{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T18:50:24Z","timestamp":1767811824979,"version":"3.49.0"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:00:00Z","timestamp":1759968000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"ETH core funding","award":["PHRT106 \/SPHN017DRI19"],"award-info":[{"award-number":["PHRT106 \/SPHN017DRI19"]}]},{"name":"Swiss Molecular Pathology Breakthrough Platform","award":["PHRT106 \/SPHN017DRI19"],"award-info":[{"award-number":["PHRT106 \/SPHN017DRI19"]}]},{"name":"Swiss Molecular Pathology Breakthrough Platform"},{"name":"LOOP Z\u00fcrich"},{"name":"The LOOP Zurich and the \u201cMonique Dornonville de la Cour\u2014Stiftung\u201d"},{"name":"Common Fund of the Office of the Director of the National Institutes of Health"},{"DOI":"10.13039\/100000054","name":"NCI","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000051","name":"NHGRI","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000051","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000050","name":"NHLBI","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000026","name":"NIDA","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000026","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000025","name":"NIMH","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000025","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000065","name":"NINDS","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>RNA sequencing enables the characterization of a cell\u2019s transcript isoforms in healthy and disease conditions. In the context of cancer, local transcript variability may translate to splicing-derived tumor-associated peptides recognized by the immune system. A software tool that extracts such candidate peptides, is of great interest for personalized cancer therapy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We present the open-source software tool ImmunoPepper, which extracts a set of biologically plausible peptides from a splicing graph, derived from a set of RNA-seq datasets. This peptide set can be personalized with germline and somatic variation and takes novel RNA splice variants into account. ImmunoPepper supports several filtering options, including subtraction of normal tissue background, prediction of MHC-binding affinity, as well as MassSpec-based validation of identified peptides. We analyzed 32 ovarian cancer (TCGA-OV) and 31 breast invasive carcinoma (TCGA-BRCA) samples, with a strict cancer-specific filtering configuration, and obtained on average 834 and 569 cancer-specific predicted MHC-I binding 9-mers per sample, for each cohort, respectively. MassSpec validation with the target-decoy competition Subset-Neighbor-Search (SNS) showed an average validation rate of 4.5% per TCGA-OV sample and 5.3% per TCGA-BRCA sample. This corresponded to 25 MHC-I binders 9-mers per TCGA-OV sample, and 20 MHC-I binders 9-mers per TCGA-BRCA sample in average. Finally, we draw conclusions about the best framework for generation of splicing-derived neoepitopes and recommend to use joint data structures when processing homogeneously a cancer and a normal cohort and to focus on reproducibility of the candidates across generation pipelines.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>ImmunoPepper is implemented in Python 3 and is available as open-source software at https:\/\/github.com\/ratschlab\/immunopepper. The online documentation can be found at https:\/\/immunopepper.readthedocs.io\/en\/latest\/.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf492","type":"journal-article","created":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T11:44:59Z","timestamp":1756899899000},"source":"Crossref","is-referenced-by-count":0,"title":["ImmunoPepper: extracting personalized peptides from complex splicing graphs"],"prefix":"10.1093","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7063-5333","authenticated-orcid":false,"given":"Laurie","family":"Pr\u00e9lot","sequence":"first","affiliation":[{"name":"Department of Computer Science, ETH Z\u00fcrich , Z\u00fcrich 8092,","place":["Switzerland"]},{"name":"Biomedical Informatics Research, University Hospital Z\u00fcrich , Z\u00fcrich 8006,","place":["Switzerland"]},{"name":"SIB Swiss Institute of Bioinformatics , Z\u00fcrich 8057,","place":["Switzerland"]}]},{"given":"Jiayu","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, ETH Z\u00fcrich , Z\u00fcrich 8092,","place":["Switzerland"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6397-1689","authenticated-orcid":false,"given":"Matthias","family":"H\u00fcser","sequence":"additional","affiliation":[{"name":"Department of Computer Science, ETH Z\u00fcrich , Z\u00fcrich 8092,","place":["Switzerland"]},{"name":"SIB Swiss Institute of Bioinformatics , Z\u00fcrich 8057,","place":["Switzerland"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3411-0692","authenticated-orcid":false,"given":"Andr\u00e9","family":"Kahles","sequence":"additional","affiliation":[{"name":"Department of Computer Science, ETH Z\u00fcrich , Z\u00fcrich 8092,","place":["Switzerland"]},{"name":"Biomedical Informatics Research, University Hospital Z\u00fcrich , Z\u00fcrich 8006,","place":["Switzerland"]},{"name":"SIB Swiss Institute of Bioinformatics , Z\u00fcrich 8057,","place":["Switzerland"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5486-8532","authenticated-orcid":false,"given":"Gunnar","family":"R\u00e4tsch","sequence":"additional","affiliation":[{"name":"Department of Computer Science, ETH Z\u00fcrich , Z\u00fcrich 8092,","place":["Switzerland"]},{"name":"Biomedical Informatics Research, University Hospital Z\u00fcrich , Z\u00fcrich 8006,","place":["Switzerland"]},{"name":"SIB Swiss Institute of Bioinformatics , Z\u00fcrich 8057,","place":["Switzerland"]},{"name":"Department of Biology, ETH Z\u00fcrich , Z\u00fcrich 8093,","place":["Switzerland"]},{"name":"ETH AI Center , Z\u00fcrich 8092,","place":["Switzerland"]}]}],"member":"286","published-online":{"date-parts":[[2025,10,9]]},"reference":[{"key":"2026010711363666700_btaf492-B1","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1093\/bioinformatics\/btv639","article-title":"Gapped sequence alignment using artificial neural networks: application to the MHC class I system","volume":"32","author":"Andreatta","year":"2016","journal-title":"Bioinformatics"},{"key":"2026010711363666700_btaf492-B2","doi-asserted-by":"crossref","first-page":"E4821","DOI":"10.1073\/pnas.1320101110","article-title":"Characterization of the human ESC transcriptome by hybrid sequencing","volume":"110","author":"Au","year":"2013","journal-title":"Proc Natl Acad Sci USA"},{"key":"2026010711363666700_btaf492-B3","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1007\/978-1-4939-7537-2_14","article-title":"Mass spectrometry based immunopeptidomics for the discovery of cancer neoantigens","volume":"1719","author":"Bassani-Sternberg","year":"2018","journal-title":"Methods Mol Biol"},{"key":"2026010711363666700_btaf492-B4","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1007\/s00262-017-2001-3","article-title":"MuPeXI: prediction of neo-epitopes from tumor sequencing data","volume":"66","author":"Bjerregaard","year":"2017","journal-title":"Cancer Immunol Immunother"},{"key":"2026010711363666700_btaf492-B5","doi-asserted-by":"crossref","first-page":"vbac032","DOI":"10.1093\/bioadv\/vbac032","article-title":"NeoSplice: a bioinformatics method for prediction of splice variant neoantigens","volume":"2","author":"Chai","year":"2022","journal-title":"Bioinform Adv"},{"key":"2026010711363666700_btaf492-B6","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1038\/s41467-020-14968-9","article-title":"Integrated proteogenomic deep sequencing and analytics accurately identify non-canonical peptides in tumor immunopeptidomes","volume":"11","author":"Chong","year":"2020","journal-title":"Nat Commun"},{"key":"2026010711363666700_btaf492-B7","doi-asserted-by":"crossref","first-page":"1589","DOI":"10.1038\/s41467-023-37266-6","article-title":"Integrated analysis of genomic and transcriptomic data for the discovery of splice-associated variants in cancer","volume":"14","author":"Cotto","year":"2023","journal-title":"Nat Commun"},{"key":"2026010711363666700_btaf492-B8","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1016\/B978-0-12-407190-2.00007-1","article-title":"Therapeutic cancer vaccines: past, present, and future","volume":"119","author":"Guo","year":"2013","journal-title":"Adv Cancer Res"},{"key":"2026010711363666700_btaf492-B9","doi-asserted-by":"crossref","DOI":"10.12688\/f1000research.7042.1","article-title":"Technical advances in proteomics: new developments in data-independent acquisition","volume":"5","author":"Hu","year":"2016","journal-title":"F1000Res"},{"key":"2026010711363666700_btaf492-B10","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1158\/2326-6066.CIR-19-0401","article-title":"pVACtools: a computational toolkit to identify and visualize cancer neoantigens","volume":"8","author":"Hundal","year":"2020","journal-title":"Cancer Immunol Res"},{"key":"2026010711363666700_btaf492-B11","doi-asserted-by":"crossref","first-page":"3360","DOI":"10.4049\/jimmunol.1700893","article-title":"NetMHCpan-4.0: improved peptide-MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data","volume":"199","author":"Jurtz","year":"2017","journal-title":"J Immunol"},{"key":"2026010711363666700_btaf492-B12","doi-asserted-by":"crossref","first-page":"1840","DOI":"10.1093\/bioinformatics\/btw076","article-title":"SplAdder: identification, quantification and testing of alternative splicing events from RNA-Seq data","volume":"32","author":"Kahles","year":"2016","journal-title":"Bioinformatics"},{"key":"2026010711363666700_btaf492-B13","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.ccell.2018.07.001","article-title":"Comprehensive analysis of alternative splicing across tumors from 8,705 patients","volume":"34","author":"Kahles","year":"2018","journal-title":"Cancer Cell"},{"key":"2026010711363666700_btaf492-B14","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1007\/s00251-011-0579-8","article-title":"NetMHCcons: a consensus method for the major histocompatibility complex class I predictions","volume":"64","author":"Karosiene","year":"2012","journal-title":"Immunogenetics"},{"key":"2026010711363666700_btaf492-B15","doi-asserted-by":"crossref","first-page":"5277","DOI":"10.1038\/ncomms6277","article-title":"MS-GF+ makes progress towards a universal database search tool for proteomics","volume":"5","author":"Kim","year":"2014","journal-title":"Nat Commun"},{"key":"2026010711363666700_btaf492-B16","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1093\/annonc\/mdy022","article-title":"Neopepsee: accurate genome-level prediction of neoantigens by harnessing sequence and amino acid immunogenicity information","volume":"29","author":"Kim","year":"2018","journal-title":"Ann Oncol"},{"key":"2026010711363666700_btaf492-B17","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1186\/s13073-020-00791-w","article-title":"Best practices for variant calling in clinical sequencing","volume":"12","author":"Koboldt","year":"2020","journal-title":"Genome Med"},{"key":"2026010711363666700_btaf492-B18","doi-asserted-by":"crossref","first-page":"vbae080","DOI":"10.1093\/bioadv\/vbae080","article-title":"Prediction of tumor-specific splicing from somatic mutations as a source of neoantigen candidates","volume":"4","author":"Lang","year":"2024","journal-title":"Bioinform Adv"},{"key":"2026010711363666700_btaf492-B19","doi-asserted-by":"crossref","first-page":"eaau5516","DOI":"10.1126\/scitranslmed.aau5516","article-title":"Noncoding regions are the main source of targetable tumor-specific antigens","volume":"10","author":"Laumont","year":"2018","journal-title":"Sci Transl Med"},{"key":"2026010711363666700_btaf492-B20","doi-asserted-by":"crossref","first-page":"eade2886","DOI":"10.1126\/scitranslmed.ade2886","article-title":"Splicing neoantigen discovery with SNAF reveals shared targets for cancer immunotherapy","volume":"16","author":"Li","year":"2024","journal-title":"Sci Transl Med"},{"key":"2026010711363666700_btaf492-B21","doi-asserted-by":"crossref","first-page":"4153","DOI":"10.1021\/acs.jproteome.1c00483","article-title":"Accurately assigning peptides to spectra when only a subset of peptides are relevant","volume":"20","author":"Lin","year":"2021","journal-title":"J Proteome Res"},{"key":"2026010711363666700_btaf492-B22","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1038\/ng.2653","article-title":"The genotype-tissue expression (gtex) project","volume":"45","author":"Lonsdale","year":"2013","journal-title":"Nat Genet"},{"key":"2026010711363666700_btaf492-B23","doi-asserted-by":"crossref","first-page":"W509","DOI":"10.1093\/nar\/gkn202","article-title":"NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11","volume":"36","author":"Lundegaard","year":"2008","journal-title":"Nucleic Acids Res"},{"key":"2026010711363666700_btaf492-B24","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.cels.2020.06.010","article-title":"MHCflurry 2.0: improved pan-allele prediction of MHC class I-presented peptides by incorporating antigen processing","volume":"11","author":"O\u2019Donnell","year":"2020","journal-title":"Cell Syst"},{"key":"2026010711363666700_btaf492-B25","doi-asserted-by":"crossref","first-page":"e2221116120","DOI":"10.1073\/pnas.2221116120","article-title":"IRIS: discovery of cancer immunotherapy targets arising from pre-mRNA alternative splicing","volume":"120","author":"Pan","year":"2023","journal-title":"Proc Natl Acad Sci USA"},{"key":"2026010711363666700_btaf492-B26","doi-asserted-by":"publisher","author":"Pr\u00e9lot","year":"2024","DOI":"10.1101\/2025.09.10.674685"},{"key":"2026010711363666700_btaf492-B27","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.cels.2019.08.009","article-title":"Neoantigen dissimilarity to the Self-Proteome predicts immunogenicity and response to immune checkpoint blockade","volume":"9","author":"Richman","year":"2019","journal-title":"Cell Syst"},{"key":"2026010711363666700_btaf492-B28","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1186\/s13073-019-0666-2","article-title":"Best practices for bioinformatic characterization of neoantigens for clinical utility","volume":"11","author":"Richters","year":"2019","journal-title":"Genome Med"},{"key":"2026010711363666700_btaf492-B29","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1021\/acs.jproteome.5b01091","article-title":"A description of the clinical proteomic tumor analysis consortium (CPTAC) common data analysis pipeline","volume":"15","author":"Rudnick","year":"2016","journal-title":"J Proteome Res"},{"key":"2026010711363666700_btaf492-B30","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1074\/mcp.M115.056226","article-title":"An analysis of the sensitivity of proteogenomic mapping of somatic mutations and novel splicing events in cancer","volume":"15","author":"Ruggles","year":"2016","journal-title":"Mol Cell Proteomics"},{"key":"2026010711363666700_btaf492-B31","doi-asserted-by":"crossref","first-page":"btad659","DOI":"10.1093\/bioinformatics\/btad659","article-title":"ScanNeo2: a comprehensive workflow for neoantigen detection and immunogenicity prediction from diverse genomic and transcriptomic alterations","volume":"39","author":"Sch\u00e4fer","year":"2023","journal-title":"Bioinformatics"},{"key":"2026010711363666700_btaf492-B32","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1126\/science.aaa4971","article-title":"Neoantigens in cancer immunotherapy","volume":"348","author":"Schumacher","year":"2015","journal-title":"Science"},{"key":"2026010711363666700_btaf492-B33","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1038\/nbt.2705","article-title":"A single-molecule long-read survey of the human transcriptome","volume":"31","author":"Sharon","year":"2013","journal-title":"Nat Biotechnol"},{"key":"2026010711363666700_btaf492-B34","doi-asserted-by":"crossref","first-page":"1056","DOI":"10.1038\/nbt.4239","article-title":"Intron retention is a source of neoepitopes in cancer","volume":"36","author":"Smart","year":"2018","journal-title":"Nat Biotechnol"},{"key":"2026010711363666700_btaf492-B35","doi-asserted-by":"crossref","first-page":"3140","DOI":"10.1093\/bioinformatics\/btx377","article-title":"TIminer: NGS data mining pipeline for cancer immunology and immunotherapy","volume":"33","author":"Tappeiner","year":"2017","journal-title":"Bioinformatics"},{"key":"2026010711363666700_btaf492-B36","doi-asserted-by":"crossref","first-page":"4159","DOI":"10.1093\/bioinformatics\/btz193","article-title":"ScanNeo: identifying indel-derived neoantigens using RNA-Seq data","volume":"35","author":"Wang","year":"2019","journal-title":"Bioinformatics"},{"key":"2026010711363666700_btaf492-B37","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.isci.2019.10.028","article-title":"The landscape of tumor fusion neoantigens: a Pan-Cancer analysis","volume":"21","author":"Wei","year":"2019","journal-title":"iScience"},{"key":"2026010711363666700_btaf492-B38","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1101\/gr.235028.118","article-title":"PepQuery enables fast, accurate, and convenient proteomic validation of novel genomic alterations","volume":"29","author":"Wen","year":"2019","journal-title":"Genome Res"},{"key":"2026010711363666700_btaf492-B39","doi-asserted-by":"crossref","first-page":"e008988","DOI":"10.1136\/jitc-2024-008988","article-title":"Comprehensive profiling of cancer neoantigens from aberrant RNA splicing","volume":"12","author":"Wickland","year":"2024","journal-title":"J Immunother Cancer"},{"key":"2026010711363666700_btaf492-B40","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1093\/bioinformatics\/btz653","article-title":"neoepiscope improves neoepitope prediction with multivariant phasing","volume":"36","author":"Wood","year":"2020","journal-title":"Bioinformatics"},{"key":"2026010711363666700_btaf492-B41","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1038\/s41571-018-0135-7","article-title":"Clinical potential of mass spectrometry-based proteogenomics","volume":"16","author":"Zhang","year":"2019","journal-title":"Nat Rev Clin Oncol"},{"key":"2026010711363666700_btaf492-B42","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1093\/bioinformatics\/btw674","article-title":"INTEGRATE-neo: a pipeline for personalized gene fusion neoantigen discovery","volume":"33","author":"Zhang","year":"2017","journal-title":"Bioinformatics"},{"key":"2026010711363666700_btaf492-B43","doi-asserted-by":"crossref","first-page":"14633","DOI":"10.18632\/aging.103516","article-title":"ASNEO: identification of personalized alternative splicing based neoantigens with RNA-seq","volume":"12","author":"Zhang","year":"2020","journal-title":"Aging (Albany NY)"},{"key":"2026010711363666700_btaf492-B44","doi-asserted-by":"publisher","author":"Zhu","year":"2024","DOI":"10.1101\/2024.03.28.587261"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btaf492\/64569015\/btaf492.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/42\/1\/btaf492\/64569015\/btaf492.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/42\/1\/btaf492\/64569015\/btaf492.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T16:36:48Z","timestamp":1767803808000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btaf492\/8279589"}},"subtitle":[],"editor":[{"given":"Christina","family":"Kendziorski","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2025,10,9]]},"references-count":44,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1,2]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btaf492","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,1]]},"published":{"date-parts":[[2025,10,9]]},"article-number":"btaf492"}}