Metadata-Version: 2.1
Name: arg-ranker
Version: 1.0.5
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 (any of these two 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`
        
        ## 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. 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 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: Run ARGs-OAP v1.0 and format the results by ARG_Ranker
        
        1. Download ARGs-OAP v1.0 pipeline and run the pipeline\
            https://github.com/biofuture/Ublastx_stageone/archive/Ublastx_stageone.tar.gz\
            https://github.com/biofuture/Ublastx_stageone/archive/Ublastx_stageone.zip
        
            A brief introduction on how to use ARGs-OAP v1.0\
            Please refer to the README.md of ARGs-OAP v1.0 for more details
        
            Prepare your metadata for your samples into example/metadata.txt (separated by tab)\
            SampleID (a number for the sample) | Name (metagenomic samples name) | Category (metadata of habitat, or group)\
            `./ublastx_stage_one  -i inputfqs -o testoutdir -m meta-data.txt -n 2`
        
                Usage: ./ublastx_stage_one -i <Fq input dir> -m <Metadata_map.txt> -o <output dir>
                -n [number of threads] -f [fa|fq] -z -h  -c   
                    -i Input files directory, required\
                    -m meta data file, required
                    -o Output files directory, default current directory
                    -n number of threads used for usearch, default 1
                    -f the format of processed files, default fq
                    -z whether the fq files were .gz format, if -z, then firstly gzip -d, default(none)
                    -c This option fulfill copy number correction by Copywriter database to transfrom 16S information into cell number [ direct searching hyper variable region database by usearch; default 1]
                    -h print this help information
        
        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
        Sample_ranking.py evaluates and assigns the risk and priority levels to environmental samples
        based on their profile of antibiotic resistant genes (ARGs).
        
        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. Tong Zhang (University of Hong Kong)
        
        ## Citation
        1. Zhang AN, ..., Alm EJ, Zhang T: Whom to Fight: Top Risk Antibiotic Resistances for Global Action (Under Review)
        2. (Optional: antibiotic resistance database)\
        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
