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

DSpace/Manakin Repository

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

Citable link to this page


Title: hiHMM: Bayesian non-parametric joint inference of chromatin state maps
Author: Sohn, Kyung-Ah; Ho, Joshua W. K.; Djordjevic, Djordje; Jeong, Hyun-hwan; Park, Peter J.; Kim, Ju Han

Note: Order does not necessarily reflect citation order of authors.

Citation: Sohn, Kyung-Ah, Joshua W. K. Ho, Djordje Djordjevic, Hyun-hwan Jeong, Peter J. Park, and Ju Han Kim. 2015. “hiHMM: Bayesian non-parametric joint inference of chromatin state maps.” Bioinformatics 31 (13): 2066-2074. doi:10.1093/bioinformatics/btv117. http://dx.doi.org/10.1093/bioinformatics/btv117.
Full Text & Related Files:
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) using genome-wide histone modification data. This flexible framework provides a new way to learn a consistent definition of chromatin states across multiple genomes, thus facilitating a direct comparison among them. We demonstrate the utility of this method using synthetic data as well as multiple modENCODE ChIP-seq datasets. Conclusion: The hierarchical and Bayesian non-parametric formulation in our approach is an important extension to the current set of methodologies for comparative chromatin landscape analysis. Availability and implementation: Source codes are available at https://github.com/kasohn/hiHMM. Chromatin data are available at http://encode-x.med.harvard.edu/data_sets/chromatin/. Contact: peter_park@harvard.edu or juhan@snu.ac.kr Supplementary information: Supplementary data are available at Bioinformatics online.
Published Version: doi:10.1093/bioinformatics/btv117
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4481846/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:17295727
Downloads of this work:

Show full Dublin Core record

This item appears in the following Collection(s)


Search DASH

Advanced Search