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: 7236730
• 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 2009-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7236730
• 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;其功能范围从中心法则的经典信使到进行酶活性的核酶，再到充当调节分子。非编码RNA的存在及其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，可以在基因组和数据库中搜索特定的RNA二级结构。我们提出的研究是针对开发计算工具来模拟RNA的结构，进行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，开发快速，准确，RNA结构同源性搜索工具的基础上的概念图树分解。这些方法可以描述茎环和伪结结构。我们的初步结果包括成功的搜索原核和真核基因组的大型和复杂的RNA的结构。具体工作包括：（1）将构象图-树分解方法发展成为生物学家可以方便使用的高通量RNA结构搜索工具;（2）发展用于自动比较分析和建模的工具，用于无注释、未比对的RNA序列;（3）应用我们的方法发现和注释特定的RNA基因家族，包括实验验证预测;（4）分发搜索和建模工具，包括用户友好的界面，以及构象图结构概况数据库。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找到基因组或数据库中的RNA结构，以帮助他们生成关于RNA基因家族的数量，功能和进化的可验证的假设。RNA分子是基础生物学和公共卫生的中心，可能是未来医学实践的关键组成部分。我们提出的研究将帮助生物学家在大量的基因组序列数据中找到感兴趣的RNA分子。