A hidden Markov model for detecting multi-gene chromatin domains
KeywordsGene Ontology Embryonic Stem Cell Hide Markov Model Histone Modification Olfactory Receptor
Epigenetic regulations are important mechanisms for transcriptional control. There is evidence that neighbouring genes, although not always involved in the same pathways, are still similarly regulated via various histone modifications. Currently, most studies are limited to local epigenetic patterns, whereas methods for analysing large-scale organizations are still lacking.
We developed a computational approach to detect multi- gene domains with coherent epigenetic patterns. We applied this method to analyse a published ChIP-seq dataset for five different histone modification marks (H3K4me2, H3K4me3, H3K27me3, H3K9me3, H3K36me3) in mouse embryonic stem cells. We first obtained a 5-dimenisinal score for all known genes based on average modification activity in select regions. Then, with hidden Markov models and corresponding algorithms, we were able to determine the most probable domain status of each gene. We find that a three-state hidden Markov model can best describe the data, where the states correspond to active, inactive, and null domains.
Our method provides a novel approach to analyse large-scale epigenetic patterns. As we continue to apply our method to other cell lines, we will provide important insight into the general structure, organization, and regulation of the mammalian genome.
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