COKGEN: a software for the identification of rare copy number variation from SNP microarrays.
Until fairly recently, it was believed that essentially all human cells harbor two copies of each locus in the autosomal genome. However, studies have now shown that there are segments of the genome that are polymorphic with regard to genomic copy number. These copy number variations (CNVs) have a role in various diseases such as Alzheimer disease, Crohn's disease, autism and schizophrenia. In the effort to scan the entire genome for these gains and losses of DNA, single nucleotide polymorphism (SNP) arrays have emerged as an important tool. As such, CNV identification from SNP array data is attracting considerable attention as an algorithmic problem, and many methods have been published over the last few years. However, many of the existing model-based methods train their models based on common variations and are therefore less successful in the identification of rare CNVs, detection of which may be very important in personalized genomics applications. In this paper, we formulate CNV identification explicitly as an optimization problem with an objective function that is characterized by several adjustable parameters. These parameters can be configured based on the characteristics of the experimental platform and target application, so that the solution to the optimization problem is the most accurate set of CNV calls. Our method, termed COKGEN, efficiently solves this problem using a variant of the well-known heuristic simulated annealing. We apply COKGEN to data from hundreds of samples, and demonstrate its ability to detect known CNVs at a high level of sensitivity without sacrificing specificity, not only for common but also rare CNVs. Furthermore, we show that it performs better than other publicly-available methods. The configurability of COKGEN, its computational efficiency, and its accuracy in calling rare CNVs make it particularly useful for personalized genomics applications. COKGEN is implemented as an R package and is freely available at http://mendel.gene.cwru.edu/laframboiselab/software.php.
Yavaş
Gökhan
G
Department of Electrical Engineering & Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA.
Koyutürk
Mehmet
M
Ozsoyoğlu
Meral
M
Gould
Meetha P
MP
Laframboise
Thomas
T
D000465:Algorithms; D019295:Computational Biology; D056915:DNA Copy Number Variations; D005819:Genetic Markers; D015894:Genome, Human; D023281:Genomics; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D020641:Polymorphism, Single Nucleotide; D012984:Software
D005819:Genetic Markers