Prediction of signal recognition particle RNA genes

- 5 - 1. Introduction Whereas there are many efficient tools for the identification of protein genes in genome sequences, we lack tools for the analysis of many noncoding RNAs. Therefore, most of the finished genome sequences published today lack annotation information about these RNAs. One difficulty with the identification of noncoding RNAs is that sequence tends to be poorly conserved and, therefore, standard tools such as BLAST [93] may be used only in the identification of orthologs in closely related organisms. On the other hand, many noncoding RNAs tend to have conserved secondary structure elements that may be used for their identification. Methods that have been used so far include the covariance models developed by Eddy and Durbin [94] as well as pattern matching, which is based on regular expression matching where also base-pairing schemes are taken into account. In the latter category, there is PatScan and PatSearch [95, 96, 97] as well as the rnabob tool of Sean Eddy [98]. In this work, we have studied methods to identify the RNA component of the signal recognition particle (SRP). The result is an automatic computer-based procedure that, starting from genomic data, finds genes coding for SRP RNA and hence scores, aligns and folds them. This procedure is available online as a public accessible web page http://bio.lundberg.gu.se/srpscan/ [1]. The first part of this thesis will give a theoretical overview of both noncoding RNA and signal recognition particle structure and function. A brief description of the statistical algorithms on which the programs are based will be given too. This section is intended to provide all the biological and computer science background necessary for a complete understanding of the material discussed in the following chapters. It intentionally includes