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  Finding Associations among Histone Modifications Using Sparse Partial Correlation Networks

Lasserre, J., Chung, H.-R., & Vingron, M. (2013). Finding Associations among Histone Modifications Using Sparse Partial Correlation Networks. PLoS Computational Biology, 9(9), e1003168-e1003168. doi:10.1371/journal.pcbi.1003168.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0019-0B2D-A Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0019-0B2F-6
Genre: Journal Article

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 Creators:
Lasserre, Julia1, Author              
Chung, Ho-Ryun2, Author              
Vingron, Martin3, Author              
Affiliations:
1Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, Ihnestr. 73, 14195 Berlin, Germany, ou_1433547              
2Computational Epigenetics (Ho-Ryun Chung), Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society, Ihnestr, 73, 14195 Berlin, Germany, ou_1479658              
3Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, Ihnestr, 73, 14195 Berlin, Germany, ou_1479639              

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Free keywords: gene-expression human genome combinatorial patterns chromatin signatures DNA transcription methylation prediction enhancers selection
 Abstract: Histone modifications are known to play an important role in the regulation of transcription. While individual modifications have received much attention in genome-wide analyses, little is known about their relationships. Some authors have built Bayesian networks of modifications, however most often they have used discretized data, and relied on unrealistic assumptions such as the absence of feedback mechanisms or hidden confounding factors. Here, we propose to infer undirected networks based on partial correlations between histone modifications. Within the partial correlation framework, correlations among two variables are controlled for associations induced by the other variables. Partial correlation networks thus focus on direct associations of histone modifications. We apply this methodology to data in CD4+ cells. The resulting network is well supported by common knowledge. When pairs of modifications show a large difference between their correlation and their partial correlation, a potential confounding factor is identified and provided as explanation. Data from different cell types (IMR90, H1) is also exploited in the analysis to assess the stability of the networks. The results are remarkably similar across cell types. Based on this observation, the networks from the three cell types are integrated into a consensus network to increase robustness. The data and the results discussed in the manuscript can be found, together with code, on http://spcn.molgen.mpg.de/index.html.

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Language(s): eng - English
 Dates: 2013-09-052013
 Publication Status: Published in print
 Pages: -
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 Rev. Method: Peer
 Identifiers: DOI: 10.1371/journal.pcbi.1003168
ISSN: 1553-7358
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Title: PLoS Computational Biology
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 9 (9) Sequence Number: - Start / End Page: e1003168 - e1003168 Identifier: ISSN: 1553-734X
CoNE: /journals/resource/1000000000017180_1