THetA: Inferring intra-tumor heterogeneity from high-throughput DNA sequencing data

Layla Oesper, Ahmad Mahmoody, Benjamin J. Raphael

Research output: Contribution to journalArticlepeer-review

133 Scopus citations

Abstract

Tumor samples are typically heterogeneous, containing admixture by normal, non-cancerous cells and one or more subpopulations of cancerous cells. Whole-genome sequencing of a tumor sample yields reads from this mixture, but does not directly reveal the cell of origin for each read. We introduce THetA (Tumor Heterogeneity Analysis), an algorithm that infers the most likely collection of genomes and their proportions in a sample, for the case where copy number aberrations distinguish subpopulations. THetA successfully estimates normal admixture and recovers clonal and subclonal copy number aberrations in real and simulated sequencing data. THetA is available at http://compbio.cs.brown.edu/software/.

Original languageEnglish (US)
Article numberR80
JournalGenome biology
Volume14
Issue number7
DOIs
StatePublished - 2013

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Genetics
  • Cell Biology

Keywords

  • Algorithms
  • Cancer genomics
  • DNA sequencing
  • Intra-tumor heterogeneity
  • Tumor evolution

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