Architecture of the human regulatory network derived from ENCODE data

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 ChengDeclan 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 Oĝgeen, 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

Research output: Contribution to journalArticle

911 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)91-100
Number of pages10
JournalNature
Volume489
Issue number7414
DOIs
StatePublished - Sep 6 2012
Externally publishedYes

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    Gerstein, M. B., Kundaje, A., Hariharan, M., Landt, S. G., Yan, K. K., Cheng, C., Mu, X. J., Khurana, E., Rozowsky, J., Alexander, R., Min, R., Alves, P., Abyzov, A., Addleman, N., Bhardwaj, N., Boyle, A. P., Cayting, P., Charos, A., Chen, D. Z., ... Snyder, M. (2012). Architecture of the human regulatory network derived from ENCODE data. Nature, 489(7414), 91-100. https://doi.org/10.1038/nature11245