| Autor: |
Wert-Carvajal, Carlos, Sánchez-García, Rubén, Macías, José R, Sanz-Pamplona, Rebeca, Pérez, Almudena Méndez, Alemany, Ramon, Veiga, Esteban, Sorzano, Carlos Óscar S., Muñoz-Barrutia, Arrate |
| Předmět: |
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| Zdroj: |
Scientific Reports; 5/24/2021, Vol. 11 Issue 1, p1-10, 10p |
| Abstrakt: |
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 (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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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