title: A multi-sample based method for identifying common CNVs in normal human genomic structure using high-resolution aCGH data.
abstract: BACKGROUND
  It is difficult to identify copy number variations (CNV) in normal human genomic data due to noise and non-linear relationships between different genomic regions and signal intensity. A high-resolution array comparative genomic hybridization (aCGH) containing 42 million probes, which is very large compared to previous arrays, was recently published. Most existing CNV detection algorithms do not work well because of noise associated with the large amount of input data and because most of the current methods were not designed to analyze normal human samples. Normal human genome analysis often requires a joint approach across multiple samples. However, the majority of existing methods can only identify CNVs from a single sample.
  
  
  RESULTS
  We developed a multi-sample-based genomic variations detector (MGVD) that uses segmentation to identify common breakpoints across multiple samples and a k-means-based clustering strategy. Unlike previous methods, MGVD simultaneously considers multiple samples with different genomic intensities and identifies CNVs and CNV zones (CNVZs); CNVZ is a more precise measure of the location of a genomic variant than the CNV region (CNVR).
  
  
  CONCLUSIONS
  We designed a specialized algorithm to detect common CNVs from extremely high-resolution multi-sample aCGH data. MGVD showed high sensitivity and a low false discovery rate for a simulated data set, and outperformed most current methods when real, high-resolution HapMap datasets were analyzed. MGVD also had the fastest runtime compared to the other algorithms evaluated when actual, high-resolution aCGH data were analyzed. The CNVZs identified by MGVD can be used in association studies for revealing relationships between phenotypes and genomic aberrations. Our algorithm was developed with standard C++ and is available in Linux and MS Windows format in the STL library. It is freely available at: http://embio.yonsei.ac.kr/~Park/mgvd.php.
authors.affiliation: Department of Computer Science, Yonsei University, Seoul, South Korea.
authors.forename: Chihyun
authors.initials: C
authors.lastname: Park
authors.forename: Jaegyoon
authors.initials: J
authors.lastname: Ahn
authors.forename: Youngmi
authors.initials: Y
authors.lastname: Yoon
authors.forename: Sanghyun
authors.initials: S
authors.lastname: Park
mesh_terms: D000465:Algorithms; D044383:Black People / Q000235:genetics; D055028:Comparative Genomic Hybridization; D056915:DNA Copy Number Variations; D005260:Female; D015894:Genome, Human; D055106:Genome-Wide Association Study; D060148:HapMap Project; D006801:Humans; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D044465:White People / Q000235:genetics
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