A Bayesian framework for genome-wide inference of DNA methylation levels

TL;DR

This paper introduces a Bayesian change-point model for analyzing genome-wide DNA methylation data, enabling detection of differential methylation patterns with high accuracy and efficiency.

Contribution

It presents a novel methylome change-point model that accounts for neighboring methylation sites and employs particle filtering for efficient inference.

Findings

High power detection of differential methylation signatures

Effective control of Type-I error rates

Applicable to genome-wide methylation data analysis

Abstract

DNA methylation is an important epigenetic mark that has been studied extensively for its regulatory role in biological processes and diseases. WGBS allows for genome-wide measurements of DNA methylation up to single-base resolutions, yet poses challenges in identifying significantly different methylation patterns across distinct biological conditions. We propose a novel methylome change-point model which describes the joint dynamics of methylation regimes of a case and a control group and benefits from taking into account the information of neighbouring methylation sites among all available samples. We also devise particle filtering and smoothing algorithms to perform efficient inference of the latent methylation patterns. We illustrate that our approach can detect and test for very flexible differential methylation signatures with high power while controlling Type-I error measures.