RNA Profiling: A New Approach to 'Denoising' Secondary Structure Prediction

• Activity Number: 630
• Type: Topic Contributed
• Date/Time: Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Christine Heitsch*+
• Companies: Georgia Institute of Technology
• Keywords: computational molecular biology ; RNA secondary structure ; information theory ; graph theory

Abstract

The biomedical importance of RNA molecules only continues to grow, yet accurate prediction of RNA secondary structures remains a significant open problem in computational molecular biology. The ability to sample secondary structures efficiently from the Gibbs distribution yields a strong signal of high probability base pairs. However, further analysis is needed to identify important correlations in these high-dimensional data sets. We present a novel method, RNA profiling, which identifies the most probable combinations of base pairs across the ensemble of possible secondary structures. As will be shown, our combinatorial approach is straightforward, stable, and clearly separates structural signal from thermodynamic noise.