Computational methods for unraveling combinatorial gene regulation

揭示组合基因调控的计算方法

• 批准号: 8612481
• 负责人: Kai Tan
• 金额: $ 29.81万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-06-15 至 2018-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8612481
• 关键词: Accounting; Address; Architecture; Cells; Chromatin; Communication; Communities; Computer software; Computing Methodologies; Cues; Data; Data Set; Enhancers; Evolution; Future; Gene Expression; Gene Expression Regulation; Gene Targeting; Genome; Genomics; Goals; Human; Individual; Invertebrates; Investigation; Knowledge; Link; Location; Mammals; Maps; Mechanics; Methods; Modeling; Molecular; Mus; Noise; Outcome; Output; Pattern; Performance; Regulation; Research; Research Personnel; Resources; Series; System; Testing; Thermodynamics; Training; Transcriptional Regulation; Translating; Validation; cell type; cellular development; combinatorial; comparative; comparative genomics; data mining; empowered; genome-wide; human disease; innovation; insight; novel; open source; predictive modeling; promoter; public health relevance; response; software development; transcription factor

Project summary 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.

项目摘要 通过多个转录因子（TF）对基因表达的组合调节是基本 调节基因表达的机制。尽管已经研究了这种现象 几十年来，我们仍然缺乏系统的策略来准确识别组合TF相互作用 在增强剂中，并在目标基因上建模其调节产量，尤其是针对 哺乳动物。如果成功，拟议的研究将消除野外的瓶颈并代表 连续研究的第一步有望进一步了解我们对基因调节的理解。 在我们看来，在本申请中提出的研究是创新的，因为它代表了一个新的和 实质性偏离现状。我们将解决研究人员面临的以下三个挑战 领域：1）缺乏识别增强剂TF之间的组合相互作用的策略； 2）缺乏 将增强剂与目标基因相关联的策略； 3）有限翻译增强子序列的能力有限 信息到其监管输出。该项目完成后，我们希望已经开发了一套 能够对增强剂和 哺乳动物组合调节的预测模型的构建。另外，通过应用我们的方法 对于大型公共数据集，我们希望获得有关进化，空间和时间动态的新见解 增强剂。开发的计算方法将作为开源软件实现，并制作 公开可供研究社区使用。