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Article: EpiRegNet: Constructing epigenetic regulatory network from high throughput gene expression data for humans

TitleEpiRegNet: Constructing epigenetic regulatory network from high throughput gene expression data for humans
Authors
Issue Date2011
PublisherLandes Bioscience. The Journal's web site is located at http://www.landesbioscience.com/journals/epigenetics
Citation
Epigenetics, 2011, v. 6 n. 12, p. 1505-1512 How to Cite?
AbstractThe advances of high throughput profiling methods, such as microarray gene profiling and RNA-seq, have enabled researchers to identify thousands of differentially expressed genes under a certain perturbation. Much work has been done to understand the genetic factors that contribute to the expression changes by searching the overrepresented regulatory motifs in the promoter regions of these genes. However, the changes could also be caused by epigenetic regulation, especially histone modifications, and no web server has been constructed to study the epigenetic factors responsible for gene expression changes. Here, we present a web tool for this purpose. Provided with different categories of genes (e.g., upregulated, downregulated or unchanged genes), the server will find epigenetic factors responsible for the difference among the categories and construct an epigenetic regulatory network. Furthermore, it will perform colocalization analyses between these epigenetic factors and transcription factors, which were collected from large scale experimental ChIP-seq or computational predicted data. In addition, for users who want to analyze dynamic change of a histone modification mark under different cell conditions, the server will find direct and indirect target genes of this mark by integrative analysis of experimental data and computational prediction, and present a regulatory network around this mark. Both networks can be visualized by a user friendly interface and the data are downloadable in batch. The server currently supports 12 cell types in human, including ESC and CD4 + T cells, and will expand as more public data are available. It also allows user to create a self-defined cell type, upload and analyze multiple ChIP-seq data. It is freely available to academic users at http://jjwanglab.org/EpiRegNet. © 2011 Landes Bioscience.
Persistent Identifierhttp://hdl.handle.net/10722/147656
ISSN
2014 Impact Factor: 4.780
ISI Accession Number ID
Funding AgencyGrant Number
University of Hong Kong
Research Grants Council of Hong KongN_HKU752/10
781511M
778609M
AoE M04/04
Food and Health Bureau of Hong Kong10091262
Funding Information:

We thank Sam Bevan for editing the manuscript. The project was supported by University Postgraduate Fellowships (to L.Y.W. and J.Q.) from the University of Hong Kong, the Research Grants Council of Hong Kong (N_HKU752/10, 781511M, 778609M, AoE M04/04), and Food and Health Bureau of Hong Kong (10091262).

References
Grants

 

DC FieldValueLanguage
dc.contributor.authorWang, LYen_US
dc.contributor.authorWang, Pen_US
dc.contributor.authorLi, MJen_US
dc.contributor.authorQin, Jen_US
dc.contributor.authorWang, Xen_US
dc.contributor.authorZhang, MQen_US
dc.contributor.authorWang, Jen_US
dc.date.accessioned2012-05-29T06:05:17Z-
dc.date.available2012-05-29T06:05:17Z-
dc.date.issued2011en_US
dc.identifier.citationEpigenetics, 2011, v. 6 n. 12, p. 1505-1512en_US
dc.identifier.issn1559-2294en_US
dc.identifier.urihttp://hdl.handle.net/10722/147656-
dc.description.abstractThe advances of high throughput profiling methods, such as microarray gene profiling and RNA-seq, have enabled researchers to identify thousands of differentially expressed genes under a certain perturbation. Much work has been done to understand the genetic factors that contribute to the expression changes by searching the overrepresented regulatory motifs in the promoter regions of these genes. However, the changes could also be caused by epigenetic regulation, especially histone modifications, and no web server has been constructed to study the epigenetic factors responsible for gene expression changes. Here, we present a web tool for this purpose. Provided with different categories of genes (e.g., upregulated, downregulated or unchanged genes), the server will find epigenetic factors responsible for the difference among the categories and construct an epigenetic regulatory network. Furthermore, it will perform colocalization analyses between these epigenetic factors and transcription factors, which were collected from large scale experimental ChIP-seq or computational predicted data. In addition, for users who want to analyze dynamic change of a histone modification mark under different cell conditions, the server will find direct and indirect target genes of this mark by integrative analysis of experimental data and computational prediction, and present a regulatory network around this mark. Both networks can be visualized by a user friendly interface and the data are downloadable in batch. The server currently supports 12 cell types in human, including ESC and CD4 + T cells, and will expand as more public data are available. It also allows user to create a self-defined cell type, upload and analyze multiple ChIP-seq data. It is freely available to academic users at http://jjwanglab.org/EpiRegNet. © 2011 Landes Bioscience.en_US
dc.languageengen_US
dc.publisherLandes Bioscience. The Journal's web site is located at http://www.landesbioscience.com/journals/epigeneticsen_US
dc.relation.ispartofEpigeneticsen_US
dc.subject.meshDatabases, Geneticen_US
dc.subject.meshEpigenesis, Geneticen_US
dc.subject.meshGene Regulatory Networks - Geneticsen_US
dc.subject.meshHistones - Metabolismen_US
dc.subject.meshHumansen_US
dc.subject.meshInterneten_US
dc.subject.meshSoftwareen_US
dc.titleEpiRegNet: Constructing epigenetic regulatory network from high throughput gene expression data for humansen_US
dc.typeArticleen_US
dc.identifier.emailWang, J:junwen@hkucc.hku.hken_US
dc.identifier.authorityWang, J=rp00280en_US
dc.description.naturelink_to_OA_fulltexten_US
dc.identifier.doi10.4161/epi.6.12.18176en_US
dc.identifier.pmid22139581en_US
dc.identifier.scopuseid_2-s2.0-83255166557en_US
dc.identifier.hkuros208294-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-83255166557&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume6en_US
dc.identifier.issue12en_US
dc.identifier.spage1505en_US
dc.identifier.epage1512en_US
dc.identifier.isiWOS:000299828200012-
dc.publisher.placeUnited Statesen_US
dc.relation.projectA Novel Hidden Markov Model to Predict microRNAs and their Targets Simultaneously and its Application to the Epstein-Barr virus-
dc.identifier.scopusauthoridWang, LY=54586178500en_US
dc.identifier.scopusauthoridWang, P=54397871300en_US
dc.identifier.scopusauthoridLi, MJ=37008547900en_US
dc.identifier.scopusauthoridQin, J=54397721700en_US
dc.identifier.scopusauthoridWang, X=8732204400en_US
dc.identifier.scopusauthoridZhang, MQ=35235931600en_US
dc.identifier.scopusauthoridWang, J=8950599500en_US
dc.identifier.citeulike11175267-

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