Computational Methods for Unraveling Combinatorial Gene Regulation

揭示组合基因调控的计算方法

• 批准号: 9260006
• 负责人: Kai Tan
• 金额: $ 32.61万
• 依托单位: CHILDREN'S HOSP OF PHILADELPHIA
• 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 2020-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9260006
• 关键词: Address; Architecture; Cells; ChIP-seq; Chromatin; Communication; Communities; Computer software; Computing Methodologies; Cues; Data; Data Set; Development; Enhancers; Evolution; Future; Gene Expression; Gene Expression Regulation; Gene Targeting; Genetic Transcription; 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; combinatorial; comparative; comparative genomics; data mining; genome-wide; human disease; innovation; insight; mouse model; novel; open source; predictive modeling; promoter; public health relevance; response; software development; transcription factor

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.

描述（由申请人提供）：通过多种转录因子（TF）组合调节基因表达是调节基因表达的基本机制。虽然这种现象已经研究了几十年，我们仍然缺乏一个系统的策略，以准确地确定组合TF相互作用的增强子和模拟其调控输出的靶基因，特别是哺乳动物物种。如果成功，拟议的研究将消除该领域的瓶颈，并代表了连续研究的第一步，预计将进一步加深我们对基因调控的理解。我们认为，这项申请中提出的研究是创新的，因为它代表了一种新的、实质性的脱离现状的做法。我们将解决该领域研究人员面临的以下三个挑战：1）缺乏识别组合相互作用的策略， 增强子处的TF; 2）缺乏将增强子与其靶基因相关联的策略;以及3）将增强子序列信息翻译为其调节输出的能力有限。在这个项目完成后，我们希望已经开发出一套计算方法，使基因组规模的识别增强子的组合相互作用和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，我们希望获得对增强子的进化、空间和时间动态的新见解。所开发的计算方法将作为开源软件实施，并向研究界公开提供。