Tree adjoining grammars for RNA structure prediction
Theoretical Computer Science - Special issue: Genome informatics
Pseudoknots in RNA secondary structures
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Dynamic programming algorithms for RNA secondary structure prediction with pseudoknots
Discrete Applied Mathematics - Special volume on combinatorial molecular biology
RNA secondary structure prediction with simple pseudoknots
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
Classifying RNA pseudoknotted structures
Theoretical Computer Science
A Faster Algorithm for RNA Co-folding
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
Sparse RNA Folding: Time and Space Efficient Algorithms
CPM '09 Proceedings of the 20th Annual Symposium on Combinatorial Pattern Matching
Time and space efficient RNA-RNA interaction prediction via sparse folding
RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
A study of accessible motifs and RNA folding complexity
RECOMB'06 Proceedings of the 10th annual international conference on Research in Computational Molecular Biology
Rich parameterization improves RNA structure prediction
RECOMB'11 Proceedings of the 15th Annual international conference on Research in computational molecular biology
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Although many RNA molecules contain pseudoknots, computational prediction of pseudoknotted RNA structure is still in its infancy due to high running time and space consumption implied by the dynamic programming formulations of the problem. In this paper, we introduce sparsification to significantly speedup the dynamic programming approaches for pseudoknotted RNA structure prediction, which also lower the space requirements. Although sparsification has been applied to a number of RNA-related structure prediction problems in the past few years, we provide the first application of sparsification to pseudoknotted RNA structure prediction specifically and to handling gapped fragments more generally - which has a much more complex recursive structure than other problems to which sparsification has been applied. We show that sparsification, when applied to the fastest, as well as the most general pseudoknotted structure prediction methods available, - respectively the Reeder-Giegerich algorithm and the Rivas-Eddy algorithm - reduces the number of "candidate" substructures to be considered significantly. In fact, experimental results on the sparsified Reeder-Giegerich algorithm suggest a linear speedup over the unsparsified implementation.