Memory efficient alignment between RNA sequences and stochastic grammar models of pseudoknots

Authors:
Yinglei Song;Chunmei Liu;Russell L. Malmberg;Congzhou He;Liming Cai
Affiliations:
Department of Computer Science, 413 Boyd Graduate Research Center, University of Georgia, Athens, GA 30602, USA.;Department of Computer Science, 413 Boyd Graduate Research Center, University of Georgia, Athens, GA 30602, USA.;Department of Plant Biology, Miller Plant Sciences Building, University of Georgia, Athens, GA 30602, USA.;Department of Computer Science, 413 Boyd Graduate Research Center, University of Georgia, Athens, GA 30602, USA.;Department of Computer Science, 413 Boyd Graduate Research Center, University of Georgia, Athens, GA 30602, USA
Venue:
International Journal of Bioinformatics Research and Applications
Year:
2006

Citing 4
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Abstract

Stochastic Context-Free Grammars (SCFG) has been shown to beeffective in modelling RNA secondary structure for searches. Ourprevious work (Cai et al., 2003) in Stochastic ParallelCommunicating Grammar Systems (SPCGS) has extended SCFG to modelRNA pseudoknots. However, the alignment algorithm requiresO(n4) memory for a sequence of length n. In this paper,we develop a memory efficient algorithm for sequence-structurealignments including pseudoknots. This new algorithm reduces thememory space requirement from O(n4) to O(n2)without increasing the computation time. Our experiments have shownthat this novel approach can achieve excellent performance onsearching for RNA pseudoknots.