Fast RNA Secondary Structure Prediction Using Fuzzy Stochastic Models

Abstract

Computational prediction of RNA secondary structures has been an active area of research over the past decades and since become of great relevance for practical applications in structural biology. To date, many popular state-of-the-art prediction tools have the same worst-case time and space requirements of $\mathcal{O}(n^3)$ and $\mathcal{O}(n^2)$ for sequence length n, limiting their applicability for practical purposes. Accordingly, biologists are interested in getting results faster, where a moderate loss of accuracy would willingly be tolerated in favor of saving a significant amount of computation time. Motivated by these facts, we invented a novel algorithm for predicting the secondary structure of RNA molecules that manages to reduce the worst-case time complexity by a linear factor to $\mathcal{O}(n^2)$, while on the other hand it is still capable of producing highly accurate results. Basically, the presented method relies on a probabilistic statistical sampling approach which is actually based on an appropriate stochastic context-free grammar (SCFG): for any given input sequence, it generates a random set of candidate structures (from the ensemble of all feasible foldings) according to a “noisy” distribution (obtained by heuristically approximating the inside-outside values for the input sequence), such that finally a corresponding prediction can be efficiently derived. Notably, this method may be employed with different sampling strategies. Therefore, we not only consider a popular common strategy but also introduce a novel one that is supposed to fit especially well in connection with fuzzy stochastic models. A major advantage of the proposed prediction approach is that sampling can easily be parallelized on modern multi-core architectures or grids. Furthermore, it can be done in-place, that is only the best (here most probable) candidate structure(s) generated so far need(s) to be stored and finally collected. The combination of these two benefits immediately allows for an efficient handling of the increased sample sizes that are often necessary to achieve competitive prediction accuracy in connection with the noisy distribution.