DeepBGC: tiny revisiosn (#2070)

* Fix variable prodigal_tf

* Remove tag in main.nf + tiny suggestions in ymls

Co-authored-by: louperelo <44900284+louperelo@users.noreply.github.com>
This commit is contained in:
Jasmin F 2022-09-15 22:57:33 +02:00 committed by GitHub
parent 8372df6fb2
commit e55012ff92
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
3 changed files with 18 additions and 18 deletions

View file

@ -1,5 +1,4 @@
process DEEPBGC_DOWNLOAD { process DEEPBGC_DOWNLOAD {
tag "download"
label 'process_low' label 'process_low'
conda (params.enable_conda ? "bioconda::deepbgc=0.1.30" : null) conda (params.enable_conda ? "bioconda::deepbgc=0.1.30" : null)
@ -8,8 +7,8 @@ process DEEPBGC_DOWNLOAD {
'quay.io/biocontainers/deepbgc:0.1.30--pyhb7b1952_1' }" 'quay.io/biocontainers/deepbgc:0.1.30--pyhb7b1952_1' }"
output: output:
path "deepbgc_db/" , emit: db path "deepbgc_db/" , emit: db
path "versions.yml" , emit: versions path "versions.yml" , emit: versions
when: when:
task.ext.when == null || task.ext.when task.ext.when == null || task.ext.when

View file

@ -1,13 +1,13 @@
name: "deepbgc_download" name: "deepbgc_download"
description: database to detect BGCs in bacterial and fungal genomes using deep learning description: Database download module for DeepBGC which detects BGCs in bacterial and fungal genomes using deep learning.
keywords: keywords:
- database - database
- download - download
- BGC - BGC
- Biosynthetic Gene Cluster - biosynthetic gene cluster
- deep learning - deep learning
- neural network - neural network
- random forrest - random forest
- genomes - genomes
- bacteria - bacteria
- fungi - fungi
@ -27,8 +27,8 @@ output:
pattern: "versions.yml" pattern: "versions.yml"
- deepbgc_db: - deepbgc_db:
type: directory type: directory
description: contains files of reference db from 'deepbgc download' description: Contains reference database files
pattern: "*db" pattern: "deepbgc_db"
authors: authors:
- "@louperelo" - "@louperelo"

View file

@ -1,7 +1,8 @@
name: "deepbgc_pipeline" name: "deepbgc_pipeline"
description: detect BGCs in bacterial and fungal genomes using deep learning description: DeepBGC detects BGCs in bacterial and fungal genomes using deep learning.
keywords: keywords:
- Biosynthetic Gene Cluster - BGC
- biosynthetic gene cluster
- deep learning - deep learning
- neural network - neural network
- random forest - random forest
@ -48,15 +49,15 @@ output:
pattern: "*.{txt}" pattern: "*.{txt}"
- json: - json:
type: file type: file
description: AntiSMASH JSON file for sideloading. description: AntiSMASH JSON file for sideloading
pattern: "*.{json}" pattern: "*.{json}"
- bgc_gbk: - bgc_gbk:
type: file type: file
description: Sequences and features of all detected BGCs in GenBank format. description: Sequences and features of all detected BGCs in GenBank format
pattern: "*.{bgc.gbk}" pattern: "*.{bgc.gbk}"
- bgc_tsv: - bgc_tsv:
type: file type: file
description: Table of detected BGCs and their properties. description: Table of detected BGCs and their properties
pattern: "*.{bgc.tsv}" pattern: "*.{bgc.tsv}"
- full_gbk: - full_gbk:
type: file type: file
@ -64,23 +65,23 @@ output:
pattern: "*.{full.gbk}" pattern: "*.{full.gbk}"
- pfam_tsv: - pfam_tsv:
type: file type: file
description: Table of Pfam domains (pfam_id) from given sequence (sequence_id) in genomic order, with BGC detection scores. description: Table of Pfam domains (pfam_id) from given sequence (sequence_id) in genomic order, with BGC detection scores
pattern: "*.{pfam.tsv}" pattern: "*.{pfam.tsv}"
- bgc_png: - bgc_png:
type: file type: file
description: Detected BGCs plotted by their nucleotide coordinates. description: Detected BGCs plotted by their nucleotide coordinates
pattern: "*.{bgc.png}" pattern: "*.{bgc.png}"
- pr_png: - pr_png:
type: file type: file
description: Precision-Recall curve based on predicted per-Pfam BGC scores. description: Precision-Recall curve based on predicted per-Pfam BGC scores
pattern: "*.{pr.png}" pattern: "*.{pr.png}"
- roc_png: - roc_png:
type: file type: file
description: ROC curve based on predicted per-Pfam BGC scores. description: ROC curve based on predicted per-Pfam BGC scores
pattern: "*.{roc.png}" pattern: "*.{roc.png}"
- score_png: - score_png:
type: file type: file
description: BGC detection scores of each Pfam domain in genomic order. description: BGC detection scores of each Pfam domain in genomic order
pattern: "*.{score.png}" pattern: "*.{score.png}"
authors: authors: