Wert Carvajal, CarlosSánchez García, RubénMacías, José RSanz Pamplona, RebecaMéndez Pérez, AlmudenaAlemany Bonastre, RamonVeiga, EstebanSorzano, Carlos Óscar S.Muñoz Barrutia, Arrate2021-07-052021-07-052021-05-24https://hdl.handle.net/2445/178832Lack 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.10 p.application/pdfengcc by (c) Wert Carvajal, Carlos et al., 2021http://creativecommons.org/licenses/by/3.0/es/CàncerImmunoteràpiaCèl·lules TCancerImmunotherapyT cellsinfo:eu-repo/semantics/article2021-07-02info:eu-repo/semantics/openAccess34031450