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PMID: 23895164 Published · epublish English Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, P.H.S.

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

Genome biology ·Vol. 14 ·No. 7 ·2013-07-29 ·Pages R80

Oesper L, Mahmoody A, Raphael BJ

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/.

MeSH Terms
Algorithms Breast Neoplasms/genetics Computer Simulation Female Genetic Heterogeneity Genome, Human/genetics High-Throughput Nucleotide Sequencing/methods Humans Likelihood Functions Software Statistics as Topic
Authors & Affiliations
3 authors, click to expand affiliations / ORCID
Oesper Layla
Mahmoody Ahmad
Raphael Benjamin J
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Article Info
Journal
Genome biology
Abbr.
Genome Biol
ISSN
1474-760X
DOI
10.1186/gb-2013-14-7-r80
Published
2013-07-29
Epub
2013-00-29
Pages
R80
Language
English
Region
England
NLM ID
100960660
PMCID
PMC4054893
Subset
IM
Grants
NHGRI NIH HHS · R01 HG005690 · United States
NHGRI NIH HHS · R01 HG5690 · United States
External Links
PubMed source DOI: 10.1186/gb-2013-14-7-r80 PMC Full Text View journal details
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