arg-ranker 2.0

# 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.txtarg_ranker -i example/ARGprofile_example_2.txt -m example/metadata.txtarg_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. Runarg_ranker -i ARG.profile.txt -m metadata.txtarg_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 blasthttps://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. Runarg_ranker -i ARG.profile.txt -m metadata.txtarg_ranker -i ARG.profile.txtIf 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.tAs 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.gzhttps://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. Runarg_ranker -f True -fo output_dirarg_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