Detecting copy number variations from array CGH data based on a conditional random field model.

ABSTRACT: Array comparative genomic hybridization (aCGH) allows identification of copy number alterations across genomes. The key computational challenge in analyzing copy number variations (CNVs) using aCGH data or other similar data generated by a variety of array technologies is the detection of segment boundaries of copy number changes and inference of the copy number state for each segment. We have developed a novel statistical model based on the framework of conditional random fields (CRFs) that can effectively combine data smoothing, segmentation and copy number state decoding into one unified framework. Our approach (termed CRF-CNV) provides great flexibilities in defining meaningful feature functions. Therefore, it can effectively integrate local spatial information of arbitrary sizes into

SUBMITTER: Yin XL 

PROVIDER: S-EPMC3326659 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature