Package: arg-ranker
arg_rankerarg_ranker evaluates the risk of ARGs in genomes and metagenomesInstallexperimental version using most updated ARG database (SARGv3)pip install arg_rankerLong term support version using the same ARG database in the publication (SARGv1)pip install arg-ranker==3.0.2Please make sure to install arg_ranker >= v3To 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 withARG_table.sum.pyand re-run the last two commands in arg_ranker.shpython $PATH_to_arg_ranker/bin/ARG_table.sum.py -i ...andarg_ranker -i ....You can find the path to ARG_table.sum.py in arg_ranker.sh.Note that thisARG_table.sum.pyis 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 thisARG_table.sum.py.We are really sorry about this inconvenience.Please feel free to reach out toanniz44@mit.eduif you have any questions.To check installed versionpip show arg_rankerTo upgradepip install arg_ranker --upgradeRequirementpython 3diamond: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 $KRAKENDBwhere $KRAKENDB is your preferred database name/locationMicrobeCensu:git clone https://github.com/snayfach/MicrobeCensus && cd MicrobeCensus && python setup.py installto estimate the average genome size for metagenomes.
(https://github.com/snayfach/MicrobeCensus)to compute the abundance of ARGs as copy number of ARGs per 16Sdownload the kraken2 16S database (73.2 MB of disk space):kraken2-build --db $DBNAME --special greengenesHow to use itput all your genomes (.fa or .fasta) and metagenomes (.fq or .fastq) into one folder ($INPUT)runarg_ranker -i $INPUT(genomes only)runarg_ranker -i $INPUT -kkdb $KRAKENDB(genomes/metagenomes + kraken2 standard database)or runarg_ranker -i $INPUT -kkdb $KRAKENDB -kkdbtype 16S(kraken2 16S database)runsh arg_ranking/script_output/arg_ranker.shOutputSample_ranking_results.txt (Table 1)arg_ranker = 3.4 (SARGv3)python >= 3.5diamond = 2.1.6 - recommendedblast = 2.13.0kraken2 = 2.1.2 - 16Gb databaseSampleRank_I_perRank_II_perRank_III_perRank_IV_perUnassessed_perTotal_abuRank_codeRank_I_riskRank_II_riskRank_III_riskRank_IV_riskARGs_unassessed_risknote1WEE300_all-trimmed-decont_1.fastq6.6E-022.1E-022.2E-016.9E-010.0E+005.5E+003.1-1.3-1.1-0.9-0.03.11.31.10.90.0hospital_metagenomeEsCo_genome.fasta7.1E-020.0E+002.1E-017.1E-010.0E+001.4E+013.3-0.0-1.1-0.9-0.03.30.01.10.90.0E.coli_genomeSample_ranking_results.txt (Table 1)arg_ranker = 3.0.2 (SARGv1)python >= 3.5diamond = 0.9.36 - not recommendedblast = 2.13.0kraken2 = 2.1.2 - 16Gb databaseSampleRank_I_perRank_II_perRank_III_perRank_IV_perUnassessed_perTotal_abuRank_codeRank_I_riskRank_II_riskRank_III_riskRank_IV_riskARGs_unassessed_risknote1WEE300_all-trimmed-decont_1.fastq4.6E-020.0E+006.8E-027.5E-011.3E-011.9E+001.5-0.0-0.4-1.7-0.41.50.00.41.70.4hospital_metagenomeEsCo_genome.fasta0.0E+000.0E+002.4E-017.6E-010.0E+002.1E+010.0-0.0-1.6-1.7-0.00.00.01.61.70.0E.coli_genomePlease 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 RankTotal_abu: total abundance of all ARGsFor 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 ARGsRank_I_risk - Unassessed_risk: the contribution of ARGs of a risk RankRank_code: a code of contribution from Rank I to UnassessedSample_ARGpresence.txt:The abundance, the gene family, and the antibiotic of resistance of ARGs detected in the input samplesTestrunarg_ranker -i example -kkdb $KRAKENDBrunsh arg_ranking/script_output/arg_ranker.shThe 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 tableMake sure that your sample names are listed as the first column (check example/metadata.txt).CopyrightDr. An-Ni Zhang (MIT), Prof. Eric Alm (MIT), Prof. Tong Zhang* (University of Hong Kong)CitationZhang, 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/s41467-021-25096-3Correction: bacA is a bacitracin resistance gene, not a beta-lactamase (Fig 3).Contactanniz44@mit.eduorcaozhichongchong@gmail.comAcknowledgementSpecial thanks to LeabaeL for their great help in testing various versions of arg_ranker and diamond!