DETECTING SUBTLE SIGNALS IN GENOMIC SEQUENCE

检测基因组序列中的细微信号

• 批准号: 6693010
• 负责人: Charles E Lawrence
• 金额: $ 0.05万
• 依托单位: WADSWORTH CENTER
• 依托单位国家: 美国
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-09-20 至 2004-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6693010
• 关键词: DNA binding protein; DNA footprinting; RNA binding protein; computer assisted sequence analysis; computer program /software; computer system design /evaluation; gene expression; genetic regulatory element; genetic techniques; genome; high throughput technology; mathematical model; model design /development; nucleic acid sequence; nucleic acid structure; regulatory gene; statistics /biometry; transcription factor

It is estimated that there are approximately 80,000 genes in the human genome (Fields C., et al. 1994). To turn this genetic blueprint into a functional organism, genes must be expressed in a specific temporal and spatial pattern. Finding signals that control this expression and understanding their language is one of the major challenges of the post- genome era. Laboratory identification of regulatory elements, modules, and regions in genomic sequences is often an arduous, time-consuming, and expensive process. If specific approaches can be developed, computational analyses promise to accelerate this process at minimal cost. The long term goal of the proposed research is to develop and apply Bayesian bioinformatics computational methods which will describe the complete wiring diagram for a genome's transcription regulation system. This description will include four components: 1) the identification of all superfamilies of transcription factors and their classification into functionally related subclasses based on both the DNA recognition motifs and the activator domains; 2) the identification and characterization of a genome's transcriptional regulatory modules and all factor binding elements within them; 3) the full delineation of the connections between factors and their binding elements; 4) a characterization of alternative transcriptional regulatory motifs, including those based on DNA composition, and DNA and RNA structure. These goals will be addressed using Bayesian statistical models and algorithms, the foundations for which we developed during the current award period. These include Gibbs sampling algorithms to assembly superfamilies of transcription factors and multiply align them, transcription factor classification algorithms, exact Bayesian algorithms for the description of compositional and structural heterogeneity, RNA secondary structure, and phylogenetic footprinting, and recursive Gibbs sampling HMM for regulatory module identification and characterization.

据估计，人类基因组中有大约 80,000 个基因（Fields C., et al. 1994）。为了将这种基因蓝图转变为功能有机体，基因必须以特定的时间和空间模式表达。寻找控制这种表达的信号并理解它们的语言是后基因组时代的主要挑战之一。基因组序列中调控元件、模块和区域的实验室鉴定通常是一个艰巨、耗时且昂贵的过程。如果可以开发出特定的方法，计算分析有望以最小的成本加速这一过程。拟议研究的长期目标是开发和应用贝叶斯生物信息学计算方法，该方法将描述基因组转录调控系统的完整接线图。该描述将包括四个部分：1）识别所有转录因子超家族，并根据 DNA 识别基序和激活结构域将其分类为功能相关的子类； 2) 基因组转录调控模块及其内所有因子结合元件的鉴定和表征； 3）充分描述因素及其结合要素之间的联系； 4) 替代转录调控基序的表征，包括基于 DNA 组成、DNA 和 RNA 结构的转录调控基序。这些目标将使用贝叶斯统计模型和算法来实现，这是我们在当前奖励期间开发的基础。其中包括组装转录因子超家族并进行多重比对的吉布斯采样算法、转录因子分类算法、用于描述组成和结构异质性的精确贝叶斯算法、RNA二级结构和系统发育足迹，以及用于调节模块识别的递归吉布斯采样 HMM和表征。