Improved algorithms and parameters for RNA secondary structure prediction

Anne Condon
Department of Computer Science, UBC

Abstract: Tools for prediction of the secondary structure of nucleic acids are useful, both to analyze naturally-occurring RNA molecules, and to inform the design of novel nucleic acids which are not found in nature. Computational methods for prediction of secondary structure from the base sequence typically rely on a thermodynamic model of structure formation, and aim to predict that structure with minimum free energy (MFE), or the probabilities of base pair formation. A significant challenge is to obtain accurate predictions in a computationally efficient manner.

In this talk, we'll describe two steps forward in meeting this challenge. The first contribution is a new set of thermodynamic parameters, which improve the accuracy of MFE structure prediction. We use optimization and machine learning methods to infer our parameters from a large repository of known structures, as well as from thermodynamic data obtained experimentally. Secondly, we will describe new combinatorial algorithms for MFE pseudoknotted structure prediction, which are more efficient than previous algorithms for certain types of pseudoknotted structures. We will also discuss possible directions for further improvements in secondary structure prediction.