diff --git a/.github/workflows/awsfulltest.yml b/.github/workflows/awsfulltest.yml
index e64ea13..562018e 100644
--- 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
index e3d3d22..1eaf602 100644
--- 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
index 2f785bc..cbc7baa 100644
--- 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.
-
+
@@ -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
index f4ee3ef..dca8295 100644
--- 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.
Example InterProScan GFF output
diff --git a/docs/usage.md b/docs/usage.md
index 5b8571a..afe2933 100644
--- 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 `/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 `/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
index 08e223e..6a10c1e 100644
--- a/ro-crate-metadata.json
+++ b/ro-crate-metadata.json
@@ -23,7 +23,7 @@
"@type": "Dataset",
"creativeWorkStatus": "Stable",
"datePublished": "2026-02-04T13:01:04+00:00",
- "description": "
\n \n \n \n \n
\n\n[](https://github.com/codespaces/new/nf-core/proteinannotator)\n[](https://github.com/nf-core/proteinannotator/actions/workflows/nf-test.yml)\n[](https://github.com/nf-core/proteinannotator/actions/workflows/linting.yml)[](https://nf-co.re/proteinannotator/results)[](https://doi.org/10.5281/zenodo.XXXXXXX)\n[](https://www.nf-test.com)\n\n[](https://www.nextflow.io/)\n[](https://github.com/nf-core/tools/releases/tag/3.5.1)\n[](https://docs.conda.io/en/latest/)\n[](https://www.docker.com/)\n[](https://sylabs.io/docs/)\n[](https://cloud.seqera.io/launch?pipeline=https://github.com/nf-core/proteinannotator)\n\n[](https://nfcore.slack.com/channels/proteinannotator)[](https://bsky.app/profile/nf-co.re)[](https://mstdn.science/@nf_core)[](https://www.youtube.com/c/nf-core)\n\n## Introduction\n\n**nf-core/proteinannotator** is a bioinformatics pipeline that runs statistics of input protein fasta files and identifies\nprotein annotations such as conserved domains, functions and secondary structure features, based on their sequence data.\n\n
\n \n \n \n \n
\n\n### Check quality and pre-process\n\nGenerate input amino acid sequence statistics with ([`SeqFu`](https://github.com/telatin/seqfu2/)) and pre-process them (i.e., gap removal, convert to upper case, validate, filter by length, replace special characters such as `/`, and remove duplicate sequences) with ([`SeqKit`](https://github.com/shenwei356/seqkit/))\n\n### Annotate sequences\n\n1. Conserved domain annotation with ([`hmmer`](https://github.com/EddyRivasLab/hmmer/)) against databases\n such as [Pfam](https://ftp.ebi.ac.uk/pub/databases/Pfam/) and [FunFam](https://download.cathdb.info/cath/releases/all-releases/)\n2. Functional annotation:\n - ([`InterProScan`](https://interproscan-docs.readthedocs.io/en/v5/)) a software tool used to analyze protein sequences by scanning them against the signatures of protein families, domains, and sites in the [InterPro](https://www.ebi.ac.uk/interpro/) database, helping to identify their functional characteristics.\n3. Predict secondary structure compositional features such as \u03b1-helices, \u03b2-strands and coils with ([`s4pred`](https://github.com/psipred/s4pred))\n4. Present QC stats for input sequences before and after initial pre-processing with ([`MultiQC`](http://multiqc.info/))\n\n## Usage\n\n> [!NOTE]\n> If you are new to Nextflow and nf-core, please refer to [this page](https://nf-co.re/docs/usage/installation) on how to set-up Nextflow. Make sure to [test your setup](https://nf-co.re/docs/usage/introduction#how-to-run-a-pipeline) with `-profile test` before running the workflow on actual data.\n\nFirst, prepare a samplesheet with your input data that looks as follows:\n\n`samplesheet.csv`:\n\n```csv\nid,fasta\nspecies1,species1_proteins.fasta\nspecies2,species2_proteins.fasta\n```\n\nEach row represents a fasta file of proteins from a single species.\n\nNow, you can run the pipeline using:\n\n```bash\nnextflow run nf-core/proteinannotator \\\n -profile \\\n --input samplesheet.csv \\\n --outdir \n```\n\n> [!WARNING]\n> Please provide pipeline parameters via the CLI or Nextflow `-params-file` option. Custom config files including those provided by the `-c` Nextflow option can be used to provide any configuration _**except for parameters**_; see [docs](https://nf-co.re/docs/usage/getting_started/configuration#custom-configuration-files).\n\nFor more details and further functionality, please refer to the [usage documentation](https://nf-co.re/proteinannotator/usage) and the [parameter documentation](https://nf-co.re/proteinannotator/parameters).\n\n## Pipeline output\n\nTo see the results of an example test run with a full size dataset refer to the [results](https://nf-co.re/proteinannotator/results) tab on the nf-core website pipeline page.\nFor more details about the output files and reports, please refer to the\n[output documentation](https://nf-co.re/proteinannotator/output).\n\n## Credits\n\nnf-core/proteinannotator was originally written by Olga Botvinnik.\n\nWe thank the following people for their extensive assistance in the development of this pipeline:\n\n- [Evangelos Karatzas](https://github.com/vagkaratzas)\n- [Martin Beracochea](https://github.com/mberacochea)\n\n## Contributions and Support\n\nIf you would like to contribute to this pipeline, please see the [contributing guidelines](.github/CONTRIBUTING.md).\n\nFor further information or help, don't hesitate to get in touch on the [Slack `#proteinannotator` channel](https://nfcore.slack.com/channels/proteinannotator) (you can join with [this invite](https://nf-co.re/join/slack)).\n\n## Citations\n\n\n\n\nAn extensive list of references for the tools used by the pipeline can be found in the [`CITATIONS.md`](CITATIONS.md) file.\n\nYou can cite the `nf-core` publication as follows:\n\n> **The nf-core framework for community-curated bioinformatics pipelines.**\n>\n> Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.\n>\n> _Nat Biotechnol._ 2020 Feb 13. doi: [10.1038/s41587-020-0439-x](https://dx.doi.org/10.1038/s41587-020-0439-x).\n",
+ "description": "
\n \n \n \n \n
\n\n[](https://github.com/codespaces/new/nf-core/proteinannotator)\n[](https://github.com/nf-core/proteinannotator/actions/workflows/nf-test.yml)\n[](https://github.com/nf-core/proteinannotator/actions/workflows/linting.yml)[](https://nf-co.re/proteinannotator/results)[](https://doi.org/10.5281/zenodo.XXXXXXX)\n[](https://www.nf-test.com)\n\n[](https://www.nextflow.io/)\n[](https://github.com/nf-core/tools/releases/tag/3.5.1)\n[](https://docs.conda.io/en/latest/)\n[](https://www.docker.com/)\n[](https://sylabs.io/docs/)\n[](https://cloud.seqera.io/launch?pipeline=https://github.com/nf-core/proteinannotator)\n\n[](https://nfcore.slack.com/channels/proteinannotator)[](https://bsky.app/profile/nf-co.re)[](https://mstdn.science/@nf_core)[](https://www.youtube.com/c/nf-core)\n\n## Introduction\n\n**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.\n\n
\n \n \n \n \n
\n\n### Check quality and pre-process\n\nGenerate input amino acid sequence statistics with ([`SeqFu`](https://github.com/telatin/seqfu2/)) and pre-process them (i.e., gap removal, convert to upper case, validate, filter by length, replace special characters such as `/`, and remove duplicate sequences) with ([`SeqKit`](https://github.com/shenwei356/seqkit/))\n\n### Annotate sequences\n\n1. Conserved domain annotation with ([`hmmer`](https://github.com/EddyRivasLab/hmmer/)) against databases\n such as [Pfam](https://ftp.ebi.ac.uk/pub/databases/Pfam/) and [FunFam](https://download.cathdb.info/cath/releases/all-releases/)\n2. Functional annotation:\n - ([`InterProScan`](https://interproscan-docs.readthedocs.io/en/v5/)) a software tool used to analyze protein sequences by scanning them against the signatures of protein families, domains, and sites in the [InterPro](https://www.ebi.ac.uk/interpro/) database, helping to identify their functional characteristics.\n3. Predict secondary structure compositional features such as \u03b1-helices, \u03b2-strands and coils with ([`s4pred`](https://github.com/psipred/s4pred))\n4. Present QC stats for input sequences before and after initial pre-processing with ([`MultiQC`](http://multiqc.info/))\n\n## Usage\n\n> [!NOTE]\n> If you are new to Nextflow and nf-core, please refer to [this page](https://nf-co.re/docs/usage/installation) on how to set-up Nextflow. Make sure to [test your setup](https://nf-co.re/docs/usage/introduction#how-to-run-a-pipeline) with `-profile test` before running the workflow on actual data.\n\nFirst, prepare a samplesheet with your input data that looks as follows:\n\n`samplesheet.csv`:\n\n```csv\nid,fasta\nspecies1,species1_proteins.fasta\nspecies2,species2_proteins.fasta\n```\n\nEach row represents a FASTA file of proteins from a single species.\n\nNow, you can run the pipeline using:\n\n```bash\nnextflow run nf-core/proteinannotator \\\n -profile \\\n --input samplesheet.csv \\\n --outdir \n```\n\n> [!WARNING]\n> Please provide pipeline parameters via the CLI or Nextflow `-params-file` option. Custom config files including those provided by the `-c` Nextflow option can be used to provide any configuration _**except for parameters**_; see [docs](https://nf-co.re/docs/usage/getting_started/configuration#custom-configuration-files).\n\nFor more details and further functionality, please refer to the [usage documentation](https://nf-co.re/proteinannotator/usage) and the [parameter documentation](https://nf-co.re/proteinannotator/parameters).\n\n## Pipeline output\n\nTo see the results of an example test run with a full size dataset refer to the [results](https://nf-co.re/proteinannotator/results) tab on the nf-core website pipeline page.\nFor more details about the output files and reports, please refer to the\n[output documentation](https://nf-co.re/proteinannotator/output).\n\n## Credits\n\nnf-core/proteinannotator was originally written by Olga Botvinnik.\n\nWe thank the following people for their extensive assistance in the development of this pipeline:\n\n- [Evangelos Karatzas](https://github.com/vagkaratzas)\n- [Martin Beracochea](https://github.com/mberacochea)\n\n## Contributions and Support\n\nIf you would like to contribute to this pipeline, please see the [contributing guidelines](.github/CONTRIBUTING.md).\n\nFor further information or help, don't hesitate to get in touch on the [Slack `#proteinannotator` channel](https://nfcore.slack.com/channels/proteinannotator) (you can join with [this invite](https://nf-co.re/join/slack)).\n\n## Citations\n\n\n\n\nAn extensive list of references for the tools used by the pipeline can be found in the [`CITATIONS.md`](CITATIONS.md) file.\n\nYou can cite the `nf-core` publication as follows:\n\n> **The nf-core framework for community-curated bioinformatics pipelines.**\n>\n> Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.\n>\n> _Nat Biotechnol._ 2020 Feb 13. doi: [10.1038/s41587-020-0439-x](https://dx.doi.org/10.1038/s41587-020-0439-x).\n",
"hasPart": [
{
"@id": "main.nf"
diff --git a/subworkflows/local/domain_annotation/main.nf b/subworkflows/local/domain_annotation/main.nf
index c0b59dc..1ec8289 100644
--- 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
index 3aeb539..240c504 100644
--- 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
index 5665c6a..fae1d7a 100644
--- 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,
@@ -62,7 +54,7 @@ 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
@@ -70,7 +62,7 @@ workflow PROTEINANNOTATOR {
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() )
}
+ ![\"nf-core/proteinannotator\"](\"docs/images/nf-core-proteinannotator_logo_dark.png\"){kind=logo}
\n ![\"Protein](\"docs/images/proteinannotator_metromap_dark.png\")
\n