BRNI: Modular analysis of transcriptional regulatory programs.
BRNI: Modular analysis of transcriptional regulatory programs.
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DOI：
10.1186/1471-2105-10-155
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发表时间：
2009-05-20
影响因子：
3
通讯作者：
Regev A
中科院分区：
文献类型：
--
作者：
Nachman I;Regev A
转录反应通常由调节模块组成--具有共同表达模式的基因组，由相同的调节机制控制。以前的方法允许从基因组学数据中剖析调控模块，如表达谱、蛋白质-DNA结合和启动子序列。在缺乏物理蛋白质-DNA数据的情况下，这种方法对于分析潜在的调控程序是必不可少的。在这里，我们提出了一种分析模块化调节程序的新方法。我们的方法-生化调控网络推理（BRNI）-是基于一种算法，从表达数据中学习一个生化动机的调控程序。它描述了由数百个基因组成的基因模块的表达谱，使用少量的调节因子和亲和力参数。我们开发了一种集成学习算法，以确保学习模型的鲁棒性。然后，我们使用的拓扑结构的学习监管程序，以指导发现库的顺式调控基序，并确定与每个模块相关联的基序组成。我们测试我们的方法上的细胞周期调控程序的裂变酵母。我们发现了16个连贯的模块，涵盖了从细胞分裂到代谢的不同过程，并将它们与18个已知的调控元件相关联，包括已知的细胞周期调控元件（MCB，Ace 2，PCB，ACCCT box）和新的元件，其中一些与G2模块相关。我们将基于表达和基序的模型中的调控关系整合到一个单一的网络中，突出显示了导致裂变酵母细胞周期中基因表达的不同动态的特定拓扑结构。我们的方法提供了一个生物驱动的，原则性的方式解构成有意义的转录模块的基因组，并确定其相关的顺式调控程序。我们的分析揭示了控制裂变酵母细胞周期的调控网络的结构和功能，类似的方法可以应用于其他模块化转录反应的调控基础。
Transcriptional responses often consist of regulatory modules – sets of genes with a shared expression pattern that are controlled by the same regulatory mechanisms. Previous methods allow dissecting regulatory modules from genomics data, such as expression profiles, protein-DNA binding, and promoter sequences. In cases where physical protein-DNA data are lacking, such methods are essential for the analysis of the underlying regulatory program. Here, we present a novel approach for the analysis of modular regulatory programs. Our method – Biochemical Regulatory Network Inference (BRNI) – is based on an algorithm that learns from expression data a biochemically-motivated regulatory program. It describes the expression profiles of gene modules consisting of hundreds of genes using a small number of regulators and affinity parameters. We developed an ensemble learning algorithm that ensures the robustness of the learned model. We then use the topology of the learned regulatory program to guide the discovery of a library of cis-regulatory motifs, and determined the motif compositions associated with each module. We test our method on the cell cycle regulatory program of the fission yeast. We discovered 16 coherent modules, covering diverse processes from cell division to metabolism and associated them with 18 learned regulatory elements, including both known cell-cycle regulatory elements (MCB, Ace2, PCB, ACCCT box) and novel ones, some of which are associated with G2 modules. We integrate the regulatory relations from the expression- and motif-based models into a single network, highlighting specific topologies that result in distinct dynamics of gene expression in the fission yeast cell cycle. Our approach provides a biologically-driven, principled way for deconstructing a set of genes into meaningful transcriptional modules and identifying their associated cis-regulatory programs. Our analysis sheds light on the architecture and function of the regulatory network controlling the fission yeast cell cycle, and a similar approach can be applied to the regulatory underpinnings of other modular transcriptional responses.
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影响因子：
30.8
作者：
Rustici, G;Mata, J;Bähler, J
通讯作者：
Bähler, J
DOI：
10.1073/pnas.2136632100
发表时间：
2003-12-23
影响因子：
11.1
作者：
Liao, JC;Boscolo, R;Roychowdhury, VP
通讯作者：
Roychowdhury, VP
影响因子：
5.8
作者：
Segal, E.;Yelensky, R.;Koller, D.
通讯作者：
Koller, D.
影响因子：
64.5
作者：
Simon, I;Barnett, J;Young, RA
通讯作者：
Young, RA
影响因子：
30.8
作者：
Segal, E;Shapira, M;Friedman, N
通讯作者：
Friedman, N