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Open Access Open Badges Methodology article

Reconstructing DNA copy number by joint segmentation of multiple sequences

Zhongyang Zhang1, Kenneth Lange2 and Chiara Sabatti3*

Author Affiliations

1 Department of Statistics, University of California, Los Angeles, CA, USA

2 Department of Human Genetics, Biomathematics and Statistics, University of California, Los Angeles, CA, USA

3 Department of Health Research and Policy and Statistics, Stanford University, Stanford, CA, USA

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BMC Bioinformatics 2012, 13:205  doi:10.1186/1471-2105-13-205

Published: 16 August 2012



Variations 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.


We 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.


The 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.

Copy number variant; Copy number polymorphism; Fused lasso; Group fused lasso; MM algorithm