arg_ranker evaluates the risk of ARGs in genomes and metagenomes
experimental version using most updated ARG database (SARGv3)
pip install arg_ranker
Long term support version using the same ARG database in the publication (SARGv1)
pip install arg-ranker==3.0.2
To all users,
We have noticed an error of arg_ranker.v2 when reporting the total ARG abundance in metagenomes.
If the total abundance is used in your research, please update arg_ranker to v3 and re-run your metagenomes (arg_ranker -i $INPUT -kkdb $KRAKENDB
).
Alternatively, you can fix arg_ranker.v2 by replacing its original ARG_table.sum.py with ARG_table.sum.py
and re-run the last two commands in arg_ranker.sh python $PATH_to_arg_ranker/bin/ARG_table.sum.py -i ...
and arg_ranker -i ...
.
You can find the path to ARG_table.sum.py in arg_ranker.sh.
Note that this ARG_table.sum.py is only meant for fixing arg_ranker.v2 and the results of arg_ranker.v2.
Please do not replace ARG_table.sum.py in arg_ranker.v3 with this ARG_table.sum.py.
We are really sorry about this inconvenience.
Please feel free to reach out to anniz44@mit.edu if you have any questions.
To check installed version pip show arg_ranker
To upgrade pip install arg_ranker --upgrade
- python 3
- diamond:
conda install -c bioconda diamond=2.1.6
(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)
- download the kraken2 standard database (50 GB of disk space):
- 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 $DBNAME --special greengenes
- download the kraken2 16S database (73.2 MB of disk space):
- to compute the abundance of ARGs as copy number of ARGs per bacterial cell (recommended)
- kraken2:
- put all your genomes (.fa or .fasta) and metagenomes (.fq or .fastq) into one folder ($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)
- or run
- run
sh arg_ranking/script_output/arg_ranker.sh
-
Sample_ranking_results.txt (Table 1)
- arg_ranker = 3.4 (SARGv3)
- python >= 3.5
- diamond = 2.1.6 - recommended
- blast = 2.13.0
- kraken2 = 2.1.2 - 16Gb database
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 6.6E-02 2.1E-02 2.2E-01 6.9E-01 0.0E+00 5.5E+00 3.1-1.3-1.1-0.9-0.0 3.1 1.3 1.1 0.9 0.0 hospital_metagenome EsCo_genome.fasta 7.1E-02 0.0E+00 2.1E-01 7.1E-01 0.0E+00 1.4E+01 3.3-0.0-1.1-0.9-0.0 3.3 0.0 1.1 0.9 0.0 E.coli_genome -
Sample_ranking_results.txt (Table 1)
- arg_ranker = 3.0.2 (SARGv1)
- python >= 3.5
- diamond = 0.9.36 - not recommended
- blast = 2.13.0
- kraken2 = 2.1.2 - 16Gb database
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 1.9E+00 1.5-0.0-0.4-1.7-0.4 1.5 0.0 0.4 1.7 0.4 hospital_metagenome EsCo_genome.fasta 0.0E+00 0.0E+00 2.4E-01 7.6E-01 0.0E+00 2.1E+01 0.0-0.0-1.6-1.7-0.0 0.0 0.0 1.6 1.7 0.0 E.coli_genome -
Please note that minor changes within ~two-fold of the Total_abu caused by different diamond and kraken versions can be considered reasonable :)
- Rank_I_per - Unassessed_per: percentage of ARGs of a risk Rank
Total_abu: total abundance of all ARGs - For genomes, we output the copy number of ARGs detected in each genome.
- 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. - 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
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)
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., 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 Correction: bacA is a bacitracin resistance gene, not a beta-lactamase (Fig 3).
anniz44@mit.edu or caozhichongchong@gmail.com
Special thanks to LeabaeL for their great help in testing various versions of arg_ranker and diamond!