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首页> 外文期刊>Journal of biomedical informatics. >A new framework for identifying combinatorial regulation of transcription factors: a case study of the yeast cell cycle.
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A new framework for identifying combinatorial regulation of transcription factors: a case study of the yeast cell cycle.

机译：识别转录因子组合调控的新框架：酵母细胞周期的案例研究。

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摘要

By integrating heterogeneous functional genomic datasets, we have developed a new framework for detecting combinatorial control of gene expression, which includes estimating transcription factor activities using a singular value decomposition method and reducing high-dimensional input gene space by considering genomic properties of gene clusters. The prediction of cooperative gene regulation is accomplished by either Gaussian Graphical Models or Pairwise Mixed Graphical Models. The proposed framework was tested on yeast cell cycle datasets: (1) 54 known yeast cell cycle genes with 9 cell cycle regulators and (2) 676 putative yeast cell cycle genes with 9 cell cycle regulators. The new framework gave promising results on inferring TF-TF and TF-gene interactions. It also revealed several interesting mechanisms such as negatively correlated protein-protein interactions and low affinity protein-DNA interactions that may be important during the yeast cell cycle. The new framework may easily be extended to study other higher eukaryotes.
机译：通过整合异构的功能基因组数据集，我们开发了一种新的框架，用于检测基因表达的组合控制，其中包括使用奇异值分解方法估算转录因子活性，并考虑基因簇的基因组特性来减少高维输入基因空间。协同基因调控的预测是通过高斯图形模型或成对混合图形模型完成的。在酵母细胞周期数据集上测试了提出的框架：（1）具有9个细胞周期调节子的54个已知酵母细胞周期基因和（2）具有9个细胞周期调节子的推定酵母细胞周期基因。新框架在推断TF-TF和TF基因相互作用方面给出了可喜的结果。它还揭示了几种有趣的机制，例如负相关的蛋白质-蛋白质相互作用和低亲和力的蛋白质-DNA相互作用，这在酵母细胞周期中可能很重要。新的框架可以很容易地扩展到研究其他高级真核生物。

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