Neoepiscope improves neoepitope prediction with multivariant phasing

Mary A. Wood, Austin Nguyen, Adam J. Struck, Kyle Ellrott, Abhinav Nellore, Reid F. Thompson

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)713-720
Number of pages8
JournalBioinformatics
Volume36
Issue number3
DOIs
StatePublished - Feb 1 2020

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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