arg_ranker

arg_ranker evaluates the risk of ARGs in genomes and metagenomes

Install

pip install arg_ranker

Requirement

• python 3
• diamond: conda install -c bioconda diamond (https://github.com/bbuchfink/diamond)
• blast+: conda install -c bioconda blast (https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/)
• For metagenomes:
  • kraken2: conda install -c bioconda kraken2(https://github.com/DerrickWood/kraken2/wiki)
    • to compute the abundance of ARGs as copy number of ARGs per bacterial cell (recommended)
      • download the kraken2 standard database (50 GB of disk space): kraken2-build --standard --db $KRAKENDB
        where $KRAKENDB is your preferred database name/location
      • MicrobeCensu: git clone https://github.com/snayfach/MicrobeCensus && cd MicrobeCensus && python setup.py install to estimate the average genome size for metagenomes. (https://github.com/snayfach/MicrobeCensus)
    • to compute the abundance of ARGs as copy number of ARGs per 16S
      • download the kraken2 16S database (73.2 MB of disk space): kraken2-build --db $DBNAME --special greengenes

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 (genomes only)
• run arg_ranker -i $INPUT -kkdb $KRAKENDB (genomes/metagenomes + kraken2 standard database)
  • or run arg_ranker -i $INPUT -kkdb $KRAKENDB -kkdbtype 16S (kraken2 16S database)
• 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. Rank_I_per - Unassessed_per: percentage of ARGs of a risk Rank
   Total_abu: total abundance of all ARGs
2. For genomes, we output the copy number of ARGs detected in each genome.
3. For metagenomes, we compute the abundance of ARGs as the copy number of ARGs divided by the bacterial cell number or 16S copy number in the same metagenome.
   If you downloaded the kraken2 standard database, we compute the copy number of ARGs divided by the bacterial cell number.
   If you downloaded the kraken2 16S database, we compute the copy number of ARGs divided by the 16S copy number.
   The copy number of ARGs, 16S, and bacterial cells were computed as the number of reads mapped to them divided by their gene/genome length.
4. We compute the contribution of each ARG risk Rank as the average abundance of ARGs of a risk Rank divided by the average abundance of all ARGs
   Rank_I_risk - Unassessed_risk: the contribution of ARGs of a risk Rank
   Rank_code: a code of contribution 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 (using kraken2 standard database)

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

Zhang, AN., Gaston, J.M., Dai, C.L. et al. An omics-based framework for assessing the health risk of antimicrobial resistance genes. Nat Commun 12, 4765 (2021). https://doi.org/10.1038/s41467-021-25096-3

Contact

anniz44@mit.edu or caozhichongchong@gmail.com