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Architecture of the human regulatory network derived from ENCODE data

Author

Listed:
  • Mark B. Gerstein

    (Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA
    Yale University, 266 Whitney Avenue, New Haven, Connecticut 06520, USA
    Yale University, 51 Prospect Street, New Haven, Connecticut 06511, USA)

  • Anshul Kundaje

    (Stanford University, 318 Campus Drive)

  • Manoj Hariharan

    (Stanford University, 300 Pasteur Drive, M-344 Stanford, California 94305, USA)

  • Stephen G. Landt

    (Stanford University, 300 Pasteur Drive, M-344 Stanford, California 94305, USA)

  • Koon-Kiu Yan

    (Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA
    Yale University, 266 Whitney Avenue, New Haven, Connecticut 06520, USA)

  • Chao Cheng

    (Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA
    Yale University, 266 Whitney Avenue, New Haven, Connecticut 06520, USA)

  • Xinmeng Jasmine Mu

    (Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA)

  • Ekta Khurana

    (Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA
    Yale University, 266 Whitney Avenue, New Haven, Connecticut 06520, USA)

  • Joel Rozowsky

    (Yale University, 266 Whitney Avenue, New Haven, Connecticut 06520, USA)

  • Roger Alexander

    (Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA
    Yale University, 266 Whitney Avenue, New Haven, Connecticut 06520, USA)

  • Renqiang Min

    (Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA
    Yale University, 266 Whitney Avenue, New Haven, Connecticut 06520, USA
    NEC Laboratories America, 4 Independence Way, Princeton, New Jersey 08540, USA)

  • Pedro Alves

    (Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA)

  • Alexej Abyzov

    (Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA
    Yale University, 266 Whitney Avenue, New Haven, Connecticut 06520, USA)

  • Nick Addleman

    (Stanford University, 300 Pasteur Drive, M-344 Stanford, California 94305, USA)

  • Nitin Bhardwaj

    (Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA
    Yale University, 266 Whitney Avenue, New Haven, Connecticut 06520, USA)

  • Alan P. Boyle

    (Stanford University, 300 Pasteur Drive, M-344 Stanford, California 94305, USA)

  • Philip Cayting

    (Stanford University, 300 Pasteur Drive, M-344 Stanford, California 94305, USA)

  • Alexandra Charos

    (Cellular, and Developmental Biology, Yale University)

  • David Z. Chen

    (Yale University, 51 Prospect Street, New Haven, Connecticut 06511, USA)

  • Yong Cheng

    (Stanford University, 300 Pasteur Drive, M-344 Stanford, California 94305, USA)

  • Declan Clarke

    (Yale University, 225 Prospect Street, New Haven, Connecticut 06520, USA)

  • Catharine Eastman

    (Stanford University, 300 Pasteur Drive, M-344 Stanford, California 94305, USA)

  • Ghia Euskirchen

    (Stanford University, 300 Pasteur Drive, M-344 Stanford, California 94305, USA)

  • Seth Frietze

    (University of Southern California, Norris Comprehensive Cancer Center, 1450 Biggy Street, NRT 6503, Los Angeles, California 90089, USA)

  • Yao Fu

    (Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA)

  • Jason Gertz

    (HudsonAlpha Institute for Biotechnology, 601 Genome Way)

  • Fabian Grubert

    (Stanford University, 300 Pasteur Drive, M-344 Stanford, California 94305, USA)

  • Arif Harmanci

    (Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA
    Yale University, 266 Whitney Avenue, New Haven, Connecticut 06520, USA)

  • Preti Jain

    (HudsonAlpha Institute for Biotechnology, 601 Genome Way)

  • Maya Kasowski

    (Stanford University, 300 Pasteur Drive, M-344 Stanford, California 94305, USA)

  • Phil Lacroute

    (Stanford University, 300 Pasteur Drive, M-344 Stanford, California 94305, USA)

  • Jing Leng

    (Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA)

  • Jin Lian

    (Yale University School of Medicine, 333 Cedar Street, New Haven, Connecticut 06510, USA)

  • Hannah Monahan

    (Cellular, and Developmental Biology, Yale University)

  • Henriette O’Geen

    (Genome Center, University of California-Davis, 451 Health Sciences Drive, Davis, California 95616, USA)

  • Zhengqing Ouyang

    (Stanford University, 300 Pasteur Drive, M-344 Stanford, California 94305, USA)

  • E. Christopher Partridge

    (HudsonAlpha Institute for Biotechnology, 601 Genome Way)

  • Dorrelyn Patacsil

    (Stanford University, 300 Pasteur Drive, M-344 Stanford, California 94305, USA)

  • Florencia Pauli

    (HudsonAlpha Institute for Biotechnology, 601 Genome Way)

  • Debasish Raha

    (Cellular, and Developmental Biology, Yale University)

  • Lucia Ramirez

    (Stanford University, 300 Pasteur Drive, M-344 Stanford, California 94305, USA)

  • Timothy E. Reddy

    (HudsonAlpha Institute for Biotechnology, 601 Genome Way
    Present address: Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina 27710, USA.)

  • Brian Reed

    (Cellular, and Developmental Biology, Yale University)

  • Minyi Shi

    (Stanford University, 300 Pasteur Drive, M-344 Stanford, California 94305, USA)

  • Teri Slifer

    (Stanford University, 300 Pasteur Drive, M-344 Stanford, California 94305, USA)

  • Jing Wang

    (Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA)

  • Linfeng Wu

    (Stanford University, 300 Pasteur Drive, M-344 Stanford, California 94305, USA)

  • Xinqiong Yang

    (Stanford University, 300 Pasteur Drive, M-344 Stanford, California 94305, USA)

  • Kevin Y. Yip

    (Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA
    Yale University, 266 Whitney Avenue, New Haven, Connecticut 06520, USA
    The Chinese University of Hong Kong)

  • Gili Zilberman-Schapira

    (Program in Computational Biology and Bioinformatics, Yale University, Bass 432, 266 Whitney Avenue, New Haven, Connecticut 06520, USA)

  • Serafim Batzoglou

    (Stanford University, 318 Campus Drive)

  • Arend Sidow

    (Stanford University, SUMC L235 (Edwards Bldg), 300 Pasteur Drive, Stanford, California 94305, USA)

  • Peggy J. Farnham

    (University of Southern California, Norris Comprehensive Cancer Center, 1450 Biggy Street, NRT 6503, Los Angeles, California 90089, USA)

  • Richard M. Myers

    (HudsonAlpha Institute for Biotechnology, 601 Genome Way)

  • Sherman M. Weissman

    (Yale University School of Medicine, 333 Cedar Street, New Haven, Connecticut 06510, USA)

  • Michael Snyder

    (Stanford University, 300 Pasteur Drive, M-344 Stanford, California 94305, USA)

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.

Suggested Citation

  • 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 & , 2012. "Architecture of the human regulatory network derived from ENCODE data," Nature, Nature, vol. 489(7414), pages 91-100, September.
    • Handle: RePEc:nat:nature:v:489:y:2012:i:7414:d:10.1038_nature11245
    DOI: 10.1038/nature11245
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    Cited by:

    1. Yue Yuan & Qiang Huo & Ziru Zhang & Qun Wang & Juanxia Wang & Shuaikang Chang & Peng Cai & Karen M. Song & David W. Galbraith & Weixiao Zhang & Long Huang & Rentao Song & Zeyang Ma, 2024. "Decoding the gene regulatory network of endosperm differentiation in maize," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    2. Milton Pividori & Sumei Lu & Binglan Li & Chun Su & Matthew E. Johnson & Wei-Qi Wei & Qiping Feng & Bahram Namjou & Krzysztof Kiryluk & Iftikhar J. Kullo & Yuan Luo & Blair D. Sullivan & Benjamin F. V, 2023. "Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    3. Hongchun Lin & Hui Peng & Yuxiang Sun & Meijun Si & Jiao Wu & Yanlin Wang & Sandhya S. Thomas & Zheng Sun & Zhaoyong Hu, 2023. "Reprogramming of cis-regulatory networks during skeletal muscle atrophy in male mice," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    4. Yuyan Cheng & Yuqin Yin & Alice Zhang & Alexander M. Bernstein & Riki Kawaguchi & Kun Gao & Kyra Potter & Hui-Ya Gilbert & Yan Ao & Jing Ou & Catherine J. Fricano-Kugler & Jeffrey L. Goldberg & Zhigan, 2022. "Transcription factor network analysis identifies REST/NRSF as an intrinsic regulator of CNS regeneration in mice," Nature Communications, Nature, vol. 13(1), pages 1-22, December.
    5. Fan Chen & Aria L. Byrd & Jinpeng Liu & Robert M. Flight & Tanner J. DuCote & Kassandra J. Naughton & Xiulong Song & Abigail R. Edgin & Alexsandr Lukyanchuk & Danielle T. Dixon & Christian M. Gosser &, 2023. "Polycomb deficiency drives a FOXP2-high aggressive state targetable by epigenetic inhibitors," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    6. Mijeong Kim & Yu Jin Jang & Muyoung Lee & Qingqing Guo & Albert J. Son & Nikita A. Kakkad & Abigail B. Roland & Bum-Kyu Lee & Jonghwan Kim, 2024. "The transcriptional regulatory network modulating human trophoblast stem cells to extravillous trophoblast differentiation," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    7. Anna Berenson & Ryan Lane & Luis F. Soto-Ugaldi & Mahir Patel & Cosmin Ciausu & Zhaorong Li & Yilin Chen & Sakshi Shah & Clarissa Santoso & Xing Liu & Kerstin Spirohn & Tong Hao & David E. Hill & Marc, 2023. "Paired yeast one-hybrid assays to detect DNA-binding cooperativity and antagonism across transcription factors," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    8. Florin Ratajczak & Mitchell Joblin & Marcel Hildebrandt & Martin Ringsquandl & Pascal Falter-Braun & Matthias Heinig, 2023. "Speos: an ensemble graph representation learning framework to predict core gene candidates for complex diseases," Nature Communications, Nature, vol. 14(1), pages 1-18, December.

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