HiHMM: Bayesian non-parametric joint inference of chromatin state maps.

ABSTRACT:

Motivation

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.

Results

Here, we present a new Bayesian non-parametric method called hierarchically linked infinite HMM (hiHMM) to jointly infer chromatin state maps in multiple genomes (different species, cell types and developmental stages) usi

SUBMITTER: Sohn KA 

PROVIDER: S-EPMC4481846 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature