Computational methods to identify noncoding RNA genes

NHGRI目前的两个主要目标是开发新的计算方法，以从人类基因组计划产生的大量数据中提取最大量的信息，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。该提案的重点是发展计算概率方法，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，从调节、RNA修饰中的介体到催化。目前迫切需要可靠地鉴定新的RNA基因，并（尽可能自动化）注释给定基因组中存在的RNA基因。目前RNA基因发现的计算方法还很年轻，不精确，仍然需要面对复杂基因组带来的挑战。在这里，我提出了几个新的算法，以改善我们目前的计算方法，以确定新的RNA的基础上随机上下文无关文法。两个最重要的新方法是：一个算法，以增加灵敏度，自动调整比较模型，以显示最适合的序列比较的进化分歧;和一个算法，以扩展这些变量的分歧比较，以执行多物种的比较，在一个可识别的方式。

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.