Charles Lawrence, Brown University, Providence, USA

A New Gibbs Sampler for Predicting RNA Secondary Structure for Unaligned Sequences
RNAsecondary structures play an important role in the functions of many RNAs, and structural features are often key to their interaction with other cellular components. Thus, there has been considerable interest in the prediction of the secondary structures of families of RNA\u2019s. In this paper, we present a new algorithm RNAG to predict consensus secondary structures for unaligned sequences using the collapsed Gibbs sampler, which has theoretical advantage in convergence time. This algorithm iteratively samples from the conditional probability distributions P(Structure |Alignment) and P(Alignment | Structure), and in so doing refines the models of both Alignment and Structure.We use the \u03b3-centroid estimator to generate a prediction from the sampled structures, hierarchical clustering method to capture the posterior ensemble space and credibility limits to characterize the uncertainty. An analysis of 17 RNA families shows improved structural prediction based on PPV-SEN curves comparisons.