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

Kyung-Ah Sohn; Joshua W K Ho; Djordje Djordjevic; Hyun-Hwan Jeong; Peter J Park; Ju Han Kim

doi:10.1093/bioinformatics/btv117

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

Bioinformatics. 2015 Jul 1;31(13):2066-74. doi: 10.1093/bioinformatics/btv117. Epub 2015 Feb 27.

Authors

Kyung-Ah Sohn¹, Joshua W K Ho¹, Djordje Djordjevic², Hyun-Hwan Jeong³, Peter J Park², Ju Han Kim²

Affiliations

¹ 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 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
² 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 of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
³ 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.

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/.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Animals
Bayes Theorem*
Chromatin / genetics*
Chromatin Immunoprecipitation
Computational Biology / methods
Drosophila melanogaster / genetics
Gene Expression Regulation, Developmental
Histones / metabolism
Humans
Promoter Regions, Genetic
Regulatory Sequences, Nucleic Acid / genetics*
Software*
Statistics, Nonparametric*

Substances

Chromatin
Histones

Grants and funding

U01HG004258/HG/NHGRI NIH HHS/United States