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

Layla Oesper1*, Ahmad Mahmoody1 and Benjamin J Raphael12*

Author affiliations

1 Department of Computer Science, Brown University, 115 Waterman Street, Providence, RI 02912, USA

2 Center for Computational Molecular Biology, Brown University, Box 1910, Providence, RI 02912, USA

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Citation and License

Genome Biology 2013, 14:R80  doi:10.1186/gb-2013-14-7-r80

Published: 29 July 2013


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 webcite

Cancer genomics; intra-tumor heterogeneity; DNA sequencing; tumor evolution; algorithms