{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T11:22:58Z","timestamp":1763810578027,"version":"3.37.3"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"21","license":[{"start":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T00:00:00Z","timestamp":1625097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science Achievement Scholarship of Thailand"},{"DOI":"10.13039\/501100004396","name":"Thailand Research Fund","doi-asserted-by":"publisher","award":["MRG6280189"],"award-info":[{"award-number":["MRG6280189"]}],"id":[{"id":"10.13039\/501100004396","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,11,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>MHC-peptide binding prediction has been widely used for understanding the immune response of individuals or populations, each carrying different MHC molecules as well as for the development of immunotherapeutics. The results from MHC-peptide binding prediction tools are mostly reported as a predicted binding affinity (IC50) and the percentile rank score, and global thresholds e.g. IC50 value &amp;lt; 500\u2009nM or percentile rank &amp;lt; 2% are generally recommended for distinguishing binding peptides from non-binding peptides. However, it is difficult to evaluate statistically the probability of an individual peptide binding prediction to be true or false solely considering predicted scores. Therefore, statistics describing the overall global false discovery rate (FDR) and local FDR, also called posterior error probability (PEP) are required to give statistical context to the natively produced scores.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Result<\/jats:title>\n                  <jats:p>We have developed an algorithm and code implementation, called MHCVision, for estimation of FDR and PEP values for the predicted results of MHC-peptide binding prediction from the NetMHCpan tool. MHCVision performs parameter estimation using a modified expectation maximization framework for a two-component beta mixture model, representing the distribution of true and false scores of the predicted dataset. We can then estimate the PEP of an individual peptide\u2019s predicted score, and conversely the probability that it is true. We demonstrate that the use of global FDR and PEP estimation can provide a better trade-off between sensitivity and precision over using currently recommended thresholds from tools.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/github.com\/PGB-LIV\/MHCVision.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab479","type":"journal-article","created":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T19:22:52Z","timestamp":1625080972000},"page":"3830-3838","source":"Crossref","is-referenced-by-count":2,"title":["MHCVision: estimation of global and local false discovery rate for MHC class I peptide binding prediction"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0953-7851","authenticated-orcid":false,"given":"Phorutai","family":"Pearngam","sequence":"first","affiliation":[{"name":"Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University , Bangkok 10330, Thailand"},{"name":"Institute of Systems, Molecular and Integrative Biology, University of Liverpool , Liverpool L69 7ZB, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4117-3632","authenticated-orcid":false,"given":"Sira","family":"Sriswasdi","sequence":"additional","affiliation":[{"name":"Research Affairs, Faculty of Medicine, Chulalongkorn University , Bangkok 10330, Thailand"},{"name":"Computational Molecular Biology Group, Faculty of Medicine, Chulalongkorn University , Bangkok 10330, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Trairak","family":"Pisitkun","sequence":"additional","affiliation":[{"name":"Center of Excellence in Systems Biology, Faculty of Medicine, Chulalongkorn University , Bangkok 10330, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6118-9327","authenticated-orcid":false,"given":"Andrew R","family":"Jones","sequence":"additional","affiliation":[{"name":"Institute of Systems, Molecular and Integrative Biology, University of Liverpool , Liverpool L69 7ZB, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,7,1]]},"reference":[{"key":"2023051608245116100_btab479-B1","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.immuni.2017.02.007","article-title":"Mass spectrometry profiling of HLA-associated peptidomes in mono-allelic cells enables more accurate epitope prediction","volume":"46","author":"Abelin","year":"2017","journal-title":"Immunity"},{"key":"2023051608245116100_btab479-B2","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":"2023051608245116100_btab479-B3","doi-asserted-by":"crossref","first-page":"D115","DOI":"10.1093\/nar\/gkh131","article-title":"UniProt: the universal protein knowledgebase","volume":"32","author":"Apweiler","year":"2004","journal-title":"Nucleic Acids Res"},{"key":"2023051608245116100_btab479-B4","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1182\/blood-2018-07-866830","article-title":"The HLA ligandome landscape of chronic myeloid leukemia delineates novel T-cell epitopes for immunotherapy","volume":"133","author":"Bilich","year":"2019","journal-title":"Blood"},{"key":"2023051608245116100_btab479-B5","doi-asserted-by":"crossref","first-page":"3360","DOI":"10.4049\/jimmunol.1700893","article-title":"NetMHCpan-4.0: improved peptide\u2013MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data","volume":"199","author":"Jurtz","year":"2017","journal-title":"J. Immunol"},{"key":"2023051608245116100_btab479-B6","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1021\/pr700739d","article-title":"Posterior error probabilities and false discovery rates: two sides of the same coin","volume":"7","author":"K\u00e4ll","year":"2008","journal-title":"J. Proteome Res"},{"key":"2023051608245116100_btab479-B7","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":"2023051608245116100_btab479-B8","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1111\/tan.12093","article-title":"Common and well-documented HLA alleles: 2012 update to the CWD catalogue","volume":"81","author":"Mack","year":"2013","journal-title":"Tissue Antigens"},{"key":"2023051608245116100_btab479-B9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13073-016-0288-x","article-title":"NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets","volume":"8","author":"Nielsen","year":"2016","journal-title":"Genome Med"},{"key":"2023051608245116100_btab479-B10","doi-asserted-by":"crossref","first-page":"e1000107","DOI":"10.1371\/journal.pcbi.1000107","article-title":"Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: netMHCIIpan","volume":"4","author":"Nielsen","year":"2008","journal-title":"PLoS Comput. Biol"},{"key":"2023051608245116100_btab479-B11","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.cels.2018.05.014","article-title":"MHCflurry: open-source class I MHC binding affinity prediction","volume":"7","author":"O'Donnell","year":"2018","journal-title":"Cell Syst"},{"key":"2023051608245116100_btab479-B12","doi-asserted-by":"crossref","first-page":"e1007757","DOI":"10.1371\/journal.pcbi.1007757","article-title":"Benchmarking predictions of MHC class I restricted T cell epitopes in a comprehensively studied model system","volume":"16","author":"Paul","year":"2020","journal-title":"PLoS Comp. Biol"},{"key":"2023051608245116100_btab479-B13","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1186\/s12859-019-2892-4","article-title":"MHCSeqNet: a deep neural network model for universal MHC binding prediction","volume":"20","author":"Phloyphisut","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"2023051608245116100_btab479-B14","doi-asserted-by":"crossref","first-page":"W449","DOI":"10.1093\/nar\/gkaa379","article-title":"NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data","volume":"48","author":"Reynisson","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2023051608245116100_btab479-B15","first-page":"D948","article-title":"IPD-IMGT\/HLA database","volume":"48","author":"Robinson","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2023051608245116100_btab479-B16","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1038\/s41587-019-0322-9","article-title":"A large peptidome dataset improves HLA class I epitope prediction across most of the human population","volume":"38","author":"Sarkizova","year":"2020","journal-title":"Nat. Biotechnol"},{"key":"2023051608245116100_btab479-B17","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1111\/tan.12282","article-title":"A comprehensive analysis of constitutive naturally processed and presented HLA-C 04: 01 (Cw4)\u2013specific peptides","volume":"83","author":"Schittenhelm","year":"2014","journal-title":"Tissue Antigens"},{"key":"2023051608245116100_btab479-B18","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/s13015-017-0112-1","article-title":"A hybrid parameter estimation algorithm for beta mixtures and applications to methylation state classification","volume":"12","author":"Schr\u00f6der","year":"2017","journal-title":"Algorithms Mol. Biol"},{"key":"2023051608245116100_btab479-B19","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1158\/2326-6066.CIR-19-0464","article-title":"High-throughput prediction of MHC class i and ii neoantigens with MHCnuggets","volume":"8","author":"Shao","year":"2020","journal-title":"Cancer Immunol. Res"},{"key":"2023051608245116100_btab479-B20","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1074\/mcp.TIR119.001641","article-title":"Mass spectrometry based immunopeptidomics leads to robust predictions of phosphorylated HLA class I ligands","volume":"19","author":"Solleder","year":"2020","journal-title":"Mol. Cell. Proteomics"},{"key":"2023051608245116100_btab479-B21","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1038\/ni1206-1277","article-title":"From antigen processing to peptide-MHC binding","volume":"7","author":"Unanue","year":"2006","journal-title":"Nat. Immunol"},{"key":"2023051608245116100_btab479-B22","doi-asserted-by":"crossref","first-page":"e1000048","DOI":"10.1371\/journal.pcbi.1000048","article-title":"A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach","volume":"4","author":"Wang","year":"2008","journal-title":"PLoS Comput. Biol"},{"key":"2023051608245116100_btab479-B23","doi-asserted-by":"crossref","first-page":"292","DOI":"10.3389\/fimmu.2017.00292","article-title":"Major histocompatibility complex (MHC) class I and MHC class II proteins: conformational plasticity in antigen presentation","volume":"8","author":"Wieczorek","year":"2017","journal-title":"Front. Immunol"},{"key":"2023051608245116100_btab479-B24","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1146\/annurev.immunol.17.1.51","article-title":"Immunodominance in major histocompatibility complex class I\u2013restricted T lymphocyte responses","volume":"17","author":"Yewdell","year":"1999","journal-title":"Annu. Rev. Immunol"},{"key":"2023051608245116100_btab479-B25","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.cels.2019.05.004","article-title":"Quantification of uncertainty in peptide-MHC binding prediction improves high-affinity peptide Selection for therapeutic design","volume":"9","author":"Zeng","year":"2019","journal-title":"Cell Syst"},{"key":"2023051608245116100_btab479-B26","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1093\/bib\/bbr060","article-title":"Toward more accurate pan-specific MHC-peptide binding prediction: a review of current methods and tools","volume":"13","author":"Zhang","year":"2012","journal-title":"Brief. Bioinform"},{"key":"2023051608245116100_btab479-B27","doi-asserted-by":"crossref","first-page":"e1006457","DOI":"10.1371\/journal.pcbi.1006457","article-title":"Systematically benchmarking peptide-MHC binding predictors: from synthetic to naturally processed epitopes","volume":"14","author":"Zhao","year":"2018","journal-title":"PLoS Comp. Biol"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btab479\/39310152\/btab479.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/21\/3830\/50336520\/btab479.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/37\/21\/3830\/50336520\/btab479.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T08:34:34Z","timestamp":1684226074000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/37\/21\/3830\/6312548"}},"subtitle":[],"editor":[{"given":"Pier Luigi","family":"Martelli","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,7,1]]},"references-count":27,"journal-issue":{"issue":"21","published-print":{"date-parts":[[2021,11,5]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btab479","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"type":"print","value":"1367-4803"},{"type":"electronic","value":"1367-4811"}],"subject":[],"published-other":{"date-parts":[[2021,11,1]]},"published":{"date-parts":[[2021,7,1]]}}}