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Architecture of the human regulatory network derived from ENCODE data

机译:从ENCODE数据得出的人类监管网络架构

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摘要

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.
机译:转录因子以组合方式结合以指定基因的开关状态。这些绑定事件的整体形成一个监管网络,构成一个单元的接线图。为了检查人类转录调控网络的原理,我们在超过450个不同的实验中确定了119个转录相关因子的基因组结合信息。我们发现转录因子的组合,共缔合具有高度的背景特异性:因子的不同组合在特定的基因组位置结合。特别地,在基因的近端和远端的结合方面存在显着差异。我们将所有转录因子的结合组织成一个层次,并将其与其他基因组信息(例如,microRNA调控)整合在一起,形成一个密集的元网络。不同级别的因素具有不同的性质;例如,顶层转录因子更强烈地影响表达,而中层转录因子共同调节靶标以减轻信息流瓶颈。而且,这些共同调节产生了许多丰富的网络图案(例如,噪声缓冲前馈环路)。最后,更多的连接网络组件处于更强的选择之下,并且展现出更高程度的等位基因特异性活性(即与两个亲本等位基因的差异结合)。这项研究中获得的监管信息对于解释个人基因组序列以及理解人类生物学和疾病的基本原理至关重要。

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  • 来源
    《Nature》 |2012年第7414期|p.91-100|共10页
  • 作者单位

    Program in Computational Biology and Bioinformatics, Yale University, Bass 432,266 Whitney Avenue, New Haven, Connecticut 06520, USA. ,Department of Molecular Biophysics and Biochemistry, YaleUniversity, 266 Whitney Avenue, New Haven, Connecticut 06520, USA. ,Department of Computer Science, Yale University, 51 Prospect Street, New Haven, Connecticut 06511, USA;

    Department ofComputer Science, Stanford University, 318 Campus Drive, Stanford, California 94305, USA;

    Department of Genetics, Stanford University, 300 Pasteur Drive, M-344 Stanford, California 94305, USA;

    Department of Genetics, Stanford University, 300 Pasteur Drive, M-344 Stanford, California 94305, USA;

    Program in Computational Biology and Bioinformatics, Yale University, Bass 432,266 Whitney Avenue, New Haven, Connecticut 06520, USA. ,Department of Molecular Biophysics and Biochemistry, YaleUniversity, 266 Whitney Avenue, New Haven, Connecticut 06520, USA.;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
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  • 正文语种 eng
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