Computational methods to identify noncoding RNA genes

识别非编码 RNA 基因的计算方法

• 批准号: 6869927
• 负责人: Elena Rivas
• 金额: $ 15.75万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-03-01 至 2009-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6869927
• 关键词: RNA; biochemical evolution; computational biology; computer assisted sequence analysis; computer program /software; mathematical model; method development; molecular biology information system; nucleic acid sequence; species difference

DESCRIPTION (provided by applicant): Two of the main current objectives of NHGRI are the development of new computational approaches to extract the maximum amount of information from the enormous amount of data generated by the Human Genome Project, and the identification of functional elements in complex genomes. For that purpose, it is also important to exploit the large amount of information derived from the comparative analysis between evolutionarily-related organisms. This proposal focuses on the development of computational probabilistic methods for the identification of new RNA genes using comparative analysis. Functional RNAs are a heterogeneous group of functional genomic elements involved in many important cellular activities ranging from regulation, mediators in RNA modifications, to catalysis. There is a pressing demand for the reliable identification of novel RNA genes, and the (as automated as possible) annotation of the RNA genes present in a given genome. Current computational methods for RNA genefinding are young, imprecise and still have to face the challenges imposed by complex genomes. Here I propose several new algorithms to improve our current computational methods to identify novel RNAs based on stochastic context-free grammars. The two most important new methods are: an algorithm to increase sensitivity by automatically tuning the comparative models to show an evolutionary divergence most adequate for the sequences being compared; and an algorithm to extend those variable-divergence comparisons to perform multi-species comparisons in a phylogeny-aware manner.

描述（由申请人提供）：NHGRI 目前的两个主要目标是开发新的计算方法，从人类基因组计划生成的大量数据中提取最大量的信息，以及识别复杂的功能元素基因组。为此，利用从进化相关生物之间的比较分析中获得的大量信息也很重要。该提案的重点是开发计算概率方法，用于使用比较分析来识别新的 RNA 基因。功能性RNA是一组异质的功能基因组元件，参与许多重要的细胞活动，包括调节、RNA修饰的介体和催化。迫切需要可靠地鉴定新的 RNA 基因，以及对给定基因组中存在的 RNA 基因进行（尽可能自动化）注释。目前用于 RNA 基因发现的计算方法还很年轻、不精确，并且仍然需要面对复杂基因组带来的挑战。在这里，我提出了几种新算法来改进我们当前的计算方法，以基于随机上下文无关语法识别新颖的 RNA。两种最重要的新方法是：一种通过自动调整比较模型来提高灵敏度的算法，以显示最适合被比较序列的进化分歧；以及一种扩展这些变量分歧比较的算法，以系统发育感知的方式执行多物种比较。