Generalized Method of Moments Estimation for Stochastic Models of DNA Methylation Patterns

TL;DR

This paper introduces an efficient GMM-based method for estimating parameters in stochastic models of DNA methylation dynamics, enabling accurate calibration even with complex, long-pattern models using epigenomic sequencing data.

Contribution

It presents a novel GMM approach for parameter estimation in complex stochastic DNA methylation models, outperforming traditional likelihood methods in efficiency and scalability.

Findings

GMM achieves accuracy comparable to maximum likelihood methods.

Method effectively calibrates models with longer methylation patterns.

Applied successfully to mouse ESCs sequencing data.

Abstract

With recent advances in sequencing technologies, large amounts of epigenomic data have become available and computational methods are contributing significantly to the progress of epigenetic research. As an orthogonal approach to methods based on machine learning, mechanistic modeling aims at a description of the mechanisms underlying epigenetic changes. Here, we propose an efficient method for parameter estimation for stochastic models that describe the dynamics of DNA methylation patterns over time. Our method is based on the Generalized Method of Moments (GMM) and gives results with an accuracy similar to that of maximum likelihood-based estimation approaches. However, in contrast to the latter, the GMM still allows an efficient and accurate calibration of parameters even if the complexity of the model is increased by considering longer methylation patterns. We show the usefulness of our method by applying it to hairpin bisulfite sequencing data from mouse ESCs for varying pattern lengths.