nextNEOpi: a comprehensive pipeline for computational neoantigen prediction
Abstract 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,...
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Veröffentlicht in: | Bioinformatics 2022-01, Vol.38 (4), p.1131-1132 |
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Sprache: | eng |
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Zusammenfassung: | Abstract
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. |
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ISSN: | 1367-4803 1460-2059 1367-4811 |
DOI: | 10.1093/bioinformatics/btab759 |