Computational methods to identify noncoding RNA genes

描述(申请人提供)：NHGRI目前的两个主要目标是开发新的计算方法，从人类基因组计划产生的海量数据中提取最大数量的信息，以及识别复杂基因组中的功能元件。为此目的，利用进化相关生物体之间的比较分析所获得的大量信息也很重要。这项建议的重点是开发计算概率方法，以利用比较分析识别新的RNA基因。功能RNA是一组不同种类的功能基因组元件，参与许多重要的细胞活动，从调节、RNA修饰的中介到催化。迫切需要可靠地识别新的RNA基因，并(尽可能自动化地)对存在于给定基因组中的RNA基因进行注释。目前用于RNA基因发现的计算方法是年轻的、不精确的，并且仍然必须面对复杂基因组施加的挑战。在这里，我提出了几个新的算法来改进我们现有的基于随机上下文无关文法的识别新RNA的计算方法。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，以显示最适合于被比较序列的进化差异；以及一种算法，用于扩展这些可变差异比较，以系统发育学感知的方式进行多物种比较。

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.