/rgi

Resistance Gene Identifier (RGI)

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Resistance Gene Identifier (RGI)

This application is used to predict resistome(s) from protein or nucleotide data based on homology and SNP models. The application uses data from CARD database.

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Table of Contents

License

Use or reproduction of these materials, in whole or in part, by any non-academic organization whether or not for non-commercial (including research) or commercial purposes is prohibited, except with written permission of McMaster University. Commercial uses are offered only pursuant to a written license and user fee. To obtain permission and begin the licensing process, see CARD website

Requirements

Install dependencies

  • pip3 install six
  • pip3 install biopython
  • pip3 install filetype
  • pip3 install pytest
  • pip3 install mock
  • pip3 install pandas
  • pip3 install matplotlib
  • pip3 install seaborn
  • pip3 install pyfaidx
  • pip3 install pyahocorasick

Install RGI from project root

pip3 install .

or

python3 setup.py build
python3 setup.py test
python3 setup.py install

Running RGI tests

cd tests
pytest -v -rxs

Help menu

rgi --help

Usage

usage: rgi <command> [<args>]
            commands are:
            main     Runs rgi application
            tab      Creates a Tab-delimited from rgi results
            parser   Creates categorical JSON files RGI wheel visualization
            load     Loads CARD database JSON file
            clean    Removes BLAST databases and temporary files
            galaxy   Galaxy project wrapper
            database Information on installed card database
            heatmap  Heatmap for multiple analysis
            ---------------------------------------------------------------------------------------
            bwt                   Metagenomics resistomes (Experimental)
            card_annotation       Create fasta files with annotations from card.json (Experimental)
            wildcard_annotation   Create fasta files with annotations from variants (Experimental)
            baits_annotation      Create fasta files with annotations from baits (Experimental)
            remove_duplicates     Removes duplicate sequences (Experimental)
            kmer_build            Build CARD*kmer database (Experimental)
            kmer_query            Query sequences through CARD*kmers (Experimental)

Resistance Gene Identifier - <version_number>

positional arguments: command Subcommand to run

optional arguments: -h, --help show this help message and exit

Use the Resistance Gene Identifier to predict resistome(s) from protein or nucleotide data based on homology and SNP models. Check https://card.mcmaster.ca/download for software and data updates. Receive email notification of monthly CARD updates via the CARD Mailing List (https://mailman.mcmaster.ca/mailman/listinfo/card-l)

Load card.json

  • local or working directory

    rgi load --card_json /path/to/card.json --local

  • system wide

    rgi load --card_json /path/to/card.json

Check database version

  • local or working directory

    rgi database --version --local

  • system wide

    rgi database --version

Run RGI

  • local or working directory

    rgi main --input_sequence /path/to/protein_input.fasta --output_file /path/to/output_file --input_type protein --local

  • system wide

    rgi main --input_sequence /path/to/nucleotide_input.fasta --output_file /path/to/output_file --input_type contig

Run RGI using GNU parallel

  • system wide and writing log files for each input file. (Note add code below to script.sh then run with ./script.sh /path/to/input_files)

    #!/bin/bash
DIR=`find . -mindepth 1 -type d`
for D in $DIR; do
      NAME=$(basename $D);
      parallel --no-notice --progress -j+0 'rgi main -i {} -o {.} -n 16 -a diamond --clean --debug > {.}.log 2>&1' ::: $NAME/*.{fa,fasta};
done

Running RGI with short contigs to predict partial genes

  • local or working directory

    rgi main --input_sequence /path/to/nucleotide_input.fasta --output_file /path/to/output_file --local --low_quality

  • system wide

    rgi main --input_sequence /path/to/nucleotide_input.fasta --output_file /path/to/output_file --low_quality

Clean previous or old databases

  • local or working directory

    rgi clean --local

  • system wide

    rgi clean

RGI Heatmap

  • Default Heatmap

    rgi heatmap --input /path/to/rgi_results_json_files_directory/

  • Heatmap with AMR Gene Family categorization

    rgi heatmap --input /path/to/rgi_results_json_files_directory/ --category gene_family

  • Heatmap with AMR Gene Family categorization and fill display

    rgi heatmap --input /path/to/rgi_results_json_files_directory/ --category gene_family --display fill

  • Heatmap with AMR Gene Family categorization and coloured y-axis labels display

    rgi heatmap --input /path/to/rgi_results_json_files_directory/ --category gene_family --display text

  • Heatmap with frequency display enabled

    rgi heatmap --input /path/to/rgi_results_json_files_directory/ --frequency

  • Heatmap with drug class category and frequency enabled

    rgi heatmap --input /path/to/rgi_results_json_files_directory/ --category drug_class --frequency --display text

  • Heatmap with samples and genes clustered

    rgi heatmap --input /path/to/rgi_results_json_files_directory/ --cluster both

  • Heatmap with resistance mechanism categorization and clustered samples

    rgi heatmap --input /path/to/rgi_results_json_files_directory/ --cluster samples --category resistance_mechanism --display fill

Run RGI from docker

  • First you you must either pull the docker container from dockerhub (latest CARD version automatically installed)

    docker pull finlaymaguire/rgi

  • Or Alternatively, build it locally from the Dockerfile (latest CARD version automatically installed)

    git clone https://github.com/arpcard/rgi
docker build -t arpcard/rgi rgi

  • Then you can either run interactively (mounting a local directory called rgi_data in your current directory to /data/ within the container

    docker run -i -v $PWD/rgi_data:/data -t arpcard/rgi bash

  • Or you can directly run the container as an executable with $RGI_ARGS being any of the commands described above. Remember paths to input and outputs files are relative to the container (i.e. /data/ if mounted as above).

    docker run -v $PWD/rgi_data:/data arpcard/rgi $RGI_ARGS

Tab-delimited results file

::

ORF_ID

 Open Reading Frame identifier (internal to RGI)
::

Contig

 Source Sequence
::

Start

 Start co-ordinate of ORF
::

Stop

 End co-ordinate of ORF
::

Orientation

 Strand of ORF
::

Cut_Off

 RGI Detection Paradigm
::

Pass_Bitscore

 STRICT detection model bitscore value cut-off
::

Best_Hit_Bitscore

 Bitscore value of match to top hit in CARD
::

Best_Hit_ARO

 ARO term of top hit in CARD
::

Best_Identities

 Percent identity of match to top hit in CARD
::

ARO

 ARO accession of top hit in CARD
::

Model_type

 CARD detection model type 
SNPs_in_Best_Hit_ARO
 Mutations observed in the ARO term of top hit in CARD (if applicable) 
Other_SNPs
 Mutations observed in ARO terms of other hits indicated by model id (if applicable)
::

Drug Class

 ARO Categorization
::

Resistance Mechanism

 ARO Categorization
::

AMR Gene Family

 ARO Categorization
::

Predicted_DNA

 ORF predicted nucleotide sequence
::

Predicted_Protein

 ORF predicted protein sequence
::

CARD_Protein_Sequence

 Protein sequence of top hit in CARD 
Percentage Length of Reference Sequence
 Calculated as percentage (length of ORF protein / length of CARD reference protein)
::

ID

 HSP identifier (internal to RGI)
::

Model_id

 CARD detection model id

Support & Bug Reports

Please log an issue on github issue.

You can email the CARD curators or developers directly at card@mcmaster.ca, via Twitter at @arpcard.