Bayesian Graphical Models for Epigenomic Heterogeneity

Abstract

Abtsrcat Histone modifications (HMs) are an important post-translational feature. Different types of HMs are believed to co-regulate biological processes such as gene expression, and are therefore intrinsically dependent on each other. We develop inference for this complex biological network of HMs based on a graphical model for the dependence structure across HMs. We report inference on HM dependence in a case study with ChIP-Seq data from a next-generation sequencing experiment. A hierarchical model extension allow us comparison of multiple graphs corresponding to different genomic regions. The described inference is an important step towards cracking the histone code.