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.