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

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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Bibliographic Details
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/

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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4995747/

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