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hiHMM: Bayesian non-parametric joint inference of chromatin state maps
- Source :
- Bioinformatics
- Publication Year :
- 2015
- Publisher :
- Oxford University Press, 2015.
-
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.
- Subjects :
- Statistics and Probability
Chromatin Immunoprecipitation
Bayesian probability
Inference
Computational biology
Biology
Regulatory Sequences, Nucleic Acid
ENCODE
Biochemistry
Genome
Statistics, Nonparametric
Histones
Bayes' theorem
Animals
Humans
Hidden Markov model
Promoter Regions, Genetic
Molecular Biology
Genetics
Computational Biology
Gene Expression Regulation, Developmental
Bayes Theorem
Genome Analysis
Original Papers
Chromatin
Computer Science Applications
Computational Mathematics
Drosophila melanogaster
Computational Theory and Mathematics
Chromatin immunoprecipitation
Software
Subjects
Details
- Language :
- English
- ISSN :
- 13674811 and 13674803
- Volume :
- 31
- Issue :
- 13
- Database :
- OpenAIRE
- Journal :
- Bioinformatics
- Accession number :
- edsair.doi.dedup.....9476ad8d9e751dc6645a7ef25ca15e7a