Computational identification of non-coding RNAs

Abstract

A large amount of genomic information is now becoming available. Suitable bioinformatic tools to organize and analyze this vast amount of information are therefore important. In the case of protein genes, the majority of these may be correctly identified using standard search methods that are based on sequence alignment. However, a different problem is presented when analysing non-coding RNA genes, since for their identification it is essential to take into consideration secondary structure features. Secondary structure is not only important for non-coding RNA genes, but it is also important in the regulation of gene expression. This work is concerned with the development of methods for ncRNA prediction and the application of these methods to identify specific ncRNA families. In a variety of organisms we report on several ncRNA sequences not previously reported. These novel RNA sequences make it possible to better predict the structure of these RNAs as well as to better understand their evolution and function. To further understand the structure and evolution of the RNases P and MRP we also analyzed the protein composition of these enzymes. Together, these new predictions aid to better understand the structure, function and evolution of RNase P and MRP.