\n\n[](https://github.com/codespaces/new/nf-core/mhcquant)\n[](https://github.com/nf-core/mhcquant/actions/workflows/nf-test.yml)\n[](https://github.com/nf-core/mhcquant/actions/workflows/linting.yml)[](https://nf-co.re/mhcquant/results)[](https://doi.org/10.5281/zenodo.8427707)\n[](https://www.nf-test.com)\n\n[](https://www.nextflow.io/)\n[](https://github.com/nf-core/tools/releases/tag/3.4.1)\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/mhcquant)\n\n[](https://nfcore.slack.com/channels/mhcquant)[](https://bsky.app/profile/nf-co.re)[](https://mstdn.science/@nf_core)[](https://www.youtube.com/c/nf-core)\n\n## Introduction\n\n**nfcore/mhcquant** is a best-practice bioinformatics pipeline to process data-dependent acquisition (DDA) immunopeptidomics data. This involves mass spectrometry-based identification and quantification of immunopeptides presented on major histocompatibility complex (MHC) molecules which mediate T cell immunosurveillance. Immunopeptidomics has central implications for clinical research, in the context of [T cell-centric immunotherapies](https://www.sciencedirect.com/science/article/pii/S1044532323000180).\n\nThe pipeline is based on the OpenMS C++ framework for computational mass spectrometry. Spectrum files (mzML/Thermo raw/Bruker tdf) serve as inputs and a database search (Comet) is performed based on a given input protein database. Peptide properties are predicted by MS\u00b2Rescore. FDR rescoring is applied using Percolator or Mokapot based on a competitive target-decoy approach. The pipeline supports both local FDR control (per sample-condition group) and global FDR control (across all samples). For label-free quantification, all input files undergo identification-based retention time alignment and targeted feature extraction matching ids between runs. The pipeline can also generate spectrum libraries suitable for DIA-based searches as well as computing consensus epitopes using epicore.\n\n\n\nThe pipeline is built using [Nextflow](https://www.nextflow.io), a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The [Nextflow DSL2](https://www.nextflow.io/docs/latest/dsl2.html) implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from [nf-core/modules](https://github.com/nf-core/modules) in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!\n\nOn release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the [nf-core website](https://nf-co.re/mhcquant/results).\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\n> to set-up Nextflow. Make sure to [test your setup](https://nf-co.re/docs/usage/introduction#how-to-run-a-pipeline)\n> 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.tsv`\n\n```tsv title=\"samplesheet.tsv\nID\tSample\tCondition\tReplicateFileName\n1\ttumor\ttreated\t/path/to/msrun1.raw|mzML|d\n2\ttumor\ttreated\t/path/to/msrun2.raw|mzML|d\n3\ttumor\tuntreated\t/path/to/msrun3.raw|mzML|d\n4\ttumor\tuntreated\t/path/to/msrun4.raw|mzML|d\n```\n\nEach row represents a mass spectrometry run in one of the formats: raw, RAW, mzML, mzML.gz, d, d.tar.gz, d.zip\n\nNow, you can run the pipeline using:\n\n```bash\nnextflow run nf-core/mhcquant \\\n -profile \\\n --input 'samplesheet.tsv' \\\n --fasta 'SWISSPROT_2020.fasta' \\\n --outdir ./results\n```\n\nOptional parameters for additional functionality:\n\n```bash\n# Enable quantification, global FDR and spectrum library generation, ion annotations, and consenus epitopes\nnextflow run nf-core/mhcquant \\\n --input 'samplesheet.tsv' \\\n --fasta 'SWISSPROT_2020.fasta' \\\n --annotate_ions \\\n --epicore \\\n --generate_speclib \\\n --global_fdr \\\n --quantify \\\n --outdir ./results \\\n -profile docker\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/mhcquant/usage) and the [parameter documentation](https://nf-co.re/mhcquant/parameters).\n\n## Pipeline summary\n\n### Default Steps\n\nBy default the pipeline currently performs identification of MHC class I peptides with HCD settings:\n\n- **Spectra Preparation**: Preparing spectra dependent on the input format (`PREPARE_SPECTRA` subworkflow)\n- **Database Preparation**: Creation of reversed decoy database (`DecoyDatabase`)\n- **Peptide Identification**: Identification of peptides in the MS/MS spectra (`CometAdapter`)\n- **Database Indexing**: Refreshes protein references for all peptide hits and adds target/decoy information (`PeptideIndexer`)\n- **Identification Merging**: Merges identification files with the same `Sample` and `Condition` label (`IDMerger`)\n- **Rescoring**: Feature prediction and peptide-spectrum-match rescoring (`RESCORE` subworkflow)\n - Prediction of retention times and MS2 intensities (`MS\u00b2Rescore`)\n - Extract PSM features for rescoring engines (`PSMFeatureExtractor`)\n - Peptide-spectrum-match rescoring using Percolator or Mokapot (`PercolatorAdapter`)\n - Filters peptide identification result according to configurable FDR threshold (`IDFilter`)\n- **Export**: Converts identification result to tab-separated files (`TextExporter`)\n\n### FDR Control Modes\n\nThe pipeline supports two FDR control strategies:\n\n- **Local FDR** (default): FDR control applied per `Sample` and `Condition` group\n- **Global FDR**: FDR control applied across all samples in the dataset (enable with `--global_fdr`)\n\n### Additional Steps\n\nAdditional functionality contained by the pipeline currently includes:\n\n#### Quantification (`QUANT` subworkflow)\n\nWhen enabled with `--quantify`, the pipeline performs label-free quantification:\n\n- **Alignment**: Corrects retention time distortions between runs (`MAP_ALIGNMENT` subworkflow)\n - Corrects retention time distortions between runs (`MapAlignerIdentification`)\n - Applies retention time transformations to runs (`MapRTTransformer`)\n- **Feature Processing**: Detects and processes features (`PROCESS_FEATURE` subworkflow)\n - Detects features in MS1 data based on peptide identifications (`FeatureFinderIdentification`)\n - Group corresponding features across label-free experiments (`FeatureLinkerUnlabeledKD`)\n - Resolves ambiguous annotations of features with peptide identifications (`IDConflictResolver`)\n\n#### Spectrum Library Generation (`SPECLIB` subworkflow)\n\nWhen enabled with `--generate_speclib`, the pipeline generates spectrum libraries suitable for DIA-based searches. Outputs one library per sample or a single library across all samples (if global FDR mode is enabled with `--global_fdr`).\n\n#### Ion Annotation (`IONANNOTATOR` subworkflow)\n\nThe pipeline annotates the final list of peptides with their respective ions and charges:\n\n- Annotates final list of peptides with their respective ions and charges (`IonAnnotator`)\n\n#### Output\n\n## Documentation\n\nTo see the the results of a test run with a full size dataset refer to the [results](https://nf-co.re/mhcquant/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/mhcquant/output).\n\n1. [Nextflow installation](https://nf-co.re/usage/installation)\n2. Pipeline configuration\n - [Pipeline installation](https://nf-co.re/docs/usage/getting_started/offline)\n - [Adding your own system config](https://nf-co.re/usage/adding_own_config)\n3. [Running the pipeline](https://nf-co.re/mhcquant/docs/usage.md)\n - This includes tutorials, FAQs, and troubleshooting instructions\n4. [Output and how to interpret the results](https://nf-co.re/mhcquant/docs/output.md)\n\n## Credits\n\nnf-core/mhcquant was originally written by [Leon Bichmann](https://github.com/Leon-Bichmann) from the [Kohlbacher Lab](https://kohlbacherlab.org/). The pipeline was re-written in Nextflow DSL2 by [Marissa Dubbelaar](https://github.com/marissaDubbelaar) and was significantly improved by [Jonas Scheid](https://github.com/jonasscheid) and [Steffen Lemke](https://github.com/steffenlem) from [Peptide-based Immunotherapy](https://www.medizin.uni-tuebingen.de/en-de/peptid-basierte-immuntherapie) and [Quantitative Biology Center](https://uni-tuebingen.de/forschung/forschungsinfrastruktur/zentrum-fuer-quantitative-biologie-qbic/) in T\u00fcbingen.\n\nHelpful contributors:\n\n- [Lukas Heumos](https://github.com/Zethson)\n- [Alexander Peltzer](https://github.com/apeltzer)\n- [Maxime Garcia](https://github.com/maxulysse)\n- [Gisela Gabernet](https://github.com/ggabernet)\n- [Susanne Jodoin](https://github.com/SusiJo)\n- [Oskar Wacker](https://github.com/WackerO)\n- [Leon Kuchenbecker](https://github.com/lkuchenb)\n- [Phil Ewels](https://github.com/ewels)\n- [Christian Fufezan](https://github.com/fu)\n- [Sven Fillinger](https://github.com/sven1103)\n- [Kevin Menden](https://github.com/KevinMenden)\n- [Julia Graf](https://github.com/JuliaGraf)\n- [Jana Hoffmann](https://github.com/janaHoffmann1)\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 `#mhcquant` channel](https://nfcore.slack.com/channels/mhcquant) (you can join with [this invite](https://nf-co.re/join/slack)).\n\n## Citations\n\nIf you use nf-core/mhcquant for your analysis, please cite the corresponding manuscript: [10.1186/s13059-025-03763-8](https://doi.org/10.1186/s13059-025-03763-8)\n\n> **MHCquant2 refines immunopeptidomics tumor antigen discovery**\n>\n> Jonas Scheid, Steffen Lemke, Naomi Hoenisch-Gravel, Anna Dengler, Timo Sachsenberg, Arthur Declerq, Ralf Gabriels, Jens Bauer, Marcel Wacker, Leon Bichmann, Lennart Martens, Marissa L. Dubbelaar, Sven Nahnsen & Juliane S. Walz\n>\n> _Genome Biology_ 2025 26 (1), 290. doi: [10.1021/acs.jproteome.9b00313](https://pubs.acs.org/doi/10.1021/acs.jproteome.9b00313)\n\n> **MHCquant: Automated and Reproducible Data Analysis for Immunopeptidomics**\n>\n> Leon Bichmann, Annika Nelde, Michael Ghosh, Lukas Heumos, Christopher Mohr, Alexander Peltzer, Leon Kuchenbecker, Timo Sachsenberg, Juliane S. Walz, Stefan Stevanovi\u0107, Hans-Georg Rammensee & Oliver Kohlbacher\n>\n> _Journal of Proteome Research_ 2019 18 (11), 3876-3884. doi: [10.1021/acs.jproteome.9b00313](https://pubs.acs.org/doi/10.1021/acs.jproteome.9b00313)\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\nIn addition, references of tools and data used in this pipeline are as follows:\n\n> **OpenMS framework**\n>\n> Pfeuffer J. et al, _Nat Methods_ 2024 Mar;21(3):365-367. doi: [0.1038/s41592-024-02197-7](https://www.nature.com/articles/s41592-024-02197-7).\n>\n> **Comet Search Engine**\n>\n> Eng J.K. et al, _J Am Soc Mass Spectrom._ 2015 Nov;26(11):1865-74. doi: [10.1007/s13361-015-1179-x](https://pubs.acs.org/doi/10.1007/s13361-015-1179-x).\n>\n> **Retention time prediction**\n>\n> Bouwmeester R. et al, _Nature Methods_ 2021 Oct;18(11):1363-1369. doi: [10.1038/s41592-021-01301-5](https://www.nature.com/articles/s41592-021-01301-5)\n>\n> **MS\u00b2 Peak intensity prediction**\n>\n> Declercq A. et al, _Nucleic Acids Res._ 2023 Jul 5;51(W1):W338-W342. doi: [10.1093/nar/gkad335](https://academic.oup.com/nar/article/51/W1/W338/7151340?login=false)\n>\n> **CCS prediction**\n>\n> Declercq A. et al _Journal of Proteome Research_ 2025 Feb 6. doi: [10.1021/acs.jproteome.4c00609](https://pubs.acs.org/doi/10.1021/acs.jproteome.4c00609)\n>\n> **MS\u00b2Rescore framework**\n>\n> Buur L. M. et al, \\_J Proteome Res. 2024 Mar 16. doi: [10.1021/acs.jproteome.3c00785](https://pubs.acs.org/doi/10.1021/acs.jproteome.3c00785)\n>\n> **Percolator**\n>\n> K\u00e4ll L. et al, _Nat Methods_ 2007 Nov;4(11):923-5. doi: [10.1038/nmeth1113](https://www.nature.com/articles/nmeth1113).\n>\n> **Identification based RT Alignment**\n>\n> Weisser H. et al, _J Proteome Res._ 2013 Apr 5;12(4):1628-44. doi: [10.1021/pr300992u](https://pubs.acs.org/doi/10.1021/pr300992u)\n>\n> **Targeted peptide quantification**\n>\n> Weisser H. et al, _J Proteome Res._ 2017 Aug 4;16(8):2964-2974. doi: [10.1021/acs.jproteome.7b00248](https://pubs.acs.org/doi/10.1021/acs.jproteome.7b00248)\n",
+ "creativeWorkStatus": "InProgress",
+ "datePublished": "2026-01-08T12:19:18+00:00",
+ "description": "
\n \n \n \n \n
\n\n[](https://github.com/codespaces/new/nf-core/mhcquant)\n[](https://github.com/nf-core/mhcquant/actions/workflows/nf-test.yml)\n[](https://github.com/nf-core/mhcquant/actions/workflows/linting.yml)[](https://nf-co.re/mhcquant/results)[](https://doi.org/10.5281/zenodo.8427707)\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/mhcquant)\n\n[](https://nfcore.slack.com/channels/mhcquant)[](https://bsky.app/profile/nf-co.re)[](https://mstdn.science/@nf_core)[](https://www.youtube.com/c/nf-core)\n\n## Introduction\n\n**nfcore/mhcquant** is a best-practice bioinformatics pipeline to process data-dependent acquisition (DDA) immunopeptidomics data. This involves mass spectrometry-based identification and quantification of immunopeptides presented on major histocompatibility complex (MHC) molecules which mediate T cell immunosurveillance. Immunopeptidomics has central implications for clinical research, in the context of [T cell-centric immunotherapies](https://www.sciencedirect.com/science/article/pii/S1044532323000180).\n\nThe pipeline is based on the OpenMS C++ framework for computational mass spectrometry. Spectrum files (mzML/Thermo raw/Bruker tdf) serve as inputs and a database search (Comet) is performed based on a given input protein database. Peptide properties are predicted by MS\u00b2Rescore. FDR rescoring is applied using Percolator or Mokapot based on a competitive target-decoy approach. The pipeline supports both local FDR control (per sample-condition group) and global FDR control (across all samples). For label-free quantification, all input files undergo identification-based retention time alignment and targeted feature extraction matching ids between runs. The pipeline can also generate spectrum libraries suitable for DIA-based searches as well as computing consensus epitopes using epicore.\n\n\n\nThe pipeline is built using [Nextflow](https://www.nextflow.io), a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The [Nextflow DSL2](https://www.nextflow.io/docs/latest/dsl2.html) implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies. Where possible, these processes have been submitted to and installed from [nf-core/modules](https://github.com/nf-core/modules) in order to make them available to all nf-core pipelines, and to everyone within the Nextflow community!\n\nOn release, automated continuous integration tests run the pipeline on a full-sized dataset on the AWS cloud infrastructure. This ensures that the pipeline runs on AWS, has sensible resource allocation defaults set to run on real-world datasets, and permits the persistent storage of results to benchmark between pipeline releases and other analysis sources. The results obtained from the full-sized test can be viewed on the [nf-core website](https://nf-co.re/mhcquant/results).\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\n> to set-up Nextflow. Make sure to [test your setup](https://nf-co.re/docs/usage/introduction#how-to-run-a-pipeline)\n> 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.tsv`\n\n```tsv title=\"samplesheet.tsv\nID\tSample\tCondition\tReplicateFileName\n1\ttumor\ttreated\t/path/to/msrun1.raw|mzML|d\n2\ttumor\ttreated\t/path/to/msrun2.raw|mzML|d\n3\ttumor\tuntreated\t/path/to/msrun3.raw|mzML|d\n4\ttumor\tuntreated\t/path/to/msrun4.raw|mzML|d\n```\n\nEach row represents a mass spectrometry run in one of the formats: raw, RAW, mzML, mzML.gz, d, d.tar.gz, d.zip\n\nNow, you can run the pipeline using:\n\n```bash\nnextflow run nf-core/mhcquant \\\n -profile \\\n --input 'samplesheet.tsv' \\\n --fasta 'SWISSPROT_2020.fasta' \\\n --outdir ./results\n```\n\nOptional parameters for additional functionality:\n\n```bash\n# Enable quantification, global FDR and spectrum library generation, ion annotations, and consenus epitopes\nnextflow run nf-core/mhcquant \\\n --input 'samplesheet.tsv' \\\n --fasta 'SWISSPROT_2020.fasta' \\\n --annotate_ions \\\n --epicore \\\n --generate_speclib \\\n --global_fdr \\\n --quantify \\\n --outdir ./results \\\n -profile docker\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/mhcquant/usage) and the [parameter documentation](https://nf-co.re/mhcquant/parameters).\n\n## Pipeline summary\n\n### Default Steps\n\nBy default the pipeline currently performs identification of MHC class I peptides with HCD settings:\n\n- **Spectra Preparation**: Preparing spectra dependent on the input format (`PREPARE_SPECTRA` subworkflow)\n- **Database Preparation**: Creation of reversed decoy database (`DecoyDatabase`)\n- **Peptide Identification**: Identification of peptides in the MS/MS spectra (`CometAdapter`)\n- **Database Indexing**: Refreshes protein references for all peptide hits and adds target/decoy information (`PeptideIndexer`)\n- **Identification Merging**: Merges identification files with the same `Sample` and `Condition` label (`IDMerger`)\n- **Rescoring**: Feature prediction and peptide-spectrum-match rescoring (`RESCORE` subworkflow)\n - Prediction of retention times and MS2 intensities (`MS\u00b2Rescore`)\n - Extract PSM features for rescoring engines (`PSMFeatureExtractor`)\n - Peptide-spectrum-match rescoring using Percolator or Mokapot (`PercolatorAdapter`)\n - Filters peptide identification result according to configurable FDR threshold (`IDFilter`)\n- **Export**: Converts identification result to tab-separated files (`TextExporter`)\n\n### FDR Control Modes\n\nThe pipeline supports two FDR control strategies:\n\n- **Local FDR** (default): FDR control applied per `Sample` and `Condition` group\n- **Global FDR**: FDR control applied across all samples in the dataset (enable with `--global_fdr`)\n\n### Additional Steps\n\nAdditional functionality contained by the pipeline currently includes:\n\n#### Quantification (`QUANT` subworkflow)\n\nWhen enabled with `--quantify`, the pipeline performs label-free quantification:\n\n- **Alignment**: Corrects retention time distortions between runs (`MAP_ALIGNMENT` subworkflow)\n - Corrects retention time distortions between runs (`MapAlignerIdentification`)\n - Applies retention time transformations to runs (`MapRTTransformer`)\n- **Feature Processing**: Detects and processes features (`PROCESS_FEATURE` subworkflow)\n - Detects features in MS1 data based on peptide identifications (`FeatureFinderIdentification`)\n - Group corresponding features across label-free experiments (`FeatureLinkerUnlabeledKD`)\n - Resolves ambiguous annotations of features with peptide identifications (`IDConflictResolver`)\n\n#### Spectrum Library Generation (`SPECLIB` subworkflow)\n\nWhen enabled with `--generate_speclib`, the pipeline generates spectrum libraries suitable for DIA-based searches. Outputs one library per sample or a single library across all samples (if global FDR mode is enabled with `--global_fdr`).\n\n#### Ion Annotation (`IONANNOTATOR` subworkflow)\n\nThe pipeline annotates the final list of peptides with their respective ions and charges:\n\n- Annotates final list of peptides with their respective ions and charges (`IonAnnotator`)\n\n#### Output\n\n## Documentation\n\nTo see the the results of a test run with a full size dataset refer to the [results](https://nf-co.re/mhcquant/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/mhcquant/output).\n\n1. [Nextflow installation](https://nf-co.re/usage/installation)\n2. Pipeline configuration\n - [Pipeline installation](https://nf-co.re/docs/usage/getting_started/offline)\n - [Adding your own system config](https://nf-co.re/usage/adding_own_config)\n3. [Running the pipeline](https://nf-co.re/mhcquant/docs/usage.md)\n - This includes tutorials, FAQs, and troubleshooting instructions\n4. [Output and how to interpret the results](https://nf-co.re/mhcquant/docs/output.md)\n\n## Credits\n\nnf-core/mhcquant was originally written by [Leon Bichmann](https://github.com/Leon-Bichmann) from the [Kohlbacher Lab](https://kohlbacherlab.org/). The pipeline was re-written in Nextflow DSL2 by [Marissa Dubbelaar](https://github.com/marissaDubbelaar) and was significantly improved by [Jonas Scheid](https://github.com/jonasscheid) and [Steffen Lemke](https://github.com/steffenlem) from [Peptide-based Immunotherapy](https://www.medizin.uni-tuebingen.de/en-de/peptid-basierte-immuntherapie) and [Quantitative Biology Center](https://uni-tuebingen.de/forschung/forschungsinfrastruktur/zentrum-fuer-quantitative-biologie-qbic/) in T\u00fcbingen.\n\nHelpful contributors:\n\n- [Lukas Heumos](https://github.com/Zethson)\n- [Alexander Peltzer](https://github.com/apeltzer)\n- [Maxime Garcia](https://github.com/maxulysse)\n- [Gisela Gabernet](https://github.com/ggabernet)\n- [Susanne Jodoin](https://github.com/SusiJo)\n- [Oskar Wacker](https://github.com/WackerO)\n- [Leon Kuchenbecker](https://github.com/lkuchenb)\n- [Phil Ewels](https://github.com/ewels)\n- [Christian Fufezan](https://github.com/fu)\n- [Sven Fillinger](https://github.com/sven1103)\n- [Kevin Menden](https://github.com/KevinMenden)\n- [Julia Graf](https://github.com/JuliaGraf)\n- [Jana Hoffmann](https://github.com/janaHoffmann1)\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 `#mhcquant` channel](https://nfcore.slack.com/channels/mhcquant) (you can join with [this invite](https://nf-co.re/join/slack)).\n\n## Citations\n\nIf you use nf-core/mhcquant for your analysis, please cite the corresponding manuscript: [10.1186/s13059-025-03763-8](https://doi.org/10.1186/s13059-025-03763-8)\n\n> **MHCquant2 refines immunopeptidomics tumor antigen discovery**\n>\n> Jonas Scheid, Steffen Lemke, Naomi Hoenisch-Gravel, Anna Dengler, Timo Sachsenberg, Arthur Declerq, Ralf Gabriels, Jens Bauer, Marcel Wacker, Leon Bichmann, Lennart Martens, Marissa L. Dubbelaar, Sven Nahnsen & Juliane S. Walz\n>\n> _Genome Biology_ 2025 26 (1), 290. doi: [10.1021/acs.jproteome.9b00313](https://pubs.acs.org/doi/10.1021/acs.jproteome.9b00313)\n\n> **MHCquant: Automated and Reproducible Data Analysis for Immunopeptidomics**\n>\n> Leon Bichmann, Annika Nelde, Michael Ghosh, Lukas Heumos, Christopher Mohr, Alexander Peltzer, Leon Kuchenbecker, Timo Sachsenberg, Juliane S. Walz, Stefan Stevanovi\u0107, Hans-Georg Rammensee & Oliver Kohlbacher\n>\n> _Journal of Proteome Research_ 2019 18 (11), 3876-3884. doi: [10.1021/acs.jproteome.9b00313](https://pubs.acs.org/doi/10.1021/acs.jproteome.9b00313)\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\nIn addition, references of tools and data used in this pipeline are as follows:\n\n> **OpenMS framework**\n>\n> Pfeuffer J. et al, _Nat Methods_ 2024 Mar;21(3):365-367. doi: [0.1038/s41592-024-02197-7](https://www.nature.com/articles/s41592-024-02197-7).\n>\n> **Comet Search Engine**\n>\n> Eng J.K. et al, _J Am Soc Mass Spectrom._ 2015 Nov;26(11):1865-74. doi: [10.1007/s13361-015-1179-x](https://pubs.acs.org/doi/10.1007/s13361-015-1179-x).\n>\n> **Retention time prediction**\n>\n> Bouwmeester R. et al, _Nature Methods_ 2021 Oct;18(11):1363-1369. doi: [10.1038/s41592-021-01301-5](https://www.nature.com/articles/s41592-021-01301-5)\n>\n> **MS\u00b2 Peak intensity prediction**\n>\n> Declercq A. et al, _Nucleic Acids Res._ 2023 Jul 5;51(W1):W338-W342. doi: [10.1093/nar/gkad335](https://academic.oup.com/nar/article/51/W1/W338/7151340?login=false)\n>\n> **CCS prediction**\n>\n> Declercq A. et al _Journal of Proteome Research_ 2025 Feb 6. doi: [10.1021/acs.jproteome.4c00609](https://pubs.acs.org/doi/10.1021/acs.jproteome.4c00609)\n>\n> **MS\u00b2Rescore framework**\n>\n> Buur L. M. et al, \\_J Proteome Res. 2024 Mar 16. doi: [10.1021/acs.jproteome.3c00785](https://pubs.acs.org/doi/10.1021/acs.jproteome.3c00785)\n>\n> **Percolator**\n>\n> K\u00e4ll L. et al, _Nat Methods_ 2007 Nov;4(11):923-5. doi: [10.1038/nmeth1113](https://www.nature.com/articles/nmeth1113).\n>\n> **Identification based RT Alignment**\n>\n> Weisser H. et al, _J Proteome Res._ 2013 Apr 5;12(4):1628-44. doi: [10.1021/pr300992u](https://pubs.acs.org/doi/10.1021/pr300992u)\n>\n> **Targeted peptide quantification**\n>\n> Weisser H. et al, _J Proteome Res._ 2017 Aug 4;16(8):2964-2974. doi: [10.1021/acs.jproteome.7b00248](https://pubs.acs.org/doi/10.1021/acs.jproteome.7b00248)\n",
"hasPart": [
{
"@id": "main.nf"
@@ -105,7 +105,7 @@
},
"mentions": [
{
- "@id": "#3dbe415b-3d82-41eb-a11f-065f425f8a83"
+ "@id": "#9e4c34cf-5530-46b2-be64-79e63d572382"
}
],
"name": "nf-core/mhcquant"
@@ -139,18 +139,18 @@
{
"@id": "https://orcid.org/0000-0001-7135-0073"
},
- {
- "@id": "https://orcid.org/0000-0002-6503-2180"
- },
{
"@id": "https://orcid.org/0000-0002-8937-3457"
},
{
"@id": "https://orcid.org/0000-0002-5923-1343"
+ },
+ {
+ "@id": "https://orcid.org/0000-0002-6503-2180"
}
],
"dateCreated": "",
- "dateModified": "2025-10-20T07:45:13Z",
+ "dateModified": "2026-01-08T12:19:18Z",
"dct:conformsTo": "https://bioschemas.org/profiles/ComputationalWorkflow/1.0-RELEASE/",
"keywords": [
"nf-core",
@@ -187,10 +187,10 @@
},
"url": [
"https://github.com/nf-core/mhcquant",
- "https://nf-co.re/mhcquant/3.1.0/"
+ "https://nf-co.re/mhcquant/dev/"
],
"version": [
- "3.1.0"
+ "3.2.0dev"
]
},
{
@@ -206,11 +206,11 @@
"version": "!>=25.04.0"
},
{
- "@id": "#3dbe415b-3d82-41eb-a11f-065f425f8a83",
+ "@id": "#9e4c34cf-5530-46b2-be64-79e63d572382",
"@type": "TestSuite",
"instance": [
{
- "@id": "#cd4e5431-8228-4f6a-a3c6-4b722e22ba0a"
+ "@id": "#b55a3b6b-29d0-41f1-a5dc-9c168ffc0064"
}
],
"mainEntity": {
@@ -219,7 +219,7 @@
"name": "Test suite for nf-core/mhcquant"
},
{
- "@id": "#cd4e5431-8228-4f6a-a3c6-4b722e22ba0a",
+ "@id": "#b55a3b6b-29d0-41f1-a5dc-9c168ffc0064",
"@type": "TestInstance",
"name": "GitHub Actions workflow for testing nf-core/mhcquant",
"resource": "repos/nf-core/mhcquant/actions/workflows/nf-test.yml",
@@ -369,12 +369,6 @@
"email": "bichmann@informatik.uni-tuebingen.de",
"name": "Leon Bichmann"
},
- {
- "@id": "https://orcid.org/0000-0002-6503-2180",
- "@type": "Person",
- "email": "apeltzer@users.noreply.github.com",
- "name": "Alexander Peltzer"
- },
{
"@id": "https://orcid.org/0000-0002-8937-3457",
"@type": "Person",
@@ -386,6 +380,12 @@
"@type": "Person",
"email": "43858870+jonasscheid@users.noreply.github.com",
"name": "Jonas Scheid"
+ },
+ {
+ "@id": "https://orcid.org/0000-0002-6503-2180",
+ "@type": "Person",
+ "email": "apeltzer@users.noreply.github.com",
+ "name": "Alexander Peltzer"
}
]
}
\ No newline at end of file
diff --git a/subworkflows/local/map_alignment/main.nf b/subworkflows/local/map_alignment/main.nf
index 76725d0d..7ca36a31 100644
--- a/subworkflows/local/map_alignment/main.nf
+++ b/subworkflows/local/map_alignment/main.nf
@@ -18,7 +18,6 @@ workflow MAP_ALIGNMENT {
// Compute group-wise alignment rt transformation
OPENMS_MAPALIGNERIDENTIFICATION( ch_runs_to_be_aligned )
- ch_versions = ch_versions.mix(OPENMS_MAPALIGNERIDENTIFICATION.out.versions)
// Join run specific trafoXMLs with meta information
merge_meta_map
@@ -32,7 +31,6 @@ workflow MAP_ALIGNMENT {
// Align mzML files using trafoXMLs
ch_trafos_mzmls = ch_mzml.join(ch_trafos)
OPENMS_MAPRTTRANSFORMERMZML(ch_trafos_mzmls)
- ch_versions = ch_versions.mix(OPENMS_MAPRTTRANSFORMERMZML.out.versions)
// Align idXMLfiles using trafoXMLs
ch_runs_to_be_aligned
@@ -45,7 +43,6 @@ workflow MAP_ALIGNMENT {
.set { ch_trafos_idxml }
OPENMS_MAPRTTRANSFORMERIDXML(ch_trafos_idxml)
- ch_versions = ch_versions.mix(OPENMS_MAPRTTRANSFORMERIDXML.out.versions)
emit:
versions = ch_versions
diff --git a/subworkflows/local/prepare_spectra/main.nf b/subworkflows/local/prepare_spectra/main.nf
index 6c199bf6..8079904a 100644
--- a/subworkflows/local/prepare_spectra/main.nf
+++ b/subworkflows/local/prepare_spectra/main.nf
@@ -36,14 +36,12 @@ workflow PREPARE_SPECTRA {
// Raw file conversion
THERMORAWFILEPARSER(branched_ms_files.raw)
- ch_versions = ch_versions.mix(THERMORAWFILEPARSER.out.versions)
+ // ch_versions = ch_versions.mix(THERMORAWFILEPARSER.out.versions)
// Decompress timsTOF archive for data conversion
UNTAR(branched_ms_files.d_tar)
- ch_versions = ch_versions.mix(UNTAR.out.versions)
UNZIP(branched_ms_files.d_zip)
- ch_versions = ch_versions.mix(UNZIP.out.versions)
ch_tdf_files = branched_ms_files.d
.mix(UNTAR.out.untar,
@@ -51,11 +49,10 @@ workflow PREPARE_SPECTRA {
// timsTOF data conversion
TDF2MZML(ch_tdf_files)
- ch_versions = ch_versions.mix(TDF2MZML.out.versions)
// Gunzip mzML files
GUNZIP(branched_ms_files.mzml_gz)
- ch_versions = ch_versions.mix(GUNZIP.out.versions)
+ // ch_versions = ch_versions.mix(GUNZIP.out.versions)
// Initialize channel for ms files that do not need to be converted
ch_ms_files = branched_ms_files.mzml
.mix(GUNZIP.out.gunzip,
@@ -65,7 +62,7 @@ workflow PREPARE_SPECTRA {
// Optional: Run Peak Picking as Preprocessing
if (params.run_centroidisation) {
OPENMS_PEAKPICKERHIRES(ch_ms_files)
- ch_versions = ch_versions.mix(OPENMS_PEAKPICKERHIRES.out.versions)
+ // ch_versions = ch_versions.mix(OPENMS_PEAKPICKERHIRES.out.versions)
ch_mzml_file = OPENMS_PEAKPICKERHIRES.out.mzml
} else {
ch_mzml_file = ch_ms_files
diff --git a/subworkflows/local/process_feature/main.nf b/subworkflows/local/process_feature/main.nf
index 8156a5f2..b9459a2c 100644
--- a/subworkflows/local/process_feature/main.nf
+++ b/subworkflows/local/process_feature/main.nf
@@ -18,15 +18,12 @@ workflow PROCESS_FEATURE {
.map { meta, featurexml -> [ groupKey([id: "${meta.sample}_${meta.condition}"], meta.group_count), featurexml] }
.groupTuple()
.set { ch_features_grouped }
- ch_versions = ch_versions.mix(OPENMS_FEATUREFINDERIDENTIFICATION.out.versions)
// Link extracted features
OPENMS_FEATURELINKERUNLABELEDKD(ch_features_grouped)
- ch_versions = ch_versions.mix(OPENMS_FEATURELINKERUNLABELEDKD.out.versions)
// Resolve conflicting ids matching to the same feature
OPENMS_IDCONFLICTRESOLVER(OPENMS_FEATURELINKERUNLABELEDKD.out.consensusxml)
- ch_versions = ch_versions.mix(OPENMS_IDCONFLICTRESOLVER.out.versions)
emit:
// Define the information that is returned by this workflow
diff --git a/subworkflows/local/quant/main.nf b/subworkflows/local/quant/main.nf
index d9202d8d..7c6824ed 100644
--- a/subworkflows/local/quant/main.nf
+++ b/subworkflows/local/quant/main.nf
@@ -38,12 +38,12 @@ workflow QUANT {
.map { meta -> [[spectra:meta.spectra], meta]} )
.map { spectra, idxmls, meta -> [meta, idxmls] }
.set { ch_ripped_idxml }
- ch_versions = ch_versions.mix(OPENMS_IDRIPPER.out.versions)
+ // ch_versions = ch_versions.mix(OPENMS_IDRIPPER.out.versions)
// Switch to xcorr for filtering since q-values are set to 1 with peptide-level-fdr
if (params.fdr_level == 'peptide_level_fdrs'){
ch_runs_score_switched = OPENMS_IDSCORESWITCHER( ch_ripped_idxml ).idxml
- ch_versions = ch_versions.mix(OPENMS_IDSCORESWITCHER.out.versions)
+ // ch_versions = ch_versions.mix(OPENMS_IDSCORESWITCHER.out.versions)
} else {
ch_runs_score_switched = ch_ripped_idxml
}
@@ -61,7 +61,7 @@ workflow QUANT {
.map { meta, idxml -> [ groupKey([id:"${meta.sample}_${meta.condition}"], meta.group_count), idxml] }
.groupTuple()
.set { ch_runs_to_be_aligned }
- ch_versions = ch_versions.mix(OPENMS_IDFILTER_QUANT.out.versions)
+ // ch_versions = ch_versions.mix(OPENMS_IDFILTER_QUANT.out.versions)
// Align retention times of runs
MAP_ALIGNMENT(
@@ -75,7 +75,7 @@ workflow QUANT {
OPENMS_IDMERGER_QUANT( MAP_ALIGNMENT.out.aligned_idxml
.map { meta, aligned_idxml -> [ groupKey([id: "${meta.sample}_${meta.condition}"], meta.group_count), aligned_idxml] }
.groupTuple())
- ch_versions = ch_versions.mix(OPENMS_IDMERGER_QUANT.out.versions)
+ // ch_versions = ch_versions.mix(OPENMS_IDMERGER_QUANT.out.versions)
// Manipulate channels such that we end up with : [meta, mzml, run_idxml, merged_runs_idxml]
MAP_ALIGNMENT.out.aligned_mzml
@@ -92,7 +92,6 @@ workflow QUANT {
ch_versions = ch_versions.mix(PROCESS_FEATURE.out.versions)
OPENMS_MZTABEXPORTER(PROCESS_FEATURE.out.consensusxml)
- ch_versions = ch_versions.mix(OPENMS_MZTABEXPORTER.out.versions)
emit:
consensusxml = PROCESS_FEATURE.out.consensusxml
diff --git a/subworkflows/local/rescore/main.nf b/subworkflows/local/rescore/main.nf
index 564a6b0c..aca93aff 100644
--- a/subworkflows/local/rescore/main.nf
+++ b/subworkflows/local/rescore/main.nf
@@ -32,7 +32,6 @@ workflow RESCORE {
// Compute features via ms2rescore
MS2RESCORE(ch_merged_runs)
- ch_versions = ch_versions.mix(MS2RESCORE.out.versions)
if (params.rescoring_engine == 'mokapot') {
log.warn "The rescoring engine is set to mokapot. This rescoring engine currently only supports psm-level-fdr via ms2rescore."
@@ -41,22 +40,20 @@ workflow RESCORE {
}
// Switch comet e-value to mokapot q-value
OPENMS_IDSCORESWITCHER(MS2RESCORE.out.idxml)
- ch_versions = ch_versions.mix(OPENMS_IDSCORESWITCHER.out.versions)
+ // ch_versions = ch_versions.mix(OPENMS_IDSCORESWITCHER.out.versions)
ch_rescored_runs = OPENMS_IDSCORESWITCHER.out.idxml
// Filter by mokapot q-value
OPENMS_IDFILTER_Q_VALUE(ch_rescored_runs.map {group_meta, idxml -> [group_meta, idxml, []]})
- ch_versions = ch_versions.mix(OPENMS_IDFILTER_Q_VALUE.out.versions)
+ // ch_versions = ch_versions.mix(OPENMS_IDFILTER_Q_VALUE.out.versions)
ch_filter_q_value = OPENMS_IDFILTER_Q_VALUE.out.filtered
} else {
// Extract PSM features for Percolator
OPENMS_PSMFEATUREEXTRACTOR(MS2RESCORE.out.idxml.join(MS2RESCORE.out.feature_names))
- ch_versions = ch_versions.mix(OPENMS_PSMFEATUREEXTRACTOR.out.versions)
// Run Percolator with local FDR
OPENMS_PERCOLATORADAPTER(OPENMS_PSMFEATUREEXTRACTOR.out.idxml)
- ch_versions = ch_versions.mix(OPENMS_PERCOLATORADAPTER.out.versions)
ch_multiqc_files = ch_multiqc_files.mix(OPENMS_PERCOLATORADAPTER.out.feature_weights.map{ meta, feature_weights -> feature_weights })
ch_pout = OPENMS_PERCOLATORADAPTER.out.idxml
@@ -68,7 +65,7 @@ workflow RESCORE {
ch_rescored_runs = OPENMS_PERCOLATORADAPTER_GLOBAL.out.idxml
// Filter by global percolator q-value
OPENMS_IDFILTER_Q_VALUE_GLOBAL(ch_rescored_runs.map {id, idxml -> [id, idxml, []]})
- ch_versions = ch_versions.mix(OPENMS_IDFILTER_Q_VALUE_GLOBAL.out.versions)
+ // ch_versions = ch_versions.mix(OPENMS_IDFILTER_Q_VALUE_GLOBAL.out.versions)
// Backfilter sample_condition runs according to global FDR
OPENMS_IDFILTER_GLOBAL(ch_pout.combine(OPENMS_IDFILTER_Q_VALUE_GLOBAL.out.filtered.map{ it[1] }))
ch_filter_q_value = OPENMS_IDFILTER_GLOBAL.out.filtered
@@ -79,7 +76,7 @@ workflow RESCORE {
ch_rescored_runs = ch_pout
// Filter by percolator q-value
OPENMS_IDFILTER_Q_VALUE(ch_rescored_runs.map {group_meta, idxml -> [group_meta, idxml, []]})
- ch_versions = ch_versions.mix(OPENMS_IDFILTER_Q_VALUE.out.versions)
+ // ch_versions = ch_versions.mix(OPENMS_IDFILTER_Q_VALUE.out.versions)
ch_filter_q_value = OPENMS_IDFILTER_Q_VALUE.out.filtered
}
}
diff --git a/subworkflows/local/speclib/main.nf b/subworkflows/local/speclib/main.nf
index dad8aa0f..cce8741b 100644
--- a/subworkflows/local/speclib/main.nf
+++ b/subworkflows/local/speclib/main.nf
@@ -28,7 +28,6 @@ workflow SPECLIB {
// Convert psms and spectra to pickle files
EASYPQP_CONVERT(fdrfiltered_comet_idxml.join(mzml), unimod)
- ch_versions = ch_versions.mix(EASYPQP_CONVERT.out.versions)
EASYPQP_CONVERT.out.psmpkl
.map { meta, psmpkl -> [groupKey([id: "${meta.sample}_${meta.condition}"], meta.group_count), psmpkl] }
@@ -41,7 +40,6 @@ workflow SPECLIB {
// Generate spectrum library for each sample-condition pair
EASYPQP_LIBRARY(ch_psmpkl.join(ch_peakpkl))
- ch_versions = ch_versions.mix(EASYPQP_LIBRARY.out.versions)
// Generate spectrum library for all MSruns in the samplesheet
if (params.global_fdr) {
diff --git a/subworkflows/local/utils_nfcore_mhcquant_pipeline/main.nf b/subworkflows/local/utils_nfcore_mhcquant_pipeline/main.nf
index de3353fc..ada8bd4d 100644
--- a/subworkflows/local/utils_nfcore_mhcquant_pipeline/main.nf
+++ b/subworkflows/local/utils_nfcore_mhcquant_pipeline/main.nf
@@ -39,7 +39,7 @@ workflow PIPELINE_INITIALISATION {
main:
- ch_versions = Channel.empty()
+ ch_versions = channel.empty()
//
// Print version and exit if required and dump pipeline parameters to JSON file
@@ -64,7 +64,7 @@ workflow PIPELINE_INITIALISATION {
\033[0;35m nf-core/mhcquant ${workflow.manifest.version}\033[0m
-\033[2m----------------------------------------------------\033[0m-
"""
- after_text = """${workflow.manifest.doi ? "\n* The pipeline\n" : ""}${workflow.manifest.doi.tokenize(",").collect { " https://doi.org/${it.trim().replace('https://doi.org/','')}"}.join("\n")}${workflow.manifest.doi ? "\n" : ""}
+ after_text = """${workflow.manifest.doi ? "\n* The pipeline\n" : ""}${workflow.manifest.doi.tokenize(",").collect { doi -> " https://doi.org/${doi.trim().replace('https://doi.org/','')}"}.join("\n")}${workflow.manifest.doi ? "\n" : ""}
* The nf-core framework
https://doi.org/10.1038/s41587-020-0439-x
@@ -96,7 +96,7 @@ workflow PIPELINE_INITIALISATION {
// Create channel from input file provided through params.input
//
- Channel
+ channel
.fromList(samplesheetToList(params.input, "${projectDir}/assets/schema_input.json"))
.map { meta, file, fasta -> [meta.subMap('sample','condition'), meta, file, fasta] }
.tap { ch_input }
diff --git a/subworkflows/nf-core/utils_nfcore_pipeline/main.nf b/subworkflows/nf-core/utils_nfcore_pipeline/main.nf
index bfd25876..2f30e9a4 100644
--- a/subworkflows/nf-core/utils_nfcore_pipeline/main.nf
+++ b/subworkflows/nf-core/utils_nfcore_pipeline/main.nf
@@ -98,7 +98,7 @@ def workflowVersionToYAML() {
// Get channel of software versions used in pipeline in YAML format
//
def softwareVersionsToYAML(ch_versions) {
- return ch_versions.unique().map { version -> processVersionsFromYAML(version) }.unique().mix(Channel.of(workflowVersionToYAML()))
+ return ch_versions.unique().map { version -> processVersionsFromYAML(version) }.unique().mix(channel.of(workflowVersionToYAML()))
}
//
diff --git a/tests/.nftignore b/tests/.nftignore
index 53b36346..b794e87c 100644
--- a/tests/.nftignore
+++ b/tests/.nftignore
@@ -1,5 +1,4 @@
.DS_Store
-multiqc/multiqc_data/fastqc_top_overrepresented_sequences_table.txt
multiqc/multiqc_data/multiqc.parquet
multiqc/multiqc_data/multiqc.log
multiqc/multiqc_data/multiqc_data.json
diff --git a/tests/default.nf.test.snap b/tests/default.nf.test.snap
index 85ba67fb..a6f7b857 100644
--- a/tests/default.nf.test.snap
+++ b/tests/default.nf.test.snap
@@ -3,11 +3,11 @@
"content": [
{
"MS2RESCORE": {
- "MS\u00b2Rescore": "3.1.5)"
+ "MS2Rescore": "3.1.5"
},
"OPENMSTHIRDPARTY_COMETADAPTER": {
- "CometAdapter": "3.4.1",
- "Comet": "2024.01 rev. 1"
+ "Comet": "2024.01 rev. 1",
+ "CometAdapter": "3.4.1"
},
"OPENMS_DECOYDATABASE": {
"openms": "3.4.1"
@@ -16,7 +16,7 @@
"openms": "3.4.1"
},
"OPENMS_IDMASSACCURACY": {
- "OpenMS": "3.4.1"
+ "openms": "3.4.1"
},
"OPENMS_IDMERGER": {
"openms": "3.4.1"
@@ -41,7 +41,7 @@
"pyopenms": "3.4.1"
},
"Workflow": {
- "nf-core/mhcquant": "v3.1.0"
+ "nf-core/mhcquant": "v3.2.0dev"
}
},
[
@@ -384,6 +384,6 @@
"nf-test": "0.9.3",
"nextflow": "25.04.8"
},
- "timestamp": "2025-10-28T21:36:13.560396546"
+ "timestamp": "2026-01-09T09:50:11.865149581"
}
}
\ No newline at end of file
diff --git a/tests/ionannotator.nf.test.snap b/tests/ionannotator.nf.test.snap
index b0fd3915..dd848f79 100644
--- a/tests/ionannotator.nf.test.snap
+++ b/tests/ionannotator.nf.test.snap
@@ -4,11 +4,11 @@
22,
{
"MS2RESCORE": {
- "MS\u00b2Rescore": "3.1.5)"
+ "MS2Rescore": "3.1.5"
},
"OPENMSTHIRDPARTY_COMETADAPTER": {
- "CometAdapter": "3.4.1",
- "Comet": "2024.01 rev. 1"
+ "Comet": "2024.01 rev. 1",
+ "CometAdapter": "3.4.1"
},
"OPENMS_DECOYDATABASE": {
"openms": "3.4.1"
@@ -17,7 +17,7 @@
"openms": "3.4.1"
},
"OPENMS_IDMASSACCURACY": {
- "OpenMS": "3.4.1"
+ "openms": "3.4.1"
},
"OPENMS_IDMERGER": {
"openms": "3.4.1"
@@ -45,7 +45,7 @@
"pyopenms": "3.4.1"
},
"Workflow": {
- "nf-core/mhcquant": "v3.1.0"
+ "nf-core/mhcquant": "v3.2.0dev"
}
},
[
@@ -405,6 +405,6 @@
"nf-test": "0.9.3",
"nextflow": "25.04.8"
},
- "timestamp": "2025-10-28T21:53:36.826911285"
+ "timestamp": "2026-01-09T10:04:59.923025945"
}
}
\ No newline at end of file
diff --git a/tests/mokapot.nf.test.snap b/tests/mokapot.nf.test.snap
index 4acd6735..045eaca7 100644
--- a/tests/mokapot.nf.test.snap
+++ b/tests/mokapot.nf.test.snap
@@ -4,11 +4,11 @@
20,
{
"MS2RESCORE": {
- "MS\u00b2Rescore": "3.1.5)"
+ "MS2Rescore": "3.1.5"
},
"OPENMSTHIRDPARTY_COMETADAPTER": {
- "CometAdapter": "3.4.1",
- "Comet": "2024.01 rev. 1"
+ "Comet": "2024.01 rev. 1",
+ "CometAdapter": "3.4.1"
},
"OPENMS_DECOYDATABASE": {
"openms": "3.4.1"
@@ -17,7 +17,7 @@
"openms": "3.4.1"
},
"OPENMS_IDMASSACCURACY": {
- "OpenMS": "3.4.1"
+ "openms": "3.4.1"
},
"OPENMS_IDMERGER": {
"openms": "3.4.1"
@@ -38,7 +38,7 @@
"pyopenms": "3.4.1"
},
"Workflow": {
- "nf-core/mhcquant": "v3.1.0"
+ "nf-core/mhcquant": "v3.2.0dev"
}
},
[
@@ -112,6 +112,6 @@
"nf-test": "0.9.3",
"nextflow": "25.04.8"
},
- "timestamp": "2025-10-28T22:04:22.998726246"
+ "timestamp": "2026-01-09T10:16:10.560078316"
}
}
\ No newline at end of file
diff --git a/tests/speclib.nf.test.snap b/tests/speclib.nf.test.snap
index 4864a01a..9d4e26c3 100644
--- a/tests/speclib.nf.test.snap
+++ b/tests/speclib.nf.test.snap
@@ -10,20 +10,23 @@
"easypqp": "0.1.53"
},
"MS2RESCORE": {
- "MS\u00b2Rescore": "3.1.5)"
+ "MS2Rescore": "3.1.5"
},
"OPENMSTHIRDPARTY_COMETADAPTER": {
- "CometAdapter": "3.4.1",
- "Comet": "2024.01 rev. 1"
+ "Comet": "2024.01 rev. 1",
+ "CometAdapter": "3.4.1"
},
"OPENMS_DECOYDATABASE": {
"openms": "3.4.1"
},
+ "OPENMS_IDFILTER_FOR_SPECLIB": {
+ "openms": "3.4.1"
+ },
"OPENMS_IDFILTER_Q_VALUE": {
"openms": "3.4.1"
},
"OPENMS_IDMASSACCURACY": {
- "OpenMS": "3.4.1"
+ "openms": "3.4.1"
},
"OPENMS_IDMERGER": {
"openms": "3.4.1"
@@ -48,7 +51,7 @@
"pyopenms": "3.4.1"
},
"Workflow": {
- "nf-core/mhcquant": "v3.1.0"
+ "nf-core/mhcquant": "v3.2.0dev"
}
},
[
@@ -393,6 +396,6 @@
"nf-test": "0.9.3",
"nextflow": "25.04.8"
},
- "timestamp": "2025-10-28T22:13:30.098880662"
+ "timestamp": "2026-01-09T10:24:20.32429559"
}
}
\ No newline at end of file
diff --git a/workflows/mhcquant.nf b/workflows/mhcquant.nf
index dad8d043..336a6ad1 100644
--- a/workflows/mhcquant.nf
+++ b/workflows/mhcquant.nf
@@ -62,13 +62,13 @@ workflow MHCQUANT {
// Prepare spectra files (Decompress archives, convert to mzML, centroid if specified)
PREPARE_SPECTRA(ch_samplesheet)
- ch_versions = ch_versions.mix(PREPARE_SPECTRA.out.versions)
+ // Decoy Database creation
// Decoy Database creation
if (!params.skip_decoy_generation) {
// Generate reversed decoy database
OPENMS_DECOYDATABASE(ch_fasta)
- ch_versions = ch_versions.mix(OPENMS_DECOYDATABASE.out.versions)
+ // ch_versions = ch_versions.mix(OPENMS_DECOYDATABASE.out.versions)
ch_decoy_db = OPENMS_DECOYDATABASE.out.decoy_fasta
} else {
ch_decoy_db = ch_fasta
@@ -77,7 +77,7 @@ workflow MHCQUANT {
// Optionally clean up mzML files
if (params.filter_mzml){
OPENMS_FILEFILTER(PREPARE_SPECTRA.out.mzml)
- ch_versions = ch_versions.mix(OPENMS_FILEFILTER.out.versions)
+ // ch_versions = ch_versions.mix(OPENMS_FILEFILTER.out.versions)
ch_clean_mzml_file = OPENMS_FILEFILTER.out.mzml
} else {
ch_clean_mzml_file = PREPARE_SPECTRA.out.mzml
@@ -85,7 +85,6 @@ workflow MHCQUANT {
// Compute MS1 TICs for QC
PYOPENMS_CHROMATOGRAMEXTRACTOR(ch_clean_mzml_file)
- ch_versions = ch_versions.mix(PYOPENMS_CHROMATOGRAMEXTRACTOR.out.versions)
ch_multiqc_files = ch_multiqc_files.mix(PYOPENMS_CHROMATOGRAMEXTRACTOR.out.csv.map{ meta, mzml -> mzml })
// Prepare the comet input channel with global fasta or per-sample_condition fasta
@@ -98,7 +97,7 @@ workflow MHCQUANT {
// Run comet database search and index decoy and target hits
OPENMSTHIRDPARTY_COMETADAPTER(ch_comet_in)
- ch_versions = ch_versions.mix(OPENMSTHIRDPARTY_COMETADAPTER.out.versions)
+ // ch_versions = ch_versions.mix(OPENMSTHIRDPARTY_COMETADAPTER.out.versions)
// Prepare the peptideindexer channel with global fasta or per-sample_condition fasta
ch_peptideindexer_in = params.fasta ?
@@ -109,11 +108,11 @@ workflow MHCQUANT {
.map { groupKey, meta, idxml, fasta -> [meta, idxml, fasta] }
OPENMS_PEPTIDEINDEXER(ch_peptideindexer_in)
- ch_versions = ch_versions.mix(OPENMS_PEPTIDEINDEXER.out.versions)
+ // ch_versions = ch_versions.mix(OPENMS_PEPTIDEINDEXER.out.versions)
// Compute mass errors for multiQC report
OPENMS_IDMASSACCURACY(PREPARE_SPECTRA.out.mzml.join(OPENMS_PEPTIDEINDEXER.out.indexed_idxml))
- ch_versions = ch_versions.mix(OPENMS_IDMASSACCURACY.out.versions)
+ // ch_versions = ch_versions.mix(OPENMS_IDMASSACCURACY.out.versions)
ch_multiqc_files = ch_multiqc_files.mix(OPENMS_IDMASSACCURACY.out.frag_err.map{ meta, frag_err -> frag_err })
// Save indexed runs for later use to keep meta-run information. Sort based on file id
@@ -129,7 +128,7 @@ workflow MHCQUANT {
// Merge aligned idXMLfiles
OPENMS_IDMERGER(ch_runs_to_merge)
- ch_versions = ch_versions.mix(OPENMS_IDMERGER.out.versions)
+ // ch_versions = ch_versions.mix(OPENMS_IDMERGER.out.versions)
// Run MS2Rescore
ch_clean_mzml_file
@@ -143,7 +142,6 @@ workflow MHCQUANT {
// SUBWORKFLOW: RESCORE WITH MOKKAPOT OR PERCOLATOR AND FILTER BY Q-VALUE ON LOCAL/GLOBAL FDR
//
RESCORE( ch_rescore_in, ch_multiqc_files )
- ch_versions = ch_versions.mix(RESCORE.out.versions)
ch_multiqc_files = ch_multiqc_files.mix(RESCORE.out.multiqc_files)
// GENERATE SPECTRUM LIBRARY
@@ -164,7 +162,6 @@ workflow MHCQUANT {
// SUBWORKFLOW: SPECLIB
//
SPECLIB(ch_fdrfilter_comet_idxml_filtered, ch_clean_mzml_file)
- ch_versions = ch_versions.mix(SPECLIB.out.versions)
}
//
@@ -172,7 +169,6 @@ workflow MHCQUANT {
//
if (params.quantify) {
QUANT(merge_meta_map, RESCORE.out.rescored_runs, RESCORE.out.fdr_filtered, ch_clean_mzml_file)
- ch_versions = ch_versions.mix(QUANT.out.versions)
ch_output = QUANT.out.consensusxml
} else {
ch_output = RESCORE.out.fdr_filtered
@@ -188,12 +184,10 @@ workflow MHCQUANT {
// Annotate spectra with ion fragmentation information
PYOPENMS_IONANNOTATOR( ch_ion_annotator_input )
- ch_versions = ch_versions.mix(PYOPENMS_IONANNOTATOR.out.versions)
}
// Prepare for check if file is empty
OPENMS_TEXTEXPORTER(ch_output)
- ch_versions = ch_versions.mix(OPENMS_TEXTEXPORTER.out.versions)
// Return an error message when there is only a header present in the document
OPENMS_TEXTEXPORTER.out.tsv.map {
meta, tsv -> if (tsv.size() < 130) {
@@ -203,14 +197,12 @@ workflow MHCQUANT {
// Process the tsv file to facilitate visualization with MultiQC
SUMMARIZE_RESULTS(OPENMS_TEXTEXPORTER.out.tsv)
- ch_versions = ch_versions.mix(SUMMARIZE_RESULTS.out.versions)
//
// EPICORE
//
if (params.epicore) {
EPICORE(ch_fasta.map{ it.last()}, SUMMARIZE_RESULTS.out.epicore_input)
- ch_versions = ch_versions.mix(EPICORE.out.versions)
ch_multiqc_files = ch_multiqc_files.mix(
EPICORE.out.length_dist,
EPICORE.out.intensity_hist
@@ -233,7 +225,25 @@ workflow MHCQUANT {
//
// Collate and save software versions
//
- softwareVersionsToYAML(ch_versions)
+ def topic_versions = Channel.topic("versions")
+ .distinct()
+ .branch { entry ->
+ versions_file: entry instanceof Path
+ versions_tuple: true
+ }
+
+ def topic_versions_string = topic_versions.versions_tuple
+ .map { process, tool, version ->
+ [ process[process.lastIndexOf(':')+1..-1], " ${tool}: ${version}" ]
+ }
+ .groupTuple(by:0)
+ .map { process, tool_versions ->
+ tool_versions.unique().sort()
+ "${process}:\n${tool_versions.join('\n')}"
+ }
+
+ softwareVersionsToYAML(ch_versions.mix(topic_versions.versions_file))
+ .mix(topic_versions_string)
.collectFile(
storeDir: "${params.outdir}/pipeline_info",
name: 'nf_core_' + 'mhcquant_software_' + 'mqc_' + 'versions.yml',
@@ -244,24 +254,24 @@ workflow MHCQUANT {
//
// MODULE: MultiQC
//
- ch_multiqc_config = Channel.fromPath(
+ ch_multiqc_config = channel.fromPath(
"$projectDir/assets/multiqc_config.yml", checkIfExists: true)
ch_multiqc_custom_config = params.multiqc_config ?
- Channel.fromPath(params.multiqc_config, checkIfExists: true) :
- Channel.empty()
+ channel.fromPath(params.multiqc_config, checkIfExists: true) :
+ channel.empty()
ch_multiqc_logo = params.multiqc_logo ?
- Channel.fromPath(params.multiqc_logo, checkIfExists: true) :
- Channel.empty()
+ channel.fromPath(params.multiqc_logo, checkIfExists: true) :
+ channel.empty()
summary_params = paramsSummaryMap(
workflow, parameters_schema: "nextflow_schema.json")
- ch_workflow_summary = Channel.value(paramsSummaryMultiqc(summary_params))
+ ch_workflow_summary = channel.value(paramsSummaryMultiqc(summary_params))
ch_multiqc_files = ch_multiqc_files.mix(
ch_workflow_summary.collectFile(name: 'workflow_summary_mqc.yaml'))
ch_multiqc_custom_methods_description = params.multiqc_methods_description ?
file(params.multiqc_methods_description, checkIfExists: true) :
file("$projectDir/assets/methods_description_template.yml", checkIfExists: true)
- ch_methods_description = Channel.value(
+ ch_methods_description = channel.value(
methodsDescriptionText(ch_multiqc_custom_methods_description))
ch_multiqc_files = ch_multiqc_files.mix(ch_collated_versions)
![\"nf-core/mhcquant\"](\"docs/images/nf-core-mhcquant_logo_dark.png\"){kind=logo}
\n