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
-
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 -
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
-
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* -
Format your results into example/ARGprofile_example_1.txt or example/ARGprofile_example_2.txt
-
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)
-
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 -
Check the "extracted.fa.blast6out.txt" and "meta_data_online.txt" in the output_dir
-
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
- Zhang AN, ..., Alm EJ, Zhang T: Choosing Your Battles: Which Resistance Genes Warrant Global Action? (bioRxiv coming soon)
- 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
1 maintainer
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