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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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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 |
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author | Rieder, Dietmar Fotakis, Georgios Ausserhofer, Markus René, Geyeregger Paster, Wolfgang Trajanoski, Zlatko Finotello, Francesca |
author_facet | Rieder, Dietmar Fotakis, Georgios Ausserhofer, Markus René, Geyeregger Paster, Wolfgang Trajanoski, Zlatko Finotello, Francesca |
author_sort | Rieder, Dietmar |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8796378 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-87963782022-01-31 nextNEOpi: a comprehensive pipeline for computational neoantigen prediction Rieder, Dietmar Fotakis, Georgios Ausserhofer, Markus René, Geyeregger Paster, Wolfgang Trajanoski, Zlatko Finotello, Francesca Bioinformatics Applications Notes 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. Oxford University Press 2021-11-12 /pmc/articles/PMC8796378/ /pubmed/34788790 http://dx.doi.org/10.1093/bioinformatics/btab759 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Applications Notes Rieder, Dietmar Fotakis, Georgios Ausserhofer, Markus René, Geyeregger Paster, Wolfgang Trajanoski, Zlatko Finotello, Francesca nextNEOpi: a comprehensive pipeline for computational neoantigen prediction |
title | nextNEOpi: a comprehensive pipeline for computational neoantigen prediction |
title_full | nextNEOpi: a comprehensive pipeline for computational neoantigen prediction |
title_fullStr | nextNEOpi: a comprehensive pipeline for computational neoantigen prediction |
title_full_unstemmed | nextNEOpi: a comprehensive pipeline for computational neoantigen prediction |
title_short | nextNEOpi: a comprehensive pipeline for computational neoantigen prediction |
title_sort | nextneopi: a comprehensive pipeline for computational neoantigen prediction |
topic | Applications Notes |
url | 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 |
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