An optimization framework for unsupervised identification of rare copy number variation from SNP array data

doi:10.1186/gb-2009-10-10-r119

Genome Biology

Volume 10
Issue 10

Viewing options:

Abstract
Full text
PDF (880KB)

Associated material:

PubMed record

Related literature:

Articles citing this article
on Google Scholar
on PubMed Central
Other articles by authors
on Google Scholar

Yavaş G
Koyutürk M
Özsoyoğlu M
Gould MP
LaFramboise T

on PubMed

Yavaş G
Koyutürk M
Özsoyoğlu M
Gould MP
LaFramboise T
Related articles/pages
on Google

on Google Scholar

on PubMed

Tools:

Post to:

Method

An optimization framework for unsupervised identification of rare copy number variation from SNP array data

Gökhan Yavaş¹ , Mehmet Koyutürk¹^,2 , Meral Özsoyoğlu¹ , Meetha P Gould³ and Thomas LaFramboise²^,3^,4

¹Department of Electrical Engineering and Computer Science, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA

²Center for Proteomics and Bioinformatics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA

³Department of Genetics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA

⁴Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, OH, 44195, USA

author email corresponding author email

Genome Biology 2009, 10:R119doi:10.1186/gb-2009-10-10-r119


Published:	23 October 2009

Subject areas: Bioinformatics, Genetics, Genome studies, Methods

Abstract

Copy number variants (CNVs) have roles in human disease, and DNA microarrays are important tools for identifying them. In this paper, we frame CNV identification as an objective function optimization problem. We apply our method to data from hundreds of samples, and demonstrate its ability to detect CNVs at a high level of sensitivity without sacrificing specificity. Its performance compares favorably with currently available methods and it reveals previously unreported gains and losses.