Identifying good predictions of RNA secondary structure.
Predicting the secondary structure of RNA molecules from the knowledge of the primary structure (the sequence of bases) is still a challenging task. There are algorithms that provide good results e.g. based on the search for an energetic optimal configuration. However the output of such algorithms does not always give the real folding of the molecule and therefore a feature to judge the reliability of the prediction would be appreciated. In this paper we present results on the expected structural behavior of LSU rRNA derived using a stochastic context-free grammar and generating functions. We show how these results can be used to judge the predictions made for LSU rRNA by any algorithm. In this way it will be possible to identify those predictions which are close to the natural folding of the molecule with a probability of 97% of success.
Johann Wolfgang Goethe-Universität, Institut für Informatik, 60325 Frankfurt am Main, Germany.
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Nebel
D000465:Algorithms; D019295:Computational Biology; D003198:Computer Simulation; D008958:Models, Molecular; D009690:Nucleic Acid Conformation; D012313:RNA / Q000737:chemistry*; D012335:RNA, Ribosomal / Q000737:chemistry; D013269:Stochastic Processes
D012335:RNA, Ribosomal; D012313:RNA