BayMeth: Improved DNA methylation quantification for affinity capture sequencing data

Speaker: Andrea Riebler, Associate Professor, Department of Mathematical Sciences, Norwegian University of Science and Technology.

Abstract

DNA methylation is a critical component of the epigenetic regulatory machinery and aberrations in DNA methylation patterns occur in many diseases, such as cancer and diabetes. Mapping and understanding methylation profiles offers considerable promise for reversing the aberrant states. There are different methylation platforms which vary widely in cost, resolution and coverage. We focus on affinity capture of methylated DNA combined with high-throughput sequencing, which represents a good tradeoff between cost and coverage. Due to region-specific sequence properties, such as CpG density, which influence capture efficiency, read density is not directly interpretable and statistical approaches are needed. In this talk, we will present an empirical Bayes approach that uses a fully methylated (SssI treated) control sample to transform observed read counts into regional methylation levels. In our model, inefficient capture can readily be distinguished from low methylation levels by means of larger posterior variances. Furthermore, we can explicitly integrate copy number variation (CNV) data, which offers improvement when applied to cancer datasets. Notably, our model offers analytic expressions for the mean and variance of the methylation level and thus is fast to compute. Our algorithm outperforms existing approaches in terms of bias, mean-squared error and coverage probabilities. A software implementation is freely available in the Bioconductor Repitools package.

This is joint work with Mark Robinson (University of Zurich).

 

Published Oct. 9, 2014 1:16 PM - Last modified Nov. 3, 2014 11:55 AM