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main.py

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+from contextlib import nullcontext
+import os
+from os import listdir
+from ete3 import NCBITaxa
+from math import log2
+import json
+from Bio import SeqIO
+from Bio import motifs
+from Bio import Entrez
+import csv
+from tqdm import trange
+ncbi=NCBITaxa()
+#ncbi.update_taxonomy_database()
+Entrez.email = '[email protected]'
+
+class HitResult:
+    def __init__(self, gen_ID,organism, tf_ID, location, score, dist_to_gene, type, gene):
+        self.tf_ID=tf_ID
+        self.organism=organism
+        self.genome_ID=gen_ID
+        self.score=score
+        self.location=location
+        if gene:
+            if 'locus_tag' in gene.qualifiers.keys():
+                self.gene_locus_tag=gene.qualifiers['locus_tag']
+            elif 'note' in gene.qualifiers.keys():
+                self.gene_locus_tag=gene.qualifiers['note']
+            else:
+                self.gene_locus_tag='undefined'
+
+            if 'product' in gene.qualifiers.keys():
+                self.gene_product=gene.qualifiers['product']
+            else:
+                self.gene_product='undefined'
+
+            self.gene_start=gene.location.start
+            self.gene_stop=gene.location.end
+            self.gene_strand=gene.strand
+            self.dist_to_gene=dist_to_gene
+            self.type=type
+        else:
+            self.gene_product='undefined'
+            self.gene_locus_tag='undefined'
+            self.gene_start='undefined'
+            self.gene_stop='undefined'
+            self.gene_strand='undefined'
+            self.dist_to_gene='undefined'
+            self.type='undefined'
+
+
+
+
+def readJSON(filename):
+    f=open(filename)
+    print(filename)
+    json_obj=json.load(f)
+    out_path=json_obj['Output_path']
+    gnome_path=json_obj['genomes_path']
+    motifs_path=json_obj['motifs_path']
+    motifs_file=json_obj['TF_filename']
+    gen_file=json_obj['Genome_filename']
+    clade=json_obj['clade']
+    up_dist=json_obj['upstream_dist']
+    down_dist=json_obj['downstream_dist']
+    pseudo=json_obj['pseudocounts']
+    return out_path,motifs_path, gnome_path, motifs_file,gen_file, clade, up_dist, down_dist,pseudo
+
+def readTF(filename, pseudo):
+    motifs_parsed = SeqIO.parse(filename,"fasta")
+
+
+    #------Convert into motif object---------
+
+    list_seq=[]
+    for record in motifs_parsed:
+        list_seq.append(record.seq.upper())
+    motif=motifs.create(list_seq)
+    tf_id=filename
+
+    #------PSSM----------
+    pwm=motif.counts.normalize(pseudocounts=pseudo)
+    pssm=pwm.log_odds()
+    reverse_pssm=pssm.reverse_complement()
+    distribution = pssm.distribution(background=None,precision=10**4)
+    threshold=distribution.threshold_patser()
+
+    return motif, threshold, pssm, reverse_pssm
+
+def readGB(filename):
+    gb_object = SeqIO.read(filename,'genbank')
+    seq=gb_object.seq
+    organism = gb_object.annotations['organism']
+    gen_ID=gb_object.name
+
+    return gb_object,seq, organism, gen_ID
+
+def passThreshold(seq,motif,pssm,reverse_pssm,threshold):
+
+    scores={}
+    sites={}
+    #---------Obtain the sites that pass the threshold using combined scores----------
+    hits=[i for i in trange(len(seq)-len(motif),desc='Finding Hits') if log2(2**pssm.calculate(seq[i:i+len(motif)])+2**reverse_pssm.calculate(seq[i:i+len(motif)]))>=threshold]
+    #hits=pssm.search(pssm.calculate(seq),threshold)
+    if hits:
+        for i in hits:
+            scores[i]=log2(2**pssm.calculate(seq[i:i+len(motif)])+2**reverse_pssm.calculate(seq[i:i+len(motif)]))
+            sites[i]=seq[i:i+len(motif)]
+    #for i in trange(len(seq)-len(motif),desc='Finding Hits'):
+        #site=seq[i:i+len(motif)]
+        #score=pssm.calculate(seq[i:i+len(motif)])
+        #reverse_score=reverse_pssm.calculate(seq[i:i+len(motif)])
+        #final_score=math.log2(2**pssm.calculate(seq[i:i+len(motif)])+2**reverse_pssm.calculate(seq[i:i+len(motif)]))
+        #if math.log2(2**pssm.calculate(seq[i:i+len(motif)])+2**reverse_pssm.calculate(seq[i:i+len(motif)]))>=threshold:
+            #scores[i]=math.log2(2**pssm.calculate(seq[i:i+len(motif)])+2**reverse_pssm.calculate(seq[i:i+len(motif)]))
+            #hits.append(i)
+            #sites[i]=seq[i:i+len(motif)]
+
+    return hits,scores
+
+def check_overlap(hits,motif,scores):
+    if hits:
+        to_delete=[]
+        for i in range(len(hits)-1):
+            if (hits[i]+len(motif)/2)>=hits[i+1]:
+                delete1=hits[i]
+                delete2=hits[i+1]
+                if scores[delete1]>=scores[delete2]:
+                    to_delete.append(i+1)
+                    #hits.pop(i)
+                    #del scores[delete2]
+                else:
+                    to_delete.append(i)
+                    #hits.pop(i+1)
+                    #del scores[delete1]
+        for i in sorted(to_delete,reverse=True):
+            del scores[hits[i]]
+            del hits[i]
+
+
+
+
+    return hits, scores
+
+def dist_classify(hits, seq, gb_object, up_dist, down_dist):
+    dist_to_genes={}
+    gene_locations={}
+    genes={}
+    classification={}
+
+    for pos in hits:
+        dist_final=len(seq)
+
+        #-----Calculate distance to closest gene----------
+        for f in gb_object.features:
+            if f.type=='CDS':
+                if f.strand is 1:
+                    dist1=pos-f.location.start
+                elif f.strand is -1:
+                    dist1=pos-f.location.end
+                    #dist2=pos-f.location.start
+                dist_aux = abs(dist1)
+                if dist_aux<dist_final:
+                    dist_final=dist_aux
+                    dist_to_genes[pos]=dist1
+                    gene_locations[pos]=f.location
+                    genes[pos]=f
+                #elif dist_aux>dist_final:
+                    #break
+
+    #------Classification of hit by their relative location to genes
+        if genes:
+            if genes[pos].strand is 1:
+                if dist_to_genes[pos]>=-(up_dist) and dist_to_genes[pos]<=down_dist:
+                    classification[pos]="OPERATOR"
+                elif dist_to_genes[pos]>down_dist and dist_to_genes[pos]<gene_locations[pos].end:
+                    classification[pos]="INTRAGENETIC"
+                elif dist_to_genes[pos]<-(up_dist) or dist_to_genes[pos]>gene_locations[pos].end:
+                    classification[pos]="INTERGENETIC"
+            elif genes[pos].strand is -1:
+                if dist_to_genes[pos] <= up_dist and dist_to_genes[pos] >= -(down_dist):
+                    classification[pos]="OPERATOR"
+                elif dist_to_genes[pos] < -(down_dist) and dist_to_genes[pos] > gene_locations[pos].start:
+                    classification[pos]="INTRAGENETIC"
+                elif dist_to_genes[pos] > up_dist or dist_to_genes[pos] < gene_locations[pos].start:
+                    classification[pos]="INTERGENETIC"
+
+    return dist_to_genes,genes,classification
+
+def save_to_csv(results, path):
+    header=[
+        "Motif",
+        "Genome",
+        "Genome ID",
+        "Score",
+        "Abs. Position",
+        "Gene locus_tag",
+        "Gene Product",
+        "Gene start",
+        "Gene stop",
+        "Gene Strand",
+        "Position relative to gene",
+        "Region"
+        ]
+    filepath=os.path.join(path, "results.csv")
+    with open(filepath,'a',newline='') as f:
+        f.truncate(0)
+        writer=csv.DictWriter(f,fieldnames=header, delimiter=',')
+        writer.writeheader()
+        for motif_dict in results.items():
+            for genome_results in motif_dict[1].items():
+                for hit in genome_results[1]:
+                    row={
+                        'Motif':hit.tf_ID,
+                        'Genome': hit.organism,
+                        'Genome ID':hit.genome_ID,
+                        'Score':hit.score,
+                        'Abs. Position':hit.location,
+                        'Gene locus_tag':hit.gene_locus_tag,
+                        'Gene Product':hit.gene_product,
+                        'Gene start':hit.gene_start,
+                        'Gene stop':hit.gene_stop,
+                        'Gene Strand':hit.gene_strand,
+                        'Position relative to gene':hit.dist_to_gene,
+                        'Region':hit.type
+                    }
+                    writer.writerow(row)
+
+
+
+def main():
+    for motif_file in tf_filenames:
+        print('=====MOTIF FILE: '+ motif_file,' ================' )
+        motif_filepath = os.path.join(motifs_path, motif_file)
+        motif, threshold, pssm, reverse_pssm=readTF(motif_filepath,pseudo)
+        result_dic={}
+        #gen_filenames=["MW546072.gb","KJ206559.gb","JX080301.gb"]
+        i=0
+        organisms=[]
+        for gnome_file in gen_filenames:
+            i=i+1
+            print('=====GEN FILE: '+ gnome_file,' ================' )
+            print('File ',i,'/',len(gen_filenames))
+            gnome_filepath = os.path.join(gnome_path, gnome_file)
+            #--------Read gb file-----------
+            gb_object,seq,organism,gen_ID=readGB(gnome_filepath)
+
+
+            if organism not in organisms:
+                organisms.append(organism)
+                print("LENGTH IN BP: ", len(seq))
+                #==========PROCESSING================#
+                hits,scores=passThreshold(seq,motif,pssm,reverse_pssm,threshold)
+
+
+                #---------Check for overlapping-------------------
+                hits, scores=check_overlap(hits,motif,scores)
+
+                print("Hits found: ",len(hits))
+
+                #---------Classification of sites by relative position to genes--
+                dist_to_genes,genes,classification=dist_classify(hits, seq, gb_object,up_dist,down_dist)
+
+
+                #===============RESULTS=====================#
+
+                #----------Saving results in list of HitResult objects--------------
+                results=[]
+                for hit in hits:
+                    if genes:
+                        result=HitResult(gen_ID,organism,motif_file,hit,scores[hit],dist_to_genes[hit],classification[hit],genes[hit])
+                    else:
+                        result=HitResult(gen_ID,organism,motif_file,hit,scores[hit],dist_to_genes,classification,genes)
+                    results.append(result)
+
+                result_dic[gnome_file]=results
+                #if i==10:
+                    #break
+
+        result_dic_GLOBAL[motif_file]=result_dic
+        save_to_csv(result_dic_GLOBAL,out_path)
+
+
+
+
+def get_genomes():
+
+
+    #===========Match hosts with descendants of clade===============
+
+    #get descendants of clade
+    descendants = ncbi.get_descendant_taxa(clade,intermediate_nodes=True,rank_limit='genus')
+
+    print("===================================================")
+    descendants = ncbi.translate_to_names(descendants)
+
+    for i in range(len(descendants)):
+        if 'Candidatus' in descendants[i]:
+            descendants[i]=descendants[i].replace('Candidatus ', '')
+
+    input_filepath = os.path.join(gnome_path, gen_file)
+
+    #open tsv with accessions
+    tsvfile = open(input_filepath, newline='')
+    phages_hosts_df = list(csv.reader(tsvfile, delimiter='\t'))
+
+    fields=phages_hosts_df[0]
+    for i in range(len(fields)):
+        if fields[i]=='Host':
+            host_column=i
+            break
+        else:
+            i+=1
+
+    print("===================================================")
+    #find descendants in hosts column and save accessions for each match
+    accessions=[]
+    descriptions=[]
+    for row in phages_hosts_df:
+        #print(row[host_column])
+        if row[host_column] in descendants:
+            accessions.append(row[0])
+            descriptions.append(row[1])
+
+
+
+    #print(accessions)
+    #print(descriptions)
+
+    #===============DOWNLOAD GENOMES============
+
+    dir_contents=listdir(gnome_path)
+    filenames=[]
+    #print(dir_contents)
+    for i in range(len(accessions)):
+        filenames.append(accessions[i]+'.gb')
+        print("Processing: "+accessions[i])
+        if not ((accessions[i]+'.gb') in dir_contents):
+            print('--> Downloading: '+accessions[i])
+            net_handle = Entrez.efetch(db='nuccore', id=accessions[i],rettype='gbwithparts',retmode='txt')
+            genome_record=net_handle.read()
+            out_handle=open(gnome_path+'/'+accessions[i]+".gb","w")
+            out_handle.write(genome_record)
+
+    return filenames
+
+def get_motifs():
+    input_filepath = os.path.join(motifs_path, motifs_file)
+    csvfile = open(input_filepath, newline='')
+    tf_motifs = list(csv.reader(csvfile, delimiter=','))
+
+    fields=tf_motifs[0]
+    for i in range(len(fields)):
+        if fields[i]=='Motif_file':
+            filename_column=i
+            break
+        else:
+            i+=1
+
+    filenames=[]
+    for row in tf_motifs:
+        if row[filename_column] != 'Motif_file':
+            filenames.append(row[filename_column])
+    return filenames
+
+
+json_filename='jsonExample.json'
+out_path,motifs_path, gnome_path, motifs_file,gen_file, clade, up_dist, down_dist,pseudo=readJSON(json_filename)
+gen_filenames=get_genomes()
+tf_filenames=get_motifs()
+
+result_dic_GLOBAL={}
+print("=====FINDING MATCHES========")
+main()
+print("==========FINISHED======")