Inference of Tumor Phylogenies with Improved Somatic Mutation Discovery

Raheleh Salari, Syed Shayon Saleh, Dorna Kashef-Haghighi, David Khavari, Daniel E. Newburger, Robert B. West, Arend Sidow, Serafim Batzoglou

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Scopus citations

Abstract

Next-generation sequencing technologies provide a powerful tool for studying genome evolution during progression of advanced diseases such as cancer. Although many recent studies have employed new sequencing technologies to detect mutations across multiple, genetically related tumors, current methods do not exploit available phylogenetic information to improve the accuracy of their variant calls. Here, we present a novel algorithm that uses somatic single nucleotide variations (SNVs) in multiple, related tissue samples as lineage markers for phylogenetic tree reconstruction. Our method then leverages the inferred phylogeny to improve the accuracy of SNV discovery. Experimental analyses demonstrate that our method achieves up to 32% improvement for somatic SNV calling of multiple related samples over the accuracy of GATK's Unified Genotyper, the state of the art multisample SNV caller. © 2013 Springer-Verlag.
Original languageEnglish (US)
Title of host publicationResearch in Computational Molecular Biology
PublisherSpringer Science + Business Media
Pages249-263
Number of pages15
ISBN (Print)9783642371943
DOIs
StatePublished - 2013
Externally publishedYes

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