Joint Loop End Modeling Improves Covariance Model Based Non-coding RNA Gene Search

Abstract

Abstract. The effect of more detailed modeling of the interface between stem and loop in non-coding RNA hairpin structures on efficacy of covariance-model-based non-coding RNA gene search is examined. Currently, the prior probabilities of the two stem nucleotides and two loop-end nucleotides at the interface are treated the same as any other stem and loop nucleotides respectively. Laboratory thermodynamic studies show that hairpin stability is dependent on the identities of these four nucleotides, but this is not taken into account in current covariance models. It is shown that separate estimation of emission priors for these nucleotides and joint treatment of substitution probabilities for the two loop-end nucleotides leads to improved non-coding RNA gene search.Keywords: sequence analysis, RNA gene search, covariance models IntroductionCovariance models are an effective method of capturing the joint probability information inherent in the intramolecularly base-paired positions of a non-coding RNA molecule [1,2]. Unlike profile hidden Markov models [3,4], which have a set of four emission probabilities over the possible nucleotides at each consensus sequence position, covariance models allow consensus base pairs to be assigned sixteen joint probabilities over the possible ordered nucleotide pairs. Covariance models also allow the probability of insertion or deletion of a base pair to be different than the sum of the marginal probabilities of insertion or deletion of the individual nucleotides. The profile hidden Markov model can be viewed as a special form of a covariance model with no base pairs specified.