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

Authors: Yinglei Song, Chunmei Liu, Russell L. Malmberg, Congzhou He, Liming Cai

Addresses: 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

Abstract: Stochastic Context-Free Grammars (SCFG) has been shown to be effective in modelling RNA secondary structure for searches. Our previous work (Cai et al., 2003) in Stochastic Parallel Communicating Grammar Systems (SPCGS) has extended SCFG to model RNA pseudoknots. However, the alignment algorithm requires O(n 4) memory for a sequence of length n. In this paper, we develop a memory efficient algorithm for sequence-structure alignments including pseudoknots. This new algorithm reduces the memory space requirement from O(n4) to O(n2) without increasing the computation time. Our experiments have shown that this novel approach can achieve excellent performance on searching for RNA pseudoknots.

Keywords: RNA pseudoknots; stochastic parallel communicating grammar systems; SPCGS; sequence-structure alignment; bioinformatics; memory efficient algorithms.

DOI: 10.1504/IJBRA.2006.010606

International Journal of Bioinformatics Research and Applications, 2006 Vol.2 No.3, pp.289 - 304

Published online: 07 Aug 2006 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article