A tiered hidden Markov model characterizes multi-scale chromatin states

Abstract

Precise characterization of chromatin states is an important but difficult task for understanding the regulatory role of chromatin. A number of computational methods have been developed with varying levels of success. However, a remaining challenge is to model epigenomic patterns over multi-scales, as each histone mark is distributed with its own characteristic length scale. We developed a tiered hidden Markov model and applied it to analyze a ChIP-seq dataset in human embryonic stem cells. We identified a two-tier structure containing 15 distinct bin-level chromatin states grouped into three domain-level states. Whereas the bin-level states capture the local variation of histone marks, the domain-level states detect large-scale variations. Compared to bin-level states, the domain-level states are more robust and coherent. We also found active regions in intergenic regions that upon closer examination were expressed non-coding RNAs and pseudogenes. These results provide insights into an additional layer of complexity in chromatin organization.