arg-ranker 1.0.5

Ranking the risk of antibiotic resistance for metagenomes

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

[e-mail] or [e-mail]