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nextNEOpi: a comprehensive pipeline for computational neoantigen prediction

SUMMARY: 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 pa...

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Detalles Bibliográficos
Autores principales: Rieder, Dietmar, Fotakis, Georgios, Ausserhofer, Markus, René, Geyeregger, Paster, Wolfgang, Trajanoski, Zlatko, Finotello, Francesca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8796378/
https://www.ncbi.nlm.nih.gov/pubmed/34788790
http://dx.doi.org/10.1093/bioinformatics/btab759
Descripción
Sumario:SUMMARY: 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. AVAILABILITY AND IMPLEMENTATION: nextNEOpi source code and documentation are available at https://github.com/icbi-lab/nextNEOpi CONTACT: dietmar.rieder@i-med.ac.at or francesca.finotello@uibk.ac.at SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.