Gecco convert

CHANGELOG.md

@@ -8,6 +8,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### `Added`
 
 - [#500](https://github.com/nf-core/funcscan/pull/500) Updated pipeline template to nf-core/tools version 3.4.1 (by @jfy133)
+- [#506](https://github.com/nf-core/funcscan/pull/506) Gecco convert (by @SkyLexS)
 
 ### `Fixed`
 

README.md

@@ -92,7 +92,7 @@ nf-core/funcscan was originally written by Jasmin Frangenberg, Anan Ibrahim, Lou
 
 We thank the following people for their extensive assistance in the development of this pipeline:
 
-Adam Talbot, Alexandru Mizeranschi, Hugo Tavares, Júlia Mir Pedrol, Martin Klapper, Mehrdad Jaberi, Robert Syme, Rosa Herbst, Vedanth Ramji, @Microbion.
+Adam Talbot, Alexandru Mizeranschi, Hugo Tavares, Júlia Mir Pedrol, Martin Klapper, Mehrdad Jaberi, Robert Syme, Rosa Herbst, Vedanth Ramji, @Microbion, Dediu Octavian-Codrin.
 
 ## Contributions and Support
 

conf/modules.config

@@ -529,6 +529,14 @@ process {
         ].join(' ').trim()
     }
 
+     withName: GECCO_CONVERT {
+        publishDir = [
+            path: { "${params.outdir}/bgc/gecco/${meta.id}" },
+            mode: params.publish_dir_mode,
+            saveAs: { filename -> filename.equals('versions.yml') ? null : filename },
+        ]
+    }
+
     withName: HAMRONIZATION_ABRICATE {
         publishDir = [
             path: { "${params.outdir}/arg/hamronization/abricate" },

docs/output.md

@@ -451,21 +451,30 @@ Note that filtered FASTA is only used for BGC workflow for run-time optimisation
 
 [deepBGC](https://github.com/Merck/deepbgc) detects BGCs in bacterial and fungal genomes using deep learning. DeepBGC employs a Bidirectional Long Short-Term Memory Recurrent Neural Network and a word2vec-like vector embedding of Pfam protein domains. Product class and activity of detected BGCs is predicted using a Random Forest classifier.
 
-#### GECCO
+#### GECCO & GECCO CONVERT
 
 <details markdown="1">
 <summary>Output files</summary>
 
 - `gecco/`
+  - **GECCO**
   - `*.genes.tsv/`: TSV file containing detected/predicted genes with BGC probability scores
   - `*.features.tsv`: TSV file containing identified domains
   - `*.clusters.tsv`: TSV file containing coordinates of predicted clusters and BGC types
   - `*_cluster_*.gbk`: GenBank file (if clusters were found) containing sequence with annotations; one file per GECCO hit
 
-</details>
+  - **GECCO CONVERT**
+  - `*.gff`: GFF3 converted cluster tables containing the position and metadata for all the predicted clusters
+  - `*.region*.gbk`: Converted and aliased GenBank files so that they can be loaded by BiG-SLiCE
+  - `*.faa`: Amino-acid FASTA converted GenBank files of all the proteins in a cluster
+  - `*.fna`:Nucleotide sequence FASTA converted GenBank files from the cluster
+  **ONLY IF --run_gecco_convert**
+  </details>
 
 [GECCO](https://gecco.embl.de) is a fast and scalable method for identifying putative novel Biosynthetic Gene Clusters (BGCs) in genomic and metagenomic data using Conditional Random Fields (CRFs).
 
+[GECCO CONVERT] (https://gecco.embl.de) is an option in gecco which does file conversion into formats like GFF3, GenBank, or FASTA for further analysis.
+
 ### Summary tools
 
 [AMPcombi](#ampcombi), [hAMRonization](#hamronization), [comBGC](#combgc), [MultiQC](#multiqc), [pipeline information](#pipeline-information), [argNorm](#argnorm).