ArticleBMC bioinformatics2012
Evolving stochastic context--free grammars for RNA secondary structure prediction.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A Hitchhiker's guide to RNA-RNA structure and interaction prediction tools.Briefings in bioinformatics · 2023Review
- tRNA renovatio: Rebirth through fragmentation.Molecular cell · 2023Review
- RNA, Action through Interactions.Trends in genetics : TIG · 2018Review
- Prediction of multi-drug resistance transporters using a novel sequence analysis method.F1000Research · 2015Article
- Asymptotic distribution of motifs in a stochastic context-free grammar model of RNA folding.Journal of mathematical biology · 2014Article
- The four ingredients of single-sequence RNA secondary structure prediction. A unifying perspective.RNA biology · 2013Article
- Quantifying variances in comparative RNA secondary structure prediction.BMC bioinformatics · 2013Article
- Characterising RNA secondary structure space using information entropy.BMC bioinformatics · 2013Article
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Authors and funding
5 authors.
Funding
Abstract
backgroundStochastic Context-Free Grammars (SCFGs) were applied successfully to RNA secondary structure prediction in the early 90s, and used in combination with comparative methods in the late 90s. The set of SCFGs potentially useful for RNA secondary structure prediction is very large, but a few intuitively designed grammars have remained dominant. In this paper we investigate two automatic search techniques for effective grammars - exhaustive search for very compact grammars and an evolutionary algorithm to find larger grammars. We also examine whether grammar ambiguity is as problematic to structure prediction as has been previously suggested.
resultsThese search techniques were applied to predict RNA secondary structure on a maximal data set and revealed new and interesting grammars, though none are dramatically better than classic grammars. In general, results showed that many grammars with quite different structure could have very similar predictive ability. Many ambiguous grammars were found which were at least as effective as the best current unambiguous grammars.
conclusionsOverall the method of evolving SCFGs for RNA secondary structure prediction proved effective in finding many grammars that had strong predictive accuracy, as good or slightly better than those designed manually. Furthermore, several of the best grammars found were ambiguous, demonstrating that such grammars should not be disregarded.
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