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bioRxiv · 10.1101/2020.10.05.327015

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

Wert-Carvajal, C.·Sanchez-Garcia, R.·Macias, J. R.·Sanz-Pamplona, R.·Mendez Perez, A.·Alemany, R.·Veiga Chacon, E.·Sorzano, C. O. S.·Munoz-Barrutia, A.
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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 immunogenicity 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 of 0.95, directly from tumor samples. As a proof-of-concept, we used the B16 melanoma model to test the systems 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.

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Wert-Carvajal, C., Sanchez-Garcia, R., Macias, J. R., Sanz-Pamplona, R., Mendez Perez, A., Alemany, R., Veiga Chacon, E., Sorzano, C. O. S., Munoz-Barrutia, A.. 2020-10-05. NAP-CNB: Bioinformatic pipeline to predict MHC-I-restricted T cell epitopes in mice. https://doi.org/10.1101/2020.10.05.327015

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