From 3a4803349091fb5c0bb2137755b6e529f13323e7 Mon Sep 17 00:00:00 2001 From: Aroon Chande Date: Tue, 5 Mar 2019 09:02:40 -0500 Subject: [PATCH] Patch version bump becayse PyPi is obtuse --- README.rst | 730 -------------------------------------------------- setup.py | 15 +- stringMLST.py | 2 +- 3 files changed, 7 insertions(+), 740 deletions(-) delete mode 100644 README.rst diff --git a/README.rst b/README.rst deleted file mode 100644 index a466057..0000000 --- a/README.rst +++ /dev/null @@ -1,730 +0,0 @@ -stringMLST -========== - -Fast k-mer based tool for multi locus sequence typing (MLST) stringMLST -is a tool for detecting the MLST of an isolate directly from the genome -sequencing reads. stringMLST predicts the ST of an isolate in a -completely assembly and alignment free manner. The tool is designed in a -light-weight, platform-independent fashion with minimum dependencies. - -Some portions of the allele selection algorithm in stringMLST are patent -pending. Please refer to the PATENTS file for additional inforamation -regarding licencing and use. - -| Reference -| *http://jordan.biology.gatech.edu/page/software/stringmlst/* - -| Abstract -| *http://bioinformatics.oxfordjournals.org/content/early/2016/09/06/bioinformatics.btw586.short?rss=1* - -| Application Note -| *http://bioinformatics.oxfordjournals.org/content/early/2016/09/06/bioinformatics.btw586.full.pdf+html* - -|install with bioconda| |PyPI version| - -**stringMLST is a *tool* not a *database*, always use the most -up-to-date database files as possible.** To facilitate keeping your -databases updated, stringMLST can download and build databases from -pubMLST using the most recent allele and profile definitions. Please see -the "Included databases and automated retrieval of databases from -pubMLST" section below for instructions. *The databases bundled here are -for convenience only, do not rely on them being up-to-date*. - -| stringMLST is licensed and distributed under `CC - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA - 4.0) `__ -| and is free for academic users and requires permission before any - commercial use for any version of this code/algorithm. -| If you are a commercial user, please contact - king.jordan@biology.gatech.edu for permissions - -Recommended installation method -------------------------------- - -:: - - pip install stringMLST - -Installation via git (Not recommended for most users) -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -:: - - git clone https://github.com/jordanlab/stringMLST - # Optional, download prebuilt databases - # We don't recommend this method, instead build the databases locally - cd stringMLST - git submodule init - git submodule update - -Quickstart guide ----------------- - -.. code:: bash - - pip install stringMLST - mkdir -p stringMLST_analysis; cd stringMLST_analysis - stringMLST.py --getMLST -P neisseria/nmb --species neisseria - # Download all available databases with: - # stringMLST.py --getMLST -P mlst_dbs --species all - wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR026/ERR026529/ERR026529_1.fastq.gz ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR026/ERR026529/ERR026529_2.fastq.gz - stringMLST.py --predict -P neisseria/nmb -1 ERR026529_1.fastq.gz -2 ERR026529_2.fastq.gz - Sample abcZ adk aroE fumC gdh pdhC pgm ST - ERR026529 231 180 306 612 269 277 260 10174 - -Python dependencies and external programs ------------------------------------------ - -stringMLST does not require any python dependencies for basic usage -(Building databases and predicting STs). - -| For advanced used (genome coverage), stringMLST depends on the - ``pyfaidx`` python module and ``bamtools``, ``bwa``, and ``samtools``. -| See the coverage section for more information - -stringMLST has been tested with: - -:: - - pyfaidx: 0.4.8.1 - samtools: 1.3 (Using htslib 1.3.1) [Requires the 1.x branch of samtools] - bedtools: v2.24.0 - bwa: 0.7.13-r1126 - -To install the dependencies -~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. code:: bash - - # pyfaidx - pip install --user pyfaidx - # samtools - wget https://github.com/samtools/samtools/releases/download/1.3.1/samtools-1.3.1.tar.bz2 -o samtools-1.3.1.tar.bz2 - tar xf samtools-1.3.1.tar.bz2 - cd samtools-1.3.1.tar - make - make prefix=$HOME install - # bedtools - wget https://github.com/arq5x/bedtools2/releases/download/v2.25.0/bedtools-2.25.0.tar.gz - tar -zxvf bedtools-2.25.0.tar.gz - cd bedtools2; make - cp ./bin/* ~/bin - # bwa - git clone https://github.com/lh3/bwa.git - cd bwa; make - cp bwa ~/bin/bwa - export PATH=$PATH:$HOME/bin - -Usage for Example Read Files (Neisseria meningitidis) ------------------------------------------------------ - -- Download stringMLST.py, example read files (ERR026529, ERR027250, - ERR036104) and the dataset for Neisseria meningitidis - (Neisseria_spp.zip). - -Build database: -~~~~~~~~~~~~~~~ - -:: - - # Add dir to path - export PATH=$PATH:$PWD - # Will connect to EBI's SRA servers - download_example_reads.sh - -- Extract the MLST loci dataset. - -:: - - unzip datasets/Neisseria_spp.zip -d datasets - -- Create or use a config file specifying the location of all the locus - and profile files. Example config file (Neisseria_spp/config.txt): - -:: - - [loci] - abcZ datasets/Neisseria_spp/abcZ.fa - adk datasets/Neisseria_spp/adk.fa - aroE datasets/Neisseria_spp/aroE.fa - fumC datasets/Neisseria_spp/fumC.fa - gdh datasets/Neisseria_spp/gdh.fa - pdhC datasets/Neisseria_spp/pdhC.fa - pgm datasets/Neisseria_spp/pgm.fa - [profile] - profile datasets/Neisseria_spp/neisseria.txt - -- Run stringMLST.py --buildDB to create DB. Choose a k value and prefix - (optional). - -:: - - stringMLST.py --buildDB -c databases/Neisseria_spp/config.txt -k 35 -P NM - -Predict: -~~~~~~~~ - -.. _single-sample-: - -Single sample : -^^^^^^^^^^^^^^^ - -:: - - stringMLST.py --predict -1 tests/fastqs/ERR026529_1.fastq -2 tests/fastqs/ERR026529_2.fastq -k 35 -P NM - -Batch mode (all the samples together): -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -:: - - stringMLST.py --predict -d ./tests/fastqs/ -k 35 -P NM - -List mode: -^^^^^^^^^^ - -Create a list file (list_paired.txt) as : - -:: - - tests/fastqs/ERR026529_1.fastq tests/fastqs/ERR026529_2.fastq - tests/fastqs/ERR027250_1.fastq tests/fastqs/ERR027250_2.fastq - tests/fastqs/ERR036104_1.fastq tests/fastqs/ERR036104_2.fastq - -Run the tool as: - -:: - - stringMLST.py --predict -l list_paired.txt -k 35 -P NM - -Working with gziped files -^^^^^^^^^^^^^^^^^^^^^^^^^ - -:: - - stringMLST.py --predict -1 tests/fastqs/ERR026529_1.fq.gz -2 tests/fastqs/ERR026529_2.fq.gz -p -P NM -k 35 -o ST_NM.txt - -Usage Documentation -------------------- - -stringMLST's workflow is divided into two routines: - -- Database building and -- ST discovery - -*Database building:* Builds the stringMLST database which is used for -assigning STs to input sample files. This step is required once for each -organism. Please note that stringMLST is capable of working on a custom -user defined typing scheme but its efficiency has not been tested on -other typing scheme. - -*ST discovery:* This routine takes the database created in the last step -and predicts the ST of the input sample(s). Please note that the -database building is required prior to this routine. stringMLST is -capable of processing single-end and paired-end files. It can run in -three modes: - -- Single sample mode - for running stringMLST on a single sample -- Batch mode - for running stringMLST on all the FASTQ files present in - a directory -- List mode - for running stringMLST on all the FASTQ files provided in - a list file - -:: - - Readme for stringMLST - ============================================================================================= - Usage - ./stringMLST.py - [--buildDB] - [--predict] - [-1 filename_fastq1][--fastq1 filename_fastq1] - [-2 filename_fastq2][--fastq2 filename_fastq2] - [-d directory][--dir directory][--directory directory] - [-l list_file][--list list_file] - [-p][--paired] - [-s][--single] - [-c][--config] - [-P][--prefix] - [-z][--fuzzy] - [-a] - [-C][--coverage] - [-k] - [-o output_filename][--output output_filename] - [-x][--overwrite] - [-t] - [-r] - [-v] - [-h][--help] - ============================================================================================== - - There are two steps to predicting ST using stringMLST. - 1. Create DB : stringMLST.py --buildDB - 2. Predict : stringMLST --predict - - 1. stringMLST.py --buildDB - - Synopsis: - stringMLST.py --buildDB -c -k -P - config file : is a tab delimited file which has the information for typing scheme ie loci, its multifasta file and profile definition file. - Format : - [loci] - locus1 locusFile1 - locus2 locusFile2 - [profile] - profile profileFile - kmer length : is the kmer length for the db. Note, while processing this should be smaller than the read length. - We suggest kmer lengths of 35, 66 depending on the read length. - DB prefix(optional) : holds the information for DB files to be created and their location. This module creates 3 files with this prefix. - You can use a folder structure with prefix to store your db at particular location. - - Required arguments - --buildDB - Identifier for build db module - -c,--config = - Config file in the format described above. - All the files follow the structure followed by pubmlst. Refer extended document for details. - - Optional arguments - -k = - Kmer size for which the db has to be formed(Default k = 35). Note the tool works best with kmer length in between 35 and 66 - for read lengths of 55 to 150 bp. Kmer size can be increased accordingly. It is advised to keep lower kmer sizes - if the quality of reads is not very good. - -P,--prefix = - Prefix for db and log files to be created(Default = kmer). Also you can specify folder where you want the dbb to be created. - -a - File location to write build log - -h,--help - Prints the help manual for this application - - -------------------------------------------------------------------------------------------- - - 2. stringMLST.py --predict - - stringMLST --predict : can run in three modes - 1) single sample (default mode) - 2) batch mode : run stringMLST for all the samples in a folder (for a particular specie) - 3) list mode : run stringMLST on samples specified in a file - stringMLST can process both single and paired end files. By default program expects paired end files. - - Synopsis - stringMLST.py --predict -1 -2 -d -l -p -s -P -k -o -x - - Required arguments - --predict - Identifier for predict miodule - - Optional arguments - -1,--fastq1 = - Path to first fastq file for paired end sample and path to the fastq file for single end file. - Should have extension fastq or fq. - -2,--fastq2 = - Path to second fastq file for paired end sample. - Should have extension fastq or fq. - -d,--dir,--directory = - BATCH MODE : Location of all the samples for batch mode. - -C,--coverage - Calculate seqence coverage for each allele. Turns on read generation (-r) and turns off fuzzy (-z 1) - Requires bwa, bamtools and samtools be in your path - -k = - Kmer length for which the db was created(Default k = 35). Could be verified by looking at the name of the db file. - Could be used if the reads are of very bad quality or have a lot of N's. - -l,--list = - LIST MODE : Location of list file and flag for list mode. - list file should have full file paths for all the samples/files. - Each sample takes one line. For paired end samples the 2 files should be tab separated on single line. - -o,--output = - Prints the output to a file instead of stdio. - -p,--paired - Flag for specifying paired end files. Default option so would work the same if you do not specify for all modes. - For batch mode the paired end samples should be differentiated by 1/2.fastq or 1/2.fq - -P,--prefix = - Prefix using which the db was created(Defaults = kmer). The location of the db could also be provided. - -r - A seperate reads file is created which has all the reads covering all the locus. - -s,--single - Flag for specifying single end files. - -t - Time for each analysis will also be reported. - -v - Prints the version of the software. - -x,--overwrite - By default stringMLST appends the results to the output_filename if same name is used. - This argument overwrites the previously specified output file. - -z,--fuzzy = - Threshold for reporting a fuzzy match (Default=300). For higher coverage reads this threshold should be set higher to avoid - indicating fuzzy match when exact match was more likely. For lower coverage reads, threshold of <100 is recommended - -h,--help - Prints the help manual for this application - - -------------------------------------------------------------------------------------------- - - 3. stringMLST.py --getMLST - - Synopsis: - stringMLST.py --getMLST --species= [-k kmer length] [-P DB prefix] - - Required arguments - --getMLST - Identifier for getMLST module - --species= - Species name from the pubMLST schemes (use --schemes to get list of available schemes) - "all" will download and build all - - Optional arguments - -k = - Kmer size for which the db has to be formed(Default k = 35). Note the tool works best with kmer length in between 35 and 66 - for read lengths of 55 to 150 bp. Kmer size can be increased accordingly. It is advised to keep lower kmer sizes - if the quality of reads is not very good. - -P,--prefix = - Prefix for db and log files to be created(Default = kmer). Also you can specify folder where you want the db to be created. - We recommend that prefix and config point to the same folder for cleanliness but this is not required - --schemes - Display the list of available schemes - -h,--help - Prints the help manual for this application - -**stringMLST expects paired end reads to be in `Illumina naming -convention `__, -minimally ending with \_1.fq and \_2.fq to delineate read1 and read2:** - -*Periods (.) are disallowed delimiters except for file extensions* - -:: - - Illumina FASTQ files use the following naming scheme: - - __L_R_.fastq.gz - - For example, the following is a valid FASTQ file name: - - NA10831_ATCACG_L002_R1_001.fastq.gz - -Running stringMLST ------------------- - -Included databases and automated retrieval of databases from pubMLST -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -| stringMLST includes all the pubMLST databases as of **February 15, - 2017**, built with the default kmer (*35*). They can be found in the - ``datasets/`` folder. -| Simply unzip the databases you need and begin using stringMSLT as - described below. - -All the databases from pubMLST can be downloaded and prepared with your -kmer choice - -*Getting all pubMLST schemes* - -:: - - stringMLST.py --getMLST -P datasets/ --species all - -Individual databases from pubMLST can also be downloaded as needed, -using the scheme identifiers - -*Downloading a scheme* - -:: - - # List available schemes - stringMLST.py --getMLST --schemes - - # Download the Neisseria spp. scheme - - stringMLST.py --getMLST -P datasets/nmb --species neisseria - -Database Preparation -^^^^^^^^^^^^^^^^^^^^ - -In order to create the database, files can be downloaded from the -database page. - -If the organism of interest is not present in the provided link, the -required files can be downloaded from PubMLST as follows: - -- On your browser, navigate to http://pubmlst.org/ -- Navigate to "Download MLST definitions" link or go to - http://pubmlst.org/data/ -- Scroll to the species of interest. For each species, user may find - the file for typing definitions and multi-FASTA files for each locus. - Download these files. - -E.g.: - -Species of interest: Neisseria spp. Corresponding definition file: -http://pubmlst.org/data/profiles/neisseria.txt Corresponding multi fasta -locus files: http://pubmlst.org/data/alleles/neisseria/abcZ.tfa -http://pubmlst.org/data/alleles/neisseria/adk.tfa -http://pubmlst.org/data/alleles/neisseria/aroE.tfa -http://pubmlst.org/data/alleles/neisseria/fumC.tfa -http://pubmlst.org/data/alleles/neisseria/gdh.tfa -http://pubmlst.org/data/alleles/neisseria/pdhC.tfa -http://pubmlst.org/data/alleles/neisseria/pgm.tfa - -Download these files at a desired location. - -Custom user files can also be used for building database. The database -building routine requires the profile definition file and allele -sequence file. The profile definition file is a tab separated file that -contains the ST and the allele profile corresponding to the ST. An -example of the profile definition file is shown below: - -:: - - ST abcZ adk aroE fumC gdh pdhC pgm clonal_complex - 1 1 3 1 1 1 1 3 ST-1 complex/subgroup I/II - 2 1 3 4 7 1 1 3 ST-1 complex/subgroup I/II - 3 1 3 1 1 1 23 13 ST-1 complex/subgroup I/II - 4 1 3 3 1 4 2 3 ST-4 complex/subgroup IV - -The allele sequence file is a standard multi-FASTA with the description -being the loci name with the allele number. An example abcZ allele -sequence is shown below: - -:: - - >abcZ_1 - TTTGATACTGTTGCCGA... - >abcZ_2 - TTTGATACCGTTGCCGA... - >abcZ_3 - TTTGATACCGTTGCGAA... - >abcZ_4 - TTTGATACCGTTGCCAA... - -These files can be obtained from PubMLST/BIGSdb or can be create by the -user themselves. - -In either case, an accompanying configuration file is also required to -describe the profile definition and allele sequence files. An example -configuration file is shown below: - -:: - - [loci] - abcZ /data/home/stringMLST/pubmlst/Neisseria_sp/abcZ.fa - adk /data/home/stringMLST/pubmlst/Neisseria_sp/adk.fa - aroE /data/home/stringMLST/pubmlst/Neisseria_sp/aroE.fa - fumC /data/home/stringMLST/pubmlst/Neisseria_sp/fumC.fa - gdh /data/home/stringMLST/pubmlst/Neisseria_sp/gdh.fa - pdhC /data/home/stringMLST/pubmlst/Neisseria_sp/pdhC.fa - pgm /data/home/stringMLST/pubmlst/Neisseria_sp/pgm.fa - - [profile] - profile /data/home/stringMLST/pubmlst/Neisseria_sp/neisseria.txt - -This file is pre-packed on stringMLSTs website and can easily be created -by the user for custom database. - -Database Building -^^^^^^^^^^^^^^^^^ - -The next step is for database building is running the buildDB module to -create the database files. buildDB module requires the user to specify -the config file. The default k-mer size is 35 but can be changed using -the -k option. Specifying the prefix for the created database files is -optional but is recommended. - -The choice of k-mer depends on the size of the sequencing read. In -general, the value of k can never be greater than the read length. The -application has been tested on a number of read lengths ranging from 55 -to 150 bps using k-mer sizes of 21 to 66. In our testing, the k-mer size -does not affect the accuracy of the read length. A smaller k-mer size -will increase the runtime and a larger k-mer size will increase the file -size. The user should ideally pick a k-mer with a length around half of -the average read length. For lower quality data, it also advised to -choose smaller k-mer values to reduce false hits. - -:: - - stringMLST.py --buildDB --config -k -P - -Example: - -:: - - stringMLST.py --buildDB --config config.txt -k 35 -P NM - -This command will produce 3 database files and a log file. The log file -is used for debugging purposes in the event an error is encountered. The -3 database files created are: - -- \_.txt : The main database file for the application. This is a tab - delimited file describing k-mer to locus relationship. -- \_weight.txt : Contains the weight factors for alleles which differ - in lengths by more than 5%. Will be empty otherwise. -- \_profile.txt : Profile definition file used for finding the ST from - the predicted allelic profile. - -For the example above, the following files will be created: NM_35.txt, -NM_weight.txt and NM_profile.txt - -Please note that in the prediction routine the database is identified -with the prefix. - -ST discovery routine As discussed earlier, StringMLST has 3 running -modes - -- Single sample mode - for running stringMLST on a single sample -- Batch mode - for running stringMLST on all the FASTQ files present in - a directory -- List mode - for running stringMLST on all the FASTQ files provided in - a list file - -Single sample mode: -^^^^^^^^^^^^^^^^^^^ - -This is the default mode for stringMLST and takes in one sample at a -time. The sample can be single-end or paired-end. The sample has to be -in FASTQ format. In order to run, the user should know the prefix of the -database created and the k-mer size. - -By default, the tool expects paired-end samples. - -:: - - stringMLST.py --predict -1 -2 -p --prefix -k -o - -*For single-end samples:* - -:: - - stringMLST.py --predict -1 -s --prefix -k -o - -Batch Mode: -^^^^^^^^^^^ - -This mode can be used for processing multiple files with one command. -All the samples will be queried against the same database. Also all -samples should be in the same directory. All the samples will be treated -either as single-end or paired-end. The paired-end samples should be -differentiated with the character \_1 and \_2 at the end (E.g.: -sampleX_1.fastq and sampleX_2.fastq). - -*Paired-end samples:* - -:: - - stringMLST.py --predict -d -p --prefix -k -o - -*Single-end samples:* - -:: - - stringMLST.py --predict -d -s --prefix -k -o - -.. _list-mode-1: - -List Mode: -^^^^^^^^^^ - -This mode could be used if user has samples at different locations or if -the paired-end samples are not stored in traditional way. All the -samples will be queried against the same database. All the samples will -be treated either as single-end or paired-end. This mode requires the -user to provide a list file which has the list of all samples along with -the location. Each line in the list file represents a new sample. A -sample list file for single-end sample looks like the following. - -:: - - - - - . - . - - -A sample list file for paired-end sample looks like the following. - -:: - - - - - . - . - - -Once the user has the list file, he can directly use the tool. - -*Paired-end samples:* - -:: - - stringMLST.py --predict -l -p --prefix -k -o - -*Single-end samples:* - -:: - - stringMLST.py --predict -l -s --prefix -k -o - -Gene coverage and match confidence -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -stringMLST provides two, complimentary methods for determining -confidence in an inferred ST. There's the ``-C|--coverage`` flag and -``-z|--fuzzy`` threshold option. - -stringMLST determines an allele based on its kmer support; the more -kmers seen for allele 1, the more likely that allele 1 is the allele -present in the genome. Unlike SRST2 and other mapping/BLAST based tools, -stringMLST always infers an ST, using the maximimally supported allele -(allele with most kmer hits). The difference between the maximum support -(the reported allele) and the second support (next closest allele) can -be informative for low coverage reads. The ``-z|--fuzzy`` threshold -(Default = 300), assigns significance to the difference between -supports. Much like SRST2 and Torsten Seemann's popular `pubMLST -script `__, stringMLST reports -potentially new or closely supported alleles in allele\* syntax. For -high coverage reads, we suggest a fuzzy threshold >500. For low coverage -reads, a fuzzy threshold of <50. - -Coverage mode requires ``bedtools``, ``bwa``, and ``samtools`` in your -PATH and an additional python module, ``pyfaidx`` (See the dependencies -section for installion information). Coverage mode by default disables -display of fuzzy alleles in favor of sequence coverage information made -by mapping potential reads to the putative allele sequence. In our -testing, coverage mode slightly increases prediction time (<1 sec -increase per sample). - -**Please note:** stringMLST *always* infers the ST from the reads, fuzzy -matches and/or <100% coverage do not necessarily mean a new allele has -been found. - -*Getting gene coverage from reads* - -:: - - stringMLST.py --predict -1 -2 -p --prefix -k -r -o - -c -C - -*Changing the fuzziness of the search for low coverage reads* - -:: - - stringMLST.py --predict -1 -2 -p --prefix -k -r -o - -f 50 - -.. _other-examples-: - -Other Examples : -^^^^^^^^^^^^^^^^ - -*Reporting time along with the output.* - -:: - - stringMLST.py --predict -1 -2 -p --prefix -k -t -o - -*Getting reads file relevant to typing scheme.* - -:: - - stringMLST.py --predict -1 -2 -p --prefix -k -r -o - -.. |install with bioconda| image:: https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat-square - :target: http://bioconda.github.io/recipes/stringmlst/README.html -.. |PyPI version| image:: https://badge.fury.io/py/stringMLST.svg - :target: https://badge.fury.io/py/stringMLST \ No newline at end of file diff --git a/setup.py b/setup.py index e88193b..d4347e1 100644 --- a/setup.py +++ b/setup.py @@ -6,20 +6,17 @@ from distutils.core import setup from os import path here = path.abspath(path.dirname(__file__)) -try: - with open("README.md", "r") as fh: - long_description = fh.read() - except: - pass - else: - long_description = 'Fast k-mer based tool for alignment and assembly-free multi locus sequence typing (MLST) directly from genome sequencing reads.' +def readme(file): + with open(path.join(here, 'README.md')) as fh: + long_description_text = fh.read() + return(long_description_text) setup( name = 'stringMLST', scripts = ['stringMLST.py'], - version = '0.6', + version = "0.6.1", description = 'Fast k-mer based tool for alignment and assembly-free multi locus sequence typing (MLST) directly from genome sequencing reads.', - long_description=long_description, + long_description=readme('README.md'), long_description_content_type="text/markdown", author = 'Jordan Lab', author_email = 'pypi@atc.io', diff --git a/stringMLST.py b/stringMLST.py index 7881065..b3c431b 100755 --- a/stringMLST.py +++ b/stringMLST.py @@ -15,7 +15,7 @@ except ImportError: from urllib import urlopen, urlretrieve import argparse -version = """ stringMLST v0.6 (updated : March, 5 2019) """ +version = """ stringMLST v0.6.1 (updated : March, 5 2019) """ """ stringMLST free for academic users and requires permission before any commercial