1. Memory efficient alignment between RNA sequences and stochastic grammar models of pseudoknots
- Author
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Song, Yinglei, Liu, Chunmei, Malmberg, Russell L., He, Congzhou, and Cai, Liming
- Subjects
Memory compaction -- Methods ,Memory management -- Methods ,Memory mapping -- Methods ,Memory partitioning -- Methods ,Memory protection -- Methods ,Memory refresh (Computers) -- Methods ,RNA processing -- Methods ,Algorithms -- Usage ,Context-free grammars -- Usage ,Context-free grammars -- Analysis ,Stochastic processes -- Usage ,Storage capacity ,Algorithm ,Biotechnology industry - Abstract
Byline: Yinglei Song, Chunmei Liu, Russell L. Malmberg, Congzhou He, Liming Cai 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.
- Published
- 2006