Computational methods to identify noncoding RNA genes

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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.