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Nature. 2012 Sep 6;489(7414):91-100. doi: 10.1038/nature11245.

Architecture of the human regulatory network derived from ENCODE data.

Author information

Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, CT 06520, USA.
Department of Molecular Biophysics and Biochemistry, Yale University, 266 Whitney Ave, New Haven, CT 06520, USA.
Department of Computer Science, Yale University, 51 Prospect Street, New Haven, CT 06511, USA.
Department of Computer Science, Stanford University, 318 Campus Drive, Stanford, CA 94305, USA.
Department of Genetics, Stanford University, 300 Pasteur Dr., M-344 Stanford, CA 94305, USA.
Department of Machine Learning, NEC Laboratories America, 4 Independence Way, Princeton, NJ 08540, USA.
Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT 06520, USA.
Department of Chemistry, Yale University, 225 Prospect Street, New Haven, CT 06520, USA.
Department of Biochemistry & Molecular Biology, University of Southern California, Norris Comprehensive Cancer Center, 1450 Biggy Street, NRT 6503, Los Angeles, CA 90089, USA.
HudsonAlpha Institute for Biotechnology, 601 Genome Way, Huntsville, AL 35806, USA.
Department of Genetics, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06510, USA.
Genome Center, University of California-Davis, 451 Health Sciences Drive, Davis, CA 95616, 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, CA 94305, USA.
Contributed equally


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.

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