| Literature DB >> 22955619 |
Mark B Gerstein1,2,3, Anshul Kundaje4, Manoj Hariharan5, Stephen G Landt5, Koon-Kiu Yan1,2, Chao Cheng1,2, Xinmeng Jasmine Mu1, Ekta Khurana1,2, Joel Rozowsky2, Roger Alexander1,2, Renqiang Min1,2,6, Pedro Alves1, Alexej Abyzov1,2, Nick Addleman5, Nitin Bhardwaj1,2, Alan P Boyle5, Philip Cayting5, Alexandra Charos7, David Z Chen2, Yong Cheng5, Declan Clarke8, Catharine Eastman5, Ghia Euskirchen5, Seth Frietze9, Yao Fu1, Jason Gertz10, Fabian Grubert5, Arif Harmanci1,2, Preti Jain10, Maya Kasowski5, Phil Lacroute5, Jing Jane Leng1, Jin Lian11, Hannah Monahan7, Henriette O'Geen12, Zhengqing Ouyang5, E Christopher Partridge10, Dorrelyn Patacsil5, Florencia Pauli10, Debasish Raha7, Lucia Ramirez5, Timothy E Reddy10, Brian Reed7, Minyi Shi5, Teri Slifer5, Jing Wang1, Linfeng Wu5, Xinqiong Yang5, Kevin Y Yip1,2,13, Gili Zilberman-Schapira1, Serafim Batzoglou4, Arend Sidow14, Peggy J Farnham9, Richard M Myers10, Sherman M Weissman11, Michael Snyder5.
Abstract
Transcription factors bind in a combinatorial fashion to specify the on-and-off states of genes; the ensemble of these binding events forms a regulatory network, constituting the wiring diagram for a cell. To examine the principles of the human transcriptional regulatory network, we determined the genomic binding information of 119 transcription-related factors in over 450 distinct experiments. We found the combinatorial, co-association of transcription factors to be highly context specific: distinct combinations of factors bind at specific genomic locations. In particular, there are significant differences in the binding proximal and distal to genes. We organized all the transcription factor binding into a hierarchy and integrated it with other genomic information (for example, microRNA regulation), forming a dense meta-network. Factors at different levels have different properties; for instance, top-level transcription factors more strongly influence expression and middle-level ones co-regulate targets to mitigate information-flow bottlenecks. Moreover, these co-regulations give rise to many enriched network motifs (for example, noise-buffering feed-forward loops). Finally, more connected network components are under stronger selection and exhibit a greater degree of allele-specific activity (that is, differential binding to the two parental alleles). The regulatory information obtained in this study will be crucial for interpreting personal genome sequences and understanding basic principles of human biology and disease.Entities:
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Year: 2012 PMID: 22955619 PMCID: PMC4154057 DOI: 10.1038/nature11245
Source DB: PubMed Journal: Nature ISSN: 0028-0836 Impact factor: 49.962