Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/178832
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dc.contributor.authorWert Carvajal, Carlos-
dc.contributor.authorSánchez García, Rubén-
dc.contributor.authorMacías, José R-
dc.contributor.authorSanz-Pamplona, Rebeca-
dc.contributor.authorMéndez Pérez, Almudena-
dc.contributor.authorAlemany Bonastre, Ramon-
dc.contributor.authorVeiga, Esteban-
dc.contributor.authorSorzano, Carlos Óscar S.-
dc.contributor.authorMuñoz Barrutia, Arrate-
dc.date.accessioned2021-07-05T09:53:47Z-
dc.date.available2021-07-05T09:53:47Z-
dc.date.issued2021-05-24-
dc.identifier.urihttp://hdl.handle.net/2445/178832-
dc.description.abstractLack of a dedicated integrated pipeline for neoantigen discovery in mice hinders cancer immunotherapy research. Novel sequential approaches through recurrent neural networks can improve the accuracy of T-cell epitope binding affinity predictions in mice, and a simplified variant selection process can reduce operational requirements. We have developed a web server tool (NAP-CNB) for a full and automatic pipeline based on recurrent neural networks, to predict putative neoantigens from tumoral RNA sequencing reads. The developed software can estimate H-2 peptide ligands, with an AUC comparable or superior to state-of-the-art methods, directly from tumor samples. As a proof-of-concept, we used the B16 melanoma model to test the system's predictive capabilities, and we report its putative neoantigens. NAP-CNB web server is freely available at http://biocomp.cnb.csic.es/NeoantigensApp/ with scripts and datasets accessible through the download section.-
dc.format.extent10 p.-
dc.format.mimetypeapplication/pdf-
dc.language.isoeng-
dc.publisherSpringer Science and Business Media LLC-
dc.relation.isformatofReproducció del document publicat a: https://doi.org/10.1038/s41598-021-89927-5-
dc.relation.ispartofScientific Reports, 2021, vol. 11, num. 10780-
dc.relation.urihttps://doi.org/10.1038/s41598-021-89927-5-
dc.rightscc by (c) Wert Carvajal, Carlos et al., 2021-
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.classificationCàncer-
dc.subject.classificationImmunoteràpia-
dc.subject.classificationCèl·lules T-
dc.subject.otherCancer-
dc.subject.otherImmunotherapy-
dc.subject.otherT cells-
dc.titlePredicting MHC I restricted T cell epitopes in mice with NAP-CNB, a novel online tool-
dc.typeinfo:eu-repo/semantics/article-
dc.date.updated2021-07-02T10:33:12Z-
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess-
Appears in Collections:Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))

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