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

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.

journal_name

Genomics

journal_title

Genomics

authors

Sheng Q,Zhao S,Li CI,Shyr Y,Guo Y

doi

10.1016/j.ygeno.2016.03.006

subject

Has Abstract

pub_date

2016-05-01 00:00:00

pages

163-9

issue

5

eissn

0888-7543

issn

1089-8646

pii

S0888-7543(16)30021-0

journal_volume

107

pub_type

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