DETECTING SUBTLE SIGNALS IN GENOMIC SEQUENCE

检测基因组序列中的细微信号

• 批准号: 6045594
• 负责人: Charles E Lawrence
• 金额: $ 33.81万
• 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 2004-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6045594
• 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，人类基因组中大约有80,000个基因（Fields C.等，1994）。为了将这种遗传蓝图变成功能性生物，必须以特定的时间和空间模式表达基因。找到控制这种表达并理解其语言的信号是后基因组时代的主要挑战之一。基因组序列中调节元素，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。如果可以开发特定的方法，则计算分析有望以最低的成本加速此过程。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系统的完整接线图。该描述将包括四个组成部分：1）基于DNA识别基序和激活因素域的所有转录因子所有超家族及其分类为功能相关的子类； 2）基因组的转录调节模块以及其中的所有因素结合元件的鉴定和表征； 3）全面描述因素及其结合元素之间的连接； 4）替代转录调节基序的表征，包括基于DNA组成的基序以及DNA和RNA结构。这些目标将使用贝叶斯统计模型和算法来解决，这是我们在当前奖励期间开发的基础。这些包括吉布斯对转录因子的组装超家族进行抽样算法，并使其对齐它们，转录因子分类算法，精确的贝叶斯算法，用于描述组成和结构异质性，RNA二级结构，以及系统发育的脚印，以及递归的Gibbs Samplations Sampletition HMM，以识别HMM。