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

TitleHiHMM: Bayesian non-parametric joint inference of chromatin state maps
Authors
Issue Date2015
Citation
Bioinformatics, 2015, v. 31, n. 13, p. 2066-2074 How to Cite?
Abstract© 2015 The Author 2015. Published by Oxford University Press. 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/.
Persistent Identifierhttp://hdl.handle.net/10722/262675
ISSN
2023 Impact Factor: 4.4
2023 SCImago Journal Rankings: 2.574
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorSohn, Kyung Ah-
dc.contributor.authorHo, Joshua W.K.-
dc.contributor.authorDjordjevic, Djordje-
dc.contributor.authorJeong, Hyun Hwan-
dc.contributor.authorPark, Peter J.-
dc.contributor.authorKim, Ju Han-
dc.date.accessioned2018-10-08T02:46:42Z-
dc.date.available2018-10-08T02:46:42Z-
dc.date.issued2015-
dc.identifier.citationBioinformatics, 2015, v. 31, n. 13, p. 2066-2074-
dc.identifier.issn1367-4803-
dc.identifier.urihttp://hdl.handle.net/10722/262675-
dc.description.abstract© 2015 The Author 2015. Published by Oxford University Press. 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/.-
dc.languageeng-
dc.relation.ispartofBioinformatics-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleHiHMM: Bayesian non-parametric joint inference of chromatin state maps-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1093/bioinformatics/btv117-
dc.identifier.pmid25725496-
dc.identifier.scopuseid_2-s2.0-84936791924-
dc.identifier.volume31-
dc.identifier.issue13-
dc.identifier.spage2066-
dc.identifier.epage2074-
dc.identifier.eissn1460-2059-
dc.identifier.isiWOS:000357425800002-

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