Improved Algorithms for Parsing ESLTAGs: A Grammatical Model Suitable for RNA Pseudoknots

Authors:
Sanguthevar Rajasekaran;Sahar Al Seesi;Reda Ammar
Affiliations:
Department of Computer Science, University of Connecticut, Storrs, CT 06269-2155;Department of Computer Science, University of Connecticut, Storrs, CT 06269-2155;Department of Computer Science, University of Connecticut, Storrs, CT 06269-2155
Venue:
ISBRA '09 Proceedings of the 5th International Symposium on Bioinformatics Research and Applications
Year:
2009

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Abstract

Formal grammars have been employed in biology to solve various important problems. In particular, grammars have been used to model and predict RNA structures. Two such grammars are Simple Linear Tree Adjoining Grammars (SLTAGs) and Extended SLTAGs (ESLTAGs). Performance of techniques that employ grammatical formalisms critically depend on the efficiency of the underlying parsing algorithms. In this paper we present efficient algorithms for parsing SLTAGs and ESLTAGs. Our algorithm for SLTAGs parsing takes O ( min {m ,n 4}) time and O ( min {m ,n 4}) space where m is the number of entries that will ever be made in the matrix M (that is normally used by TAG parsing algorithms). Our algorithm for ESLTAGs parsing takes O (n min {m ,n 4}) time and O ( min {m ,n 4}) space. We show that these algorithms perform better in practice than the algorithms of Uemura, et al. [19].