Bayesian computational approaches for gene regulation studies of bioethanol and biohydrogen production

It has recently become clear that regulatory RNAs play a major role in regulation of gene expression in bacteria. RNA secondary structures play a major role in the function of many regulatory 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 for RNA families. A paper describing our new algorithm, RNAG, to predict consensus secondary structures for unaligned sequences using the blocked Gibbs sampler has been published[1]. This sampling algorithm iteratively samples from the conditional probability distributions: P(Structure | Alignment) and P(Alignment | Structure). Subsequent to publication of the RNAG paper we have employed the technology from RNAG in the development of an RNA motif finding algorithm. To develop and RNA motif finding algorithm, RGibbs, we capitalized on our long experience in DNA motif finding and RNA secondary structure prediction. We applied RGibbs to three data sets from the literature and compared it to existing methods: one for training and two others for tests sets. In both test sets we found RGibbs out performed existing procedures.