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Article: EpiRegNet: Constructing epigenetic regulatory network from high throughput gene expression data for humans
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TitleEpiRegNet: Constructing epigenetic regulatory network from high throughput gene expression data for humans
 
AuthorsWang, LY1
Wang, P1
Li, MJ1
Qin, J1
Wang, X3
Zhang, MQ2 3
Wang, J1
 
Issue Date2011
 
PublisherLandes Bioscience. The Journal's web site is located at http://www.landesbioscience.com/journals/epigenetics
 
CitationEpigenetics, 2011, v. 6 n. 12, p. 1505-1512 [How to Cite?]
DOI: http://dx.doi.org/10.4161/epi.6.12.18176
 
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.
 
ISSN1559-2294
2012 Impact Factor: 4.92
2012 SCImago Journal Rankings: 1.965
 
DOIhttp://dx.doi.org/10.4161/epi.6.12.18176
 
ISI Accession Number IDWOS:000299828200012
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).

 
ReferencesReferences in Scopus
 
GrantsA Novel Hidden Markov Model to Predict microRNAs and their Targets Simultaneously and its Application to the Epstein-Barr virus
 
DC FieldValue
dc.contributor.authorWang, LY
 
dc.contributor.authorWang, P
 
dc.contributor.authorLi, MJ
 
dc.contributor.authorQin, J
 
dc.contributor.authorWang, X
 
dc.contributor.authorZhang, MQ
 
dc.contributor.authorWang, J
 
dc.date.accessioned2012-05-29T06:05:17Z
 
dc.date.available2012-05-29T06:05:17Z
 
dc.date.issued2011
 
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.
 
dc.description.natureLink_to_OA_fulltext
 
dc.identifier.citationEpigenetics, 2011, v. 6 n. 12, p. 1505-1512 [How to Cite?]
DOI: http://dx.doi.org/10.4161/epi.6.12.18176
 
dc.identifier.citeulike11175267
 
dc.identifier.doihttp://dx.doi.org/10.4161/epi.6.12.18176
 
dc.identifier.epage1512
 
dc.identifier.hkuros208294
 
dc.identifier.isiWOS:000299828200012
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).

 
dc.identifier.issn1559-2294
2012 Impact Factor: 4.92
2012 SCImago Journal Rankings: 1.965
 
dc.identifier.issue12
 
dc.identifier.pmid22139581
 
dc.identifier.scopuseid_2-s2.0-83255166557
 
dc.identifier.spage1505
 
dc.identifier.urihttp://hdl.handle.net/10722/147656
 
dc.identifier.volume6
 
dc.languageeng
 
dc.publisherLandes Bioscience. The Journal's web site is located at http://www.landesbioscience.com/journals/epigenetics
 
dc.publisher.placeUnited States
 
dc.relation.ispartofEpigenetics
 
dc.relation.projectA Novel Hidden Markov Model to Predict microRNAs and their Targets Simultaneously and its Application to the Epstein-Barr virus
 
dc.relation.referencesReferences in Scopus
 
dc.subject.meshDatabases, Genetic
 
dc.subject.meshEpigenesis, Genetic
 
dc.subject.meshGene Regulatory Networks - Genetics
 
dc.subject.meshHistones - Metabolism
 
dc.subject.meshHumans
 
dc.subject.meshInternet
 
dc.subject.meshSoftware
 
dc.titleEpiRegNet: Constructing epigenetic regulatory network from high throughput gene expression data for humans
 
dc.typeArticle
 
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Author Affiliations
  1. The University of Hong Kong Li Ka Shing Faculty of Medicine
  2. University of Texas at Dallas
  3. Tsinghua University