nf-core_modules/modules/deepbgc/pipeline/meta.yml

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name: "deepbgc_pipeline"
description: DeepBGC detects BGCs in bacterial and fungal genomes using deep learning.
keywords:
- BGC
- biosynthetic gene cluster
- deep learning
- neural network
- random forest
- genomes
- bacteria
- fungi
tools:
- "deepbgc":
description: "DeepBGC - Biosynthetic Gene Cluster detection and classification"
homepage: "https://github.com/Merck/deepbgc"
documentation: "https://github.com/Merck/deepbgc"
tool_dev_url: "https://github.com/Merck/deepbgc"
doi: "10.1093/nar/gkz654"
licence: "['MIT']"
input:
- meta:
type: map
description: |
Groovy Map containing sample information
e.g. [ id:'test' ]
- genome:
type: file
description: FASTA/GenBank/Pfam CSV file
pattern: "*.{fasta,fa,fna,gbk,csv}"
output:
- meta:
type: map
description: |
Groovy Map containing sample information
e.g. [ id:'test']
- versions:
type: file
description: File containing software versions
pattern: "versions.yml"
- readme:
type: file
description: txt file containing description of output files
pattern: "*.{txt}"
- log:
type: file
description: Log output of DeepBGC
pattern: "*.{txt}"
- json:
type: file
description: AntiSMASH JSON file for sideloading
pattern: "*.{json}"
- bgc_gbk:
type: file
description: Sequences and features of all detected BGCs in GenBank format
pattern: "*.{bgc.gbk}"
- bgc_tsv:
type: file
description: Table of detected BGCs and their properties
pattern: "*.{bgc.tsv}"
- full_gbk:
type: file
description: Fully annotated input sequence with proteins, Pfam domains (PFAM_domain features) and BGCs (cluster features)
pattern: "*.{full.gbk}"
- pfam_tsv:
type: file
description: Table of Pfam domains (pfam_id) from given sequence (sequence_id) in genomic order, with BGC detection scores
pattern: "*.{pfam.tsv}"
- bgc_png:
type: file
description: Detected BGCs plotted by their nucleotide coordinates
pattern: "*.{bgc.png}"
- pr_png:
type: file
description: Precision-Recall curve based on predicted per-Pfam BGC scores
pattern: "*.{pr.png}"
- roc_png:
type: file
description: ROC curve based on predicted per-Pfam BGC scores
pattern: "*.{roc.png}"
- score_png:
type: file
description: BGC detection scores of each Pfam domain in genomic order
pattern: "*.{score.png}"
authors:
- "@louperelo"
- "@jfy133"