ScanNeo: Identifying indel-derived neoantigens using RNA-Seq data

Ting You Wang, Li Wang, Sk Kayum Alam, Luke H. Hoeppner, Rendong Yang, Inanc Birol

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

Insertion and deletion (indels) have been recognized as an important source generating tumor-specific mutant peptides (neoantigens). The focus of indel-derived neoantigen identification has been on leveraging DNA sequencing such as whole exome sequencing, with the effort of using RNA-seq less well explored. Here we present ScanNeo, a fast-streamlined computational pipeline for analyzing RNA-seq to predict neoepitopes derived from small to large-sized indels. We applied ScanNeo in a prostate cancer cell line and validated our predictions with matched mass spectrometry data. Finally, we demonstrated that indel neoantigens predicted from RNA-seq were associated with checkpoint inhibitor response in a cohort of melanoma patients. Availability and implementation: ScanNeo is implemented in Python. It is freely accessible at the GitHub repository (https://github.com/ylab-hi/ScanNeo). Supplementary information: Supplementary data are available at Bioinformatics online.

Original languageEnglish (US)
Pages (from-to)4159-4161
Number of pages3
JournalBioinformatics
Volume35
Issue number20
DOIs
StatePublished - Oct 15 2019

PubMed: MeSH publication types

  • Journal Article
  • Research Support, Non-U.S. Gov't

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