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.
Department of Electrical Engineering and Computer Science, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, USA. gokhan.yavas@case.edu
Gökhan
G
Yavas
Mehmet
M
Koyutürk
Meral
M
Ozsoyoğlu
Meetha P
MP
Gould
Thomas
T
LaFramboise
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