Comput. Biol. Med. - CNV detection method optimized for high-resolution arrayCGH by normality test.

Tópicos

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Resumo

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.

Resumo Limpo

highresolut arraycgh platform make possibl detect small gain loss previous measur howev current cnv detect tool fit earli lowresolut data applic larger highresolut data cnv detect tool appli highresolut data suffer high falseposit increas valid cost exist cnv detect tool also requir optim paramet valu case obtain valu difficult task studi develop cnv detect algorithm optim highresolut arraycgh data tool oper time faster exist tool highresolut arraycgh whole human chromosom million probe whose averag length base preserv fals positiveneg rate algorithm also use normal test therebi remov need optim paramet knowledg first formul cnv detect problem result nearlinear empir overal complex real highresolut data

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