hiHMM: Bayesian non-parametric joint inference of chromatin state maps

Kyung-Ah Sohn, Joshua Wing Kei Ho, Djordje Djordjevic, Hyun-hwan Jeong, Peter J. Park, Ju Han Kim

Abstract: Genome-wide mapping of chromatin states is essential for defining regulatory elements and inferring their activities in eukaryotic genomes. A number of hidden Markov model (HMM)-based methods have been developed to infer chromatin state maps from genome-wide histone modification data for an individual genome. To perform a principled comparison of evolutionarily distant epigenomes, we must consider species-specific biases such as differences in genome size, strength of signal enrichment and co-occurrence patterns of histone modifications.