Predicting MHC I restricted T cell epitopes in mice with NAP-CNB, a novel online tool

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dc.contributor.author Wert Carvajal, Carlos
dc.contributor.author Sánchez García, Rubén
dc.contributor.author Macías, José R.
dc.contributor.author Sanz Pamplona, Rebeca
dc.contributor.author Méndez Pérez, Almudena
dc.contributor.author Alemany, Ramon
dc.contributor.author Veiga, Esteban
dc.contributor.author Sánchez Sorzano, Carlos Óscar
dc.contributor.author Muñoz Barrutia, María Arrate
dc.date.accessioned 2022-05-16T10:37:16Z
dc.date.available 2022-05-16T10:37:16Z
dc.date.issued 2021-05-24
dc.identifier.bibliographicCitation Wert-Carvajal, C., Sánchez-García, R., Macías, J. R., Sanz-Pamplona, R., Pérez, A. M., Alemany, R., Veiga, E., Sorzano, C. Ó. S., & Muñoz-Barrutia, A. (2021). Predicting MHC I restricted T cell epitopes in mice with NAP-CNB, a novel online tool. In Scientific Reports (Vol. 11, Issue 1). Springer Science and Business Media LLC.
dc.identifier.issn 2045-2322
dc.identifier.uri http://hdl.handle.net/10016/34809
dc.description.abstract Lack 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 (NAPCNB) 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:// bioco mp. cnb. csic. es/ Neoan tigen sApp/ with scripts and datasets accessible through the download section.
dc.description.sponsorship This work was funded by the Spanish Ministry of Economy, Industry and Competitiveness (TEC2016-28052-R, RTC2017-6600-1, SAF2017-84091-R, BFERO2020.04), the Spanish Ministry of Science and Innovation (FPU18/03199, PID2019-109820RB-I00), the “la Caixa” Foundation (LCF/BQ/EU19/11710071), FERO foundation and Centro Superior de Investigaciones Científicas (JAEINT18/EX/0636).
dc.format.extent 10
dc.language.iso eng
dc.publisher Springer Science and Business Media LLC
dc.rights © The Author(s) 2021
dc.rights Atribución 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by/3.0/es/
dc.title Predicting MHC I restricted T cell epitopes in mice with NAP-CNB, a novel online tool
dc.type article
dc.subject.eciencia Biología y Biomedicina
dc.identifier.doi https://doi.org/10.1038/s41598-021-89927-5
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. PID2019-109820RB-I00
dc.relation.projectID Gobierno de España. TEC2016-28052-R
dc.relation.projectID Gobierno de España. RTC2017-6600-1
dc.relation.projectID Gobierno de España. SAF2017-84091-R
dc.relation.projectID Gobierno de España. BFERO2020.04
dc.relation.projectID Gobierno de España. FPU18/03199
dc.type.version publishedVersion
dc.identifier.publicationfirstpage 1
dc.identifier.publicationissue 1
dc.identifier.publicationlastpage 10
dc.identifier.publicationtitle Scientific Reports
dc.identifier.publicationvolume 11
dc.identifier.uxxi AR/0000030533
dc.contributor.funder Ministerio de Economía y Competitividad (España)
dc.contributor.funder Ministerio de Ciencia e Innovación (España)
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