Probabilistic models of RNA secondary structure

This thesis develops probabilistic models of RNA secondary structure. The first chapter introduces RNA secondary structure prediction, in particular stochastic context-free grammars (SCFGs), and considers a novel method for automated design of SCFGs. Many SCFGs are found with a similar predictive quality as those commonly used for RNA secondary structure prediction. The second chapter discusses the effect alignment quality, evolutionary distance between sequences, and number of sequences in an alignment have on RNA secondary structure prediction. By combining statistical alignment and SCFG models we can, in a statistically sound setting, average structure predictions over the space of alignments to decrease loss created by poor alignments. The third chapter incorporates additional biological information about RNA secondary structure formation into the decoding of the SCFG posterior distribution. Combining iterative helix formation, phylogenetic modelling, and a distance function between alignment columns leads to the an improvement in the accuracy of comparative RNA secondary structure prediction. Finally, appendices briefly discuss further work concerning probabilistic models of RNA secondary structure which may be of interest to the reader.This thesis is not currently available in ORA