nf-core · vagkaratzas · Feb 5, 2026 · Feb 4, 2026
diff --git a/.github/workflows/awsfulltest.yml b/.github/workflows/awsfulltest.yml
@@ -24,9 +24,6 @@ jobs:
 
       - name: Launch workflow via Seqera Platform
         uses: seqeralabs/action-tower-launch@v2
-        # TODO nf-core: You can customise AWS full pipeline tests as required
-        # Add full size test data (but still relatively small datasets for few samples)
-        # on the `test_full.config` test runs with only one set of parameters
         with:
           workspace_id: ${{ vars.TOWER_WORKSPACE_ID }}
           access_token: ${{ secrets.TOWER_ACCESS_TOKEN }}

diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -3,12 +3,10 @@
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/)
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-## v1.0.0 - [2026/02/04]
+## v1.0.0 - Yellow Saiga - [2026/02/04]
 
 Initial release of nf-core/proteinannotator, created with the [nf-core](https://nf-co.re/) template.
 
-### `Added`
-
 - [#68](https://github.com/nf-core/proteinannotator/pull/68) - Using the `ARIA2` and `UNTAR` nf-core modules to download and decompress the InterProScan database. (by @vagkaratzas)
 - [#67](https://github.com/nf-core/proteinannotator/pull/67) - Swapped to the updated, non-buggy, nf-core version of `INTERPROSCAN`. (by @vagkaratzas)
 - [#65](https://github.com/nf-core/proteinannotator/pull/65) - Converted the pipeline schematic to nf-core metromap. (by @vagkaratzas)

diff --git a/README.md b/README.md
@@ -21,13 +21,12 @@
 
 ## Introduction
 
-**nf-core/proteinannotator** is a bioinformatics pipeline that runs statistics of input protein fasta files and identifies
-protein annotations such as conserved domains, functions and secondary structure features, based on their sequence data.
+**nf-core/proteinannotator** is a bioinformatics pipeline that computes statistics on input protein FASTA files and identifies protein annotations such as conserved domains, predicted functions, and secondary structure features based on sequence data.
 
 <p>
   <picture>
     <source media="(prefers-color-scheme: dark)" srcset="docs/images/proteinannotator_metromap_dark.png">
-    <img alt="Protein annotator metromap. Protein fasta files are summarized with `seqkit stats`, then functionally annotated with InterProScan, DIAMOND-blastp, UniFire, and Kmerseek" src="docs/images/proteinannotator_metromap_light.png">
+    <img alt="nf-core/proteinannotator" src="docs/images/proteinannotator_metromap_light.png">
   </picture>
 </p>
 
@@ -59,7 +58,7 @@ species1,species1_proteins.fasta
 species2,species2_proteins.fasta
 ```
 
-Each row represents a fasta file of proteins from a single species.
+Each row represents a FASTA file of proteins from a single species.
 
 Now, you can run the pipeline using:
 

diff --git a/docs/output.md b/docs/output.md
@@ -10,8 +10,8 @@ The directories listed below will be created in the results directory after the
 
 The pipeline is built using [Nextflow](https://www.nextflow.io/) and processes data using the following steps:
 
-- [Quality check and preprocessing](#quality-check-and-preprocessing)
-  - [SeqFu](#seqfu) for input amino acid sequences quality check (QC)
+- [Quality control and preprocessing](#quality-control-and-preprocessing)
+  - [SeqFu](#seqfu) for input amino acid sequences quality control (QC)
   - [SeqKit](#seqkit) for preprocessing input amino acid sequences (i.e., gap removal, convert to upper case, validate, filter by length, replace special characters such as `/`, and remove duplicate sequences)
 
 - [Database download](#database-download) Optionally download selected databases for annotation.
@@ -23,12 +23,12 @@ The pipeline is built using [Nextflow](https://www.nextflow.io/) and processes d
 - [Functional annotation](#functional-annotation) Annotate proteins with functional domains
   - [InterProScan](#Interproscan) - Search the InterProScan database for functional domains
 
-- [s4pred](#s4pred) - Predict secondary structures of sequences, producing per amino acid probabilities of being an α-helix, a β-strand or a coil.
+- [s4pred](#s4pred) - Predict secondary structures of sequences, producing amino acid level probabilities of forming an α-helix, a β-strand or a coil.
 
 - [MultiQC](#multiqc) - Aggregate report describing results and QC from the whole pipeline
 - [Pipeline information](#pipeline-information) - Report metrics generated during the workflow execution
 
-### Quality check and preprocessing
+### Quality control and preprocessing
 
 #### SeqFu
 
@@ -127,7 +127,7 @@ See also [InterProScan output documentation](https://interproscan-docs.readthedo
 
 ##### Generic Feature Format Version 3 (GFF3) Output
 
-The GFF3 format is a flat tab-delimited file, which is much richer then the TSV output format. It allows you to trace back from matches to predicted proteins and to nucleic acid sequences. It also contains a FASTA format representation of the predicted protein sequences and their matches. You will find a documentation of all the columns and attributes used on http://www.sequenceontology.org/gff3.shtml.
+The GFF3 format is a flat tab-delimited file, which is much richer then the TSV output. It allows you to trace back from matches to predicted proteins and to nucleic acid sequences. It also contains a FASTA format representation of the predicted protein sequences and their matches. You will find a documentation of all the columns and attributes used on http://www.sequenceontology.org/gff3.shtml.
 
 <details markdown="1">
 <summary>Example InterProScan GFF output</summary>

diff --git a/docs/usage.md b/docs/usage.md
@@ -80,7 +80,7 @@ You can also generate such `YAML`/`JSON` files via [nf-core/launch](https://nf-c
 
 ### InterProScan
 
-[InterProScan](https://github.com/ebi-pf-team/interproscan) is used to provide more information about the proteins annotated on the contigs. By default, turning on this subworkflow without `--skip_interproscan` will download and unzip the [InterPro database](http://ftp.ebi.ac.uk/pub/software/unix/iprscan/5/5.72-103.0/) version 5.72-103.0. The database will then be saved in the output directory `<output_directory>/databases/interproscan/`.
+[InterProScan](https://github.com/ebi-pf-team/interproscan) is used to provide more information about the proteins annotated on the contigs. By default, turning on this subworkflow without `--skip_interproscan` will download and unzip the InterPro database. The database will then be saved in the output directory `<output_directory>/downloaded_dbs/interproscan_db/`.
 
 :::note
 The huge database download (5.5GB) can take up to 4 hours depending on the bandwidth.

diff --git a/ro-crate-metadata.json b/ro-crate-metadata.json
diff --git a/subworkflows/local/domain_annotation/main.nf b/subworkflows/local/domain_annotation/main.nf
@@ -30,11 +30,11 @@ workflow DOMAIN_ANNOTATION {
             ch_pfam_db = channel.of([ [ id: 'pfam' ], pfam_db ])
         }
 
-        ch_input_for_hmmsearch = ch_fasta
+        ch_input_for_hmmsearch_pfam = ch_fasta
             .combine(ch_pfam_db)
             .map{ meta, seqs, _meta2, models -> [meta, models, seqs, false, false, true] }
 
-        HMMSEARCH_PFAM( ch_input_for_hmmsearch )
+        HMMSEARCH_PFAM( ch_input_for_hmmsearch_pfam )
         ch_versions = ch_versions.mix( HMMSEARCH_PFAM.out.versions.first() )
         ch_pfam_domains = HMMSEARCH_PFAM.out.domain_summary
     }
@@ -50,11 +50,11 @@ workflow DOMAIN_ANNOTATION {
             ch_funfam_db = channel.of([ [ id: 'funfam' ], funfam_db ])
         }
 
-        ch_input_for_hmmsearch = ch_fasta
+        ch_input_for_hmmsearch_funfam = ch_fasta
             .combine(ch_funfam_db)
             .map{ meta, seqs, _meta2, models -> [meta, models, seqs, false, false, true] }
 
-        HMMSEARCH_FUNFAM( ch_input_for_hmmsearch )
+        HMMSEARCH_FUNFAM( ch_input_for_hmmsearch_funfam )
         ch_versions = ch_versions.mix( HMMSEARCH_FUNFAM.out.versions.first() )
         ch_funfam_domains = HMMSEARCH_FUNFAM.out.domain_summary
     }

diff --git a/subworkflows/local/functional_annotation/main.nf b/subworkflows/local/functional_annotation/main.nf
@@ -14,7 +14,7 @@ workflow FUNCTIONAL_ANNOTATION {
     ch_versions         = channel.empty()
 
     if (!skip_interproscan) {
-        if (interproscan_db != null) {
+        if (interproscan_db) {
             ch_interproscan_db = channel.fromPath(interproscan_db).first()
         }
         else {

diff --git a/workflows/proteinannotator.nf b/workflows/proteinannotator.nf
@@ -42,16 +42,8 @@ workflow PROTEINANNOTATOR {
     FAA_SEQFU_SEQKIT( ch_samplesheet, skip_preprocessing )
     ch_versions = ch_versions.mix( FAA_SEQFU_SEQKIT.out.versions )
 
-    // Replace input fasta and join back in samplesheet to ensure in sync in case of multiple sequence files
-    ch_samplesheet_updated = ch_samplesheet
-        .combine(FAA_SEQFU_SEQKIT.out.fasta, by: 0)
-        .map {
-            meta, _fasta, updated_fasta ->
-            [ meta, updated_fasta ]
-        }
-
     DOMAIN_ANNOTATION (
-        ch_samplesheet_updated,
+        FAA_SEQFU_SEQKIT.out.fasta,
         skip_pfam,
         pfam_db,
         pfam_latest_link,
@@ -62,15 +54,15 @@ workflow PROTEINANNOTATOR {
     ch_versions = ch_versions.mix( DOMAIN_ANNOTATION.out.versions )
 
     FUNCTIONAL_ANNOTATION (
-        ch_samplesheet_updated,
+        FAA_SEQFU_SEQKIT.out.fasta,
         skip_interproscan,
         interproscan_db_url,
         interproscan_db
     )
     ch_versions = ch_versions.mix( FUNCTIONAL_ANNOTATION.out.versions )
 
     if (!skip_s4pred) {
-        S4PRED_RUNMODEL( ch_samplesheet_updated )
+        S4PRED_RUNMODEL( FAA_SEQFU_SEQKIT.out.fasta )
         ch_versions = ch_versions.mix( S4PRED_RUNMODEL.out.versions.first() )
     }