Somatic mutations and gene fusions can produce immunogenic neoantigens mediating anticancer immune responses. However, their computational prediction from sequencing data requires complex computational workflows to identify tumor-specific aberrations, derive the resulting peptides, infer patients' Human Leukocyte Antigen types and predict neoepitopes binding to them, together with a set of features underlying their immunogenicity. Here, we present nextNEOpi (nextflow NEOantigen prediction pipeline) a comprehensive and fully automated bioinformatic pipeline to predict tumor neoantigens from raw DNA and RNA sequencing data. In addition, nextNEOpi quantifies neoepitope- and patient-specific features associated with tumor immunogenicity and response to immunotherapy.
CITATION STYLE
Rieder, D., Fotakis, G., Ausserhofer, M., Rene, G., Paster, W., Trajanoski, Z., & Finotello, F. (2022). nextNEOpi: a comprehensive pipeline for computational neoantigen prediction. Bioinformatics, 38(4), 1131–1132. https://doi.org/10.1093/bioinformatics/btab759
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