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.

Motivation: Probabilistic models for methylation dynamics of CpG sites are usually based on sequence evolution models that assume indepedence between sites. In vertebrate genomes, CpG sites can be clustered in CpG islands, and the amount of methylation in a CpG island can change due to gene regulation processes. We propose a probabilistic 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 method to fit this model to methylation data from cell type phylogenies and apply this method to available data from murine haematopoietic cells. Results: Branch lengths in cell phylogenies show the amount of changes in methylation in the development of one cell type from another. We 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.