Memory efficient alignment between RNA sequences and stochastic grammar models of pseudoknots.
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(n4) 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.
Song
Yinglei
Y
Department of Computer Science, 413 Boyd Graduate Research Center, University of Georgia, Athens, GA 30602, USA. song@cs.uga.edu
Liu
Chunmei
C
Malmberg
Russell L
RL
He
Congzhou
C
Cai
Liming
L
D000465:Algorithms; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods*; D016678:Genome; D016679:Genome, Viral; D008954:Models, Biological; D008957:Models, Genetic; D009690:Nucleic Acid Conformation; D017433:Protein Structure, Secondary; D012313:RNA / Q000737:chemistry / Q000235:genetics*; D012367:RNA, Viral / Q000737:chemistry; D012680:Sensitivity and Specificity; D016415:Sequence Alignment; D017423:Sequence Analysis, RNA; D012984:Software; D013269:Stochastic Processes
D012367:RNA, Viral; D012313:RNA