MHCquant2 refines immunopeptidomics tumor antigen discovery
Confident identification of human leukocyte antigen (HLA)-presented peptides is crucial for advancing cancer immunotherapy. We present MHCquant2, a scalable and modular Nextflow pipeline integrated into nf-core as a reproducible, portable, and standardized workflow for immunopeptidomics. This integr...
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| Vydané v: | Genome Biology Ročník 26; číslo 1; s. 290 - 17 |
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| Hlavní autori: | , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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London
BioMed Central
22.09.2025
Springer Nature B.V BMC |
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| ISSN: | 1474-760X, 1474-7596, 1474-760X |
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| Abstract | Confident identification of human leukocyte antigen (HLA)-presented peptides is crucial for advancing cancer immunotherapy. We present MHCquant2, a scalable and modular Nextflow pipeline integrated into nf-core as a reproducible, portable, and standardized workflow for immunopeptidomics. This integration allows a community-driven, robust solution for high-throughput analyses across operating systems and cloud infrastructures. MHCquant2 integrates open-source tools including OpenMS, DeepLC, and MS
2
PIP, improving peptide identifications by up to 27% across diverse MS platforms, particularly enriching low-abundant peptides. MHCquant2 demonstrates state-of-the-art performance on our novel benign
MHCquant2
dataset (
n
= 92) and expands the benign human immunopeptidome by over 160,000 unique naturally presented HLA peptides. |
|---|---|
| AbstractList | Confident identification of human leukocyte antigen (HLA)-presented peptides is crucial for advancing cancer immunotherapy. We present MHCquant2, a scalable and modular Nextflow pipeline integrated into nf-core as a reproducible, portable, and standardized workflow for immunopeptidomics. This integration allows a community-driven, robust solution for high-throughput analyses across operating systems and cloud infrastructures. MHCquant2 integrates open-source tools including OpenMS, DeepLC, and MS
PIP, improving peptide identifications by up to 27% across diverse MS platforms, particularly enriching low-abundant peptides. MHCquant2 demonstrates state-of-the-art performance on our novel benign
dataset (n = 92) and expands the benign human immunopeptidome by over 160,000 unique naturally presented HLA peptides. Abstract Confident identification of human leukocyte antigen (HLA)-presented peptides is crucial for advancing cancer immunotherapy. We present MHCquant2, a scalable and modular Nextflow pipeline integrated into nf-core as a reproducible, portable, and standardized workflow for immunopeptidomics. This integration allows a community-driven, robust solution for high-throughput analyses across operating systems and cloud infrastructures. MHCquant2 integrates open-source tools including OpenMS, DeepLC, and MS2PIP, improving peptide identifications by up to 27% across diverse MS platforms, particularly enriching low-abundant peptides. MHCquant2 demonstrates state-of-the-art performance on our novel benignMHCquant2 dataset (n = 92) and expands the benign human immunopeptidome by over 160,000 unique naturally presented HLA peptides. Confident identification of human leukocyte antigen (HLA)-presented peptides is crucial for advancing cancer immunotherapy. We present MHCquant2, a scalable and modular Nextflow pipeline integrated into nf-core as a reproducible, portable, and standardized workflow for immunopeptidomics. This integration allows a community-driven, robust solution for high-throughput analyses across operating systems and cloud infrastructures. MHCquant2 integrates open-source tools including OpenMS, DeepLC, and MS 2 PIP, improving peptide identifications by up to 27% across diverse MS platforms, particularly enriching low-abundant peptides. MHCquant2 demonstrates state-of-the-art performance on our novel benign MHCquant2 dataset ( n = 92) and expands the benign human immunopeptidome by over 160,000 unique naturally presented HLA peptides. Confident identification of human leukocyte antigen (HLA)-presented peptides is crucial for advancing cancer immunotherapy. We present MHCquant2, a scalable and modular Nextflow pipeline integrated into nf-core as a reproducible, portable, and standardized workflow for immunopeptidomics. This integration allows a community-driven, robust solution for high-throughput analyses across operating systems and cloud infrastructures. MHCquant2 integrates open-source tools including OpenMS, DeepLC, and MS2PIP, improving peptide identifications by up to 27% across diverse MS platforms, particularly enriching low-abundant peptides. MHCquant2 demonstrates state-of-the-art performance on our novel benignMHCquant2 dataset (n = 92) and expands the benign human immunopeptidome by over 160,000 unique naturally presented HLA peptides.Confident identification of human leukocyte antigen (HLA)-presented peptides is crucial for advancing cancer immunotherapy. We present MHCquant2, a scalable and modular Nextflow pipeline integrated into nf-core as a reproducible, portable, and standardized workflow for immunopeptidomics. This integration allows a community-driven, robust solution for high-throughput analyses across operating systems and cloud infrastructures. MHCquant2 integrates open-source tools including OpenMS, DeepLC, and MS2PIP, improving peptide identifications by up to 27% across diverse MS platforms, particularly enriching low-abundant peptides. MHCquant2 demonstrates state-of-the-art performance on our novel benignMHCquant2 dataset (n = 92) and expands the benign human immunopeptidome by over 160,000 unique naturally presented HLA peptides. Confident identification of human leukocyte antigen (HLA)-presented peptides is crucial for advancing cancer immunotherapy. We present MHCquant2, a scalable and modular Nextflow pipeline integrated into nf-core as a reproducible, portable, and standardized workflow for immunopeptidomics. This integration allows a community-driven, robust solution for high-throughput analyses across operating systems and cloud infrastructures. MHCquant2 integrates open-source tools including OpenMS, DeepLC, and MS 2 PIP, improving peptide identifications by up to 27% across diverse MS platforms, particularly enriching low-abundant peptides. MHCquant2 demonstrates state-of-the-art performance on our novel benign MHCquant2 dataset ( n = 92) and expands the benign human immunopeptidome by over 160,000 unique naturally presented HLA peptides. Confident identification of human leukocyte antigen (HLA)-presented peptides is crucial for advancing cancer immunotherapy. We present MHCquant2, a scalable and modular Nextflow pipeline integrated into nf-core as a reproducible, portable, and standardized workflow for immunopeptidomics. This integration allows a community-driven, robust solution for high-throughput analyses across operating systems and cloud infrastructures. MHCquant2 integrates open-source tools including OpenMS, DeepLC, and MS2PIP, improving peptide identifications by up to 27% across diverse MS platforms, particularly enriching low-abundant peptides. MHCquant2 demonstrates state-of-the-art performance on our novel benignMHCquant2 dataset (n = 92) and expands the benign human immunopeptidome by over 160,000 unique naturally presented HLA peptides. |
| ArticleNumber | 290 |
| Author | Gabriels, Ralf Bichmann, Leon Hoenisch-Gravel, Naomi Sachsenberg, Timo Dubbelaar, Marissa L. Walz, Juliane S. Wacker, Marcel Scheid, Jonas Lemke, Steffen Nahnsen, Sven Martens, Lennart Dengler, Anna Declerq, Arthur Bauer, Jens |
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University of Tübingen, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen – sequence: 3 givenname: Naomi surname: Hoenisch-Gravel fullname: Hoenisch-Gravel, Naomi organization: Department of Peptide-based Immunotherapy, Institute of Immunology, University and University Hospital Tübingen, Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen – sequence: 4 givenname: Anna surname: Dengler fullname: Dengler, Anna organization: Department of Peptide-based Immunotherapy, Institute of Immunology, University and University Hospital Tübingen, Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen – sequence: 5 givenname: Timo surname: Sachsenberg fullname: Sachsenberg, Timo organization: Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Department of Computer Science, Applied Bioinformatics, 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Department of Peptide-based Immunotherapy, Institute of Immunology, University and University Hospital Tübingen, Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen – sequence: 10 givenname: Leon surname: Bichmann fullname: Bichmann, Leon organization: Center for Systems and Engineering Immunology (CSEI), School of Medicine, Yale University – sequence: 11 givenname: Lennart surname: Martens fullname: Martens, Lennart organization: CompOmics, VIB Center for Medical Biotechnology, VIB, Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, BioOrganic Mass Spectrometry Laboratory (LSMBO), IPHC UMR 7178, University of Strasbourg, CNRS, ProFI FR2048 – sequence: 12 givenname: Marissa L. surname: Dubbelaar fullname: Dubbelaar, Marissa L. organization: Department of Peptide-based Immunotherapy, Institute of Immunology, University and University Hospital Tübingen, Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Quantitative Biology Center (QBiC), University of Tübingen – sequence: 13 givenname: Sven surname: Nahnsen fullname: Nahnsen, Sven organization: Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Quantitative Biology Center (QBiC), University of Tübingen, Department of Computer Science, Biomedical Data Science, University of Tübingen, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, M3 Research Center, University Hospital of Tübingen – sequence: 14 givenname: Juliane S. surname: Walz fullname: Walz, Juliane S. email: juliane.walz@med.uni-tuebingen.de organization: Department of Peptide-based Immunotherapy, Institute of Immunology, University and University Hospital Tübingen, Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), partner site Tübingen, Clinical Collaboration Unit Translational Immunology, Department of Internal Medicine, University Hospital Tübingen |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40983925$$D View this record in MEDLINE/PubMed |
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| Keywords | Open-source Immunopeptidomics Immunotherapy Nextflow Nf-core HLA Mass spectrometry Pipeline |
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| Snippet | Confident identification of human leukocyte antigen (HLA)-presented peptides is crucial for advancing cancer immunotherapy. We present MHCquant2, a scalable... Abstract Confident identification of human leukocyte antigen (HLA)-presented peptides is crucial for advancing cancer immunotherapy. We present MHCquant2, a... |
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| Title | MHCquant2 refines immunopeptidomics tumor antigen discovery |
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