Metadata-Version: 2.1
Name: arg-ranker
Version: 2.3
Summary: Ranking the risk of antibiotic resistance for metagenomes
Home-page: https://github.com/caozhichongchong/ARG_Ranker
Author: An-Ni Zhang
Author-email: anniz44@mit.edu
License: MIT
Description: # arg_ranker
        
        ## Install
        `pip install arg_ranker`
        
        `conda install -c caozhichongchong arg_ranker`
        
        ## Test (download examples and use any of these commands)
        `arg_ranker -i example/ARGprofile_example_1.txt -m example/metadata.txt`\
        `arg_ranker -i example/ARGprofile_example_2.txt -m example/metadata.txt`\
        `arg_ranker -i test`
        
        ## How to use it
        ### Prepare your ARG profile
        
        arg_ranker is suitable for the units of ppm, gene copy per 16S or gene copy per cell
        
        #### Option 1: Use our pipeline
        
        1. Use my traits_finder to search ARGs in genomes and metagenomes (in preparation)\
        Now we have both nucleotides and amino acids databases!\
        https://github.com/caozhichongchong/traits_finder
        
        2. Run\
        `arg_ranker -i ARG.profile.txt -m metadata.txt`\
        `arg_ranker -i ARG.profile.txt`
        
        #### Option 2: Run your own pipeline using our database
        
        1. Search ARGs-OAP v1.0 database (amino acids) in your data using diamond or blast\
        https://github.com/caozhichongchong/arg_ranker/tree/master/arg_ranker/data/SARG.db.fasta*
        
        2. Format your results into example/ARGprofile_example_1.txt or example/ARGprofile_example_2.txt
        
        3. Run\
        `arg_ranker -i ARG.profile.txt -m metadata.txt`\
        `arg_ranker -i ARG.profile.txt`\
        If you see a lot of errors saying: "ARGs in mothertable do not match with the ARGs in ARG_rank.txt.\
        Please check something something in ARG.summary.cell.txt!"\
        It means that the samples are placed as row names instead of colomn names (which arg_ranker expects).\
        Don't worry, please try: `arg_ranker -i ARG.profile.txt.t`\
        As we automatically transpose your table to make it work.
        
        #### Option 3: Use results from ARGs-OAP v1.0 (not recommended)
        
        1. If you have already run the ARGs-OAP v1.0 pipeline\
            https://github.com/biofuture/Ublastx_stageone/archive/Ublastx_stageone.tar.gz\
            https://github.com/biofuture/Ublastx_stageone/archive/Ublastx_stageone.zip
        
        2. Check the "extracted.fa.blast6out.txt" and "meta_data_online.txt" in the output_dir
        
        3. Run\
        `arg_ranker -f True -fo output_dir`\
        `arg_ranker -i formated_table.normalize_cellnumber.gene.tab -m metadata.txt`
        
        ### Prepare your metadata for your samples (optional)
        
        Format your metadata of metagenomic samples into example/metadata.txt (not necessarily the same)\
        First column matches the sample ID in your ARG profile;\
        Other columns contain the metadata of your samples (such as habitat/eco-type, accession number, group...)
        
        ## Introduction
        ARG_ranker evaluates the risk of antibiotic resistance in metagenomes.\
        We designed a framework to rank the risk of ARGs based on three factors: “anthropogenic enrichment”, “mobility”, and “host pathogenicity”, informed by all available bacterial genomes, plasmids, integrons, and 850 metagenomes covering diverse global eco-habitats. The framework prioritizes 3% of ARGs in Rank I (the most at risk of dissemination among pathogens) and 0.3% of ARGs in Rank II (high potential emergence of new resistance in pathogens). 
        
        Requirement: python packages (pandas, argparse)
        
        Requirement: a mothertable of the ARG abundance in all your samples
        annotated by ARGs-OAP v1.0 \
        (see example/All_sample_cellnumber.txt).
        
        Optimal: a table of the metadata of your samples \
        (see example/All_sample_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
