Sparsification of RNA Structure Prediction Including Pseudoknots

Peer reviewed: 
Yes, item is peer reviewed.
Scholarly level: 
Faculty/Staff
Final version published as: 

Möhl et al. Algorithms for Molecular Biology 2010, 5:39
http://www.almob.org/content/5/1/39

Date created: 
2010
Abstract: 

Background: Although many RNA molecules contain pseudoknots, computational prediction of pseudoknottedRNA structure is still in its infancy due to high running time and space consumption implied by the dynamicprogramming formulations of the problem.Results: In this paper, we introduce sparsification to significantly speedup the dynamic programming approachesfor pseudoknotted RNA structure prediction, which also lower the space requirements. Although sparsification hasbeen applied to a number of RNA-related structure prediction problems in the past few years, we provide the firstapplication of sparsification to pseudoknotted RNA structure prediction specifically and to handling gappedfragments more generally - which has a much more complex recursive structure than other problems to whichsparsification 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-Giegerichalgorithm). 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 overthe unsparsified implementation.

Language: 
English
Document type: 
Article
Rights: 
You are free to copy, distribute and transmit this work under the following conditions: You must give attribution to the work (but not in any way that suggests that the author endorses you or your use of the work); You may not use this work for commercial purposes; You may not alter, transform, or build upon this work. Any further uses require the permission of the rights holder (or author if no rights holder is listed). These rights are based on the Creative Commons Attribution-NonCommercial-NoDerivatives License.
Statistics: