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_per	Rank_II_per	Rank_III_per	Rank_IV_per	Unassessed_per	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	4.6E-02	0.0E+00	6.8E-02	7.5E-01	1.3E-01	5.4E-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
   For metagenomes, the copy number of ARGs and 16S were computed as the number of reads mapped to them divided by their gene length.
   Rank_I_per - Unassessed_per: percentage 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