Evolving stochastic context-free grammars for RNA secondary structure prediction

Stochastic Context-Free Grammars (SCFGs) were applied successfully to RNA secondarystructure prediction in the early 90s, and used in combination with comparative methods in thelate 90s. The set of SCFGs potentially useful for RNA secondary structure prediction is verylarge, but a few intuitively designed grammars have remained dominant.

In this paper weinvestigate two automatic search techniques for effective grammars - exhaustive search forvery compact grammars and an evolutionary algorithm to find larger grammars. We alsoexamine whether grammar ambiguity is as problematic to structure prediction as has beenpreviously suggested.

Results: These search techniques were applied to predict RNA secondary structure on a maximal dataset and revealed new and interesting grammars, though none are dramatically better thanclassic grammars.

In general, results showed that many grammars with quite different structurecould have very similar predictive ability. Many ambiguous grammars were found which wereat least as effective as the best current unambiguous grammars.

Conclusions: Overall the method of evolving SCFGs for RNA secondary structure prediction provedeffective in finding many grammars that had strong predictive accuracy, as good or slightlybetter than those designed manually.

Furthermore, several of the best grammars found wereambiguous, demonstrating that such grammars should not be disregarded.

Author: James WJ AndersonPaula TataruJoe StainesJotun HeinRune Lyngso
Credits/Source: BMC Bioinformatics 2012, 13:78

Published on: 2012-05-04

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