Metadata-Version: 2.1
Name: arg_ranker
Version: 2.5
Summary: Ranking the risk of antibiotic resistance for genomes/metagenomes
Home-page: https://github.com/caozhichongchong/ARG_Ranker
Author: An-Ni Zhang
Author-email: anniz44@mit.edu
License: MIT
Description: # 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_abu|Rank_II_abu|Rank_III_abu|Rank_IV_abu|Unassessed_abu|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|2.9E-02|0.0E+00|7.4E-02|7.8E-01|1.2E-01|4.2E-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\
        Rank_I - Unassessed_abu: total abundance 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
        
Keywords: antibiotic resistance,risk,one health,clinical AMR,mobile AMR
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
