title: CNV detection method optimized for high-resolution arrayCGH by normality test.
abstract: High-resolution arrayCGH platform makes it possible to detect small gains and losses which previously could not be measured. However, current CNV detection tools fitted to early low-resolution data are not applicable to larger high-resolution data. When CNV detection tools are applied to high-resolution data, they suffer from high false-positives, which increases validation cost. Existing CNV detection tools also require optimal parameter values. In most cases, obtaining these values is a difficult task. This study developed a CNV detection algorithm that is optimized for high-resolution arrayCGH data. This tool operates up to 1500 times faster than existing tools on a high-resolution arrayCGH of whole human chromosomes which has 42 million probes whose average length is 50 bases, while preserving false positive/negative rates. The algorithm also uses a normality test, thereby removing the need for optimal parameters. To our knowledge, this is the first formulation for CNV detecting problems that results in a near-linear empirical overall complexity for real high-resolution data.
authors.affiliation: Department of Computer Science, Yonsei University, 134 Sinchon-dong, Seodaemun-gu, Seoul 120-749, South Korea.
authors.forename: Jaegyoon
authors.initials: J
authors.lastname: Ahn
authors.forename: Youngmi
authors.initials: Y
authors.lastname: Yoon
authors.forename: Chihyun
authors.initials: C
authors.lastname: Park
authors.forename: Sanghyun
authors.initials: S
authors.lastname: Park
mesh_terms: D000465:Algorithms; D055028:Comparative Genomic Hybridization / Q000379:methods*; D056915:DNA Copy Number Variations; D057225:Data Mining; D030541:Databases, Genetic; D006801:Humans