Architecture of the human regulatory network derived from ENCODE data View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2012-09-05

AUTHORS

Mark B. Gerstein, Anshul Kundaje, Manoj Hariharan, Stephen G. Landt, Koon-Kiu Yan, Chao Cheng, Xinmeng Jasmine Mu, Ekta Khurana, Joel Rozowsky, Roger Alexander, Renqiang Min, Pedro Alves, Alexej Abyzov, Nick Addleman, Nitin Bhardwaj, Alan P. Boyle, Philip Cayting, Alexandra Charos, David Z. Chen, Yong Cheng, Declan Clarke, Catharine Eastman, Ghia Euskirchen, Seth Frietze, Yao Fu, Jason Gertz, Fabian Grubert, Arif Harmanci, Preti Jain, Maya Kasowski, Phil Lacroute, Jing Leng, Jin Lian, Hannah Monahan, Henriette O’Geen, Zhengqing Ouyang, E. Christopher Partridge, Dorrelyn Patacsil, Florencia Pauli, Debasish Raha, Lucia Ramirez, Timothy E. Reddy, Brian Reed, Minyi Shi, Teri Slifer, Jing Wang, Linfeng Wu, Xinqiong Yang, Kevin Y. Yip, Gili Zilberman-Schapira, Serafim Batzoglou, Arend Sidow, Peggy J. Farnham, Richard M. Myers, Sherman M. Weissman, Michael Snyder

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. More... »

PAGES

91-100

Journal

TITLE

Nature

ISSUE

7414

VOLUME

489

Author Affiliations

  • Department of Computer Science, Yale University, 51 Prospect Street, New Haven, Connecticut 06511, USA
  • Department of Computer Science, Stanford University, 318 Campus Drive, 94305, Stanford, California, USA
  • Department of Genetics, Stanford University, 300 Pasteur Drive, M-344 Stanford, California 94305, USA
  • Department of Molecular Biophysics and Biochemistry, Yale University, 266 Whitney Avenue, New Haven, Connecticut 06520, USA
  • Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA
  • Department of Machine Learning, NEC Laboratories America, 4 Independence Way, Princeton, New Jersey 08540, USA
  • Department of Molecular, Cellular, and Developmental Biology, Yale University, 06520, New Haven, Connecticut, USA
  • Department of Chemistry, Yale University, 225 Prospect Street, New Haven, Connecticut 06520, USA
  • Department of Biochemistry and Molecular Biology, University of Southern California, Norris Comprehensive Cancer Center, 1450 Biggy Street, NRT 6503, Los Angeles, California 90089, USA
  • HudsonAlpha Institute for Biotechnology, 601 Genome Way, 35806, Huntsville, Alabama, USA
  • Department of Genetics, Yale University School of Medicine, 333 Cedar Street, New Haven, Connecticut 06510, USA
  • Genome Center, University of California-Davis, 451 Health Sciences Drive, Davis, California 95616, USA
  • Present address: Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina 27710, USA.
  • Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong
  • Department of Pathology, Stanford University, SUMC L235 (Edwards Bldg), 300 Pasteur Drive, Stanford, California 94305, USA
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/nature11245

    DOI

    http://dx.doi.org/10.1038/nature11245

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1028790868

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/22955619


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