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NAP-CNB: Bioinformatic pipeline to predict MHC-I-restricted T cell epitopes in mice

Motivation 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 immunogenicity predictions in mice and a simplified variant selection process can reduce operational requirements. Results We have developed a web server tool (NAP-CNB) for a full and automatic pipeline, based on recurrent neural networks, able to predict putative neoantigens from tumoral RNA sequencing reads. The developed software is able to estimate H-2 peptide ligands, with an AUC of 0.95, directly from tumor samples. As a proof-of-concept, we used the B16 melanoma model to test the predictive capabilities of the system and we report its putative neoantigens. Availability and implementation NAP-CNB web server is freely available at http://biocomp.cnb.csic.es/NeoantigensApp/ with scripts and datasets accessible through the download section.