Toward Turbo Decoding of RNA Secondary Structure
We propose an iterative probabilistic algorithm for estimation of RNA secondary structure using sequence data from two homologous sequences. The method is intended to exploit intersequence correlations "encoded" in the form of probabilistic models for alignment and for common secondary structure. In analogy with turbo-decoding in digital communications, we formulate a maximum a posteriori probability objective function for joint structural prediction and sequence alignment using iterations over individual structural and sequential alignment models with soft-input soft-output estimators. As a preliminary step toward realizing this methodology, we present results obtained from incorporating (hard) constraints based on posterior sequence alignment probabilities in joint secondary structure prediction. Through experimental evaluations over available databases of known secondary structure, we demonstrate that this results in a significant decrease in computation time while simultaneously providing a marginal increase in structural prediction accuracy.