  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.
