Detecting Differentially Methylated Genomic Regions with Generalized Gaussian Process Regression

• Activity Number: 303
• Type: Contributed
• Date/Time: Tuesday, August 6, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Dong Wang*+
• Companies: University of Nebraska-Lincoln
• Keywords: DNA methylation ; generalized gaussian process regression ; bisulfite sequencing ; differentially methylated regions

Abstract

Cytosine methylation is one of the major forms of epigenetic modification and has been shown to play critical roles in various biological processes. The emergence of high throughput Bisulfite sequencing makes it possible to obtain a comprehensive view of methylation patterns over the entire genome with reasonable cost. Thus genome-wide comparisons of DNA methylation patterns have become increasingly common. Though a lot of information can be gained by studying differentially methylated individual cytosines, identifying genomic regions that are differentially methylated can potentially provide more information and be more reliable. However, computational and statistical methods for defining differentially methylated regions are mostly ad hoc in nature, typically by clustering differentially methylated cytosines after position-wise tests. Here we propose a generalized Gaussian process regression based approach, which provides a natural framework for identifying differentially methylated regions while taking into account the spatial structure of DNA methylation. We illustrate the proposed method with a data set regarding Arabidopsis plants with different genotypes.