%0 Journal Article %A Yu Fan %A Xi Liu %A Hughes Daniel S. T. %A Jianjua Zhang %A Jianhua Zhang %A P. Andrew Futreal %A David A. Wheeler %A Wang Wenyi %T Accounting for tumor heterogeneity using a sample-specific error model improves sensitivity and specificity in mutation calling for sequencing data %D 2016 %R 10.1101/055467 %J bioRxiv %P 055467 %X Subclonal mutations reveal important features of the genetic architecture of tumors. However, accurate detection of mutations in genetically heterogeneous tumor cell populations using NGS remains challenging. We developed MuSE (http://bioinformatics.mdanderson.org/main/MuSE), mutation calling using a Markov substitution model for evolution, a novel approach 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 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 sequence. %U https://www.biorxiv.org/content/biorxiv/early/2016/05/25/055467.full.pdf