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Add deeparg
(#1234)
* Add deeparg download data * Tweak downloaddata test.yml * Add deeparg tests * Apply suggestions from code review * Fix output file paths in test * Fix tests again md5sum to string
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30
modules/deeparg/downloaddata/main.nf
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30
modules/deeparg/downloaddata/main.nf
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def VERSION='1.0.2'
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process DEEPARG_DOWNLOADDATA {
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label 'process_low'
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conda (params.enable_conda ? "bioconda::deeparg=1.0.2" : null)
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container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ?
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'https://depot.galaxyproject.org/singularity/deeparg:1.0.2--pyhdfd78af_1' :
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'quay.io/biocontainers/deeparg:1.0.2--pyhdfd78af_1' }"
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input:
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output:
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path "db/" , emit: db
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path "versions.yml" , emit: versions
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script:
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def args = task.ext.args ?: ''
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"""
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deeparg \\
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download_data \\
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$args \\
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-o db/
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cat <<-END_VERSIONS > versions.yml
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"${task.process}":
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deeparg: $VERSION
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END_VERSIONS
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"""
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}
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33
modules/deeparg/downloaddata/meta.yml
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modules/deeparg/downloaddata/meta.yml
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name: deeparg_downloaddata
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description: A deep learning based approach to predict Antibiotic Resistance Genes (ARGs) from metagenomes
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keywords:
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- download
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- database
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- deeparg
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- antimicrobial resistance genes
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- deep learning
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- prediction
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tools:
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- deeparg:
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description: A deep learning based approach to predict Antibiotic Resistance Genes (ARGs) from metagenomes
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homepage: https://bench.cs.vt.edu/deeparg
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documentation: https://bitbucket.org/gusphdproj/deeparg-ss/src/master/
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tool_dev_url: https://bitbucket.org/gusphdproj/deeparg-ss/src/master/
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doi: "10.1186/s40168-018-0401-z"
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licence: ['MIT']
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input:
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- none: There is no input. This module downloads a pre-built database for use with deepARG.
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output:
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- versions:
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type: file
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description: File containing software versions
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pattern: "versions.yml"
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- db:
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type: directory
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description: Directory containing database required for deepARG.
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pattern: "db/"
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authors:
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- "@jfy133"
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40
modules/deeparg/predict/main.nf
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40
modules/deeparg/predict/main.nf
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def VERSION="1.0.2"
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process DEEPARG_PREDICT {
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tag "$meta.id"
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label 'process_medium'
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conda (params.enable_conda ? "bioconda::deeparg=1.0.2" : null)
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container "${ workflow.containerEngine == 'singularity' && !task.ext.singularity_pull_docker_container ?
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'https://depot.galaxyproject.org/singularity//deeparg:1.0.2--pyhdfd78af_1' :
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'quay.io/biocontainers/deeparg:1.0.2--pyhdfd78af_1' }"
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input:
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tuple val(meta), path(fasta), val(model)
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tuple path(db)
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output:
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tuple val(meta), path("*.align.daa") , emit: daa
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tuple val(meta), path("*.align.daa.tsv") , emit: daa_tsv
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tuple val(meta), path("*.mapping.ARG") , emit: arg
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tuple val(meta), path("*.mapping.potential.ARG"), emit: potential_arg
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path "versions.yml" , emit: versions
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script:
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def args = task.ext.args ?: ''
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def prefix = task.ext.prefix ?: "${meta.id}"
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"""
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deeparg \\
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predict \\
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$args \\
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-i $fasta \\
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-o ${prefix} \\
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-d $db \\
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--model $model
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cat <<-END_VERSIONS > versions.yml
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"${task.process}":
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deeparg: $VERSION
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END_VERSIONS
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"""
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}
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68
modules/deeparg/predict/meta.yml
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modules/deeparg/predict/meta.yml
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name: deeparg_predict
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description: A deep learning based approach to predict Antibiotic Resistance Genes (ARGs) from metagenomes
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keywords:
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- deeparg
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- antimicrobial resistance
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- antimicrobial resistance genes
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- arg
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- deep learning
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- prediction
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- contigs
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- metagenomes
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tools:
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- deeparg:
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description: A deep learning based approach to predict Antibiotic Resistance Genes (ARGs) from metagenomes
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homepage: https://bench.cs.vt.edu/deeparg
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documentation: https://bitbucket.org/gusphdproj/deeparg-ss/src/master/
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tool_dev_url: https://bitbucket.org/gusphdproj/deeparg-ss/src/master/
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doi: "10.1186/s40168-018-0401-z"
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licence: ['MIT']
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input:
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- meta:
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type: map
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description: |
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Groovy Map containing sample information
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e.g. [ id:'test' ]
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- fasta:
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type: file
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description: FASTA file containing gene-like sequences
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pattern: "*.{fasta,fa,fna}"
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- model:
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type: string
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description: Which model to use, depending on input data. Either 'LS' or 'SS' for long or short sequences respectively
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pattern: "LS|LS"
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- db:
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type: directory
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description: Path to a directory containing the deepARG pre-built models
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pattern: "*/"
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output:
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- meta:
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type: map
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description: |
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Groovy Map containing sample information
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e.g. [ id:'test', single_end:false ]
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- versions:
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type: file
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description: File containing software versions
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pattern: "versions.yml"
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- daa:
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type: file
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description: Sequences of ARG-like sequences from DIAMOND alignment
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pattern: "*.align.daa"
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- daa_tsv:
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type: file
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description: Alignments scores against ARG-like sequences from DIAMOND alignment
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pattern: "*.align.daa.tsv"
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- arg:
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type: file
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description: Table containing sequences with an ARG-like probability of more than specified thresholds
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pattern: "*.mapping.ARG"
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- potential_arg:
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type: file
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description: Table containing sequences with an ARG-like probability of less than specified thresholds, and requires manual inspection
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pattern: "*.mapping.potential.ARG"
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authors:
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- "@jfy133"
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@ -380,6 +380,14 @@ dedup:
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- modules/dedup/**
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- tests/modules/dedup/**
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deeparg/downloaddata:
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- modules/deeparg/downloaddata/**
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- tests/modules/deeparg/downloaddata/**
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deeparg/predict:
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- modules/deeparg/predict/**
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- tests/modules/deeparg/predict/**
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deeptools/computematrix:
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- modules/deeptools/computematrix/**
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- tests/modules/deeptools/computematrix/**
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9
tests/modules/deeparg/downloaddata/main.nf
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9
tests/modules/deeparg/downloaddata/main.nf
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#!/usr/bin/env nextflow
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nextflow.enable.dsl = 2
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include { DEEPARG_DOWNLOADDATA } from '../../../../modules/deeparg/downloaddata/main.nf'
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workflow test_deeparg_downloaddata {
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DEEPARG_DOWNLOADDATA ()
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}
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5
tests/modules/deeparg/downloaddata/nextflow.config
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5
tests/modules/deeparg/downloaddata/nextflow.config
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process {
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publishDir = { "${params.outdir}/${task.process.tokenize(':')[-1].tokenize('_')[0].toLowerCase()}" }
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}
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9
tests/modules/deeparg/downloaddata/test.yml
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9
tests/modules/deeparg/downloaddata/test.yml
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- name: deeparg downloaddata test_deeparg_downloaddata
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command: nextflow run tests/modules/deeparg/downloaddata -entry test_deeparg_downloaddata -c tests/config/nextflow.config
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tags:
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- deeparg
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- deeparg/downloaddata
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files:
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- path: output/deeparg/db/
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- path: output/deeparg/db/data/gg13/dataset.rev.2.bt2
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md5sum: 99d90f132fc2795d5a527ce31f1c4d30
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19
tests/modules/deeparg/predict/main.nf
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19
tests/modules/deeparg/predict/main.nf
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#!/usr/bin/env nextflow
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nextflow.enable.dsl = 2
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include { DEEPARG_DOWNLOADDATA } from '../../../../modules/deeparg/downloaddata/main.nf'
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include { DEEPARG_PREDICT } from '../../../../modules/deeparg/predict/main.nf'
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workflow test_deeparg_predict {
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input = [
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[ id:'test', single_end:false ], // meta map
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file(params.test_data['bacteroides_fragilis']['genome']['genome_fna_gz'], checkIfExists: true),
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'LS'
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]
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DEEPARG_DOWNLOADDATA()
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DEEPARG_PREDICT ( input, DEEPARG_DOWNLOADDATA.out.db )
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}
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5
tests/modules/deeparg/predict/nextflow.config
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5
tests/modules/deeparg/predict/nextflow.config
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process {
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publishDir = { "${params.outdir}/${task.process.tokenize(':')[-1].tokenize('_')[0].toLowerCase()}" }
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}
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17
tests/modules/deeparg/predict/test.yml
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17
tests/modules/deeparg/predict/test.yml
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- name: deeparg predict test_deeparg_predict
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command: nextflow run tests/modules/deeparg/predict -entry test_deeparg_predict -c tests/config/nextflow.config
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tags:
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- deeparg/predict
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- deeparg
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files:
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- path: output/deeparg/test.align.daa
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md5sum: c52d0af8362244f214da25bc45f2bf42
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- path: output/deeparg/test.align.daa.tsv
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md5sum: a4aa1da2db98274ede2b927fa8227e5a
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- path: output/deeparg/test.mapping.ARG
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md5sum: 0e049e99eab4c55666062df21707d5b9
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- path: output/deeparg/test.mapping.potential.ARG
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contains:
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- "#ARG"
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- path: output/deeparg/versions.yml
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md5sum: e848ddab324e8c6fd18eaa6b2656f195
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