Detection and benchmarking of somatic mutations in cancer genomes using RNA-seq data
To detect functional somatic mutations in tumor samples, whole-exome sequencing (WES) is often used for its reliability and relative low cost. RNA-seq, while generally used to measure gene expression, can potentially also be used for identification of somatic mutations. However there has been little...
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doaj-033abd0d4c4a4444aef50fd63b8d7bc52020-11-24T21:14:27ZengPeerJ Inc.PeerJ2167-83592018-07-016e536210.7717/peerj.5362Detection and benchmarking of somatic mutations in cancer genomes using RNA-seq dataAlexandre Coudray0Anna M. Battenhouse1Philipp Bucher2Vishwanath R. Iyer3School of Life Sciences, École Polytechnique Federale de Lausanne, Lausanne, SwitzerlandDepartment of Molecular Biosciences, University of Texas at Austin, Austin, TX, USASchool of Life Sciences, École Polytechnique Federale de Lausanne, Lausanne, SwitzerlandDepartment of Molecular Biosciences, Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, Livestrong Cancer Institutes, University of Texas at Austin, Austin, TX, USATo detect functional somatic mutations in tumor samples, whole-exome sequencing (WES) is often used for its reliability and relative low cost. RNA-seq, while generally used to measure gene expression, can potentially also be used for identification of somatic mutations. However there has been little systematic evaluation of the utility of RNA-seq for identifying somatic mutations. Here, we develop and evaluate a pipeline for processing RNA-seq data from glioblastoma multiforme (GBM) tumors in order to identify somatic mutations. The pipeline entails the use of the STAR aligner 2-pass procedure jointly with MuTect2 from genome analysis toolkit (GATK) to detect somatic variants. Variants identified from RNA-seq data were evaluated by comparison against the COSMIC and dbSNP databases, and also compared to somatic variants identified by exome sequencing. We also estimated the putative functional impact of coding variants in the most frequently mutated genes in GBM. Interestingly, variants identified by RNA-seq alone showed better representation of GBM-related mutations cataloged by COSMIC. RNA-seq-only data substantially outperformed the ability of WES to reveal potentially new somatic mutations in known GBM-related pathways, and allowed us to build a high-quality set of somatic mutations common to exome and RNA-seq calls. Using RNA-seq data in parallel with WES data to detect somatic mutations in cancer genomes can thus broaden the scope of discoveries and lend additional support to somatic variants identified by exome sequencing alone.https://peerj.com/articles/5362.pdfRNA-seqCancerVariantsSomatic mutations |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Alexandre Coudray Anna M. Battenhouse Philipp Bucher Vishwanath R. Iyer |
spellingShingle |
Alexandre Coudray Anna M. Battenhouse Philipp Bucher Vishwanath R. Iyer Detection and benchmarking of somatic mutations in cancer genomes using RNA-seq data PeerJ RNA-seq Cancer Variants Somatic mutations |
author_facet |
Alexandre Coudray Anna M. Battenhouse Philipp Bucher Vishwanath R. Iyer |
author_sort |
Alexandre Coudray |
title |
Detection and benchmarking of somatic mutations in cancer genomes using RNA-seq data |
title_short |
Detection and benchmarking of somatic mutations in cancer genomes using RNA-seq data |
title_full |
Detection and benchmarking of somatic mutations in cancer genomes using RNA-seq data |
title_fullStr |
Detection and benchmarking of somatic mutations in cancer genomes using RNA-seq data |
title_full_unstemmed |
Detection and benchmarking of somatic mutations in cancer genomes using RNA-seq data |
title_sort |
detection and benchmarking of somatic mutations in cancer genomes using rna-seq data |
publisher |
PeerJ Inc. |
series |
PeerJ |
issn |
2167-8359 |
publishDate |
2018-07-01 |
description |
To detect functional somatic mutations in tumor samples, whole-exome sequencing (WES) is often used for its reliability and relative low cost. RNA-seq, while generally used to measure gene expression, can potentially also be used for identification of somatic mutations. However there has been little systematic evaluation of the utility of RNA-seq for identifying somatic mutations. Here, we develop and evaluate a pipeline for processing RNA-seq data from glioblastoma multiforme (GBM) tumors in order to identify somatic mutations. The pipeline entails the use of the STAR aligner 2-pass procedure jointly with MuTect2 from genome analysis toolkit (GATK) to detect somatic variants. Variants identified from RNA-seq data were evaluated by comparison against the COSMIC and dbSNP databases, and also compared to somatic variants identified by exome sequencing. We also estimated the putative functional impact of coding variants in the most frequently mutated genes in GBM. Interestingly, variants identified by RNA-seq alone showed better representation of GBM-related mutations cataloged by COSMIC. RNA-seq-only data substantially outperformed the ability of WES to reveal potentially new somatic mutations in known GBM-related pathways, and allowed us to build a high-quality set of somatic mutations common to exome and RNA-seq calls. Using RNA-seq data in parallel with WES data to detect somatic mutations in cancer genomes can thus broaden the scope of discoveries and lend additional support to somatic variants identified by exome sequencing alone. |
topic |
RNA-seq Cancer Variants Somatic mutations |
url |
https://peerj.com/articles/5362.pdf |
work_keys_str_mv |
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