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

Genome Biol. 2016 Aug 24;17(1):178. doi: 10.1186/s13059-016-1029-6.

Abstract

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.

Keywords: Bayesian inference; Model-based cutoff finding; Next-generation sequencing; Sensitivity and specificity; Somatic mutation calling.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Alleles
  • Computational Biology
  • Exome / genetics
  • Genetic Heterogeneity*
  • High-Throughput Nucleotide Sequencing*
  • Humans
  • Mutation / genetics
  • Neoplasms / genetics*
  • Sensitivity and Specificity
  • Software*