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Updated: Apr 16, 2026
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
Published on: December 10, 2012
hiHMM: Bayesian non-parametric joint inference of chromatin state maps
Kyung-Ah Sohn1, Joshua W K Ho1, Djordje Djordjevic2
1Department of Information and Computer Engineering, Ajou University, Suwon 443-749, South Korea, Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul 110799, Korea, Systems Biomedical Informatics Research Center, Seoul National University, Seoul 110799, Korea, Victor Chang Cardiac Research Institute, Sydney, NSW 2010, Australia, The University of New South Wales, Sydney, NSW 2052, Australia, Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA and Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA Department of Information and Computer Engineering, Ajou University, Suwon 443-749, South Korea, Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul 110799, Korea, Systems Biomedical Informatics Research Center, Seoul National University, Seoul 110799, Korea, Victor Chang Cardiac Research Institute, Sydney, NSW 2010, Australia, The University of New South Wales, Sydney, NSW 2052, Australia, Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA and Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA Department of Information and Computer Engineering, Ajou University, Suwon 443-749, South Korea, Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul 110799, Korea, Systems Biomedical Informatics Research Center, Seoul National University, Seoul 110799, Korea, Victor Chang Cardiac Research Institute, Sydney, NSW 2010, Australia, The University of New South Wales, Sydney, NSW 2052, Australia, Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA and Division of Genetics, Department o
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/.
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