Modeling the structure & evolution of regulatory regions in eukaryotic gemomes

结构建模

• 批准号: 7254422
• 负责人: Uwe Ohler
• 金额: $ 35万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-07-26 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7254422
• 关键词: Address; Algorithms; Animal Model; Architecture; Base Sequence; Case Study; Class; Code; Communities; Comparative Study; Complement; Complex; Computational Biology; Computer Analysis; Computer Simulation; DNA; DNA Sequence Rearrangement; Data; Development; Disease; Drosophila genus; E2F transcription factors; Elements; Enhancers; Environment; Evolution; Face; Family; Functional RNA; Gene Expression; Gene Expression Regulation; Gene Targeting; Genes; Genetic Transcription; Genome; Genomics; Goals; Heart; Human; Image; In Situ Hybridization; Indium; Institutes; Lead; Learning; Methods; Modeling; Molecular Biology; Nucleic Acid Regulatory Sequences; Numbers; Organism; Phylogenetic Analysis; Process; Proteins; Public Health; RNA; RNA Polymerase II; Regulation; Research; Research Personnel; Science Policy; Series; Site; Statistical Models; Structure; Time; Today; Transcript; Transcription Initiation Site; Transcriptional Regulation; Work; base; combinatorial; comparative; digital imaging; experience; fly; functional genomics; genome sequencing; interest; markov model; programs; promoter; research study; size; spatiotemporal; tool

DESCRIPTION (provided by applicant): Transcription is at the heart of the regulation of gene expression, yet the computational analysis of transcription regulation currently faces a number of challenges and opportunities: The large number of sequenced genomes allows to study and exploit the conservation of regulatory sequences, but algorithms that do so in a rigorous framework are still scarce. Detailed data of spatiotemporal gene expression has become available, enabling us to use this information to elucidate regulatory interactions in the development of complex organisms. The long-term goal is to build computational models to infer regulatory networks and their evolution in the development of model organisms and ultimately humans. The objective of this particular proposal is to develop algorithms to analyze the conservation of gene regulation on the sequence level, as well as an integrated approach to model conserved regulatory regions important for development. Its specific aims are: (1) To decipher the precise requirements to define a functional transcription start site, based on a comparative study of the conservation of core promoter elements in two fly genomes, and build a model for genome-wide comparative annotation. (2) To develop and implement an efficient progressive multiple alignment algorithm for non-coding regulatory sequences based on phylogenetic hidden Markov models, and to study the evolution of core promoters in a wider set of species. (3) To extend the framework set by this algorithm to more complex regulatory modules (such as developmental enhancers and E2F target genes), and to incorporate prior information on putative upstream factors to predict regulatory interactions. Computational predictions will be validated by a small number of experiments. The proposed research is expected to advance the understanding on the evolution of regulatory regions, and how to build computational models that accurately utilize sequence information from several species. Relevance to public health: Understanding how gene regulation is encoded in the genome is undoubtedly one of the most interesting challenges in molecular biology today, and it is intuitive that errors occurring in this machinery lead to mis-expression of genes, and may often be important in genetically based diseases. Our research will help to find the exact regulatory regions in DNA, both computationally and experimentally, and to learn the mechanisms that control the expression of genes in model organisms and humans.

描述（由申请人提供）：转录是基因表达调控的核心，然而转录调控的计算分析目前面临着许多挑战和机遇：大量测序的基因组允许研究和利用调控序列的保守性，但在严格框架中这样做的算法仍然很少。时空基因表达的详细数据已经成为可用的，使我们能够利用这些信息来阐明复杂生物体发育中的调控相互作用。长期目标是建立计算模型来推断调控网络及其在模型生物和最终人类发展中的演变。这个特别的建议的目的是开发算法来分析序列水平上的基因调控的保守性，以及一个综合的方法来模拟保守的调控区域的发展是重要的。其具体目标是：（1）通过对两个果蝇基因组中核心启动子保守性的比较研究，阐明了确定功能性转录起始位点的精确要求，并建立了全基因组比较注释模型。(2)基于系统发育隐马尔可夫模型，开发并实现非编码调控序列的高效渐进式多重比对算法，并研究核心启动子在更广泛物种中的进化。(3)将该算法设定的框架扩展到更复杂的调节模块（例如发育增强子和E2 F靶基因），并结合有关推定上游因子的先验信息来预测调节相互作用。计算预测将通过少量实验进行验证。拟议的研究预计将促进对调控区进化的理解，以及如何建立准确利用多个物种序列信息的计算模型。与公共卫生的相关性：理解基因调控如何在基因组中编码无疑是当今分子生物学中最有趣的挑战之一，并且直观地说，这种机制中发生的错误会导致基因的错误表达，并且在遗传性疾病中通常很重要。我们的研究将有助于在计算和实验上找到DNA中的确切调控区域，并了解控制模式生物和人类基因表达的机制。