{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T17:06:54Z","timestamp":1776272814426,"version":"3.50.1"},"reference-count":13,"publisher":"Oxford University Press (OUP)","issue":"18","license":[{"start":{"date-parts":[[2020,9,16]],"date-time":"2020-09-16T00:00:00Z","timestamp":1600214400000},"content-version":"vor","delay-in-days":1,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Grant-in-Aid for Scientific Researc","award":["18H03328"],"award-info":[{"award-number":["18H03328"]}]},{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,9,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Summary<\/jats:title>\n                  <jats:p>It is known that some mutant peptides, such as those resulting from missense mutations and frameshift insertions, can bind to the major histocompatibility complex and be presented to antitumor T cells on the surface of a tumor cell. These peptides are termed neoantigen, and it is important to understand this process for cancer immunotherapy. Here, we introduce an R package termed Neoantimon that can predict a list of potential neoantigens from a variety of mutations, which include not only somatic point mutations but insertions, deletions and structural variants. Beyond the existing applications, Neoantimon is capable of attaching and reflecting several additional information, e.g. wild-type binding capability, allele specific RNA expression levels, single nucleotide polymorphism information and combinations of mutations to filter out infeasible peptides as neoantigen.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The R package is available at http:\/\/github\/hase62\/Neoantimon.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa616","type":"journal-article","created":{"date-parts":[[2020,7,2]],"date-time":"2020-07-02T11:09:59Z","timestamp":1593688199000},"page":"4813-4816","source":"Crossref","is-referenced-by-count":18,"title":["Neoantimon: a multifunctional R package for identification of tumor-specific neoantigens"],"prefix":"10.1093","volume":"36","author":[{"given":"Takanori","family":"Hasegawa","sequence":"first","affiliation":[{"name":"Division of Health Medical Data Science, Health Intelligence Center, The Institute of Medical Science, The University of Tokyo, Tokyo 108-8639, Japan"}]},{"given":"Shuto","family":"Hayashi","sequence":"additional","affiliation":[{"name":"Laboratory of DNA Information Analysis, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo 108-8639, Japan"}]},{"given":"Eigo","family":"Shimizu","sequence":"additional","affiliation":[{"name":"Laboratory of DNA Information Analysis, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo 108-8639, Japan"}]},{"given":"Shinichi","family":"Mizuno","sequence":"additional","affiliation":[{"name":"Devision of Cancer Research, Center for Advanced Medical Innovation, Kyushu University, Fukuoka 812-8582, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6851-8004","authenticated-orcid":false,"given":"Atsushi","family":"Niida","sequence":"additional","affiliation":[{"name":"Division of Health Medical Data Science, Health Intelligence Center, The Institute of Medical Science, The University of Tokyo, Tokyo 108-8639, Japan"}]},{"given":"Rui","family":"Yamaguchi","sequence":"additional","affiliation":[{"name":"Laboratory of DNA Information Analysis, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo 108-8639, Japan"}]},{"given":"Satoru","family":"Miyano","sequence":"additional","affiliation":[{"name":"Laboratory of DNA Information Analysis, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo 108-8639, Japan"}]},{"given":"Hidewaki","family":"Nakagawa","sequence":"additional","affiliation":[{"name":"Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences , Yokohama 230-0045, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2989-308X","authenticated-orcid":false,"given":"Seiya","family":"Imoto","sequence":"additional","affiliation":[{"name":"Division of Health Medical Data Science, Health Intelligence Center, The Institute of Medical Science, The University of Tokyo, Tokyo 108-8639, Japan"}]}],"member":"286","published-online":{"date-parts":[[2020,9,16]]},"reference":[{"key":"2023062213572466300_btaa616-B1","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1007\/s00251-015-0873-y","article-title":"Accurate pan-specific prediction of peptide\u2013MHC class II binding affinity with improved binding core identification","volume":"67","author":"Andreatta","year":"2015","journal-title":"Immunogenetics"},{"key":"2023062213572466300_btaa616-B2","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. 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A dendritic cell vaccine increases the breadth and diversity of melanoma neoantigen-specific T cells","volume":"348","author":"Carreno","year":"2015","journal-title":"Science"},{"key":"2023062213572466300_btaa616-B4","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1186\/s12864-018-5169-9","article-title":"ALPHLARD: a Bayesian method for analyzing HLA genes from whole genome sequence data","volume":"19","author":"Hayashi","year":"2018","journal-title":"BMC Genomics"},{"key":"2023062213572466300_btaa616-B5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13073-016-0264-5","article-title":"pVAC-Seq: a genome-guided in silico approach to identifying tumor neoantigens","volume":"8","author":"Hundal","year":"2016","journal-title":"Genome Med"},{"key":"2023062213572466300_btaa616-B6","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. 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