neoepiscope improves neoepitope prediction with multivariant phasing.

Autor:	Wood MA; Computational Biology Program, Oregon Health & Science University, Portland, OR 97201, USA.; Portland VA Research Foundation, Portland, OR 97239, USA., Nguyen A; Computational Biology Program, Oregon Health & Science University, Portland, OR 97201, USA., Struck AJ; Computational Biology Program, Oregon Health & Science University, Portland, OR 97201, USA., Ellrott K; Computational Biology Program, Oregon Health & Science University, Portland, OR 97201, USA.; Department of Biomedical Engineering, OR 97239, USA., Nellore A; Computational Biology Program, Oregon Health & Science University, Portland, OR 97201, USA.; Department of Biomedical Engineering, OR 97239, USA.; Department of Surgery, OR 97239, USA., Thompson RF; Computational Biology Program, Oregon Health & Science University, Portland, OR 97201, USA.; Portland VA Research Foundation, Portland, OR 97239, USA.; Department of Radiation Medicine, OR 97239, USA.; Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University Portland, OR 97239, USA.; Division of Hospital and Specialty Medicine, VA Portland Healthcare System, Portland, OR 97239, USA.
Jazyk:	angličtina
Zdroj:	Bioinformatics (Oxford, England) [Bioinformatics] 2020 Feb 01; Vol. 36 (3), pp. 713-720.
DOI:	10.1093/bioinformatics/btz653
Abstrakt:	Motivation: The vast majority of tools for neoepitope prediction from DNA sequencing of complementary tumor and normal patient samples do not consider germline context or the potential for the co-occurrence of two or more somatic variants on the same mRNA transcript. Without consideration of these phenomena, existing approaches are likely to produce both false-positive and false-negative results, resulting in an inaccurate and incomplete picture of the cancer neoepitope landscape. We developed neoepiscope chiefly to address this issue for single nucleotide variants (SNVs) and insertions/deletions (indels). Results: Herein, we illustrate how germline and somatic variant phasing affects neoepitope prediction across multiple datasets. We estimate that up to ∼5% of neoepitopes arising from SNVs and indels may require variant phasing for their accurate assessment. neoepiscope is performant, flexible and supports several major histocompatibility complex binding affinity prediction tools. Availability and Implementation: neoepiscope is available on GitHub at https://github.com/pdxgx/neoepiscope under the MIT license. Scripts for reproducing results described in the text are available at https://github.com/pdxgx/neoepiscope-paper under the MIT license. Additional data from this study, including summaries of variant phasing incidence and benchmarking wallclock times, are available in Supplementary Files 1, 2 and 3. Supplementary File 1 contains Supplementary Table 1, Supplementary Figures 1 and 2, and descriptions of Supplementary Tables 2-8. Supplementary File 2 contains Supplementary Tables 2-6 and 8. Supplementary File 3 contains Supplementary Table 7. Raw sequencing data used for the analyses in this manuscript are available from the Sequence Read Archive under accessions PRJNA278450, PRJNA312948, PRJNA307199, PRJNA343789, PRJNA357321, PRJNA293912, PRJNA369259, PRJNA305077, PRJNA306070, PRJNA82745 and PRJNA324705; from the European Genome-phenome Archive under accessions EGAD00001004352 and EGAD00001002731; and by direct request to the authors. Supplementary Information: Supplementary data are available at Bioinformatics online. (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)
Databáze:	MEDLINE
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