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

Abstract excerpt

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...

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