l=0, r=0, b=0)) - - plotsdir = os.path.join(outputdir, "plots", "samples_st.html") - - fig.write_html(plotsdir) - - return True - - -# · * · * · * · * · * · * · * · * · * · * · # -# Update ST profile file adding new ST found # -# · * · * · * · * · * · * · * · * · * · * · # - - -def update_st_profile( - updateprofile, profile_csv_path, outputdir, inf_ST, core_gene_list_files, logger -): - ## Create a copy of ST profile file if updateprofile = 'new' - if updateprofile == "new": - no_updated_profile_csv_path = profile_csv_path - profile_csv_path_name = os.path.basename(no_updated_profile_csv_path).split( - "." - )[0] - profile_csv_path = os.path.join( - outputdir, profile_csv_path_name + "_updated" + ".csv" - ) - shutil.copyfile(no_updated_profile_csv_path, profile_csv_path) - logger.info("Copying ST profile file to update profiles") - - ## Update ST profile file - logger.info("Updating ST profile file adding new INF ST") - - with open(profile_csv_path, "r") as csvfile: - csvreader = csv.reader(csvfile, delimiter="\t") - for line in csvreader: - profile_header = line[0][1 : len(core_gene_list_files) + 1] - break - - with open(profile_csv_path, "a") as profile_fh: - for ST in inf_ST: - locus_ST_list = [] - locus_ST_list.append(ST) - for locus in profile_header: - locus_ST_list.append(inf_ST[ST][locus]) - profile_fh.write("\t".join(locus_ST_list) + "\n") - - return True - - -# · * · * · * · * · * · * · * · * · * · # -# Create allele calling results reports # -# · * · * · * · * · * · * · * · * · * · # - - -def save_allele_call_results( - outputdir, - full_gene_list, - samples_matrix_dict, - exact_dict, - paralog_dict, - inf_dict, - plot_dict, - matching_genes_dict, - list_asm, - list_alm, - lnf_tpr_dict, - snp_dict, - match_alignment_dict, - protein_dict, - prodigal_report, - shorter_seq_coverage, - longer_seq_coverage, - equal_seq_coverage, - shorter_blast_seq_coverage, - longer_blast_seq_coverage, - equal_blast_seq_coverage, - logger, -): - header_matching_alleles_contig = [ - "Sample Name", - "Contig", - "Core Gene", - "Allele", - "Contig Start", - "Contig Stop", - "Direction", - "Codification", - ] - header_exact = [ - "Core Gene", - "Sample Name", - "Gene Annotation", - "Product Annotation", - "Allele", - "Allele Quality", - "Contig", - "Query length", - "Contig start", - "Contig end", - "Sequence", - "Predicted Sequence", - ] - header_paralogs = [ - "Core Gene", - "Sample Name", - "Gene Annotation", - "Product Annotation", - "Paralog Tag", - "ID %", - "Allele", - "Allele Quality", - "Contig", - "Bitscore", - "Contig start", - "Contig end", - "Sequence", - "Predicted Sequence", - ] - header_inferred = [ - "Core Gene", - "Sample Name", - "INF tag", - "Gene Annotation", - "Product Annotation", - "Allele", - "Allele Quality", - "Contig", - "Bitscore", - "Query length", - "Contig length", - "New sequence length", - "Mismatch", - "gaps", - "Contig start", - "Contig end", - "New sequence", - "Predicted Sequence", - ] - header_asm = [ - "Core Gene", - "Sample Name", - "ASM tag", - "Gene Annotation", - "Product Annotation", - "Allele", - "Allele Quality", - "Contig", - "Bitscore", - "Query length", - "Contig length", - "New sequence length", - "Mismatch", - "gaps", - "Contig start", - "Contig end", - "New sequence", - "Additional info", - "Predicted Sequence", - ] - header_alm = [ - "Core Gene", - "Sample Name", - "ALM tag", - "Gene Annotation", - "Product Annotation", - "Allele", - "Allele Quality", - "Contig", - "Bitscore", - "Query length", - "Contig length", - "New sequence length", - "Mismatch", - "gaps", - "Contig start", - "Contig end", - "New sequence", - "Additional info", - "Predicted Sequence", - ] - header_plot = [ - "Core Gene", - "Sample Name", - "Gene Annotation", - "Product Annotation", - "Allele", - "Allele Quality", - "Contig", - "Bitscore", - "Contig start", - "Contig end", - "Sequence", - "Predicted Sequence", - ] - header_lnf_tpr = [ - "Core Gene", - "Sample Name", - "Gene Annotation", - "Product Annotation", - "Tag", - "Allele", - "Allele Quality", - "ID %", - "Blast sequence coverage %", - "New sequence coverage %", - "Query length", - "Contig length", - "New sequence length", - "Additional info", - ] - header_snp = [ - "Core Gene", - "Sample Name", - "Allele", - "Position", - "Mutation Schema/Sample", - "Codon Schema/Sample", - "Amino acid in Schema/Sample", - "Mutation type", - "Annotation Schema/Sample", - ] - header_protein = ["Core Gene", "Sample Name", "Protein in ", "Protein sequence"] - header_match_alignment = ["Core Gene", "Sample Name", "Alignment", "Sequence"] - header_stprofile = ["Sample Name", "ST"] - - # Añadido header_prodigal_report para report prodigal - # header_prodigal_report = ['Core gene', 'Sample Name', 'Allele', 'Sequence type', 'BLAST start', 'BLAST end', 'Prodigal start', 'Prodigal end', 'BLAST sequence', 'Prodigal sequence'] - # Añadido header_newsseq_coverage_report para determinar coverage threshold a imponer - # header_newsseq_coverage_report = ['Core gene', 'Sample Name', 'Query length', 'New sequence length', 'Locus mean', 'Coverage (new sequence/allele)', 'Coverage (new sequence/locus mean)'] - # Añadido header_blast_coverage_report para determinar coverage threshold a imponer - # header_blast_coverage_report = ['Core gene', 'Sample Name', 'Query length', 'Blast sequence length', 'Locus mean', 'Coverage (blast sequence/allele)', 'Coverage (blast sequence/locus mean)'] - - ## Saving the result information to file - print("Saving results to files \n") - result_file = os.path.join(outputdir, "result.tsv") - logger.info("Saving result information to file..") - with open(result_file, "w") as out_fh: - out_fh.write("Sample Name\t" + "\t".join(full_gene_list) + "\n") - for key in sorted(samples_matrix_dict): - out_fh.write(key + "\t" + "\t".join(samples_matrix_dict[key]) + "\n") - - ## Saving exact matches to file - logger.info("Saving exact matches information to file..") - exact_file = os.path.join(outputdir, "exact.tsv") - with open(exact_file, "w") as exact_fh: - exact_fh.write("\t".join(header_exact) + "\n") - for sample in sorted(exact_dict): - for core in sorted(exact_dict[sample]): - exact_fh.write( - core - + "\t" - + sample - + "\t" - + "\t".join(exact_dict[sample][core]) - + "\n" - ) - - ## Saving paralog alleles to file - logger.info("Saving paralog information to file..") - paralog_file = os.path.join(outputdir, "paralog.tsv") - with open(paralog_file, "w") as paralog_fh: - paralog_fh.write("\t".join(header_paralogs) + "\n") - for sample in sorted(paralog_dict): - for core in sorted(paralog_dict[sample]): - for paralog in paralog_dict[sample][core]: - paralog_fh.write( - core + "\t" + sample + "\t" + "\t".join(paralog) + "\n" - ) - - ## Saving inferred alleles to file - logger.info("Saving inferred alleles information to file..") - inferred_file = os.path.join(outputdir, "inferred_alleles.tsv") - with open(inferred_file, "w") as infer_fh: - infer_fh.write("\t".join(header_inferred) + "\n") - for core in sorted(inf_dict): - for sample in sorted(inf_dict[core]): - for inferred in inf_dict[core][sample]: - # seq_in_inferred_allele = '\t'.join (inf_dict[sample]) - infer_fh.write( - core - + "\t" - + sample - + "\t" - + inferred - + "\t" - + "\t".join(inf_dict[core][sample][inferred]) - + "\n" - ) - - ## Saving PLOTs to file - logger.info("Saving PLOT information to file..") - plot_file = os.path.join(outputdir, "plot.tsv") - with open(plot_file, "w") as plot_fh: - plot_fh.write("\t".join(header_plot) + "\n") - for core in sorted(plot_dict): - for sample in sorted(plot_dict[core]): - plot_fh.write( - core - + "\t" - + sample - + "\t" - + "\t".join(plot_dict[core][sample]) - + "\n" - ) - - ## Saving matching contigs to file - logger.info("Saving matching information to file..") - matching_file = os.path.join(outputdir, "matching_contigs.tsv") - with open(matching_file, "w") as matching_fh: - matching_fh.write("\t".join(header_matching_alleles_contig) + "\n") - for samples in sorted(matching_genes_dict): - for contigs in matching_genes_dict[samples]: - for contig in matching_genes_dict[samples][contigs]: - matching_alleles = "\t".join(contig) - matching_fh.write( - samples + "\t" + contigs + "\t" + matching_alleles + "\n" - ) - - ## Saving ASMs to file - logger.info("Saving asm information to file..") - asm_file = os.path.join(outputdir, "asm.tsv") - with open(asm_file, "w") as asm_fh: - asm_fh.write("\t".join(header_asm) + "\n") - for core in list_asm: - for sample in list_asm[core]: - for asm in list_asm[core][sample]: - asm_fh.write( - core - + "\t" - + sample - + "\t" - + asm - + "\t" - + "\t".join(list_asm[core][sample][asm]) - + "\n" - ) - - ## Saving ALMs to file - logger.info("Saving alm information to file..") - alm_file = os.path.join(outputdir, "alm.tsv") - with open(alm_file, "w") as alm_fh: - alm_fh.write("\t".join(header_alm) + "\n") - for core in list_alm: - for sample in list_alm[core]: - for alm in list_alm[core][sample]: - alm_fh.write( - core - + "\t" - + sample - + "\t" - + alm - + "\t" - + "\t".join(list_alm[core][sample][alm]) - + "\n" - ) - - ## Saving LNFs to file - logger.info("Saving lnf information to file..") - lnf_file = os.path.join(outputdir, "lnf_tpr.tsv") - with open(lnf_file, "w") as lnf_fh: - lnf_fh.write("\t".join(header_lnf_tpr) + "\n") - for core in lnf_tpr_dict: - for sample in lnf_tpr_dict[core]: - for lnf in lnf_tpr_dict[core][sample]: - lnf_fh.write(core + "\t" + sample + "\t" + "\t".join(lnf) + "\n") - - ## Saving SNPs information to file - logger.info("Saving SNPs information to file..") - snp_file = os.path.join(outputdir, "snp.tsv") - with open(snp_file, "w") as snp_fh: - snp_fh.write("\t".join(header_snp) + "\n") - for core in sorted(snp_dict): - for sample in sorted(snp_dict[core]): - for allele_id_snp in snp_dict[core][sample]: - for snp in snp_dict[core][sample][allele_id_snp]: - snp_fh.write( - core - + "\t" - + sample - + "\t" - + allele_id_snp - + "\t" - + "\t".join(snp) - + "\n" - ) - - ## Saving DNA sequences alignments to file - logger.info("Saving matching alignment information to files..") - alignment_dir = os.path.join(outputdir, "alignments") - if os.path.exists(alignment_dir): - shutil.rmtree(alignment_dir) - logger.info("deleting the alignment files from previous execution") - os.makedirs(alignment_dir) - for core in sorted(match_alignment_dict): - for sample in sorted(match_alignment_dict[core]): - match_alignment_file = os.path.join( - alignment_dir, str("match_alignment_" + core + "_" + sample + ".txt") - ) - with open(match_alignment_file, "w") as match_alignment_fh: - match_alignment_fh.write("\t".join(header_match_alignment) + "\n") - for match_align in match_alignment_dict[core][sample]: - match_alignment_fh.write( - core + "\t" + sample + "\t" + "\t".join(match_align) + "\n" - ) - - ## Saving protein sequences alignments to file - logger.info("Saving protein information to files..") - protein_dir = os.path.join(outputdir, "proteins") - if os.path.exists(protein_dir): - shutil.rmtree(protein_dir) - logger.info("deleting the proteins files from previous execution") - os.makedirs(protein_dir) - for core in sorted(protein_dict): - for sample in sorted(protein_dict[core]): - protein_file = os.path.join( - protein_dir, str("protein_" + core + "_" + sample + ".txt") - ) - with open(protein_file, "w") as protein_fh: - protein_fh.write("\t".join(header_protein) + "\n") - for protein in protein_dict[core][sample]: - protein_fh.write( - core + "\t" + sample + "\t" + "\t".join(protein) + "\n" - ) - - ## Saving summary information to file - logger.info("Saving summary information to file..") - summary_result = create_summary(samples_matrix_dict, logger) - summary_file = os.path.join(outputdir, "summary_result.tsv") - with open(summary_file, "w") as summ_fh: - for line in summary_result: - summ_fh.write(line + "\n") - - ## Modify the result file to remove the PLOT_ string for creating the file to use in the tree diagram - # logger.info('Saving result information for tree diagram') - # tree_diagram_file = os.path.join ( outputdir, 'result_for_tree_diagram.tsv') - # with open (result_file, 'r') as result_fh: - # with open(tree_diagram_file, 'w') as td_fh: - # for line in result_fh: - # tree_line = line.replace('PLOT_','') - # td_fh.write(tree_line) - - ########################################################################################### - # Guardando report de prodigal. Temporal - # prodigal_report_file = os.path.join (outputdir, 'prodigal_report.tsv') - # saving prodigal predictions to file - # with open (prodigal_report_file, 'w') as out_fh: - # out_fh.write ('\t'.join(header_prodigal_report)+ '\n') - # for prodigal_result in prodigal_report: - # out_fh.write ('\t'.join(prodigal_result)+ '\n') - - # Guardando coverage de new_sseq para estimar el threshold a establecer. Temporal - # newsseq_coverage_file = os.path.join (outputdir, 'newsseq_coverage_report.tsv') - # saving the coverage information to file - # with open (newsseq_coverage_file, 'w') as out_fh: - # out_fh.write ('\t' + '\t'.join(header_newsseq_coverage_report)+ '\n') - # for coverage in shorter_seq_coverage: - # out_fh.write ('Shorter new sequence' + '\t' + '\t'.join(coverage)+ '\n') - # for coverage in longer_seq_coverage: - # out_fh.write ('Longer new sequence' + '\t' + '\t'.join(coverage)+ '\n') - # for coverage in equal_seq_coverage: - # out_fh.write ('Same length new sequence' + '\t' + '\t'.join(coverage)+ '\n') - - # Guardando coverage de la sseq obtenida tras blast para estimar el threshold a establecer. Temporal - # blast_coverage_file = os.path.join (outputdir, 'blast_coverage_report.tsv') - # saving the result information to file - # with open (blast_coverage_file, 'w') as out_fh: - # out_fh.write ('\t' + '\t'.join(header_blast_coverage_report)+ '\n') - # for coverage in shorter_blast_seq_coverage: - # out_fh.write ('Shorter blast sequence' + '\t' + '\t'.join(coverage)+ '\n') - # for coverage in longer_blast_seq_coverage: - # out_fh.write ('Longer blast sequence' + '\t' + '\t'.join(coverage)+ '\n') - # for coverage in equal_blast_seq_coverage: - # out_fh.write ('Same length blast sequence' + '\t' + '\t'.join(coverage)+ '\n') - ########################################################################################### - - return True - - -def save_allele_calling_plots( - outputdir, - sample_list_files, - count_exact, - count_inf, - count_asm, - count_alm, - count_lnf, - count_tpr, - count_plot, - count_niph, - count_niphem, - count_error, - logger, -): - ## Create result plots directory - plots_dir = os.path.join(outputdir, "plots") - try: - os.makedirs(plots_dir) - except: - logger.info( - "Deleting the results plots directory for a previous execution without cleaning up" - ) - shutil.rmtree(os.path.join(outputdir, "plots")) - try: - os.makedirs(plots_dir) - logger.info("Results plots folder %s has been created again", plots_dir) - except: - logger.info( - "Unable to create again the results plots directory %s", plots_dir - ) - print("Cannot create Results plots directory on ", plots_dir) - exit(0) - - for sample_file in sample_list_files: - sample_name = ".".join(os.path.basename(sample_file).split(".")[:-1]) - - ## Obtain interactive piechart - logger.info("Creating interactive results piecharts") - create_sunburst_allele_call( - outputdir, - sample_name, - count_exact[sample_name], - count_inf[sample_name], - count_asm[sample_name], - count_alm[sample_name], - count_lnf[sample_name], - count_tpr[sample_name], - count_plot[sample_name], - count_niph[sample_name], - count_niphem[sample_name], - count_error[sample_name], - ) - - return True - - -def create_sunburst_allele_call( - outputdir, - sample_name, - count_exact, - count_inf, - count_asm, - count_alm, - count_lnf, - count_tpr, - count_plot, - count_niph, - count_niphem, - count_error, -): - ### logger - - total_locus = ( - count_exact["total"] - + count_inf["total"] - + count_asm["total"] - + count_alm["total"] - + count_lnf["total"] - + count_tpr["total"] - + count_plot["total"] - + count_niph["total"] - + count_niphem["total"] - + count_error["total"] - ) - - tag_counts = [ - total_locus, - count_exact["total"], - count_exact["good_quality"], - count_exact["bad_quality"], - count_exact["no_start"], - count_exact["no_start_stop"], - count_exact["no_stop"], - count_exact["multiple_stop"], - count_inf["total"], - count_inf["good_quality"], - count_inf["bad_quality"], - count_inf["no_start"], - count_inf["no_start_stop"], - count_inf["no_stop"], - count_inf["multiple_stop"], - count_asm["total"], - count_asm["insertion"], - count_asm["deletion"], - count_asm["substitution"], - count_alm["total"], - count_alm["insertion"], - count_alm["deletion"], - count_alm["substitution"], - count_plot["total"], - count_niph["total"], - count_niph["good_quality"], - count_niph["bad_quality"], - count_niph["no_start"], - count_niph["no_start_stop"], - count_niph["no_stop"], - count_niph["multiple_stop"], - count_niphem["total"], - count_niphem["good_quality"], - count_niphem["bad_quality"], - count_niphem["no_start"], - count_niphem["no_start_stop"], - count_niphem["no_stop"], - count_niphem["multiple_stop"], - count_lnf["total"], - count_lnf["not_found"], - count_lnf["low_id"], - count_lnf["low_coverage"], - count_tpr["total"], - count_tpr["good_quality"], - count_tpr["bad_quality"], - count_tpr["no_start"], - count_tpr["no_start_stop"], - count_tpr["no_stop"], - count_tpr["multiple_stop"], - count_error["total"], - count_error["good_quality"], - count_error["bad_quality"], - count_error["no_start"], - count_error["no_start_stop"], - count_error["no_stop"], - count_error["multiple_stop"], - ] - - fig = go.Figure( - go.Sunburst( - ids=[ - sample_name, - "Exact Match", - "Good Quality - Exact Match", - "Bad Quality - Exact Match", - "No start - Bad Quality - Exact Match", - "No start-stop - Bad Quality - Exact Match", - "No stop - Bad Quality - Exact Match", - "Multiple stop - Bad Quality - Exact Match", - "INF", - "Good Quality - INF", - "Bad Quality - INF", - "No start - Bad Quality - INF", - "No start-stop - Bad Quality - INF", - "No stop - Bad Quality - INF", - "Multiple stop - Bad Quality - INF", - "ASM", - "Insertion - ASM", - "Deletion - ASM", - "Substitution - ASM", - "ALM", - "Insertion - ALM", - "Deletion - ALM", - "Substitution - ALM", - "PLOT", - "NIPH", - "Good Quality - NIPH", - "Bad Quality - NIPH", - "No start - Bad Quality - NIPH", - "No start-stop - Bad Quality - NIPH", - "No stop - Bad Quality - NIPH", - "Multiple stop - Bad Quality - NIPH", - "NIPHEM", - "Good Quality - NIPHEM", - "Bad Quality - NIPHEM", - "No start - Bad Quality - NIPHEM", - "No start-stop - Bad Quality - NIPHEM", - "No stop - Bad Quality - NIPHEM", - "Multiple stop - Bad Quality - NIPHEM", - "LNF", - "Not found", - "Low ID", - "Low coverage", - "TPR", - "Good Quality - TPR", - "Bad Quality - TPR", - "No start - Bad Quality - TPR", - "No start-stop - Bad Quality - TPR", - "No stop - Bad Quality - TPR", - "Multiple stop - Bad Quality - TPR", - "Error", - "Good Quality - Error", - "Bad Quality - Error", - "No start - Bad Quality - Error", - "No start-stop - Bad Quality - Error", - "No stop - Bad Quality - Error", - "Multiple stop - Bad Quality - Error", - ], - labels=[ - sample_name, - "Exact
Match", - "Good
Quality", - "Bad
Quality", - "No
start", - "No
start-stop", - "No
stop", - "Multiple
stop", - "INF", - "Good
Quality", - "Bad
Quality", - "No
start", - "No
start-stop", - "No
stop", - "Multiple
stop", - "ASM", - "Insertion", - "Deletion", - "Substitution", - "ALM", - "Insertion", - "Deletion", - "Substitution", - "PLOT", - "NIPH", - "Good
Quality", - "Bad
Quality", - "No
start", - "No
start-stop", - "No
stop", - "Multiple
stop", - "NIPHEM", - "Good
Quality", - "Bad
Quality", - "No
start", - "No
start-stop", - "No
stop", - "Multiple
stop", - "LNF", - "Not
found", - "Low
ID", - "Low
coverage", - "TPR", - "Good
Quality", - "Bad
Quality", - "No
start", - "No
start-stop", - "No
stop", - "Multiple
stop", - "Error", - "Good
Quality", - "Bad
Quality", - "No
start", - "No
start-stop", - "No
stop", - "Multiple
stop", - ], - parents=[ - "", - sample_name, - "Exact Match", - "Exact Match", - "Bad Quality - Exact Match", - "Bad Quality - Exact Match", - "Bad Quality - Exact Match", - "Bad Quality - Exact Match", - sample_name, - "INF", - "INF", - "Bad Quality - INF", - "Bad Quality - INF", - "Bad Quality - INF", - "Bad Quality - INF", - sample_name, - "ASM", - "ASM", - "ASM", - sample_name, - "ALM", - "ALM", - "ALM", - sample_name, - sample_name, - "NIPH", - "NIPH", - "Bad Quality - NIPH", - "Bad Quality - NIPH", - "Bad Quality - NIPH", - "Bad Quality - NIPH", - sample_name, - "NIPHEM", - "NIPHEM", - "Bad Quality - NIPHEM", - "Bad Quality - NIPHEM", - "Bad Quality - NIPHEM", - "Bad Quality - NIPHEM", - sample_name, - "LNF", - "LNF", - "LNF", - sample_name, - "TPR", - "TPR", - "Bad Quality - TPR", - "Bad Quality - TPR", - "Bad Quality - TPR", - "Bad Quality - TPR", - sample_name, - "Error", - "Error", - "Bad Quality - Error", - "Bad Quality - Error", - "Bad Quality - Error", - "Bad Quality - Error", - ], - values=tag_counts, - branchvalues="total", - ) - ) - - fig.update_layout(margin=dict(t=0, l=0, r=0, b=0)) - - plotsdir = os.path.join(outputdir, "plots", sample_name + ".html") - - fig.write_html(plotsdir) - - return True - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # -# Update core genes schema adding new inferred alleles found for each locus in allele calling analysis # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # - - -def update_schema( - updateschema, - schemadir, - outputdir, - core_gene_list_files, - inferred_alleles_dict, - alleles_in_locus_dict, - logger, -): - if len(inferred_alleles_dict) > 0: - ## Create a copy of core genes schema if updateschema = 'new' / 'New' - if updateschema == "new": - no_updated_schemadir = schemadir - ##schemadir_name = os.path.dirname(no_updated_schemadir) ---> se puede usar si guardo finalmente el nuevo esquema en el mismo directorio que el antiguo esquema, pero para ello debo verificar si termina o no el path en / para eliminarlo o no del path antes de hacer el dirname - schemadir_name = no_updated_schemadir.split("/") - if no_updated_schemadir.endswith("/"): - schemadir_name = schemadir_name[-2] - else: - schemadir_name = schemadir_name[-1] - - schemadir = os.path.join(outputdir, schemadir_name + "_updated") - - try: - shutil.copytree(no_updated_schemadir, schemadir) - logger.info( - "Schema copy %s has been created to update schema", schemadir - ) - except: - logger.info("Deleting preexisting directory") - shutil.rmtree(schemadir) - try: - shutil.copytree(no_updated_schemadir, schemadir) - logger.info( - "Schema copy %s has been created to update schema", schemadir - ) - except: - logger.info("Unable to create schema copy %s", schemadir) - print("Cannot create schema copy on ", schemadir) - exit(0) - - ## Get INF alleles for each core gene and update each locus fasta file - for core_file in core_gene_list_files: - core_name = os.path.basename(core_file).split(".")[0] - if core_name in inferred_alleles_dict: - logger.info( - "Updating core gene file %s adding new INF alleles", core_file - ) - - inf_list = inferred_alleles_dict[core_name] - - try: - alleles_ids = [ - int(allele) for allele in alleles_in_locus_dict[core_name] - ] - allele_number = max(alleles_ids) - - locus_schema_file = os.path.join(schemadir, core_name + ".fasta") - with open(locus_schema_file, "a") as core_fh: - for inf in inf_list: - allele_number += 1 - core_fh.write( - "\n" - + ">" - + str(allele_number) - + " # " - + "INF by taranys" - + "\n" - + inf - + "\n" - ) - except: - alleles_ids = [ - int(allele.split("_")[-1]) - for allele in alleles_in_locus_dict[core_name] - ] - allele_number = max(alleles_ids) - - locus_schema_file = os.path.join(schemadir, core_name + ".fasta") - with open(locus_schema_file, "a") as core_fh: - for inf in inf_list: - allele_number += 1 - complete_inf_id = core_name + "_" + str(allele_number) - core_fh.write( - "\n" - + ">" - + complete_inf_id - + " # " - + "INF by taranys" - + "\n" - + inf - + "\n" - ) - - return True - - -""" -def update_schema (updateschema, schemadir, storedir, core_gene_list_files, inferred_alleles_dict, alleles_in_locus_dict, logger): - - ## Create a copy of core genes schema if updateschema = 'new' / 'New' - if updateschema == 'new' or updateschema == 'New': - no_updated_schemadir = schemadir - schemadir_name = os.path.basename(no_updated_schemadir) - schemadir = os.path.join(storedir, schemadir_name + '_updated') - shutil.copytree(no_updated_schemadir, schemadir) - logger.info('Copying core genes fasta files to update schema') - - ## Get INF alleles for each core gene and update each locus fasta file - for core_file in core_gene_list_files: - core_name = os.path.basename(core_file).split('.')[0] - if core_name in inferred_alleles_dict: - logger.info('Updating core gene file %s adding new INF alleles', core_file) - - inf_list = inferred_alleles_dict[core_name] - - alleles_ids = [int(allele) for allele in alleles_in_locus_dict[core_name]] - allele_number = max(alleles_ids) - - locus_schema_file = os.path.join(schemadir, core_name + '.fasta') - with open (locus_schema_file, 'a') as core_fh: - for inf in inf_list: - allele_number += 1 - core_fh.write('\n' + '>' + str(allele_number) + ' # ' + 'INF by taranys' + '\n' + inf + '\n') - - return True -""" - - -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # -# Allele calling analysis to find each core gene in schema and its variants in samples # -# · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * · * # - - -def allele_call_nucleotides( - core_gene_list_files, - sample_list_files, - alleles_in_locus_dict, - contigs_in_sample_dict, - query_directory, - reference_alleles_directory, - blast_db_directory, - prodigal_directory, - blast_results_seq_directory, - blast_results_db_directory, - inputdir, - outputdir, - cpus, - percentlength, - coverage, - evalue, - perc_identity_ref, - perc_identity_loc, - reward, - penalty, - gapopen, - gapextend, - max_target_seqs, - max_hsps, - num_threads, - flankingnts, - schema_variability, - schema_statistics, - schema_quality, - annotation_core_dict, - profile_csv_path, - logger, -): - prodigal_report = ( - [] - ) # TEMPORAL. prodigal_report para checkear las secuencias obtenidas con prodigal vs blast y las posiciones sstart y send - # listas añadidas para calcular coverage medio de new_sseq con respecto a alelo para establecer coverage mínimo por debajo del cual considerar LNF - shorter_seq_coverage = [] # TEMPORAL - longer_seq_coverage = [] # TEMPORAL - equal_seq_coverage = [] # TEMPORAL - # listas añadidas para calcular coverage medio de sseq con respecto a alelo tras blast para establecer coverage mínimo por debajo del cual considerar LNF - shorter_blast_seq_coverage = [] # TEMPORAL - longer_blast_seq_coverage = [] # TEMPORAL - equal_blast_seq_coverage = [] # TEMPORAL - - full_gene_list = [] - samples_matrix_dict = {} # to keep allele number - matching_genes_dict = {} # to keep start and stop positions - exact_dict = {} # c/m: to keep exact matches found for each sample - inferred_alleles_dict = {} # to keep track of the new inferred alleles - inf_dict = {} # to keep inferred alleles found for each sample - paralog_dict = {} # to keep paralogs found for each sample - asm_dict = {} # c/m: to keep track of asm - alm_dict = {} # c/m: to keep track of alm - list_asm = {} # c/m: to keep asm found for each sample - list_alm = {} # c/m: to keep alm found for each sample - lnf_tpr_dict = {} # c/m: to keep locus not found for each sample - plot_dict = {} # c/m: to keep plots for each sample - snp_dict = {} # c/m: to keep snp information for each sample - protein_dict = {} - match_alignment_dict = {} - - # (recuento tags para plots) - count_exact = {} - count_inf = {} - count_asm = {} - count_alm = {} - count_lnf = {} - count_tpr = {} - count_plot = {} - count_niph = {} - count_niphem = {} - count_error = {} - - blast_parameters = '"6 , qseqid , sseqid , pident , qlen , length , mismatch , gapopen , evalue , bitscore , sstart , send , qstart , qend , sseq , qseq"' - - print("Allele calling starts") - pbar = ProgressBar() - - ## # # # # # # # # # # # # # # # # # # # # # # # # ## - ## Processing the search for each schema core gene ## - ## # # # # # # # # # # # # # # # # # # # # # # # # ## - - for core_file in pbar(core_gene_list_files): - core_name = os.path.basename(core_file).split(".")[0] - full_gene_list.append(core_name) - logger.info("Processing core gene file %s ", core_file) - - # Get path to this locus fasta file - locus_alleles_path = os.path.join(query_directory, str(core_name + ".fasta")) - - # Get path to reference allele fasta file for this locus - core_reference_allele_path = os.path.join( - reference_alleles_directory, core_name + ".fasta" - ) - - # Get length thresholds for INF, ASM and ALM classification - max_length_threshold, min_length_threshold = length_thresholds( - core_name, schema_statistics, percentlength - ) - - # Get length thresholds for LNF, ASM and ALM classification - max_coverage_threshold, min_coverage_threshold = length_thresholds( - core_name, schema_statistics, coverage - ) - - ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## - ## Processing the search for each schema core gene in each sample ## - ## # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ## - - for sample_file in sample_list_files: - logger.info("Processing sample file %s ", sample_file) - - sample_name = ".".join(os.path.basename(sample_file).split(".")[:-1]) - - # (recuento tags para plots) - if sample_name not in count_exact: - count_exact[sample_name] = { - "good_quality": 0, - "bad_quality": 0, - "no_start": 0, - "no_start_stop": 0, - "no_stop": 0, - "multiple_stop": 0, - "total": 0, - } - - if sample_name not in count_inf: - count_inf[sample_name] = { - "good_quality": 0, - "bad_quality": 0, - "no_start": 0, - "no_start_stop": 0, - "no_stop": 0, - "multiple_stop": 0, - "total": 0, - } - - if sample_name not in count_asm: - count_asm[sample_name] = { - "insertion": 0, - "deletion": 0, - "substitution": 0, - "total": 0, - } - - if sample_name not in count_alm: - count_alm[sample_name] = { - "insertion": 0, - "deletion": 0, - "substitution": 0, - "total": 0, - } - - if sample_name not in count_lnf: - count_lnf[sample_name] = { - "not_found": 0, - "low_id": 0, - "low_coverage": 0, - "total": 0, - } - - if sample_name not in count_tpr: - count_tpr[sample_name] = { - "good_quality": 0, - "bad_quality": 0, - "no_start": 0, - "no_start_stop": 0, - "no_stop": 0, - "multiple_stop": 0, - "total": 0, - } - - if sample_name not in count_plot: - count_plot[sample_name] = {"total": 0} - - if sample_name not in count_niph: - count_niph[sample_name] = { - "good_quality": 0, - "bad_quality": 0, - "no_start": 0, - "no_start_stop": 0, - "no_stop": 0, - "multiple_stop": 0, - "total": 0, - } - - if sample_name not in count_niphem: - count_niphem[sample_name] = { - "good_quality": 0, - "bad_quality": 0, - "no_start": 0, - "no_start_stop": 0, - "no_stop": 0, - "multiple_stop": 0, - "total": 0, - } - - if sample_name not in count_error: - count_error[sample_name] = { - "good_quality": 0, - "bad_quality": 0, - "no_start": 0, - "no_start_stop": 0, - "no_stop": 0, - "multiple_stop": 0, - "total": 0, - } - - # Initialize the sample list to add the number of alleles and the start, stop positions - if not sample_name in samples_matrix_dict: - samples_matrix_dict[sample_name] = [] - matching_genes_dict[sample_name] = {} - - # Path to this sample BLAST database created when processing samples - blast_db_name = os.path.join(blast_db_directory, sample_name, sample_name) - - # * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * # - # Sample contigs VS reference allele(s) BLAST for locus detection in sample # - # * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * # - - cline = NcbiblastnCommandline( - db=blast_db_name, - evalue=evalue, - perc_identity=perc_identity_ref, - reward=reward, - penalty=penalty, - gapopen=gapopen, - gapextend=gapextend, - outfmt=blast_parameters, - max_target_seqs=max_target_seqs, - max_hsps=max_hsps, - num_threads=num_threads, - query=core_reference_allele_path, - ) - out, err = cline() - out_lines = out.splitlines() - - bigger_bitscore = 0 - - # ······························································ # - # LNF if there are no BLAST results for this gene in this sample # - # ······························································ # - if len(out_lines) == 0: - # Trying to get the allele number to avoid that a bad quality assembly impact on the tree diagram - cline = NcbiblastnCommandline( - db=blast_db_name, - evalue=evalue, - perc_identity=70, - reward=reward, - penalty=penalty, - gapopen=gapopen, - gapextend=gapextend, - outfmt=blast_parameters, - max_target_seqs=1, - max_hsps=1, - num_threads=1, - query=core_reference_allele_path, - ) - out, err = cline() - out_lines = out.splitlines() - - if len(out_lines) > 0: - for line in out_lines: - values = line.split("\t") - if float(values[8]) > bigger_bitscore: - ( - qseqid, - sseqid, - pident, - qlen, - s_length, - mismatch, - r_gapopen, - r_evalue, - bitscore, - sstart, - send, - qstart, - qend, - sseq, - qseq, - ) = values - bigger_bitscore = float(bitscore) - - # Keep LNF info - lnf_tpr_tag( - core_name, - sample_name, - alleles_in_locus_dict, - samples_matrix_dict, - lnf_tpr_dict, - schema_statistics, - locus_alleles_path, - qseqid, - pident, - "-", - "-", - perc_identity_ref, - "-", - schema_quality, - annotation_core_dict, - count_lnf, - logger, - ) - - else: - # Keep LNF info - lnf_tpr_tag( - core_name, - sample_name, - "-", - samples_matrix_dict, - lnf_tpr_dict, - schema_statistics, - locus_alleles_path, - "-", - "-", - "-", - "-", - "-", - "-", - schema_quality, - annotation_core_dict, - count_lnf, - logger, - ) - - continue - - ## Continue classification process if the core gene has been detected in sample after BLAST search - if len(out_lines) > 0: - # Parse contigs for this sample - # contig_file = os.path.join(inputdir, sample_name + ".fasta") ## parse - # records = list(SeqIO.parse(contig_file, "fasta")) ## parse - - ## Keep BLAST results after locus detection in sample using reference allele - - # Path to BLAST results fasta file - path_to_blast_seq = os.path.join( - blast_results_seq_directory, sample_name, core_name + "_blast.fasta" - ) - - with open(path_to_blast_seq, "w") as outblast_fh: - seq_number = 1 - for line in out_lines: - values = line.split("\t") - qseqid = values[0] - if values[1] not in contigs_in_sample_dict[sample_name]: - sseqid = "|".join(values[1].split("|")[1:-1]) - else: - sseqid = values[1] - sstart = values[9] - send = values[10] - - # Get flanked BLAST sequences from contig for correct allele tagging - - accession_sequence = contigs_in_sample_dict[sample_name][sseqid] - # for record in records: ## parse - # if record.id == sseqid : ## parse - # break ## parse - # accession_sequence = str(record.seq) ## parse - - if int(send) > int(sstart): - max_index = int(send) - min_index = int(sstart) - else: - max_index = int(sstart) - min_index = int(send) - - if (flankingnts + 1) <= min_index: - if flankingnts <= (len(accession_sequence) - max_index): - flanked_sseq = accession_sequence[ - min_index - - 1 - - flankingnts : max_index - + flankingnts - ] - else: - flanked_sseq = accession_sequence[ - min_index - 1 - flankingnts : - ] - else: - flanked_sseq = accession_sequence[: max_index + flankingnts] - - seq_id = str(seq_number) + "_" + sseqid - outblast_fh.write( - ">" - + seq_id - + " # " - + " # ".join(values[0:13]) - + "\n" - + flanked_sseq - + "\n" - + "\n" - ) - - seq_number += 1 - - ## Create local BLAST database for BLAST results after locus detection in sample using reference allele - db_name = os.path.join(blast_results_db_directory, sample_name) - if not create_blastdb(path_to_blast_seq, db_name, "nucl", logger): - print( - "Error when creating the blastdb for blast results file for locus %s at sample %s. Check log file for more information. \n ", - core_name, - sample_name, - ) - return False - - # Path to local BLAST database for BLAST results after locus detection in sample using reference allele - locus_blast_db_name = os.path.join( - blast_results_db_directory, - sample_name, - os.path.basename(core_name) + "_blast", - os.path.basename(core_name) + "_blast", - ) - - # * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * # - # BLAST result sequences VS ALL alleles in locus BLAST for allele identification detection in sample # - # * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * # - - cline = NcbiblastnCommandline( - db=locus_blast_db_name, - evalue=evalue, - perc_identity=perc_identity_loc, - reward=reward, - penalty=penalty, - gapopen=gapopen, - gapextend=gapextend, - outfmt=blast_parameters, - max_target_seqs=max_target_seqs, - max_hsps=max_hsps, - num_threads=num_threads, - query=locus_alleles_path, - ) - - out, err = cline() - out_lines = out.splitlines() - - allele_found = {} # To keep filtered BLAST results - - ## Check if there is any BLAST result with ID = 100 ## - for line in out_lines: - values = line.split("\t") - pident = values[2] - - if float(pident) == 100: - ( - qseqid, - sseqid, - pident, - qlen, - s_length, - mismatch, - r_gapopen, - r_evalue, - bitscore, - sstart, - send, - qstart, - qend, - sseq, - qseq, - ) = values - - # Parse core gene fasta file to get matching allele sequence and length - # alleles_in_locus = list (SeqIO.parse(locus_alleles_path, "fasta")) ## parse - # for allele in alleles_in_locus : ## parse - # if allele.id == qseqid : ## parse - # break ## comentar parse - # matching_allele_seq = str(allele.seq) ## parse - # matching_allele_length = len(matching_allele_seq) ## parse - - matching_allele_seq = alleles_in_locus_dict[core_name][qseqid] - matching_allele_length = len(matching_allele_seq) - - # Keep BLAST results with ID = 100 and same length as matching allele - if int(s_length) == matching_allele_length: - # get_blast_results (values, records, allele_found, logger) - get_blast_results( - sample_name, - values, - contigs_in_sample_dict, - allele_found, - logger, - ) - - # ·································································································································· # - # NIPHEM (paralog) if there are multiple BLAST results with ID = 100 and same length as matching allele for this gene in this sample # - # ·································································································································· # - if len(allele_found) > 1: - # Keep NIPHEM info - paralog_exact_tag( - sample_name, - core_name, - "NIPHEM", - schema_quality, - matching_genes_dict, - samples_matrix_dict, - allele_found, - paralog_dict, - prodigal_report, - prodigal_directory, - blast_parameters, - annotation_core_dict, - count_niphem, - logger, - ) - - continue - - ## Check for possible paralogs with ID < 100 if there is only one BLAST result with ID = 100 and same length as matching allele - elif len(allele_found) == 1: - for line in out_lines: - values = line.split("\t") - - sseq_no_gaps = values[13].replace("-", "") - s_length_no_gaps = len(sseq_no_gaps) - - # Keep BLAST result if its coverage is within min and max thresholds - if ( - min_length_threshold - <= s_length_no_gaps - <= max_length_threshold - ): - # get_blast_results (values, records, allele_found, logger) - get_blast_results( - sample_name, - values, - contigs_in_sample_dict, - allele_found, - logger, - ) - - # ································································ # - # EXACT MATCH if there is any paralog for this gene in this sample # - # ································································ # - if len(allele_found) == 1: - paralog_exact_tag( - sample_name, - core_name, - "EXACT", - schema_quality, - matching_genes_dict, - samples_matrix_dict, - allele_found, - exact_dict, - prodigal_report, - prodigal_directory, - blast_parameters, - annotation_core_dict, - count_exact, - logger, - ) - - continue - - # ··········································································· # - # NIPH if there there are paralogs with ID < 100 for this gene in this sample # - # ··········································································· # - else: - paralog_exact_tag( - sample_name, - core_name, - "NIPH", - schema_quality, - matching_genes_dict, - samples_matrix_dict, - allele_found, - paralog_dict, - prodigal_report, - prodigal_directory, - blast_parameters, - annotation_core_dict, - count_niph, - logger, - ) - - continue - - ## Look for the best BLAST result if there are no results with ID = 100 ## - elif len(allele_found) == 0: - bigger_bitscore_seq_values = [] - - for line in out_lines: - values = line.split("\t") - - if float(values[8]) > bigger_bitscore: - s_length_no_gaps = len(values[13].replace("-", "")) - - # Keep BLAST result if its coverage is within min and max thresholds and its bitscore is bigger than the one previously kept - if ( - min_coverage_threshold - <= s_length_no_gaps - <= max_coverage_threshold - ): - bigger_bitscore_seq_values = values - bigger_bitscore = float(bigger_bitscore_seq_values[8]) - - ## Check if best BLAST result out of coverage thresholds is a possible PLOT or LNF due to low coverage ## - # if len(allele_found) == 0: - if len(bigger_bitscore_seq_values) == 0: - # Look for best bitscore BLAST result out of coverage thresholds to check possible PLOT or reporting LNF due to low coverage - - for line in out_lines: - values = line.split("\t") - - if float(values[8]) > bigger_bitscore: - ( - qseqid, - sseqid, - pident, - qlen, - s_length, - mismatch, - r_gapopen, - r_evalue, - bitscore, - sstart, - send, - qstart, - qend, - sseq, - qseq, - ) = values - bigger_bitscore_seq_values_out_cov = values ### - bigger_bitscore = float(bitscore) - - # Get BLAST values relatives to contig for bigger bitscore result - lnf_plot_found = {} ### - - get_blast_results( - sample_name, - bigger_bitscore_seq_values_out_cov, - contigs_in_sample_dict, - lnf_plot_found, - logger, - ) ### - - allele_id = str(list(lnf_plot_found.keys())[0]) ### - ( - qseqid, - sseqid, - pident, - qlen, - s_length, - mismatch, - r_gapopen, - r_evalue, - bitscore, - sstart, - send, - qstart, - qend, - sseq, - qseq, - ) = lnf_plot_found[allele_id] - - # Get contig sequence and length for best bitscore BLAST result ID - - # for record in records: ## parse - # if record.id == sseqid : ## parse - # break ## parse - # accession_sequence = record.seq ## parse - # length_sseqid = len(accession_sequence) ## parse - - accession_sequence = contigs_in_sample_dict[sample_name][sseqid] - length_sseqid = len(accession_sequence) - - # Check if best BLAST result out of coverage thresholds is a possible PLOT. If so, keep result info for later PLOT classification - if ( - int(sstart) == length_sseqid - or int(send) == length_sseqid - or int(sstart) == 1 - or int(send) == 1 - ): - bigger_bitscore_seq_values = ( - bigger_bitscore_seq_values_out_cov ### - ) - - # ·············································································································································· # - # LNF if there are no BLAST results within coverage thresholds for this gene in this sample and best out threshold result is not a possible PLOT # - # ·············································································································································· # - else: - # Get sequence length - s_length_no_gaps = len( - bigger_bitscore_seq_values_out_cov[13].replace("-", "") - ) - - # Keep LNF info - lnf_tpr_tag( - core_name, - sample_name, - alleles_in_locus_dict, - samples_matrix_dict, - lnf_tpr_dict, - schema_statistics, - locus_alleles_path, - qseqid, - pident, - s_length_no_gaps, - "-", - "-", - coverage, - schema_quality, - annotation_core_dict, - count_lnf, - logger, - ) - - ## Keep result with bigger bitscore in allele_found dict and look for possible paralogs ## - if len(bigger_bitscore_seq_values) > 0: - ( - qseqid, - sseqid, - pident, - qlen, - s_length, - mismatch, - r_gapopen, - r_evalue, - bitscore, - sstart, - send, - qstart, - qend, - sseq, - qseq, - ) = bigger_bitscore_seq_values - - # get_blast_results (bigger_bitscore_seq_values, records, allele_found, logger) - get_blast_results( - sample_name, - bigger_bitscore_seq_values, - contigs_in_sample_dict, - allele_found, - logger, - ) - - # Possible paralogs search - for line in out_lines: - values = line.split("\t") - - ( - qseqid, - sseqid, - pident, - qlen, - s_length, - mismatch, - r_gapopen, - r_evalue, - bitscore, - sstart, - send, - qstart, - qend, - sseq, - qseq, - ) = values - sseq_no_gaps = sseq.replace("-", "") - s_length_no_gaps = len(sseq_no_gaps) - - if ( - min_length_threshold - <= s_length_no_gaps - <= max_length_threshold - ): - # get_blast_results (values, records, allele_found, logger) - get_blast_results( - sample_name, - values, - contigs_in_sample_dict, - allele_found, - logger, - ) - - # ····························································· # - # NIPH if there there are paralogs for this gene in this sample # - # ····························································· # - if len(allele_found) > 1: - paralog_exact_tag( - sample_name, - core_name, - "NIPH", - schema_quality, - matching_genes_dict, - samples_matrix_dict, - allele_found, - paralog_dict, - prodigal_report, - prodigal_directory, - blast_parameters, - annotation_core_dict, - count_niph, - logger, - ) - - continue - - ## Continue classification if there are no paralogs ## - elif len(allele_found) == 1: - allele_id = str(list(allele_found.keys())[0]) - ( - qseqid, - sseqid, - pident, - qlen, - s_length, - mismatch, - r_gapopen, - r_evalue, - bitscore, - sstart, - send, - qstart, - qend, - sseq, - qseq, - ) = allele_found[allele_id] - - sseq_no_gaps = sseq.replace("-", "") - s_length_no_gaps = len(sseq_no_gaps) - - # Get matching allele quality - allele_quality = schema_quality[core_name][qseqid] - - # Get matching allele sequence and length - - # alleles_in_locus = list (SeqIO.parse(locus_alleles_path, "fasta")) ## parse - # for allele in alleles_in_locus : ## parse - # if allele.id == qseqid : ## parse - # break ## parse - # matching_allele_seq = allele.seq ## parse - # matching_allele_length = len(matching_allele_seq) ## parse - - matching_allele_seq = alleles_in_locus_dict[core_name][ - qseqid - ] - matching_allele_length = len(matching_allele_seq) - - # Get contig sequence and length for ID found in BLAST - - # for record in records: ## parse - # if record.id == sseqid : ## parse - # break ## parse - # accession_sequence = record.seq ## parse - # length_sseqid = len(accession_sequence) ## parse - - accession_sequence = contigs_in_sample_dict[sample_name][ - sseqid - ] - length_sseqid = len(accession_sequence) - - # ········································································································· # - # PLOT if found sequence is shorter than matching allele and it is located on the edge of the sample contig # - # ········································································································· # - if ( - int(sstart) == length_sseqid - or int(send) == length_sseqid - or int(sstart) == 1 - or int(send) == 1 - ): - if int(s_length) < matching_allele_length: - ### sacar sec prodigal para PLOT? - # Get prodigal predicted sequence if matching allele quality is "bad quality" - if "bad_quality" in allele_quality: - ( - complete_predicted_seq, - start_prodigal, - end_prodigal, - ) = get_prodigal_sequence( - sseq_no_gaps, - sseqid, - prodigal_directory, - sample_name, - blast_parameters, - logger, - ) - - # Keep info for prodigal report - prodigal_report.append( - [ - core_name, - sample_name, - qseqid, - "PLOT", - sstart, - send, - start_prodigal, - end_prodigal, - sseq_no_gaps, - complete_predicted_seq, - ] - ) - - else: - complete_predicted_seq = "-" - start_prodigal = "-" - end_prodigal = "-" - - # Keep PLOT info - inf_asm_alm_tag( - core_name, - sample_name, - "PLOT", - allele_found[allele_id], - allele_quality, - "-", - matching_allele_length, - "-", - plot_dict, - samples_matrix_dict, - matching_genes_dict, - prodigal_report, - start_prodigal, - end_prodigal, - complete_predicted_seq, - annotation_core_dict, - count_plot, - logger, - ) - - continue - - # * * * * * * * * * * * * * * * * * * * * # - # Search for complete final new sequence # - # * * * * * * * * * * * * * * * * * * * * # - - ## Get Prodigal predicted sequence ## - ( - complete_predicted_seq, - start_prodigal, - end_prodigal, - ) = get_prodigal_sequence( - sseq_no_gaps, - sseqid, - prodigal_directory, - sample_name, - blast_parameters, - logger, - ) - - ## Search for new codon stop using contig sequence info ## - - # Check matching allele sequence direction - query_direction = check_sequence_order( - matching_allele_seq, logger - ) - - # Get extended BLAST sequence for stop codon search - if query_direction == "reverse": - if int(send) > int(sstart): - sample_gene_sequence = accession_sequence[ - : int(send) - ] - sample_gene_sequence = str( - Seq.Seq( - sample_gene_sequence - ).reverse_complement() - ) - else: - sample_gene_sequence = accession_sequence[ - int(send) - 1 : - ] - - else: - if int(sstart) > int(send): - sample_gene_sequence = accession_sequence[ - : int(sstart) - ] - sample_gene_sequence = str( - Seq.Seq( - sample_gene_sequence - ).reverse_complement() - ) - else: - sample_gene_sequence = accession_sequence[ - int(sstart) - 1 : - ] - - # Get new stop codon index - stop_index = get_stop_codon_index(sample_gene_sequence) - - ## Classification of final new sequence if it is found ## - if stop_index != False: - new_sequence_length = stop_index + 3 - new_sseq = str( - sample_gene_sequence[0:new_sequence_length] - ) - - ######################################################################################################################### - ### c/m: introducido para determinar qué umbral de coverage poner. TEMPORAL - new_sseq_coverage = ( - new_sequence_length / matching_allele_length - ) ### introduciendo coverage new_sseq /// debería ser con respecto a la media? - - if new_sseq_coverage < 1: - shorter_seq_coverage.append( - [ - core_name, - sample_name, - str(matching_allele_length), - str(new_sequence_length), - str(schema_statistics[core_name][0]), - str(new_sseq_coverage), - str( - new_sequence_length - / schema_statistics[core_name][0] - ), - ] - ) - elif new_sseq_coverage > 1: - longer_seq_coverage.append( - [ - core_name, - sample_name, - str(matching_allele_length), - str(new_sequence_length), - str(schema_statistics[core_name][0]), - str(new_sseq_coverage), - str( - new_sequence_length - / schema_statistics[core_name][0] - ), - ] - ) - elif new_sseq_coverage == 1: - equal_seq_coverage.append( - [ - core_name, - sample_name, - str(matching_allele_length), - str(new_sequence_length), - str(schema_statistics[core_name][0]), - str(new_sseq_coverage), - str( - new_sequence_length - / schema_statistics[core_name][0] - ), - ] - ) - ######################################################################################################################### - - # Get and keep SNP and DNA and protein alignment - keep_snp_alignment_info( - sseq, - new_sseq, - matching_allele_seq, - qseqid, - query_direction, - core_name, - sample_name, - reward, - penalty, - gapopen, - gapextend, - snp_dict, - match_alignment_dict, - protein_dict, - logger, - ) - - # ····································································································· # - # INF if final new sequence length is within min and max length thresholds for this gene in this sample # - # ····································································································· # - if ( - min_length_threshold - <= new_sequence_length - <= max_length_threshold - ): - # Keep INF info - inf_asm_alm_tag( - core_name, - sample_name, - "INF", - allele_found[allele_id], - allele_quality, - new_sseq, - matching_allele_length, - inferred_alleles_dict, - inf_dict, - samples_matrix_dict, - matching_genes_dict, - prodigal_report, - start_prodigal, - end_prodigal, - complete_predicted_seq, - annotation_core_dict, - count_inf, - logger, - ) ### introducido start_prodigal, end_prodigal, complete_predicted_seq, prodigal_report como argumento a inf_asm_alm_tag para report prodigal, temporal - - # ············································································································································ # - # ASM if final new sequence length is under min length threshold but its coverage is above min coverage threshold for this gene in this sample # - # ············································································································································ # - elif ( - min_coverage_threshold - <= new_sequence_length - < min_length_threshold - ): - # Keep ASM info - inf_asm_alm_tag( - core_name, - sample_name, - "ASM", - allele_found[allele_id], - allele_quality, - new_sseq, - matching_allele_length, - asm_dict, - list_asm, - samples_matrix_dict, - matching_genes_dict, - prodigal_report, - start_prodigal, - end_prodigal, - complete_predicted_seq, - annotation_core_dict, - count_asm, - logger, - ) - - # ············································································································································ # - # ALM if final new sequence length is above max length threshold but its coverage is under max coverage threshold for this gene in this sample # - # ············································································································································ # - elif ( - max_length_threshold - < new_sequence_length - <= max_coverage_threshold - ): - # Keep ALM info - inf_asm_alm_tag( - core_name, - sample_name, - "ALM", - allele_found[allele_id], - allele_quality, - new_sseq, - matching_allele_length, - alm_dict, - list_alm, - samples_matrix_dict, - matching_genes_dict, - prodigal_report, - start_prodigal, - end_prodigal, - complete_predicted_seq, - annotation_core_dict, - count_alm, - logger, - ) ### introducido start_prodigal, end_prodigal, complete_predicted_seq, prodigal_report como argumento a inf_asm_alm_tag para report prodigal, temporal - - # ························································································· # - # TPR if final new sequence coverage is not within thresholds for this gene in this sample # - # ························································································· # - else: - # Keep TPR info - lnf_tpr_tag( - core_name, - sample_name, - alleles_in_locus_dict, - samples_matrix_dict, - lnf_tpr_dict, - schema_statistics, - locus_alleles_path, - qseqid, - pident, - s_length_no_gaps, - new_sequence_length, - "-", - coverage, - schema_quality, - annotation_core_dict, - count_tpr, - logger, - ) - - # ········································ # - # ERROR if final new sequence is not found # - # ········································ # - else: - logger.error( - "ERROR : Stop codon was not found for the core %s and the sample %s", - core_name, - sample_name, - ) - samples_matrix_dict[sample_name].append( - "ERROR not stop codon" - ) - if not sseqid in matching_genes_dict[sample_name]: - matching_genes_dict[sample_name][sseqid] = [] - if sstart > send: - # matching_genes_dict[sample_name][sseqid].append([core_name, sstart, send,'-', 'ERROR']) - matching_genes_dict[sample_name][sseqid].append( - [core_name, qseqid, sstart, send, "-", "ERROR"] - ) - else: - # matching_genes_dict[sample_name][sseqid].append([core_name, sstart,send,'+', 'ERROR']) - matching_genes_dict[sample_name][sseqid].append( - [core_name, qseqid, sstart, send, "+", "ERROR"] - ) - - # (recuento tags para plot) - count_error[sample_name]["total"] += 1 - for count_class in count_error[sample_name]: - if count_class in allele_quality: - if ( - "no_start_stop" not in count_class - and "no_start_stop" in allele_quality - ): - if count_class == "bad_quality": - count_error[sample_name][ - count_class - ] += 1 - else: - count_error[sample_name][count_class] += 1 - - ## Save results and create reports - - if not save_allele_call_results( - outputdir, - full_gene_list, - samples_matrix_dict, - exact_dict, - paralog_dict, - inf_dict, - plot_dict, - matching_genes_dict, - list_asm, - list_alm, - lnf_tpr_dict, - snp_dict, - match_alignment_dict, - protein_dict, - prodigal_report, - shorter_seq_coverage, - longer_seq_coverage, - equal_seq_coverage, - shorter_blast_seq_coverage, - longer_blast_seq_coverage, - equal_blast_seq_coverage, - logger, - ): - print( - "There is an error while saving the allele calling results. Check the log file to get more information \n" - ) - # exit(0) - - ## Saving sample results plots - - if not save_allele_calling_plots( - outputdir, - sample_list_files, - count_exact, - count_inf, - count_asm, - count_alm, - count_lnf, - count_tpr, - count_plot, - count_niph, - count_niphem, - count_error, - logger, - ): - print( - "There is an error while saving the allele calling results plots. Check the log file to get more information \n" - ) - - return True, inferred_alleles_dict, inf_dict, exact_dict - - -# * * * * * * * * * * * * * * * * * * * # -# Processing gene by gene allele calling # -# * * * * * * * * * * * * * * * * * * * # - - -def processing_allele_calling(arguments): - """ - Description: - This is the main function for allele calling. - With the support of additional functions it will create the output files - with the summary report. - Input: - arguments # Input arguments given on command line - Functions: - ???? - Variables: - ???? - Return: - ???? - """ - - start_time = datetime.now() - print("Start the execution at :", start_time) - - # Open log file - logger = open_log("taranys_wgMLST.log") - # print('Checking the pre-requisites.') - - ############################################################ - ## Check additional programs are installed in your system ## - ############################################################ - # pre_requisites_list = [['blastp', '2.9'], ['makeblastdb', '2.9']] - # if not check_prerequisites (pre_requisites_list, logger): - # print ('your system does not fulfill the pre-requistes to run the script ') - # exit(0) - - ###################################################### - ## Check that given directories contain fasta files ## - ###################################################### - print("Validating schema fasta files in ", arguments.coregenedir, "\n") - valid_core_gene_files = get_fasta_file_list(arguments.coregenedir, logger) - if not valid_core_gene_files: - print( - "There are not valid fasta files in ", - arguments.coregenedir, - " directory. Check log file for more information ", - ) - exit(0) - - print("Validating reference alleles fasta files in ", arguments.refalleles, "\n") - valid_reference_alleles_files = get_fasta_file_list(arguments.refalleles, logger) - if not valid_reference_alleles_files: - print( - "There are not valid reference alleles fasta files in ", - arguments.refalleles, - " directory. Check log file for more information ", - ) - exit(0) - - print("Validating sample fasta files in ", arguments.inputdir, "\n") - valid_sample_files = get_fasta_file_list(arguments.inputdir, logger) - if not valid_sample_files: - print( - "There are not valid fasta files in ", - arguments.inputdir, - " directory. Check log file for more information ", - ) - exit(0) - - ################################# - ## Prepare the coreMLST schema ## - ################################# - tmp_core_gene_dir = os.path.join(arguments.outputdir, "tmp", "cgMLST") - try: - os.makedirs(tmp_core_gene_dir) - except: - logger.info( - "Deleting the temporary directory for a previous execution without cleaning up" - ) - shutil.rmtree(os.path.join(arguments.outputdir, "tmp")) - try: - os.makedirs(tmp_core_gene_dir) - logger.info( - "Temporary folder %s has been created again", tmp_core_gene_dir - ) - except: - logger.info( - "Unable to create again the temporary directory %s", tmp_core_gene_dir - ) - print("Cannot create temporary directory on ", tmp_core_gene_dir) - exit(0) - - ( - alleles_in_locus_dict, - annotation_core_dict, - schema_variability, - schema_statistics, - schema_quality, - ) = prepare_core_gene( - valid_core_gene_files, - tmp_core_gene_dir, - arguments.refalleles, - arguments.genus, - arguments.species, - str(arguments.usegenus).lower(), - logger, - ) - # alleles_in_locus_dict, annotation_core_dict, schema_variability, schema_statistics, schema_quality = prepare_core_gene (valid_core_gene_files, tmp_core_gene_dir, arguments.refalleles, arguments.outputdir, logger) - if not alleles_in_locus_dict: - print( - "There is an error while processing the schema preparation phase. Check the log file to get more information \n" - ) - logger.info( - "Deleting the temporary directory to clean up the temporary files created" - ) - shutil.rmtree(os.path.join(arguments.outputdir, "tmp")) - exit(0) - - ############################### - ## Prepare the samples files ## - ############################### - tmp_samples_dir = os.path.join(arguments.outputdir, "tmp", "samples") - try: - os.makedirs(tmp_samples_dir) - except: - logger.info( - "Deleting the temporary directory for a previous execution without properly cleaning up" - ) - shutil.rmtree(tmp_samples_dir) - try: - os.makedirs(tmp_samples_dir) - logger.info("Temporary folder %s has been created again", tmp_samples_dir) - except: - logger.info( - "Unable to create again the temporary directory %s", tmp_samples_dir - ) - shutil.rmtree(os.path.join(arguments.outputdir, "tmp")) - logger.info( - "Cleaned up temporary directory ", - ) - print( - "Cannot create temporary directory on ", - tmp_samples_dir, - "Check the log file to get more information \n", - ) - exit(0) - - contigs_in_sample_dict = prepare_samples( - valid_sample_files, tmp_samples_dir, arguments.refgenome, logger - ) - if not contigs_in_sample_dict: - print( - "There is an error while processing the saving temporary files. Check the log file to get more information \n" - ) - logger.info( - "Deleting the temporary directory to clean up the temporary files created" - ) - shutil.rmtree(os.path.join(arguments.outputdir, "tmp")) - exit(0) - - ################################## - ## Run allele callling analysis ## - ################################## - query_directory = arguments.coregenedir - reference_alleles_directory = arguments.refalleles - blast_db_directory = os.path.join(tmp_samples_dir, "blastdb") - prodigal_directory = os.path.join(tmp_samples_dir, "prodigal") - blast_results_seq_directory = os.path.join( - tmp_samples_dir, "blast_results", "blast_results_seq" - ) ### path a directorio donde guardar secuencias encontradas tras blast con alelo de referencia - blast_results_db_directory = os.path.join( - tmp_samples_dir, "blast_results", "blast_results_db" - ) ### path a directorio donde guardar db de secuencias encontradas tras blast con alelo de referencia - - ( - complete_allele_call, - inferred_alleles_dict, - inf_dict, - exact_dict, - ) = allele_call_nucleotides( - valid_core_gene_files, - valid_sample_files, - alleles_in_locus_dict, - contigs_in_sample_dict, - query_directory, - reference_alleles_directory, - blast_db_directory, - prodigal_directory, - blast_results_seq_directory, - blast_results_db_directory, - arguments.inputdir, - arguments.outputdir, - int(arguments.cpus), - arguments.percentlength, - arguments.coverage, - float(arguments.evalue), - int(arguments.perc_identity_ref), - int(arguments.perc_identity_loc), - int(arguments.reward), - int(arguments.penalty), - int(arguments.gapopen), - int(arguments.gapextend), - int(arguments.max_target_seqs), - int(arguments.max_hsps), - int(arguments.num_threads), - int(arguments.flankingnts), - schema_variability, - schema_statistics, - schema_quality, - annotation_core_dict, - arguments.profile, - logger, - ) - if not complete_allele_call: - print( - "There is an error while processing the allele calling. Check the log file to get more information \n" - ) - exit(0) - - ######################################################### - ## Update core gene schema adding new inferred alleles ## - ######################################################### - if inferred_alleles_dict: - if ( - str(arguments.updateschema).lower() == "true" - or str(arguments.updateschema).lower() == "new" - ): - if not update_schema( - str(arguments.updateschema).lower(), - arguments.coregenedir, - arguments.outputdir, - valid_core_gene_files, - inferred_alleles_dict, - alleles_in_locus_dict, - logger, - ): - print( - "There is an error adding new inferred alleles found to the core genes schema. Check the log file to get more information \n" - ) - exit(0) - - if str(arguments.profile).lower() != "false": - ############################ - ## Get ST for each sample ## - ############################ - complete_ST, inf_ST = get_ST_profile( - arguments.outputdir, - arguments.profile, - exact_dict, - inf_dict, - valid_core_gene_files, - valid_sample_files, - logger, - ) - - if not complete_ST: - print( - "There is an error while processing ST analysis. Check the log file to get more information \n" - ) - exit(0) - - ########################################### - ## Update ST profile file adding new STs ## - ########################################### - if ( - str(arguments.updateprofile).lower() == "true" - or str(arguments.updateprofile).lower() == "new" - ): - if len(inf_ST) > 0: - if not update_st_profile( - str(arguments.updateprofile).lower(), - arguments.profile, - arguments.outputdir, - inf_ST, - valid_core_gene_files, - logger, - ): - print( - "There is an error adding new STs found to the ST profile file. Check the log file to get more information \n" - ) - exit(0) - - shutil.rmtree(os.path.join(arguments.outputdir, "tmp")) - - end_time = datetime.now() - print("completed execution at :", end_time) - - return True diff --git a/taranis/pruebas.py b/taranis/pruebas.py deleted file mode 100644 index c38e2b8..0000000 --- a/taranis/pruebas.py +++ /dev/null @@ -1,96 +0,0 @@ -# from Bio.Seq import Seq - -from Bio import SeqIO -from Bio.Blast.Applications import NcbiblastnCommandline -import subprocess - -# import taranys.utils - -import random - -""" - Para hacer las pruebas con alfaclust activo el entorno de conda alfatclust_env - despues me voy a la carpeta donde me he descargado, de git, alfatclust y - ejecuto : - ./alfatclust.py -i /media/lchapado/Reference_data/proyectos_isciii/taranys/taranys_testing_data/listeria_testing_schema/lmo0003.fasta -o /media/lchapado/Reference_data/proyectos_isciii/taranys/test/alfacluster_test/resultado_alfaclust_lmo003 -l 0.9 - despues ejecuto este programa de prueba cambiando los ficheros de resultados - -""" - -# read result of alfatclust - -alfa_clust_file = "/media/lchapado/Reference_data/proyectos_isciii/taranys/test/resultado_alfatclust-090" -with open(alfa_clust_file, "r") as fh: - lines = fh.readlines() -alleles_found = False -locus_list = [] -for line in lines: - line = line.strip() - if line == "#Cluster 5": - if alleles_found is False: - alleles_found = True - continue - if alleles_found: - if "#Cluster" in line: - break - locus_list.append(line) - -rand_locus = random.choice(locus_list) -schema_file = "/media/lchapado/Reference_data/proyectos_isciii/taranys/taranys_testing_data/listeria_testing_schema/lmo0002.fasta" -new_schema_file = ( - "/media/lchapado/Reference_data/proyectos_isciii/taranys/test/cluster_lmo0002.fasta" -) -q_file = "/media/lchapado/Reference_data/proyectos_isciii/taranys/test/q_file.fasta" -with open(schema_file) as fh: - with open(new_schema_file, "w") as fo: - for record in SeqIO.parse(schema_file, "fasta"): - if record.id in locus_list: - SeqIO.write(record, fo, "fasta") - -# choose a random locus for testing -with open(new_schema_file) as fh: - with open(q_file, "w") as fo: - for record in SeqIO.parse(new_schema_file, "fasta"): - if record.id == rand_locus: - SeqIO.write(record, fo, "fasta") - break -print("Selected locus: ", rand_locus) -db_name = "/media/lchapado/Reference_data/proyectos_isciii/taranys/test/testing_clster/lmo0002" -blast_command = [ - "makeblastdb", - "-in", - new_schema_file, - "-parse_seqids", - "-dbtype", - "nucl", - "-out", - db_name, -] -blast_result = subprocess.run( - blast_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE -) - -blast_parameters = '"6 , qseqid , sseqid , pident , qlen , length , mismatch , gapopen , evalue , bitscore , sstart , send , qstart , qend , sseq , qseq"' -# db=self.blast_dir, evalue=evalue, perc_identity=perc_identity_ref, reward=reward, penalty=penalty, gapopen=gapopen, gapextend=gapextend, outfmt=blast_parameters, max_target_seqs=max_target_seqs, max_hsps=max_hsps, num_threads=num_threads, query=core_reference_allele_path) -cline = NcbiblastnCommandline( - db=db_name, - evalue=0.001, - perc_identity=90, - reward=1, - penalty=-2, - gapopen=1, - gapextend=1, - outfmt=blast_parameters, - max_target_seqs=1100, - max_hsps=1000, - num_threads=4, - query=q_file, -) - -try: - out, _ = cline() -except Exception as e: - print(e) -b_lines = out.splitlines() -print("longitud del cluster = ", len(locus_list)) -print("numero de matches = ", len(b_lines)) diff --git a/taranis/__init__.py b/taranys/__init__.py similarity index 100% rename from taranis/__init__.py rename to taranys/__init__.py diff --git a/taranis/__main__.py b/taranys/__main__.py similarity index 100% rename from taranis/__main__.py rename to taranys/__main__.py diff --git a/taranis/allele_calling.py b/taranys/allele_calling.py similarity index 100% rename from taranis/allele_calling.py rename to taranys/allele_calling.py diff --git a/taranis/analyze_schema.py b/taranys/analyze_schema.py similarity index 100% rename from taranis/analyze_schema.py rename to taranys/analyze_schema.py diff --git a/taranis/blast.py b/taranys/blast.py similarity index 100% rename from taranis/blast.py rename to taranys/blast.py diff --git a/taranis/clustering.py b/taranys/clustering.py similarity index 100% rename from taranis/clustering.py rename to taranys/clustering.py diff --git a/taranis/distance.py b/taranys/distance.py similarity index 100% rename from taranis/distance.py rename to taranys/distance.py diff --git a/taranis/eval_cluster.py b/taranys/eval_cluster.py similarity index 100% rename from taranis/eval_cluster.py rename to taranys/eval_cluster.py diff --git a/taranis/inferred_alleles.py b/taranys/inferred_alleles.py similarity index 100% rename from taranis/inferred_alleles.py rename to taranys/inferred_alleles.py diff --git a/taranis/reference_alleles.py b/taranys/reference_alleles.py similarity index 100% rename from taranis/reference_alleles.py rename to taranys/reference_alleles.py diff --git a/taranis/seq_cluster.py b/taranys/seq_cluster.py similarity index 100% rename from taranis/seq_cluster.py rename to taranys/seq_cluster.py diff --git a/taranis/utils.py b/taranys/utils.py similarity index 100% rename from taranis/utils.py rename to taranys/utils.py From da7762720116e8db09b136966bdbe8accfb0ae68 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Thu, 2 Jan 2025 10:31:45 +0100 Subject: [PATCH 202/214] changed permissions to file --- taranys/seq_cluster.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) mode change 100755 => 100644 taranys/seq_cluster.py diff --git a/taranys/seq_cluster.py b/taranys/seq_cluster.py old mode 100755 new mode 100644 From 0d1099e47957e4ec70b310c5cfe6b7d2d68eb7c1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Thu, 2 Jan 2025 11:04:24 +0100 Subject: [PATCH 203/214] deleted setup.py, added pip publish workflow to github actions --- .github/pypi_publish.yml | 49 ++++++++++++++ setup.py | 37 ---------- test/test.sh | 141 --------------------------------------- 3 files changed, 49 insertions(+), 178 deletions(-) create mode 100644 .github/pypi_publish.yml delete mode 100644 setup.py delete mode 100755 test/test.sh diff --git a/.github/pypi_publish.yml b/.github/pypi_publish.yml new file mode 100644 index 0000000..ceec80a --- /dev/null +++ b/.github/pypi_publish.yml @@ -0,0 +1,49 @@ +name: Publish package python distribution to Pypi + +on: + release: + types: [published] + workflow_dispatch: + +jobs: + build: + name: Build distribution + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: 3.12.7 + - name: Install pypi/build + run: >- + python3 -m + pip install + build + --user + - name: Build a binary wheel and a source tarball + run: python3 -m build + - name: Store the distribution packages + uses: actions/upload-artifact@v4 + with: + name: python-package-distributions + path: dist/ + + publish-to-pypi: + name: Publish dist to PyPI + needs: + - build + runs-on: ubuntu-latest + environment: + name: pypi + url: https://pypi.org/p/buisciii-tools + permissions: + id-token: write + steps: + - name: Download all the dists + uses: actions/download-artifact@v4 + with: + name: python-package-distributions + path: dist/ + - name: Publish to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 \ No newline at end of file diff --git a/setup.py b/setup.py deleted file mode 100644 index 5f6c85e..0000000 --- a/setup.py +++ /dev/null @@ -1,37 +0,0 @@ -#!/usr/bin/env python - -from setuptools import setup, find_packages - -version = "3.0.0" - -with open("README.md") as f: - readme = f.read() - -with open("requirements.txt") as f: - required = f.read().splitlines() - -setup( - name="taranys", - version=version, - description="Tools for gene-by-gene allele calling analysis", - long_description=readme, - long_description_content_type="text/markdown", - keywords=[ - "buisciii", - "bioinformatics", - "pipeline", - "sequencing", - "NGS", - "next generation sequencing", - ], - author="Sara Monzon", - author_email="smonzon@isciii.es", - url="https://github.com/BU-ISCIII/taranys", - license="GNU GENERAL PUBLIC LICENSE v.3", - entry_points={"console_scripts": ["taranys=taranys.__main__:run_taranys"]}, - python_requires=">=3.9, <4", - install_requires=required, - packages=find_packages(exclude=("docs")), - include_package_data=True, - zip_safe=False, -) diff --git a/test/test.sh b/test/test.sh deleted file mode 100755 index a6e573c..0000000 --- a/test/test.sh +++ /dev/null @@ -1,141 +0,0 @@ -#!/bin/bash --login - -# Exit immediately if a pipeline, which may consist of a single simple command, a list, -#or a compound command returns a non-zero status: If errors are not handled by user -set -e -# Treat unset variables and parameters other than the special parameters ‘@’ or ‘*’ as an error when performing parameter expansion. - -#Print everything as if it were executed, after substitution and expansion is applied: Debug|log option -#set -x - -#============================================================= -# HEADER -#============================================================= - -#INSTITUTION:ISCIII -#CENTRE:BU-ISCIII -# -#ACKNOLEDGE: longops2getops.sh: https://gist.github.com/adamhotep/895cebf290e95e613c006afbffef09d7 -# -#DESCRIPTION: test.sh uses test data for testing taranys installation. -# -# -#================================================================ -# END_OF_HEADER -#================================================================ - -#SHORT USAGE RULES -#LONG USAGE FUNCTION -usage() { - cat << EOF - -plasmidID is a computational pipeline tha reconstruct and annotate the most likely plasmids present in one sample - -usage : $0 - - -v | --version version - -h | --help display usage message - -example: ./test.sh - -EOF -} - -#================================================================ -# OPTION_PROCESSING -#================================================================ -# Error handling -error(){ - local parent_lineno="$1" - local script="$2" - local message="$3" - local code="${4:-1}" - - RED='\033[0;31m' - NC='\033[0m' - - if [[ -n "$message" ]] ; then - echo -e "\n---------------------------------------\n" - echo -e "${RED}ERROR${NC} in Script $script on or near line ${parent_lineno}; exiting with status ${code}" - echo -e "MESSAGE:\n" - echo -e "$message" - echo -e "\n---------------------------------------\n" - else - echo -e "\n---------------------------------------\n" - echo -e "${RED}ERROR${NC} in Script $script on or near line ${parent_lineno}; exiting with status ${code}" - echo -e "\n---------------------------------------\n" - fi - - exit "${code}" -} - -# translate long options to short -reset=true -for arg in "$@" -do - if [ -n "$reset" ]; then - unset reset - set -- # this resets the "$@" array so we can rebuild it - fi - case "$arg" in - --help) set -- "$@" -h ;; - --version) set -- "$@" -v ;; - # pass through anything else - *) set -- "$@" "$arg" ;; - esac -done - -#DECLARE FLAGS AND VARIABLES -script_dir=$(dirname $(readlink -f $0)) -assemblies="./samples_listeria/" -schema="./MLST_listeria/" -profile="./profile_MLST_listeria/profiles_csv.csv" -refgenome="./reference_listeria/GCF_002213505.1_ASM221350v1_genomic.fna" - -#PARSE VARIABLE ARGUMENTS WITH getops -#common example with letters, for long options check longopts2getopts.sh -options=":1:2:d:s:g:c:a:i:o:C:S:f:l:L:T:M:X:y:Y:RVtvh" -while getopts $options opt; do - case $opt in - h ) - usage - exit 1 - ;; - v ) - echo $VERSION - exit 1 - ;; - \?) - echo "Invalid Option: -$OPTARG" 1>&2 - usage - exit 1 - ;; - : ) - echo "Option -$OPTARG requires an argument." >&2 - exit 1 - ;; - * ) - echo "Unimplemented option: -$OPTARG" >&2; - exit 1 - ;; - - esac -done -shift $((OPTIND-1)) - -## Execute plasmidID with test data. -echo "Executing:../taranys.py allele_calling -coregenedir $schema -inputdir $assemblies -refgenome $refgenome -outputdir allele_calling_test -percentlength 20 -refalleles $refallele -profile $profile" -echo "Assemblies: $assemblies" -echo "Schema: $schema" -echo "$PWD" -cd -echo "Executing taranys analyze_schema" -$script_dir/../taranys.py analyze_schema -i $script_dir/MLST_listeria -o analyze_schema_test --output-allele-annot --cpus 1 - -# $script_dir/../taranys.py reference_alleles -coregenedir $script_dir/MLST_listeria -outputdir reference_alleles_test - -# $script_dir/../taranys.py allele_calling -coregenedir $script_dir/$schema -inputdir $script_dir/$assemblies -refgenome $script_dir/$refgenome -outputdir allele_calling_test -percentlength 20 -refalleles reference_alleles_test -profile $script_dir/$profile - -# $script_dir/../taranys.py distance_matrix -alleles_matrix allele_calling_test/result.tsv -outputdir distance_matrix_test - -echo "ALL DONE. TEST COMPLETED SUCCESSFULLY." From 332ddd8a92fb6233d984028102f7cdeedb598a9a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Thu, 2 Jan 2025 11:30:09 +0100 Subject: [PATCH 204/214] modified pyproject.toml --- pyproject.toml | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 7cdfdfe..6d76ea1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,5 +1,5 @@ [build-system] -requires = ["setuptools", "wheel"] +requires = ["setuptools", "setuptools-scm"] build-backend = "setuptools.build_meta" [project] @@ -13,12 +13,22 @@ authors = [ {name = "Luis Chapado", email = "lchapado@externos.isciii.es"}, ] maintainers = [ + {name = "Sara Monzon", email = "smonzon@isciii.es"}, {name = "Luis Chapado", email = "lchapado@externos.isciii.es"} ] -description = "Tools for gene-by-gene allele calling analysis" +description = "cg/wgMLST allele calling software, schema evaluation and allele distance estimation for outbreak reserch." readme = "README.md" license = {file = "LICENSE"} - +keywords = [ + "bioinformatics", + "assembly", + "cgMLST", + "wgMLST", + "MLST schema" +] +[project.urls] +Homepage = "https://github.com/bu-isciii/taranys" +Issues = "https://github.com/bu-isciii/taranys/issues" [tool.setuptools.dynamic] dependencies = {file = ["requirements.txt"]} From a3e29c3b42394ad69560ed8bf0c60e82e54d7861 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Thu, 2 Jan 2025 11:30:49 +0100 Subject: [PATCH 205/214] moved to correct path pypi_publish workflow --- .github/{ => workflows}/pypi_publish.yml | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename .github/{ => workflows}/pypi_publish.yml (100%) diff --git a/.github/pypi_publish.yml b/.github/workflows/pypi_publish.yml similarity index 100% rename from .github/pypi_publish.yml rename to .github/workflows/pypi_publish.yml From 7b6bd657f06724db62ee6d9225c71a9629aaf535 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Thu, 2 Jan 2025 11:40:30 +0100 Subject: [PATCH 206/214] added contributing guidelines file --- .github/CONTRIBUTING.md | 72 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 72 insertions(+) create mode 100644 .github/CONTRIBUTING.md diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md new file mode 100644 index 0000000..92f76ec --- /dev/null +++ b/.github/CONTRIBUTING.md @@ -0,0 +1,72 @@ +# taranys: Contributing Guidelines + +## Contribution workflow + +If you'd like to write or modify some code for taranys, the standard workflow is as follows: + +1. Check that there isn't already an issue about your idea in the [taranys issues](https://github.com/BU-ISCIII/taranys/issues) to avoid duplicating work. **If there isn't one already, please create one so that others know you're working on this**. +2. [Fork](https://help.github.com/en/github/getting-started-with-github/fork-a-repo) the [taranys repository](https://github.com/BU-ISCIII/taranys/) to your GitHub account. +3. Make the necessary changes / additions within your forked repository following the [code style guidelines](#code-style-guidelines). +4. Modify the [`CHANGELOG`](../CHANGELOG.md) file according to your changes in the appropiate section ([X.X.Xdev]), you should register your changes regarding: + 1. Added enhancements + 2. New modules + 3. Fixes + 4. Removed stuff + 5. Requirements added or version update +5. Update any documentation as needed. +6. [Submit a Pull Request](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request) against the `develop` branch and send the url to the #pipelines-dev channel in slack (if you are not in the slack channel just wait fot the PR to be reviewed and rebased). + +If you're not used to this workflow with git, you can start with: + +- Some [docs in the bu-isciii wiki](https://github.com/BU-ISCIII/BU-ISCIII/wiki/Github--gitflow). +- [some slides](https://docs.google.com/presentation/d/1PruqGxPQVxtNcuEbOd86mylXorgYIU5a/edit?pli=1#slide=id.p1) (in spanish). +- some github generic docs [docs from GitHub](https://help.github.com/en/github/collaborating-with-issues-and-pull-requests). +- even their [excellent `git` resources](https://try.github.io/). + +### taranys repo branches + +taranys repo works with a two branching scheme: `main` and `develop`. + +- `main`: stable code only for releases. +- `develop`: new code development for the different modules. + +You need to submit your PR always against `develop`. Once approbed, this changes must be **`rebased`** so we do not create empty unwanted merges. + +## Tests + +When you create a pull request with changes, [GitHub Actions](https://github.com/features/actions) will run automatic tests. +Typically, pull-requests are only fully reviewed when these tests are passing, though of course we can help out before then. + +There are typically two types of tests that run: + +### Lint tests + +We use black and flake8 linting based on PEP8 guidelines for python coding. You can check more information [here](https://github.com/BU-ISCIII/BU-ISCIII/wiki/Python#linting). + +### Code tests + +Taranys modules are executed using a test dataset. + +Anyhow you should always submit locally tested code!! + +### New version bumping and release + +In order to create a new release you need to follow the next steps: + +1. Set the new version according to [semantic versioning](https://semver.org/), in our particular case, changes in the `hotfix` branch will change the PATCH version (third one), and changes in develop will typicaly change the MINOR version, unless the developing team decides otherwise. +2. Create a PR bumping the new version against `develop`. For bumping a new version just change [this line](https://github.com/BU-ISCIII/taranys/blob/09c00c1ddd11f7489de7757841aff506ef4b7e1d/setup.py#L5) with the new version. +3. Once that PR is merged, create via web another PR against `main` (origin `develop`). This PR would need 2 approvals. +4. [Create a new release](https://docs.github.com/en/repositories/releasing-projects-on-github/managing-releases-in-a-repository) copying the appropiate notes from the `CHANGELOG`. +5. Once the release is approved and merged, you're all set! + +PRs from one branch to another, like in a release should be **`merged`** not rebased, so we avoid conflicts and the branch merge is correctly visualize in the commits history. + +### Code style guidelines + +We follow PEP8 conventions as code style guidelines, please check [here](https://github.com/BU-ISCIII/BU-ISCIII/wiki/Python#pep-8-guidelines-read-the-full-pep-8-documentation) for more detail. + +When developing new code, we strongly recommend to implement LogSum() functions from log_summary.py instead of the classic python logging in order to keep track of all the warnings and errors that may appear during any of the processes. + +## Getting help + +For further information/help, please ask on the `#pipelines-dev` slack channel or write us an email! ([bionformatica@isciii.es](emailto:bioinformatica@isciii.es)). \ No newline at end of file From cfb5db68092f204ec75fc0dac8e118eafe494d16 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Thu, 2 Jan 2025 12:16:17 +0100 Subject: [PATCH 207/214] fix path in pypi workflow --- .github/workflows/pypi_publish.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pypi_publish.yml b/.github/workflows/pypi_publish.yml index ceec80a..3112496 100644 --- a/.github/workflows/pypi_publish.yml +++ b/.github/workflows/pypi_publish.yml @@ -36,7 +36,7 @@ jobs: runs-on: ubuntu-latest environment: name: pypi - url: https://pypi.org/p/buisciii-tools + url: https://pypi.org/p/taranys permissions: id-token: write steps: From bdf21a9854fc863c2a31cabf6215634c54b4960d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Thu, 2 Jan 2025 12:16:44 +0100 Subject: [PATCH 208/214] updated and fixed dependencies --- environment.yml | 9 +++++---- requirements.txt | 20 ++++++++++---------- 2 files changed, 15 insertions(+), 14 deletions(-) diff --git a/environment.yml b/environment.yml index 4203d9f..a35f16d 100644 --- a/environment.yml +++ b/environment.yml @@ -4,13 +4,14 @@ channels: - bioconda - defaults dependencies: - - python=3.10 - - conda-forge::poetry=1.7.1 + - python>=3.10 + - conda-forge::poetry>=1.7.1 - bioconda::prokka>=1.14 - bioconda::blast>=2.9 - bioconda::mash>=2 - - bioconda::prodigal=2.6.3 - - bioconda::mafft=7.525 + - bioconda::prodigal>=2.6.3 + - bioconda::mafft>=7.505 - pip - pip : - -r requirements.txt + - . diff --git a/requirements.txt b/requirements.txt index 5b97e97..6fb50fb 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,10 +1,10 @@ -biopython -igraph -rich -click -leidenalg -questionary -bio -scikit-learn -plotly -kaleido +igraph>=0.9.8 +rich>=13.4.1 +click>=8.1.3 +leidenalg>=0.9.1 +questionary>=1.10.0 +bio>=1.6.0 +scikit-learn>=1.2.0 +plotly>=5.11.0 +kaleido>=0.2.1 +six>=1.16.0 From c5110581440ad1eefce01d99681121c2efab134d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Thu, 2 Jan 2025 12:17:01 +0100 Subject: [PATCH 209/214] renamed param in analyze schema --- taranys/__main__.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/taranys/__main__.py b/taranys/__main__.py index a864ff8..c184089 100644 --- a/taranys/__main__.py +++ b/taranys/__main__.py @@ -142,7 +142,7 @@ def taranys_cli(verbose, log_file): @taranys_cli.command(help_priority=1) @click.option( "-i", - "--inputdir", + "--input", required=True, multiple=False, type=click.Path(), @@ -207,7 +207,7 @@ def taranys_cli(verbose, log_file): help="Number of cpus used for execution", ) def analyze_schema( - inputdir: str, + input: str, output: str, remove_subset: bool, remove_duplicated: bool, @@ -219,7 +219,7 @@ def analyze_schema( cpus: int, ): _ = taranys.utils.check_additional_programs_installed([["prokka", "--version"]]) - schema_files = taranys.utils.get_files_in_folder(inputdir, "fasta") + schema_files = taranys.utils.get_files_in_folder(input, "fasta") results = [] max_cpus = taranys.utils.cpus_available() From e0f7f25b3d8c7551126e96afa70dffdc3c496614 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Thu, 2 Jan 2025 13:01:10 +0100 Subject: [PATCH 210/214] fixes in params help, added show default when appropiate --- taranys/__main__.py | 60 +++++++++++++++++++++++++++++++++------------ 1 file changed, 45 insertions(+), 15 deletions(-) diff --git a/taranys/__main__.py b/taranys/__main__.py index c184089..1789f82 100644 --- a/taranys/__main__.py +++ b/taranys/__main__.py @@ -146,7 +146,7 @@ def taranys_cli(verbose, log_file): required=True, multiple=False, type=click.Path(), - help="Directory where the schema with the core gene files are located. ", + help="Directory where the schema with the core gene files are located.", ) @click.option( "-o", @@ -160,43 +160,50 @@ def taranys_cli(verbose, log_file): "--remove-subset/--no-remove-subset", required=False, default=False, + show_default=True, help="Remove allele subsequences from the schema.", ) @click.option( "--remove-duplicated/--no-remove-duplicated", required=False, default=False, + show_default=True, help="Remove duplicated subsequences from the schema.", ) @click.option( "--remove-no-cds/--no-remove-no-cds", required=False, default=False, + show_default=True, help="Remove no CDS alleles from the schema.", ) @click.option( "--output-allele-annot/--no-output-allele-annot", required=False, default=True, - help="output prokka/allele annotation for all alleles in locus", + show_default=True, + help="output prokka/allele annotation for all alleles in locus.", ) @click.option( "--genus", required=False, default="Genus", - help="Genus name for Prokka schema genes annotation. Default is Genus.", + show_default=True, + help="Genus name for Prokka schema genes annotation.", ) @click.option( "--species", required=False, default="species", - help="Species name for Prokka schema genes annotation. Default is species", + show_default=True, + help="Species name for Prokka schema genes annotation.", ) @click.option( "--usegenus", required=False, default="Genus", - help="Use genus-specific BLAST databases for Prokka schema genes annotation (needs --genus). Default is False.", + show_default=True, + help="Use genus-specific BLAST databases for Prokka schema genes annotation (needs --genus).", ) @click.option( "--cpus", @@ -204,7 +211,8 @@ def taranys_cli(verbose, log_file): multiple=False, type=int, default=1, - help="Number of cpus used for execution", + show_default=True, + help="Number of cpus used for execution.", ) def analyze_schema( input: str, @@ -277,6 +285,7 @@ def analyze_schema( "--eval-cluster/--no-eval-cluster", required=False, default=True, + show_default=True, help="Evaluate if the reference alleles match against blast with the identity set in eval-identity param", ) @click.option( @@ -285,6 +294,7 @@ def analyze_schema( required=False, type=int, default=21, + show_default=True, help="Mash parameter for K-mer size.", ) @click.option( @@ -293,6 +303,7 @@ def analyze_schema( required=False, type=int, default=2000, + show_default=True, help="Mash parameter for Sketch size", ) @click.option( @@ -301,6 +312,7 @@ def analyze_schema( required=False, type=float, default=0.75, + show_default=True, help="Resolution value used for clustering.", ) @click.option( @@ -309,13 +321,15 @@ def analyze_schema( required=False, type=float, default=85, - help="Resolution value used for clustering.", + show_default=True, + help="Blast percentage identity to use for evaluation of identification.", ) @click.option( "--seed", required=False, type=int, default=None, + show_default=True, help="Seed value for clustering", ) @click.option( @@ -324,12 +338,14 @@ def analyze_schema( multiple=False, type=int, default=1, + show_default=True, help="Number of cpus used for execution", ) @click.option( "--force/--no-force", required=False, default=False, + show_default=True, help="Overwrite the output folder if it exists", ) def reference_alleles( @@ -417,17 +433,19 @@ def reference_alleles( required=False, nargs=1, default=0.8, + show_default=True, type=float, - help="Threshold value to consider in blast hit percentage regarding the reference length. Values from 0 to 1. default 0.8", + help="Threshold value to consider in blast hit percentage regarding the reference length. Values from 0 to 1.", ) @click.option( "-p", "--perc-identity", required=False, nargs=1, - default=90, + default=85, + show_default=True, type=int, - help="Percentage of identity to consider in blast. default 90", + help="Percentage of identity to consider in blast.", ) @click.option( "-o", @@ -441,6 +459,7 @@ def reference_alleles( "--force/--no-force", required=False, default=False, + show_default=True, help="Overwrite the output folder if it exists", ) @click.argument( @@ -454,12 +473,14 @@ def reference_alleles( "--snp/--no-snp", required=False, default=False, + show_default=True, help="Create SNP file for alleles in assembly in relation with reference allele", ) @click.option( "--alignment/--no-alignment", required=False, default=False, + show_default=True, help="Create alignment files", ) @click.option( @@ -468,8 +489,9 @@ def reference_alleles( required=False, nargs=1, default=80, + show_default=True, type=int, - help="Threshold of protein coverage to consider as TPR. default 90", + help="Threshold of protein coverage to consider as TPR", ) @click.option( "-i", @@ -477,8 +499,9 @@ def reference_alleles( required=False, nargs=1, default=20, + show_default=True, type=int, - help="Increase the number of triplet sequences to find the stop codon. default 20", + help="Increase the number of triplet sequences to find the stop codon", ) @click.option( "--cpus", @@ -486,6 +509,7 @@ def reference_alleles( multiple=False, type=int, default=1, + show_default=True, help="Number of cpus used for execution", ) def allele_calling( @@ -591,6 +615,7 @@ def allele_calling( "--force/--no-force", required=False, default=False, + show_default=True, help="Overwrite the output folder if it exists", ) @click.option( @@ -600,6 +625,7 @@ def allele_calling( multiple=False, type=int, default=0, + show_default=True, help="Maximum percentaje of missing values a locus can have, otherwise is filtered. By default core genome is calculated, locus must be found in all samples.", ) @click.option( @@ -609,6 +635,7 @@ def allele_calling( multiple=False, type=int, default=20, + show_default=True, help="Maximum percentaje for missing values a sample can have, otherwise it is filtered", ) @click.option( @@ -617,7 +644,8 @@ def allele_calling( multiple=False, type=bool, default=True, - help="Consider paralog tags (NIPH, NIPHEM) as missing values. Default is True", + show_default=True, + help="Consider paralog tags (NIPH, NIPHEM) as missing values.", ) @click.option( "--lnf-filter/--no-lnf-filter", @@ -625,7 +653,8 @@ def allele_calling( multiple=False, type=bool, default=True, - help="Consider LNF as missing values. Default is True", + show_default=True, + help="Consider LNF as missing values.", ) @click.option( "--plot-filter/--no-plot-filter", @@ -633,7 +662,8 @@ def allele_calling( multiple=False, type=bool, default=True, - help="Consider PLOT as missing values. Default is True", + show_default=True, + help="Consider PLOT as missing values.", ) def distance_matrix( alleles: str, From 4901b3098771681aea7e88da5e2c8c14a8e1b0b7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Thu, 2 Jan 2025 13:01:57 +0100 Subject: [PATCH 211/214] readme docs are updated --- README.md | 430 +++++++++++++++++++++----------------- assets/allele_calling.png | Bin 0 -> 72509 bytes assets/taranis_schema.png | Bin 0 -> 99332 bytes 3 files changed, 241 insertions(+), 189 deletions(-) create mode 100644 assets/allele_calling.png create mode 100644 assets/taranis_schema.png diff --git a/README.md b/README.md index 8676463..87bca80 100644 --- a/README.md +++ b/README.md @@ -1,14 +1,17 @@ # taranys -- [Introduction](#introduction) -- [Dependencies](#dependencies) -- [Installation](#installation) - - [Install from source](#install-from-source) - - [Install using conda](#install-using-conda) -- [Quick usage](#quick-usage) -- [Usage](#usage) -- [Output](#output) -- [Illustrated pipeline](#illustrated-pipeline) +- [taranys](#taranys) + - [Introduction](#introduction) + - [Dependencies](#dependencies) + - [Installation](#installation) + - [Install from source](#install-from-source) + - [Install using conda](#install-using-conda) + - [Usage](#usage) + - [**analyze\_schema:**](#analyze_schema) + - [**reference\_alleles:**](#reference_alleles) + - [**allele\_calling:**](#allele_calling) + - [**distance\_matrix:**](#distance_matrix) + - [Output](#output) ## Introduction @@ -16,195 +19,140 @@ taranys includes four main functionalities: MLST **schema analysis**, gene-by-gene **allele calling**, **reference alleles** obtainment for allele calling analysis and the final **distance matrix** construction. +![taranis_schema](assets/taranis_schema.png) + ## Dependencies -- Python >=3.8 +- Python >=3.10 - NCBI_blast >= v2.9 - prokka >=1.14.6 -- mafft = 7.520 -- mash >=2 -- biopython v1.81 -- pandas v2.1.1 -- plotly v5.17.0 -- numpy v1.26.0 +- mafft >= 7.505 +- mash >= 2 +- python deps: + - igraph>=0.9.8 + - rich>=13.4.1 + - click>=8.1.3 + - leidenalg>=0.9.1 + - questionary>=1.10.0 + - bio>=1.6.0 + - scikit-learn>=1.2.0 + - plotly>=5.11.0 + - kaleido>=0.2.1 + - six>=1.16.0 ## Installation -#### Install from source - -Install all dependencies and add them to $PATH. - -`git clone https://github.com/BU-ISCIII/taranys.git` - -Add taranys and ./bin to $PATH. - -#### Install using conda - -This option is recomended. - -Install Anaconda3. - -`conda install -c conda-forge -c bioconda -c defaults taranys` - -Wait for the environment to solve.
-Ignore warnings/errors. - -## Quick usage - -- **analyze_schema mode:** - - Schema analysis: - -``` -taranys analyze_schema \ --inputdir schema_dir \ --output output_analyze_schema_dir ---ouput-allele-annotation annotation_dir -``` - - Schema analysis for removing duplicated, subsequences and no CDS alleles: - -``` -taranys analyze_schema \ --inputdir schema_dir \ --output output_analyze_schema_dir \ ---remove-subsets \ ---remove-duplicated \ ---remove-no-cds \ ---ouput-allele-annotation annotation_dir \ ---genus prokka_genus_name \ ---usegenus prokka genus-specific BLAST database \ ---species prokka_species_name \ ---cpus number_of_cpus +### Install from source +```bash +git clone https://github.com/BU-ISCIII/taranys.git ``` -- **reference_alleles mode:** - - Get reference alleles: - -``` -taranys reference_alleles \ --s schema_dir \ --o output_reference_alleles_dir \ ---eval-cluster \ ---cpus number_of_cpus \ ---force overwrite output dir -``` - - Reference alleles with clustering settings: +Install dependencies and taranys using conda or micromamba: -``` -taranys reference_alleles \ --s schema_dir \ --o output_reference_alleles_dir \ ---eval-cluster \ --k k-mer size for mash \ --S Sketch size for mash \ --r resolution used for clustering \ ---cpus number_of_cpus \ ---force overwrite output dir +```bash +cd /path/to/clonedrepo +micromamba install -f environment.yml ``` -- **allele_calling mode:** - - Run allele calling: - -``` -taranys allele_calling \ --s schema_dir \ --a annotation_file \ --r reference_alleles_dir \ --o output_allele_calling_dir \ --t threshold to consider in blast \ --p percentage of identity to consider in blast \ --q threshold to consider as TPR \ --i increase number of nucleotides to find stop codon \ ---snp Create SNP file \ ---cpus number_of_cpus \ ---alignment Create aligment files \ -samples_dir -``` +### Install using conda - Allele calling for blast and threshold settings: +This option is the recommended option for installing taranys. -``` -taranys allele_calling \ --s schema_dir \ --a annotation_file \ --r reference_alleles_dir \ --o output_allele_calling_dir \ --t threshold to consider in blast \ --p percentage of identity to consider in blast \ --q threshold to consider as TPR \ --i increase number of nucleotides to find stop codon \ ---snp Create SNP file \ ---cpus number_of_cpus \ ---alignment Create aligment files \ -samples_dir +```bash +micromamba install -c conda-forge -c bioconda -c defaults taranys ``` -- **distance_matrix mode:** +## Usage - Get distance matrix: +### **analyze_schema:** -``` -taranys distance_matrix \ --a allele_calling_match.csv file \ --o distance_matrix_dir ---force overwrite output folder -``` +To assess the quality of the schema, the following analysis is performed for each locus in the schema: -Distance matrix with threshold settings: +- The existence of potential duplicate alleles within the same locus is examined. +- The presence of allelic sequences that are partial sequences or subsequences of other alleles is checked. +- Each allele is evaluated to verify whether it is a coding region (CDS) using the translate function from Biopython's Seq class. -``` -taranys distance_matrix \ --a allele_calling_match.csv file \ --o distance_matrix_dir --l threshold for missing locus \ --s threshold for missing samples \ ---paralog-filter \ ---lnf-filter \ ---plot-filter \ ---force overwrite output folder -``` +The following quality categories are defined: -## Usage +- “Good” quality: The sequence meets the criteria to be considered a hypothetical CDS. +- “Bad” quality: The sequence fails to meet the criteria for a hypothetical CDS due to one of the following reasons: + - Lack of a start codon (Bad quality: no start codon). + - Lack of a stop codon (Bad quality: no stop codon). + - Simultaneous absence of both start and stop codons (Bad quality: no start stop). + - The sequence length is not a multiple of 3 (Bad quality: no multiple of three). + - Presence of multiple stop codons (Bad quality: multiple stop). -- **analyze_schema mode:** +Usage: -``` +```bash Usage: taranys analyze-schema [OPTIONS] Options: - -i, --inputdir PATH Directory where the schema with the core + -i, --input PATH Directory where the schema with the core gene files are located. [required] -o, --output PATH Output folder to save analyze schema [required] --remove-subset / --no-remove-subset Remove allele subsequences from the schema. + [default: no-remove-subset] --remove-duplicated / --no-remove-duplicated Remove duplicated subsequences from the - schema. + schema. [default: no-remove-duplicated] --remove-no-cds / --no-remove-no-cds Remove no CDS alleles from the schema. + [default: no-remove-no-cds] --output-allele-annot / --no-output-allele-annot output prokka/allele annotation for all - alleles in locus + alleles in locus. Default is True. + [default: output-allele-annot] --genus TEXT Genus name for Prokka schema genes - annotation. Default is Genus. + annotation. Default is Genus. [default: + Genus] --species TEXT Species name for Prokka schema genes - annotation. Default is species + annotation. Default is species [default: + species] --usegenus TEXT Use genus-specific BLAST databases for Prokka schema genes annotation (needs - --genus). Default is False. - --cpus INTEGER Number of cpus used for execution + --genus). Default is False. [default: + Genus] + --cpus INTEGER Number of cpus used for execution. Default + is 1 [default: 1] --help Show this message and exit. ``` -- **reference_alleles mode:** +Example when removing bad quality alleles is not wanted, just statistics outputted: +```bash +taranys analyze-schema \ +-input schema_dir \ +-output output_analyze_schema_dir +--ouput-allele-annotation annotation_dir +``` + +Example for removing duplicated, subsequences and no CDS alleles: + +```bash +taranys analyze-schema \ +-input schema_dir \ +-output output_analyze_schema_dir \ +--remove-subsets \ +--remove-duplicated \ +--remove-no-cds \ +--ouput-allele-annotation annotation_dir \ +--genus prokka_genus_name \ +--usegenus prokka genus-specific BLAST database \ +--species prokka_species_name \ +--cpus number_of_cpus ``` + +### **reference_alleles:** + +As a preliminary step to the typing analysis, the representative allele or alleles for each locus in the schema are determined. The selected allele will be the one that shows the least dissimilarity compared to all other known alleles for that locus and can be used to detect, with a certain degree of similarity, all alleles in the schema. Leiden algorithm is used for clusting similar sequences. + +Usage: + +```bash Usage: taranys reference-alleles [OPTIONS] Options: @@ -214,19 +162,58 @@ Options: [required] --eval-cluster / --no-eval-cluster Evaluate if the reference alleles match - against blast with a 90% identity - -k, --kmer-size INTEGER Mash parameter for K-mer size. - -S, --sketch-size INTEGER Mash parameter for Sketch size + against blast with the identity set in eval- + identity param [default: eval-cluster] + -k, --kmer-size INTEGER Mash parameter for K-mer size. [default: + 21] + -S, --sketch-size INTEGER Mash parameter for Sketch size [default: + 2000] -r, --cluster-resolution FLOAT Resolution value used for clustering. + [default: 0.75] + -e, --eval-identity FLOAT Blast percentage identity to use for evaluation of identification. + [default: 85] --seed INTEGER Seed value for clustering - --cpus INTEGER Number of cpus used for execution + --cpus INTEGER Number of cpus used for execution [default: + 1] --force / --no-force Overwrite the output folder if it exists + [default: no-force] --help Show this message and exit. ``` -- **allele_calling mode:** +Example for reference-alleles using defaults: +```bash +taranys reference-alleles \ +--schema schema_dir \ +--output output_reference_alleles_dir \ +--eval-cluster \ +--cpus number_of_cpus \ +--force overwrite output dir ``` + +Command example changing params: + +```bash +taranys reference-alleles \ +--schema schema_dir \ +--output output_reference_alleles_dir \ +--eval-cluster \ +--kmer-size k-mer size for mash \ +--sketch-size Sketch size for mash \ +--cluster-resolution resolution used for clustering \ +--cpus number_of_cpus \ +--force overwrite output dir +``` + +### **allele_calling:** + +La llamada de alelos es la función principal de Taranis con la que se realiza la tipificación propiamente dicha. Utilizando este módulo se identifican los locus del esquema presentes en las muestras analizadas. Para ello se utilizan como query los alelos de referencia identificados anteriormente y se realiza un alineamiento con blast utilizando como base de datos los ensamblados en formato fasta. Este alineamiento nos permite obtener el alelo que está presente en la muestra, realizando una clasificación basada en las categorías descritas por el software [chewBBACA](https://chewBBACA.readthedocs.io/en/latest/user/modules/AlleleCall.html#outputs). + +![allele_calling](assets/allele_calling.png) + +Usage: + +```bash Usage: taranys allele-calling [OPTIONS] ASSEMBLIES... Options: @@ -235,29 +222,70 @@ Options: -r, --reference PATH Directory where the schema reference allele files are located. [required] -a, --annotation PATH Annotation file. [required] - -t, --threshold FLOAT Threshold value to consider in blast. Values - from 0 to 1. default 0.8 + -t, --hit_lenght_perc FLOAT Threshold value to consider in blast hit + percentage regarding the reference length. + Values from 0 to 1. [default: + 0.8] -p, --perc-identity INTEGER Percentage of identity to consider in blast. - default 90 + [default: 85] -o, --output PATH Output folder to save reference alleles [required] --force / --no-force Overwrite the output folder if it exists + [default: no-force] --snp / --no-snp Create SNP file for alleles in assembly in - relation with reference allele - --alignment / --no-alignment Create alignment files + relation with reference allele [default: + no-snp] + --alignment / --no-alignment Create alignment files [default: no- + alignment] -q, --proteine-threshold INTEGER Threshold of protein coverage to consider as - TPR. default 90 + TPR [default: 80] -i, --increase-sequence INTEGER Increase the number of triplet sequences to - find the stop codon. default 20 - --cpus INTEGER Number of cpus used for execution + find the stop codon [default: 20] + --cpus INTEGER Number of cpus used for execution [default: + 1] --help Show this message and exit. ``` + +Command example for allele calling using defaults: + +```bash +taranys allele-calling \ +-s schema_dir \ +-a annotation_file \ +-r reference_alleles_dir \ +-o output_allele_calling_dir \ +--snp \ +--cpus 10 \ +samples_dir +``` -- **distance_matrix mode:** +Allele calling for blast and threshold settings: +```bash +taranys allele_calling \ +-s schema_dir \ +-a annotation_file \ +-r reference_alleles_dir \ +-o output_allele_calling_dir \ +-t coverage threshold to consider in blast \ +-p percentage of identity to consider in blast \ +-q threshold to consider as TPR \ +-i increase number of nucleotides to find stop codon \ +--snp Create SNP file \ +--cpus 10 \ +--alignment\ +samples_dir ``` + +### **distance_matrix:** + +The similarity between two or more genomes is estimated by comparing their respective allelic profiles and calculating the total number of differing alleles. These allelic differences between genomes are obtained by generating a distance matrix using the distance_matrix module, which takes as input the allele matrix resulting from the allele_calling process. The Hamming distance is then calculated between pairs of samples. + +Usage: + +```bash Usage: taranys distance-matrix [OPTIONS] Options: @@ -266,35 +294,61 @@ Options: -o, --output PATH Output folder to save distance matrix [required] --force / --no-force Overwrite the output folder if it exists + [default: no-force] -l, --locus-missing-threshold INTEGER - Threshold for missing alleles in locus, - which loci is excluded from distance matrix + Maximum percentaje of missing values a locus + can have, otherwise is filtered. By default + core genome is calculated, locus must be + found in all samples. [default: 0] -s, --sample-missing-threshold INTEGER - Threshold for missing samples, which sample - is excluded from distance matrix + Maximum percentaje for missing values a + sample can have, otherwise it is filtered + [default: 20] --paralog-filter / --no-paralog-filter Consider paralog tags (NIPH, NIPHEM) as - missing values. Default is True - --lnf-filter / --no-lnf-filter Consider LNF as missing values. Default is - True + missing values. [default: paralog-filter] + --lnf-filter / --no-lnf-filter Consider LNF as missing values. [default: + lnf-filter] --plot-filter / --no-plot-filter - Consider PLOT as missing values. Default is - True + Consider PLOT as missing values. [default: + plot-filter] --help Show this message and exit. ``` +Command example: + +```bash +taranys distance-matrix \ +-alleles allele_calling_match.csv file \ +--output distance_matrix_dir +--force overwrite output folder +``` + +Distance matrix with threshold settings: + +```bash +taranys distance-matrix \ +--alleles allele_calling_match.csv file \ +--output distance_matrix_dir +--locus-missing-threshold threshold for missing locus \ +--sample-missing-threshold threshold for missing samples \ +--paralog-filter \ +--lnf-filter \ +--plot-filter \ +--force overwrite output folder +``` + ## Output -- **analyze_schema mode:** +- **analyze_schema:** - **FOLDERS and FILES structure:** - + - **new_schema** Contains the new schema. - - **prokka** Contains the prokka results + - **prokka** Contains prokka results - **statistics** Statistics data - **graphics** Plot graphics folder - **statistics.csv** Quality statistics showing the following data: - - allele_name, - min_length, - max_length, @@ -308,16 +362,16 @@ Options: - Duplicate allele, - Sub set allele - - **allele_annotation.tar.gz** Annotation schema file + - **allele_annotation.tar.gz** Annotation schema file to be inputted in allele calling module. -- **reference_alleles mode:** +- **reference_alleles:** - **FOLDERS and FILES structure:** - **Clusters** Contains the cluster allele files - **[cluster_alleles].txt** cluster allele file - **evaluate_cluster** - - **cluster_evaluuation.csv** Evaluation result with the following info: + - **cluster_evaluation.csv** Evaluation result with the following info: - Locus name - cluster number - result @@ -340,7 +394,7 @@ Options: - **[ref_alleles_locusX].fasta:** One fasta file for each schema locus containing reference alleles for that locus -- **allele_calling mode:** +- **allele_calling:** - **FOLDERS and FILES structure:** - **alignments:** Nucleotide alignment between sequence found in the sample and allele @@ -356,7 +410,7 @@ Options: - **NIPH_graphic.pnd** Number of NIPH in samples. - **PLOT_graphic.pnd** Number of PLOT in samples. - **TPR_graphic.pnd** Number of TPR in samples. - - **[locus_name]_snp_data** One file per sample + - **[locus_name]_snp_data.csv** One file per sample - **allele_calling_match.csv** Contains the classification for each locus and for all samples - **allele_calling_summary.csv** Contains the number of each classification per samples - **matching_contig.csv** Summary for each locus in sample with the following data: @@ -379,11 +433,9 @@ Options: - allele sequence - predicted protein sequence -- **distance_matrix mode:** +- **distance_matrix:** - **FILES:** - - **filtered_result.tsv:** Filtered allele calling matrix filtered - - **matrix_distance.tsv:** Samples matrix distance - - **matrix_distance_filter_report.tsv:** Allele calling matrix filtering report - -## Illustrated pipeline + - **allele_matrix_fil.tsv:** Filtered allele calling matrix filtered + - **distance_matrix_core.tsv:** distance matrix with only core 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+0100 Subject: [PATCH 212/214] added changelog --- CHANGELOG.md | 88 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 88 insertions(+) create mode 100644 CHANGELOG.md diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000..2a7f455 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,88 @@ +# Taranys Changelog + +All notable changes to this project will be documented in this file. + +The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). + +## [3.0.1dev] - 202X-XX-XX : https://github.com/BU-ISCIII/relecov-tools/releases/tag/ + +### Credits + +Code contributions to the release: + +### Modules + +#### Added enhancements + +#### Fixes + +#### Changed + +#### Removed + +### Requirements + +## [3.0.0] - 2025-01-02 : https://github.com/BU-ISCIII/taranys/releases/tag/3.0.0 + +- Code refactor to create a proper python package +- Implementation of parallel computation for execution speed. +- New implementation of reference-alleles module using leiden algorithm for allele clustering. +- New param defaults based on empirical testing. +- Distance matrix is rewritten with more params available. + +## [2.0.1beta] - 2021-07-14 : https://github.com/BU-ISCIII/taranys/releases/tag/2.0.1 + +### New features + +- New default missing values threshold imposed for samples in distance matrix creation +- New default perc_identity_ref value for loci search in allele calling analysis +- Github actions for testing and docker generation. + +### Bug fixes + +- multiple statistic modes bug fixed +- BLAST database creation bug due to punctuation signs in file name fixed +- ST profile identification bug fixed +- Python modules installed from conda-forge in environment.yml + +### Documentation + +- Channel order in conda installation command + +## [2.0.0beta] - 2021-06-24 : https://github.com/BU-ISCIII/taranys/releases/tag/2.0.0edge + +Pre-release + +## [0.3.3beta] - 2018-11-05 : https://github.com/BU-ISCIII/taranys/releases/tag/0.3.3 + +**BUG FIX:** + +- Fix num_threads in blast commands + +## [0.3.2beta] - 2018-11-02 : https://github.com/BU-ISCIII/taranys/releases/tag/0.3.2 + +**BUG FIX:** + +- Added ERROR posibility to allele classification, when the allele is not PLOT but is too near to contig end, and protein codification finishes without finding a stop codon. + +## [0.3.1beta] - 2018-10-27 : https://github.com/BU-ISCIII/taranys/releases/tag/0.3.1 + +- Added cpus as parameter. + +## [0.3.0beta] - 2018-10-25 : https://github.com/BU-ISCIII/taranys/releases/tag/0.3.0 + +**Bug fixes:** + +- Allow dependency check for versions greater than needed. + +**Features:** + +- Added graphics for schema evaluation(beta) +- Added SNP calling (beta) + +## [0.0.1beta] - 2018-10-22 : https://github.com/BU-ISCIII/taranys/releases/tag/0.1 + +- cg/wgMLST analysis using assembled contigs as input using a defined schema. Comparison matrix is generated for phyloviz visualization. Blast hit are classified as Exact match , new allele, etc. +- cg/wgMLST statistics. +- beta: SNP analysis. +- Insertions, deletions and paralogues detection. \ No newline at end of file From fb2c7abb72516b5d7a4bd5552c069f76f53800aa Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Thu, 2 Jan 2025 13:54:35 +0100 Subject: [PATCH 213/214] updated gitignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 56f7103..74f194f 100644 --- a/.gitignore +++ b/.gitignore @@ -9,6 +9,7 @@ __pycache__/ *.log.* # Projects files *.komodoproject +*.vscode/ # Distribution / packaging .Python From 1de6d7a3d19bf0348b7131a4cd82268f3f64bfbd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sara=20Monz=C3=B3n?= Date: Thu, 2 Jan 2025 15:28:16 +0100 Subject: [PATCH 214/214] fix tests workflow gact --- .github/workflows/tests.yml | 26 ++++++++++++++++++++++---- 1 file changed, 22 insertions(+), 4 deletions(-) diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 6342cdb..fc9ce5c 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -6,7 +6,7 @@ on: push: jobs: - test_analyze_schema: + test_taranys: runs-on: ubuntu-latest steps: @@ -19,7 +19,7 @@ jobs: activate-environment: taranys_env environment-file: environment.yml - - name: Verify Conda environment + - name: Verify conda environment run: conda env list - name: Activate env and install taranys @@ -27,11 +27,29 @@ jobs: source $CONDA/etc/profile.d/conda.sh conda activate taranys_env python -m pip install . + + - name: Testing analyze schema allele + run: | + source $CONDA/etc/profile.d/conda.sh + conda activate taranys_env taranys analyze-schema -i test/MLST_listeria/analyze_schema -o analyze_schema_test --cpus 1 --output-allele-annot --remove-no-cds --remove-duplicated --remove-subset - - name: Testing Reference allele + - name: Testing reference allele run: | source $CONDA/etc/profile.d/conda.sh conda activate taranys_env taranys reference-alleles -s test/MLST_listeria/reference_allele -o reference_allele_test --cpus 1 - \ No newline at end of file + + - name: Testing allele calling + run: | + source $CONDA/etc/profile.d/conda.sh + conda activate taranys_env + taranys reference-alleles -s analyze_schema_test/new_schema -o reference_allele --cpus 1 + taranys allele-calling --force --schema analyze_schema_test/new_schema --reference reference_allele --annotation analyze_schema_test/allele_annotation.tar.gz --output allele_calling_test --cpus 1 --snp --alignment test/samples_listeria/*.fasta + + - name: Testing distance matrix + run: | + source $CONDA/etc/profile.d/conda.sh + conda activate taranys_env + mkdir distance_matrix_result + taranys distance-matrix --alleles allele_calling_test/allele_calling_match.csv --force --output distance_matrix_result \ No newline at end of file