Description

A deep learning based approach to predict Antibiotic Resistance Genes (ARGs) from metagenomes

Input

Name (Type)
Description
Pattern

meta (map)

Groovy Map containing sample information
e.g. [ id:‘test’ ]

fasta (file)

FASTA file containing gene-like sequences

*.{fasta,fa,fna}

model (string)

Which model to use, depending on input data. Either ‘LS’ or ‘SS’ for long or short sequences respectively

LS|LS

db (directory)

Path to a directory containing the deepARG pre-built models

*/

Output

Name (Type)
Description
Pattern

meta (map)

Groovy Map containing sample information
e.g. [ id:‘test’, single_end

]

versions (file)

File containing software versions

versions.yml

daa (file)

Sequences of ARG-like sequences from DIAMOND alignment

*.align.daa

daa_tsv (file)

Alignments scores against ARG-like sequences from DIAMOND alignment

*.align.daa.tsv

arg (file)

Table containing sequences with an ARG-like probability of more than specified thresholds

*.mapping.ARG

potential_arg (file)

Table containing sequences with an ARG-like probability of less than specified thresholds, and requires manual inspection

*.mapping.potential.ARG
included in
funcscan
maintainer
Github user jfy133
get in touch
Ask a question on Slack Open an issue on GitHub

Tools

deeparg
MIT

A deep learning based approach to predict Antibiotic Resistance Genes (ARGs) from metagenomes

github.com/gaarangoa/deeparg 10.1186/s40168-018-0401-z