Practicability of detecting somatic point mutation from RNA high throughput sequencing data

Quanhu Sheng, Shilin Zhao, Chung I. Li, Yu Shyr, Yan Guo

Research output: Contribution to journalArticle

14 Citations (Scopus)

Abstract

Traditionally, somatic mutations are detected by examining DNA sequence. The maturity of sequencing technology has allowed researchers to screen for somatic mutations in the whole genome. Increasingly, researchers have become interested in identifying somatic mutations through RNAseq data. With this motivation, we evaluated the practicability of detecting somatic mutations from RNAseq data. Current somatic mutation calling tools were designed for DNA sequencing data. To increase performance on RNAseq data, we developed a somatic mutation caller GLMVC based on bias reduced generalized linear model for both DNA and RNA sequencing data. Through comparison with MuTect and Varscan we showed that GLMVC performed better for somatic mutation detection using exome sequencing or RNAseq data. GLMVC is freely available for download at the following website: https://github.com/shengqh/GLMVC/wiki.

Original languageEnglish
Pages (from-to)163-169
Number of pages7
JournalGenomics
Volume107
Issue number5
DOIs
Publication statusPublished - 2016 May 1

All Science Journal Classification (ASJC) codes

  • Genetics

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