Publication: Predicting MHC I restricted T cell epitopes in mice with NAP-CNB, a novel online tool
dc.affiliation.dpto | UC3M. Departamento de Bioingeniería | es |
dc.affiliation.grupoinv | UC3M. Grupo de Investigación: BSEL - Laboratorio de Ciencia e Ingeniería Biomédica | es |
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.contributor.funder | Ministerio de Economía y Competitividad (España) | es |
dc.contributor.funder | Ministerio de Ciencia e Innovación (España) | es |
dc.date.accessioned | 2022-05-16T10:37:16Z | |
dc.date.available | 2022-05-16T10:37:16Z | |
dc.date.issued | 2021-05-24 | |
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. | en |
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). | en |
dc.format.extent | 10 | |
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. | en |
dc.identifier.doi | https://doi.org/10.1038/s41598-021-89927-5 | |
dc.identifier.issn | 2045-2322 | |
dc.identifier.publicationfirstpage | 1 | |
dc.identifier.publicationissue | 1 | |
dc.identifier.publicationlastpage | 10 | |
dc.identifier.publicationtitle | Scientific Reports | en |
dc.identifier.publicationvolume | 11 | |
dc.identifier.uri | http://hdl.handle.net/10016/34809 | |
dc.identifier.uxxi | AR/0000030533 | |
dc.language.iso | eng | en |
dc.publisher | Springer Science and Business Media LLC | en |
dc.relation.projectID | Gobierno de España. PID2019-109820RB-I00 | es |
dc.relation.projectID | Gobierno de España. TEC2016-28052-R | es |
dc.relation.projectID | Gobierno de España. RTC2017-6600-1 | es |
dc.relation.projectID | Gobierno de España. SAF2017-84091-R | es |
dc.relation.projectID | Gobierno de España. BFERO2020.04 | es |
dc.relation.projectID | Gobierno de España. FPU18/03199 | es |
dc.rights | © The Author(s) 2021 | en |
dc.rights | Atribución 3.0 España | * |
dc.rights.accessRights | open access | en |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject.eciencia | Biología y Biomedicina | es |
dc.title | Predicting MHC I restricted T cell epitopes in mice with NAP-CNB, a novel online tool | en |
dc.type | research article | * |
dc.type.hasVersion | VoR | * |
dspace.entity.type | Publication |
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