Computational Methods for Unraveling Combinatorial Gene Regulation

描述(申请人提供)：多个转录因子(TF)对基因表达的组合调节是调节基因表达的基本机制。虽然这一现象已经被研究了几十年，但我们仍然缺乏一个系统的策略来准确识别增强子上的组合转铁蛋白相互作用，并模拟它们对靶基因的调控输出(S)，特别是对于哺乳动物。如果成功，这项拟议的研究将消除该领域的一个瓶颈，并代表着一系列研究的第一步，预计这将进一步加深我们对基因调控的理解。在我们看来，这项申请中提出的研究是创新的，因为它代表了对现状的新的实质性偏离。我们将解决该领域研究人员面临的以下三个挑战：1)缺乏确定组合相互作用的策略 2)缺乏将增强子与其靶基因相关联的策略；3)将增强子序列信息转化为其调控输出的能力有限。在这个项目完成时，我们预计已经开发出一套计算方法，能够在基因组规模上识别增强子的组合相互作用，并构建哺乳动物组合调控的预测模型。此外，通过将我们的方法应用于大型公共数据集，我们希望对增强剂的进化、空间和时间动力学获得新的见解。开发的计算方法将作为开放源码软件实施，并向研究界公开提供。

DESCRIPTION (provided by applicant): Combinatorial regulation of gene expression by multiple transcription factors (TFs) is a fundamental mechanism for regulating gene expression. Although this phenomenon has been studied for several decades, we still lack a systematic strategy to accurately identify combinatorial TF interactions at enhancers and to model their regulatory output on target gene(s), especially for mammalian species. If successful, the proposed research will remove a bottleneck in the field and represent the first step in a continuum of research that is expected to further our understanding of gene regulation. Research proposed in this application is innovative, in our opinion, because it represents a new and substantive departure from the status quo. We will address the following three challenges facing researchers in the field: 1) lack of strategies to identify combinatorial interactions among TFs at enhancers; 2) lack of strategies to associate enhancers with their target genes; and 3) limited ability to translate enhancer sequence information to its regulatory output. At the completion of this project, we expect to have developed a set of computational methods that enable genome-scale identification of combinatorial interactions at enhancers and construction of predictive models of combinatorial regulation in mammals. In addition, by applying our methods to large public datasets, we expect to obtain new insights into the evolutionary, spatial, and temporal dynamics of enhancers. The computational methods developed will be implemented as open-source software and made publicly available to the research community.