A novel method for cluster analysis of RNA structural data

Abstract

Functional RNA is known to contribute to a host of important biological pathways, with new discoveries being made daily. Because function is dependent on structure, computational tools that predict secondary structure of RNA are crucial to researchers. By far the most popular method is to predict the minimum free energy structure as the native. However, well-known limitations of this method have led the computational RNA community to move on to Boltzmann sampling. This method predicts an ensemble of structures sampled from the Boltzmann distribution under the Nearest Neighbor Thermodynamic Model (NNTM). Although providing a more thorough view of the folding landscape of a sequence, the Boltzmann sampling method also has the drawback of needing post-processing (i.e. data mining) in order to be meaningful. This dissertation presents a novel method of representing and clustering secondary structures of a Boltzmann sample. In addition, it demonstrates its ability to extract the meaningful structural signal of a Boltzmann sample by identifying significant commonalities and differences. Applications include two outstanding problems in the computational RNA community: investigating the ill-conditioning of thermodynamic optimization under the NNTM, and predicting a consensus structure for a set of sequences. Finally, this dissertation concludes with research performed as an intern for the Department of Defense's Defense Forensic Science Center. This work concerns analyzing the results of a DNA mixture interpretation study, highlighting the current state of forensic interpretation today.