Evolving stochastic context--free grammars for RNA secondary structure prediction.

BMC Bioinformatics
James Wj AndersonRune Lyngsø

Abstract

Stochastic 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. These 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. Overall 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...Continue Reading

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Citations

May 22, 2013·Obstetrics and Gynecology International·Oscar Peralta-ZaragozaNadia Judith Jacobo-Herrera
May 3, 2013·BMC Bioinformatics·James W J AndersonJotun Hein
Jan 5, 2014·Journal of Mathematical Biology·Svetlana Poznanović, Christine E Heitsch
Aug 12, 2009·PloS One·Robert K BradleyIan Holmes
Mar 3, 2011·PloS One·Michiaki HamadaKiyoshi Asai
Aug 11, 2011·PLoS Computational Biology·Jan Gorodkin, Ivo L Hofacker
Apr 25, 2007·Seminars in Cell & Developmental Biology·Russell S Hamilton, Ilan Davis
Feb 26, 2016·F1000Research·Jason E McDermottStephen R Lindemann
Feb 13, 2013·BMC Bioinformatics·Zsuzsanna SükösdChristian N S Pedersen
Sep 5, 2018·Trends in Genetics : TIG·Tri C NguyenSheng Zhong

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Software Mentioned

Turbofold
GG2
RNAfold
IO
RNAalifold
UNAfold
RNASTRAND
CYK
RNAfoldv1
Pfold

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