| name | deeparg |
| description | Deep-learning prediction of antibiotic resistance genes from metagenomic data. Use this skill when working with deeparg: installing, choosing parameters, running commands, interpreting outputs, or troubleshooting. Upstream: https://github.com/gaarangoa/DeepARG. For routing across mining tasks use microbial-mining. For stage order use metagenomics-workflow.
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| license | MIT |
| category | analysis-tools |
| tags | ["AMR","ARG","genome-mining"] |
| upstream | https://github.com/gaarangoa/DeepARG |
| stage | mining |
DeepARG
Upstream: DeepARG
Citation
Arango-Argoty, G. et al. DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data. Microbiome 6, 23 (2018). https://doi.org/10.1186/s40168-018-0401-z
See also docs/references.md.
Analytical thinking
Complement to RGI/CARD for ARG mining from reads or genes. Model-based predictions need careful thresholds; always cross-check critical ARGs with RGI and report both DB/model versions.
How to run
conda install -c bioconda deeparg
deepARG --align --type prot --genes genes.faa --model LS --out temp/deeparg/${id}
Key parameters
| mode | reads vs genes | |
| model | LS/SS | See docs |
Decision notes
- Run mining on QC-filtered genomes/MAGs (or contigs for MGE/virus screens)
- Computational hits are hypotheses — state validation limits in Methods
- Pin software + database releases
Related skills
rgi · genomad · humann · microbial-mining · tool-selection · metagenomics-workflow
