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

Genome Biology

Benjamin J Raphael¹  Ahmad Mahmoody²  Layla Oesper² 

[1] Center for Computational Molecular Biology, Brown University, Box 1910, Providence, RI 02912, USA;Department of Computer Science, Brown University, 115 Waterman Street, Providence, RI 02912, USA

关键词: algorithms;    tumor evolution;    DNA sequencing;    intra-tumor heterogeneity;    Cancer genomics;   

Others  :  864121
DOI  :  10.1186/gb-2013-14-7-r80

 received in 2013-04-26, accepted in 2013-07-29,  发布年份 2013

【 摘 要 】

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/. webcite

【 授权许可】

   
2013 Oesper et al.; licensee BioMed Central Ltd.

【 图 表 】

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