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.

项目总结