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Ranking the risk of antibiotic resistance for metagenomes

Project 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`

## Availability
https://anaconda.org/caozhichongchong/arg_ranker\
https://pypi.org/project/arg-ranker/

## 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: Run your own pipeline against our database

1. Download the ARGs-OAP v1.0 database\
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

#### Option 2: 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
Copyright:An-Ni Zhang, Prof. Tong Zhang, University of Hong Kong
Citation:
1. This study
2. Yang Y, Jiang X, Chai B, Ma L, Li B, Zhang A, Cole JR, 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. (optional: antibiotic resistance database)
Contact caozhichongchong@gmail.com

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