Detection of copy number variation from array intensity and sequencing read depth using a stepwise Bayesian model

Background: Copy number variants (CNVs) have been demonstrated to occur at a high frequency and are now widely believed to make a significant contribution to the phenotypic variation in human populations. Array-based comparative genomic hybridization (array-CGH) and newly developed read-depth approach through ultrahigh throughput genomic sequencing both provide rapid, robust, and comprehensive methods to identify CNVs on a whole-genome scale. Results: We developed a Bayesian statistical analysis algorithm for the detection of CNVs from both types of genomic data. The algorithm can analyze such data obtained from PCR-based bacterial artificial chromosome arrays, high-density oligonucleotide arrays, and more recently developed high-throughput DNA sequencing. Treating parameters

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Zhengdong D. Zhang, Mark B. Gerstein
• Keywords: BMCBI 2010, Comparative Genomic Hybridization, Genomic, Posterior Distribution