Reconstructing DNA copy number by joint segmentation of multiple sequences

BMC Bioinformatics

Methodology Article

Chiara Sabatti¹  Kenneth Lange²  Zhongyang Zhang³ 

[1] Department of Health Research and Policy and Statistics, Stanford University, Stanford, CA, USA;Department of Human Genetics, Biomathematics and Statistics, University of California, Los Angeles, CA, USA;Department of Statistics, University of California, Los Angeles, CA, USA;

关键词: Copy number variant;    Copy number polymorphism;    Fused lasso;    Group fused lasso;    MM algorithm;   

DOI  :  10.1186/1471-2105-13-205

 received in 2012-03-20, accepted in 2012-07-27,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundVariations in DNA copy number carry information on the modalities of genome evolution and mis-regulation of DNA replication in cancer cells. Their study can help localize tumor suppressor genes, distinguish different populations of cancerous cells, and identify genomic variations responsible for disease phenotypes. A number of different high throughput technologies can be used to identify copy number variable sites, and the literature documents multiple effective algorithms. We focus here on the specific problem of detecting regions where variation in copy number is relatively common in the sample at hand. This problem encompasses the cases of copy number polymorphisms, related samples, technical replicates, and cancerous sub-populations from the same individual.ResultsWe present a segmentation method named generalized fused lasso (GFL) to reconstruct copy number variant regions. GFL is based on penalized estimation and is capable of processing multiple signals jointly. Our approach is computationally very attractive and leads to sensitivity and specificity levels comparable to those of state-of-the-art specialized methodologies. We illustrate its applicability with simulated and real data sets.ConclusionsThe flexibility of our framework makes it applicable to data obtained with a wide range of technology. Its versatility and speed make GFL particularly useful in the initial screening stages of large data sets.

【 授权许可】

Unknown   
© Zhang et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.