Modeling Methylation Patterns with Long Read Sequencing Data

Modeling methylation dynamics with simultaneous changes in CpG islands

Background

In vertebrate genomes, CpG sites can be clustered into CpG islands, and the amount of methylation in a CpG island can change due to gene regulation processes. Thus, single regulatory events can simultaneously change the methylation states of many CpG sites within a CpG island. This should be taken into account when quantifying the amount of change in methylation, for example in form of a branch length in a phylogeny of cell types.

Results

We propose a probabilistic model (the IWE-SSE model) of methylation dynamics that accounts for simultaneous methylation changes in multiple CpG sites belonging to the same CpG island. We further propose a Markov-chain Monte-Carlo (MCMC) method to fit this model to methylation data from cell type phylogenies and apply this method to available data from murine haematopoietic cells and from human cell lines. Combined with simulation studies, these analyses show that accounting for CpG island wide methylation changes has a strong effect on the inferred branch lengths and leads to a significantly better model fit for the methylation data from murine haematopoietic cells and human cell lines.

Conclusion

The MCMC based parameter estimation method for the IWE-SSE model in combination with our MCMC based inference method allows to quantify the amount of methylation changes at single CpG sites as well as on entire CpG islands. Accounting for changes affecting entire islands can lead to more accurate branch length estimation in the presence of simultaneous methylation change.

Hidden Markov Modelling Reveals Neighborhood Dependence of Dnmt3a and 3b Activity

DNA methylation is an epigenetic mark whose important role in development has been widely recognized. This epigenetic modification results in heritable information not encoded by the DNA sequence. The underlying mechanisms controlling DNA methylation are only partly understood. Several mechanistic models of enzyme activities responsible for DNA methylation have been proposed. Here, we extend existing Hidden Markov Models (HMMs) for DNA methylation by describing the occurrence of spatial methylation patterns over time and propose several models with different neighborhood dependences. Furthermore, we investigate correlations between the neighborhood dependence and other genomic information. We perform numerical analysis of the HMMs applied to comprehensive hairpin and non-hairpin bisulfite sequencing measurements and accurately predict wild-type data. We find evidence that the activities of Dnmt3a and Dnmt3b responsible for de novo methylation depend on 5' (left) but not on 3' (right) neighboring CpGs in a sequencing string.