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 多种转录因子（TF）对基因表达的组合调节是一个基本的 调节基因表达的机制。虽然这种现象已经研究了几年， 几十年来，我们仍然缺乏一个系统的战略，以准确地确定组合TF相互作用 在增强子上，并对它们在靶基因上的调节输出进行建模，特别是对于 哺乳动物物种。如果成功，拟议的研究将消除该领域的瓶颈，并代表 这是一系列研究的第一步，有望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，因为它代表了一种新的， 从本质上脱离现状。我们将解决研究人员面临的以下三个挑战， 该领域：1）缺乏鉴定TF在增强子处的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;以及3）有限的翻译增强子序列的能力 信息到其监管输出。在这个项目完成后，我们预计已经制定了一套 能够在基因组规模上鉴定增强子处的组合相互作用的计算方法， 构建哺乳动物组合调控的预测模型。此外，通过应用我们的方法， 到大型公共数据集，我们希望获得新的见解的演变，空间和时间的动态 增强剂。开发的计算方法将作为开源软件实施， 向研究界公开。