Computational methods to identify noncoding RNA genes

识别非编码 RNA 基因的计算方法

• 批准号: 7025050
• 负责人: Elena Rivas
• 金额: $ 15.38万
• 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 2006-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7025050
• 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; 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的两个主要目的目标是开发新的计算方法，以从人类基因组项目产生的大量数据中提取最大信息量，以及复杂基因组中功能元素的鉴定。为此，利用从进化相关的生物之间的比较分析中得出的大量信息也很重要。该建议着重于使用比较分析来鉴定新RNA基因的计算概率方法的发展。功能性RNA是一组异质的功能基因组元素，涉及许多重要的细胞活性，从调节，RNA修饰中的介体到催化。对新型RNA基因的可靠鉴定有紧迫的需求，并且（尽可能自动化的）给定基因组中存在的RNA基因的注释（尽可能自动化）。当前用于RNA基因定态的计算方法是年轻的，不精确的，仍然必须面对复杂基因组施加的挑战。在这里，我提出了几种新算法，以改善我们当前的计算方法，以基于随机上下文的语法识别新型RNA。两种最重要的新方法是：一种算法，通过自动调整比较模型来提高灵敏度，以显示进化差异最适合要比较的序列；以及一种以系统发育方式进行多种物种比较的算法，以扩展这些可变差异比较。