Parametric modeling of whole-genome sequencing data for CNV identification.

ABSTRACT: Copy number variants (CNVs) constitute an important class of genetic variants in human genome and are shown to be associated with complex diseases. Whole-genome sequencing provides an unbiased way of identifying all the CNVs that an individual carries. In this paper, we consider parametric modeling of the read depth (RD) data from whole-genome sequencing with the aim of identifying the CNVs, including both Poisson and negative-binomial modeling of such count data. We propose a unified approach of using a mean-matching variance stabilizing transformation to turn the relatively complicated problem of sparse segment identification for count data into a sparse segment identification problem for a sequence of Gaussian data. We apply the optimal sparse segment identification procedure to the tra

SUBMITTER: Vardhanabhuti S 

PROVIDER: S-EPMC4059462 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature