MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data

Subclonal mutations reveal important features of the genetic architecture of tumors. However, accurate detection of mutations in genetically heterogeneous tumor cell populations using next-generation sequencing remains challenging. We develop MuSE (http://bioinformatics.mdanderson.org/main/MuSE), Mu...

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Main Authors:	Fan, Yu, Xi, Liu, Hughes, Daniel S. T., Zhang, Jianjun, Zhang, Jianhua, Futreal, P. Andrew, Wheeler, David A., Wang, Wenyi
Format:	Online
Language:	English
Published:	BioMed Central 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4995747/

id	pubmed-4995747
recordtype	oai_dc
spelling	pubmed-49957472016-08-25 MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data Fan, Yu Xi, Liu Hughes, Daniel S. T. Zhang, Jianjun Zhang, Jianhua Futreal, P. Andrew Wheeler, David A. Wang, Wenyi Method Subclonal mutations reveal important features of the genetic architecture of tumors. However, accurate detection of mutations in genetically heterogeneous tumor cell populations using next-generation sequencing remains challenging. We develop MuSE (http://bioinformatics.mdanderson.org/main/MuSE), Mutation calling using a Markov Substitution model for Evolution, a novel approach for modeling the evolution of the allelic composition of the tumor and normal tissue at each reference base. MuSE adopts a sample-specific error model that reflects the underlying tumor heterogeneity to greatly improve the overall accuracy. We demonstrate the accuracy of MuSE in calling subclonal mutations in the context of large-scale tumor sequencing projects using whole exome and whole genome sequencing. BioMed Central 2016-08-24 /pmc/articles/PMC4995747/ /pubmed/27557938 http://dx.doi.org/10.1186/s13059-016-1029-6 Text en © The Author(s) 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Fan, Yu Xi, Liu Hughes, Daniel S. T. Zhang, Jianjun Zhang, Jianhua Futreal, P. Andrew Wheeler, David A. Wang, Wenyi
spellingShingle	Fan, Yu Xi, Liu Hughes, Daniel S. T. Zhang, Jianjun Zhang, Jianhua Futreal, P. Andrew Wheeler, David A. Wang, Wenyi MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data
author_facet	Fan, Yu Xi, Liu Hughes, Daniel S. T. Zhang, Jianjun Zhang, Jianhua Futreal, P. Andrew Wheeler, David A. Wang, Wenyi
author_sort	Fan, Yu
title	MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data
title_short	MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data
title_full	MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data
title_fullStr	MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data
title_full_unstemmed	MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data
title_sort	muse: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data
description	Subclonal mutations reveal important features of the genetic architecture of tumors. However, accurate detection of mutations in genetically heterogeneous tumor cell populations using next-generation sequencing remains challenging. We develop MuSE (http://bioinformatics.mdanderson.org/main/MuSE), Mutation calling using a Markov Substitution model for Evolution, a novel approach for modeling the evolution of the allelic composition of the tumor and normal tissue at each reference base. MuSE adopts a sample-specific error model that reflects the underlying tumor heterogeneity to greatly improve the overall accuracy. We demonstrate the accuracy of MuSE in calling subclonal mutations in the context of large-scale tumor sequencing projects using whole exome and whole genome sequencing.
publisher	BioMed Central
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4995747/
_version_	1613633586249859072

MuSE: accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling from sequencing data

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