A Bayesian Graphical Model for ChIP-Seq Data on Histone Modifications

Histone modifications (HMs) are an important post-translational feature. Different types of HMs are believed to co-exist and co-regulate biological processes such as gene expression, and therefore are intrinsically dependent on each other. We develop inference for this complex biological network of HMs based on a graphical model using ChIP-Seq data. A critical computational hurdle in the inference for the proposed graphical model is the evaluation of a normalization constant in an autologistic model that builds on the graphical model. We tackle the problem by Monte Carlo evaluation of ratios of normalization constants. We carry out a set of simulations to validate the proposed approach and to compare it with a standard approach using Bayesian networks. We report inference on HM dependence in a case study with ChIP-Seq data from a next-generation sequencing experiment. An important feature of our approach is that we can report coherent probabilities and estimates related to any event or parameter of interest, including honest uncertainties. Posterior inference is obtained from a joint probability model on latent indicators for the recorded HMs. We illustrate this in the motivating case study. An R package including an implementation of posterior simulation in C is available.