By Topic

Detection of copy number variation from next generation sequencing data with total variation penalized least square optimization

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Junbo Duan ; Department of Biostatistics and Bioinformatics, 1440 Canal Street, New Orleans, USA ; Ji-Gang Zhang ; John Lefante ; Hong-Wen Deng
more authors

The detection of copy number variation is important to understand complex diseases such as autism, schizophrenia, cancer, etc. In this paper we propose a method to detect copy number variation from next generation sequencing data. Compared with conventional methods to detect copy number variation like array comparative genomic hybridization (aCGH), the next generation sequencing data provide higher resolution of genomic variations. There are a lot of methods to detect copy number variation from next sequencing data, and most of them are based on statistical hypothesis testing. In this paper, we consider this problem from an optimization point of view. The proposed method is based on optimizing a total variation penalized least square criterion, which involves ℓ-1 norm. Inspired by the analytical study of a statics system, we propose an iterative algorithm to find the optimal solution of this optimization problem. The comparative study with other existing methods on simulated data demonstrates that our method can detect relatively small copy number variants (low copy number and small single copy length) with low false positive rate.

Published in:

Bioinformatics and Biomedicine Workshops (BIBMW), 2011 IEEE International Conference on

Date of Conference:

12-15 Nov. 2011