An optimization framework for unsupervised identification of rare copy number variation from SNP array data.
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.
Yavas
Gökhan
G
Department of Electrical Engineering and Computer Science, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, USA. gokhan.yavas@case.edu
Koyutürk
Mehmet
M
Ozsoyoğlu
Meral
M
Gould
Meetha P
MP
LaFramboise
Thomas
T
D000465:Algorithms; D002881:Chromosomes, Human, Pair 12 / Q000235:genetics; D056915:DNA Copy Number Variations / Q000235:genetics*; D005006:Ethnicity / Q000235:genetics; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D015203:Reproducibility of Results; D012984:Software; D013997:Time Factors