<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Scheid J</submitter><funding>CHIST-ERA</funding><funding>Deutsche Forschungsgemeinschaft</funding><funding>Fonds Wetenschappelijk Onderzoek,Belgium</funding><funding>HORIZON EUROPE Project BAXERNA 2.0</funding><funding>Invest BW Innovation grant</funding><funding>Deutsche Krebshilfe</funding><funding>Else Kröner-Fresenius-Stiftung</funding><pagination>290</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12455830</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>26(1)</volume><pubmed_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&lt;sup>2&lt;/sup&gt;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&lt;sub>MHCquant2&lt;/sub> dataset (n = 92) and expands the benign human immunopeptidome by ove</pubmed_abstract><journal>Genome biology</journal><pubmed_title>MHCquant2 refines immunopeptidomics tumor antigen discovery.</pubmed_title><pmcid>PMC12455830</pmcid><funding_grant_id>2022_EKSE.79</funding_grant_id><funding_grant_id>EXC2180-390900677</funding_grant_id><funding_grant_id>BW1_4064/03/TruVac</funding_grant_id><funding_grant_id>70114948</funding_grant_id><funding_grant_id>ODEEP-EU</funding_grant_id><funding_grant_id>101080544</funding_grant_id><funding_grant_id>12B7123N, G010023N, G028821N, 1SE3724N</funding_grant_id><pubmed_authors>Martens L</pubmed_authors><pubmed_authors>Lemke S</pubmed_authors><pubmed_authors>Sachsenberg T</pubmed_authors><pubmed_authors>Declerq A</pubmed_authors><pubmed_authors>Hoenisch-Gravel N</pubmed_authors><pubmed_authors>Wacker M</pubmed_authors><pubmed_authors>Gabriels R</pubmed_authors><pubmed_authors>Bichmann L</pubmed_authors><pubmed_authors>Dubbelaar ML</pubmed_authors><pubmed_authors>Bauer J</pubmed_authors><pubmed_authors>Scheid J</pubmed_authors><pubmed_authors>Dengler A</pubmed_authors><pubmed_authors>Nahnsen S</pubmed_authors><pubmed_authors>Walz JS</pubmed_authors></additional><is_claimable>false</is_claimable><name>MHCquant2 refines immunopeptidomics tumor antigen discovery.</name><description>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&lt;sup>2&lt;/sup&gt;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&lt;sub>MHCquant2&lt;/sub> dataset (n = 92) and expands the benign human immunopeptidome by ove</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Sep</publication><modification>2026-06-03T19:40:25.585Z</modification><creation>2026-05-01T03:10:19.253Z</creation></dates><accession>S-EPMC12455830</accession><cross_references><pubmed>40983925</pubmed><doi>10.1186/s13059-025-03763-8</doi></cross_references></HashMap>