Skip to content

Latest commit

 

History

History
99 lines (80 loc) · 5.53 KB

README.md

File metadata and controls

99 lines (80 loc) · 5.53 KB
Raw

ShigaPass

ShigaPass is a new in silico tool used to predict Shigella serotypes and to differentiate between Shigella, EIEC (Enteroinvasive E. coli), and non Shigella/EIEC using assembled whole genomes.

Dependencies

ShigaPass is a command line tool written in Bash version 4.4.20 and requires Blast+ version 2.12.0 to run.

Installation

1. Clone this repository with the following command line:

git clone https://github.com/imanyass/ShigaPass.git

2. Give the execute permission to the file ShigaPass.sh:

chmod +x ShigaPass.sh

3. Execute ShigaPass with the following command line model:

./ShigaPass.sh  [options]

Usage

Run ShigaPass without option to read the following documentation:

###### This tool is used to predict Shigella serotypes  #####
        Usage : ShigaPass.sh [options]
   
        options :
        -l	List of input file(s) (FASTA) with their path(s) (mandatory)
        -o	Output directory (mandatory)
        -p	Path to databases directory (mandatory)
        -t	Number of threads (optional, default: 2)
        -u	Call the makeblastdb utility for databases initialisation (optional, but required when running the script for the first time)
        -k	Do not remove subdirectories (optional)
       	-v	Display the version and exit
        -h	Display this help and exit
        Example: ShigaPass.sh -l list_of_fasta.txt -o ShigaPass_Results -p ShigaPass/ShigaPass_DataBases -t 4 -u -k
        Please note that the -u option should be used when running the script for the first time and after databases updates

Example

  • The Fasta sequence files are available in the directory Example/Input

    • Please unzip the sequences (using gunzip) before running ShigaPass

  • All output files are available in the directory Example/ShigaPass_Results

Running ShigaPass

Create a list file containing the paths to the FASTA files then run ShigaPass

ShigaPass.sh -l ShigaPass_test.txt -o ShigaPass_Results -p ShigaPass_DataBases -u -k

Here's an example of ShigaPass summary file

Name rfb rfb_hits,(%) MLST fliC CRISPR ipaH Predicted_Serotype Predicted_FlexSerotype Comments
ERR5888634 C2 79,(48.2%) ST145 ShH57(ShH3cplx) A-var2 ipaH+ SB2
ERR5952732 B1-5 139,(93.3%) ST245 ShH2(ShH2cplx) A-var3,x,16 ipaH+ SF1-5 1b
ERR5976293 D 202,(70.6%) ST152 ShH25(ShH1cplx) A-var0,27 ipaH+ SS
ERR5982186 A2 100,(61.7%) ST147 none A-var1,12,3,5,11-var1 ipaH+ SD2

"none" means that no allele/profile is detected (in the ERR5982186 example no fliC allele was detected)

SB: S. boydii; SD: S. dysenteriae; SF: S. flexneri; SS: S. sonnei

Output Files

  • In the output directory, two files will be written:
    1. ShigaPass_summary.csv: semicolon-delimited file with one row per genome inclinding the sample name; type of rfb; number of rfb hits, (% of rfb coverage); MLST profile; type of fliC; CRISPR spacers; the presence of ipaH; the predicted serotype and S. flexneri subserotype; comments to show the number of rfb when more than one is detected
    2. ShigaPass_Flex_summary.csv: semicolon-delimited file detailing the phage and plasmid-encoded O-antigen modification (POAC) genes detected for the predicted S. flexneri genomes
  • In case -k option is used, a directory will be created for every assembled genome and will contain the following files:
Extension Description
blastout.txt Blast results in tabular format
allrecords.txt Blast hits that passed the selected thresholds
records.txt The best blast hit that passed the selected thresholds
hits.txt Name and number of hits that passed the selected thresholds (only for k-mers databases: rfb, ipaH and POAC genes)
hitscoverage.txt This file displays in addition to the name and the number of hits detected present in hits.txt, the total hits number for the identified gene (3rd column) and the percentage of the hits detected (number of hits detected/total number of hits) (4th column)

Notes

The Fasta sequences were assembled using SPAdes version 3.15 (Bankevich et al. Journal of Computational Biology, 2012) with the following options: -k 21,33,55,77 --only-assembler --careful --cov-cutoff auto

You can download the short reads using the following command lines:

wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR588/004/ERR5888634/ERR5888634_1.fastq.gz
wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR588/004/ERR5888634/ERR5888634_2.fastq.gz
wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR595/002/ERR5952732/ERR5952732_1.fastq.gz
wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR595/002/ERR5952732/ERR5952732_2.fastq.gz
wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR597/003/ERR5976293/ERR5976293_1.fastq.gz
wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR597/003/ERR5976293/ERR5976293_2.fastq.gz
wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR598/006/ERR5982186/ERR5982186_1.fastq.gz
wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/ERR598/006/ERR5982186/ERR5982186_2.fastq.gz

All reads were filtered with FqCleanER version 3.0 (https://gitlab.pasteur.fr/GIPhy/fqCleanER) with options -q 15 -l 50