Ranking the risk of antibiotic resistance for genomes/metagenomes
arg_ranker evaluates the risk of ARGs in genomes and metagenomes
pip install arg_ranker
- python 3
conda install -c bioconda kraken2
download kraken2 database:
kraken2-build --standard --db $KRAKENDB
where $krakenDB is your preferred database name/location\
conda install -c bioconda diamond\
conda install -c bioconda blast
How to use it
- put all your genomes (.fa or .fasta) and metagenomes (.fq or .fastq) into one folder ($INPUT)
arg_ranker -i $INPUT --kkdb $KRAKENDB
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
- 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
- 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
The abundance, the gene family, and the antibiotic of resistance of ARGs detected in the input samples
arg_ranker -i example --kkdb $KRAKENDB
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).
Dr. An-Ni Zhang (MIT), Prof. Eric Alm (MIT), Prof. Tong Zhang* (University of Hong Kong)
- Zhang AN, ..., Alm EJ, Zhang T: Choosing Your Battles: Which Resistance Genes Warrant Global Action? (bioRxiv coming soon)
- 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.
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