Systematic determination of genetic network architecture
Systematic determination of genetic network architecture
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DOI：
10.1038/10343
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发表时间：
1999-07-01
期刊：
影响因子：
30.8
通讯作者：
Church, GM
中科院分区：
文献类型：
--
作者：
Tavazoie, S;Hughes, JD;Church, GM
测量全基因组mRNA丰度（1-3）的技术和组织和显示此类数据的方法（4-10）是作为系统级探索转录调节网络的有价值工具。例如，已经表明，来自118个基因的mRNA数据，在发育中的小鼠后部脑中的几个时间点测量，可以分层聚集成各种模式（或“波”），它们的成员倾向于参与共同过程（5）（5） 。我们先前已经表明，层次聚类可以将其顺式调节元件在体内受相同蛋白质的基因组合在一起（6）。分层聚类也已用于根据多种生长条件的表达来将基因组织成层次结构图（7）。傅立叶分析在同步酵母mRNA表达数据中的应用已鉴定出细胞周期的周期基因，其中许多基因都期望顺式调节元件（8）。在这里，我们应用了一组系统的统计算法，基于全基因组mRNA数据，分区聚类和图案发现，以识别酵母中的转录调节子网络与其结构或任何关于其动力学的任何假设的先验知识。这种方法发现了新的调节（共同调节的基因）及其假定的顺式调节元件。我们使用已知调节子和基序的统计表征来得出标准，通过这些标准，我们推断了新发现的调节子和基序的生物学意义。我们的方法有望快速阐明几乎没有生物学的测序生物中遗传网络结构。
Technologies to measure whole-genome mRNA abundances(1-3) and methods to organize and display such data(4-10) are emerging as valuable tools for systems-level exploration of transcriptional regulatory networks. For instance, it has been shown that mRNA data from 118 genes, measured at several time points in the developing hindbrain of mice, can be hierarchically clustered into various patterns (or 'waves') whose members tend to participate in common processes(5). We have previously shown that hierarchical clustering can group together genes whose cis-regulatory elements are bound by the same proteins in vivo(6). Hierarchical clustering has also been used to organize genes into hierarchical dendograms on the basis of their expression across multiple growth conditions(7). The application of Fourier analysis to synchronized yeast mRNA expression data has identified cell-cycle periodic genes, many of which have expected cis-regulatory elements(8). Here we apply a systematic set of statistical algorithms, based on whole-genome mRNA data, partitional clustering and motif discovery, to identify transcriptional regulatory sub-networks in yeast-without any a priori knowledge of their structure or any assumptions about their dynamics. This approach uncovered new regulons (sets of co-regulated genes) and their putative cis-regulatory elements. We used statistical characterization of known regulons and motifs to derive criteria by which we infer the biological significance of newly discovered regulons and motifs. Our approach holds promise for the rapid elucidation of genetic network architecture in sequenced organisms in which little biology is known.