RNA secondary structure forms a scaffold for tertiary structure formation, hence is a major determinant for both structure and function of an RNA
molecule. While protein secondary structure is predicted by machine learning methods, RNA secondary structure can be predicted by dynamic programming
methods that use an ab initio energy model will experimentally measured free energy parameters. Such thermodynamics-based methods constitute important
tools especially when confronted with novel RNAs of unknown function.
In this talk, we describe two recent results. First, we briefly describe a new algorithm, RNAsc, to optimally predict RNA secondary structure when
integrating chemical/enzymatic probing data, such a s in-line probing or SHAPE data (s elective 2' - hydroxyl acylation analyzed by primer extension).
Secondly, we describe a thermodynamics - based algorithm, FFTbor, for conformational switch prediction that employs the FFT to determine partition functions by polynomial interpolation.