Statistical Methods for DNA Methylation Next-Generation Sequencing Data

• Activity Number: 253
• Type: Contributed
• Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Gayla Olbricht*+
• Companies: Missouri University of Science and Technology
• Keywords: DNA Methylation ; Epigenetics ; Next-Generation Sequencing

Abstract

DNA methylation is an epigenetic modification that plays an important role in many biological processes and has been linked to many diseases, including cancer. DNA methylation has the ability to alter gene expression without any change in the DNA sequence via the addition of a methyl chemical group to cytosine bases on the DNA sequence. Historically, it has been difficult to study DNA methylation at a genome-wide level since it can occur anywhere in the genome, and because technology limited investigations to smaller genomic regions. However, with current next-generation sequencing methods such as BS-Seq or MethylC-Seq, it is now possible to identify DNA methylation sites across the entire genome (i.e., the methylome). Many statistical challenges accompany studies of the methylome, which typically aim to identify locations of methylated cytosines as well as detect differences in methylation status between sample types (e.g., disease vs. control). In this work, current methods for investigating DNA methylation on a genome-wide level using next-generation sequencing technology are reviewed and statistical methods to address some of the data analysis challenges are proposed.