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

BMC Bioinformatics

Methodology Article

Zhengdong D Zhang¹  Mark B Gerstein² 

[1] Department of Genetics, Albert Einstein College of Medicine, 10461, Bronx, NY, USA;Department of Molecular Biophysics and Biochemistry, Yale University, 06520, New Haven, CT, USA;Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, 06520, New Haven, CT, USA;Department of Computer Science, Yale University, 06520, New Haven, CT, USA;

关键词: Posterior Distribution;    Markov Chain Monte Carlo;    Markov Chain Monte Carlo Simulation;    Reversible Jump Markov Chain Monte Carlo;    Gibbs Sampling Algorithm;   

DOI  :  10.1186/1471-2105-11-539

 received in 2010-05-17, accepted in 2010-10-31,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundCopy 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.ResultsWe 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--e.g., the number of CNVs, the position of each CNV, and the data noise level--that define the underlying data generating process as random variables, our approach derives the posterior distribution of the genomic CNV structure given the observed data. Sampling from the posterior distribution using a Markov chain Monte Carlo method, we get not only best estimates for these unknown parameters but also Bayesian credible intervals for the estimates. We illustrate the characteristics of our algorithm by applying it to both synthetic and experimental data sets in comparison to other segmentation algorithms.ConclusionsIn particular, the synthetic data comparison shows that our method is more sensitive than other approaches at low false positive rates. Furthermore, given its Bayesian origin, our method can also be seen as a technique to refine CNVs identified by fast point-estimate methods and also as a framework to integrate array-CGH and sequencing data with other CNV-related biological knowledge, all through informative priors.

【 授权许可】

CC BY   
© Zhang and Gerstein; licensee BioMed Central Ltd. 2010