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Sparsification of RNA structure prediction including pseudoknots

Author(s)
Mohl, Mathias; Salari, Raheleh; Will, Sebastian; Backofen, Rolf; Sahinalp, S. Cenk
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Abstract
Abstract Background 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. Results 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 analyse how to sparsify four pseudoknot structure prediction algorithms, among those the most general method available (the Rivas-Eddy algorithm) and the fastest one (Reeder-Giegerich algorithm). In all algorithms the number of "candidate" substructures to be considered is reduced. Conclusions Our experimental results on the sparsified Reeder-Giegerich algorithm suggest a linear speedup over the unsparsified implementation.
Date issued
2010-12
URI
http://hdl.handle.net/1721.1/63116
Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Journal
Algorithms for Molecular Biology
Publisher
BioMed Central Ltd
Citation
Mohl, Mathias et al. “Sparsification of RNA Structure Prediction Including Pseudoknots.” Algorithms for Molecular Biology 5.1 (2010) : 39.
Version: Final published version
ISSN
1748-7188

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