Skip to main content

Ranking the risk of antibiotic resistance for genomes/metagenomes

Project description

arg_ranker

arg_ranker evaluates the risk of ARGs in genomes and metagenomes

Install

pip install arg_ranker

Requirement

  • python 3
  • kraken2: conda install -c bioconda kraken2
    download kraken2 database: kraken2-build --standard --db $KRAKENDB
    where $krakenDB is your preferred database name/location\
  • diamond: conda install -c bioconda diamond\
  • blast+: conda install -c bioconda blast

How to use it

  • put all your genomes (.fa or .fasta) and metagenomes (.fq or .fastq) into one folder ($INPUT)
  • run arg_ranker -i $INPUT --kkdb $KRAKENDB
  • run sh arg_ranking/script_output/arg_ranker.sh

Output

  • Sample_ranking_results.txt (Table 1)

    Sample Rank_I_abu Rank_II_abu Rank_III_abu Rank_IV_abu Unassessed_abu Total_abu Rank_code Rank_I_risk Rank_II_risk Rank_III_risk Rank_IV_risk ARGs_unassessed_risk note1
    WEE300_all-trimmed-decont_1.fastq 2.9E-02 0.0E+00 7.4E-02 7.8E-01 1.2E-01 4.2E-04 1.0-0.0-0.5-1.7-0.3 1.0 0.0 0.5 1.7 0.3 hospital_metagenome
    EsCo_genome.fasta 0.0E+00 0.0E+00 0.0E+00 1.0E+00 0.0E+00 2.0E+00 0.0-0.0-0.0-2.2-0.0 0.0 0.0 0.0 2.2 0.0 E.coli_genome
  1. We compute the abundance of ARGs as the copy number of ARGs divided by the 16S copy number in a sample
    Rank_I - Unassessed_abu: total abundance of ARGs of a risk rank
    Total_abu: total abundance of all ARGs
  2. We compute the risk of ARGs as the average abundance of ARGs of a risk rank divided the average abundance of all ARGs
    Rank_I_risk - Unassessed_risk: the risk of ARGs of a risk rank
    Rank_code: a code of ARG risk from Rank I to Unassessed
  • Sample_ARGpresence.txt:
    The abundance, the gene family, and the antibiotic of resistance of ARGs detected in the input samples

Test

run arg_ranker -i example --kkdb $KRAKENDB
run sh arg_ranking/script_output/arg_ranker.sh
The arg_ranking/Sample_ranking_results.txt should look like Table 1

Metadata for your samples (optional)

arg_ranker can merge your sample metadata into the results of ARG ranking (i.e. note1 in Table 1).
Simply put all information you would like to include into a tab-delimited table
Make sure that your sample names are listed as the first column (check example/metadata.txt).

Copyright

Dr. An-Ni Zhang (MIT), Prof. Eric Alm (MIT), Prof. Tong Zhang* (University of Hong Kong)

Citation

  1. Zhang AN, ..., Alm EJ, Zhang T: Choosing Your Battles: Which Resistance Genes Warrant Global Action? (bioRxiv coming soon)
  2. Yang Y, ..., Tiedje JM, Zhang T: ARGs-OAP: online analysis pipeline for antibiotic resistance genes detection from metagenomic data using an integrated structured ARG-database. Bioinformatics 2016.

Contact

anniz44@mit.edu or caozhichongchong@gmail.com

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for arg-ranker, version 2.4
Filename, size File type Python version Upload date Hashes
Filename, size arg_ranker-2.4-py3.6.egg (92.7 MB) File type Egg Python version 3.6 Upload date Hashes View
Filename, size arg_ranker-2.4.tar.gz (6.3 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page