Searching Genomes for Non-Coding RNAs by Their Structure

通过结构搜索基因组中的非编码 RNA

• 批准号: 7432577
• 负责人: LIMING CAI
• 金额: $ 23.49万
• 依托单位: UNIVERSITY OF GEORGIA
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-06-01 至 2010-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7432577
• 关键词: Algorithms; Bioinformatics; Biological; Biology; Catalytic RNA; Complex; Computer software; Computing Methodologies; Consensus; Data; Data Set; Databases; Development; Distant; Evolution; Extensible Markup Language; Family; Functional RNA; Fungal Genome; Future; Gene Family; Genome; Goals; Graph; Homologous Gene; Internet; Limes; Medical; Methods; Modeling; Molecular and Cellular Biology; Numbers; Plant Roots; Public Health; RNA; RNA Databases; RNA Sequence Analysis; RNA Sequences; RNA analysis; Range; Reporting; Research; Research Personnel; Running; Speed; Structure; Techniques; Telomerase RNA Component; Testing; Time; Trees; Validation; Work; base; comparative; computerized tools; follow-up; genome database; genome sequencing; improved; interest; novel; novel strategies; programs; research study; stem; tool; tool development; user-friendly

DESCRIPTION (provided by applicant): RNA molecules and their functions are central to cellular and molecular biology; their functions range from the classic messengers of the central dogma, to ribozymes that carry out enzymatic activities, to acting as regulatory molecules. The existence and important of non-coding RNAs poses a challenge to bioinformatics. How are biologists to identify these molecules in newly sequenced genomes or sequence databases? One key approach is to use bioinformatic methods that can search genomes and databases for specific RNA secondary structures. The research we propose is directed towards developing computational tools to model the structures of RNAs, to perform RNA structure alignment and database searches, and then to experimentally test our predictions. Our approach is based upon conformational graph models and graph theoretic techniques, developing fast, yet accurate, RNA structural homology search tools based upon the notion of graph tree decomposition. These methods can describe both stem-loop and pseudoknot structures. Our preliminary results include successful searches of prokaryotic and eukaryotic genomes for large and complex RNAs by their structure. The specific steps of the proposed work are: (1) development of the conformational graph - tree decomposition method into high throughput tools for RNA structure search that biologists can readily use; (2) development of tools for automated comparative analysis and modeling of non-annotated, unaligned, RNA sequences; (3) application of our method to discovery and annotation of specific RNA gene families, including experimental verification of the predictions; (4) distribution of the search and modeling tools, including a user-friendly interface, together with a conformational graph structure profile database. Our goal is to develop a practical RNA structure modeling and search tool set that biologists can readily use to find RNA structures in genomes or databases, to help them generate testable hypotheses about the numbers, functions and evolution of RNA gene families. RNA molecules are at the center of basic biology as well as public health, and may be a key component of future medical practice. Our proposed research will help biologists find RNA molecules of interest in the mass of genome sequence data being generated.

描述（由申请人提供）：RNA分子及其功能是细胞和分子生物学的核心；它们的功能范围从经典的中心法则的信使，到执行酶活动的核酶，再到充当调节分子。非编码rna的存在和重要性对生物信息学提出了挑战。生物学家如何在新测序的基因组或序列数据库中识别这些分子？一个关键的方法是使用生物信息学方法，可以搜索基因组和数据库中特定的RNA二级结构。我们提出的研究方向是开发计算工具来模拟RNA的结构，执行RNA结构比对和数据库搜索，然后实验测试我们的预测。我们的方法是基于构象图模型和图论技术，基于图树分解的概念开发快速而准确的RNA结构同源性搜索工具。这些方法既可以描述茎环结构，也可以描述假结结构。我们的初步结果包括通过结构成功地在原核和真核生物基因组中搜索大型和复杂的rna。具体工作步骤如下：(1)将构象图-树分解方法发展为生物学家易于使用的RNA结构搜索的高通量工具；(2)开发非注释、未对齐RNA序列的自动比较分析和建模工具；(3)将我们的方法应用于特定RNA基因家族的发现和注释，包括对预测的实验验证；(4)搜索和建模工具的分布，包括用户友好的界面，以及构象图结构轮廓数据库。我们的目标是开发一个实用的RNA结构建模和搜索工具集，生物学家可以很容易地使用它来查找基因组或数据库中的RNA结构，以帮助他们产生关于RNA基因家族的数量、功能和进化的可测试的假设。RNA分子是基础生物学和公共卫生的核心，可能是未来医疗实践的关键组成部分。我们提出的研究将帮助生物学家在生成的大量基因组序列数据中找到感兴趣的RNA分子。