Predicting MHC I restricted T cell epitopes in mice with NAP-CNB, a novel online tool.
Sci Rep
; 11(1): 10780, 2021 05 24.
Article
em En
| MEDLINE
| ID: mdl-34031450
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 (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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Melanoma Experimental
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Antígenos de Histocompatibilidade Classe I
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Epitopos de Linfócito T
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Biologia Computacional
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Animals
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article