Thermodynamics of RNA--RNA binding
Bioinformatics
A Faster Algorithm for RNA Co-folding
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
A grammatical approach to RNA-RNA interaction prediction
Pattern Recognition
Bioinformatics
Sparse RNA Folding: Time and Space Efficient Algorithms
CPM '09 Proceedings of the 20th Annual Symposium on Combinatorial Pattern Matching
biRNA: fast RNA-RNA binding sites prediction
WABI'09 Proceedings of the 9th international conference on Algorithms in bioinformatics
Fast prediction of RNA-RNA interaction
WABI'09 Proceedings of the 9th international conference on Algorithms in bioinformatics
Sparsification of RNA structure prediction including pseudoknots
WABI'10 Proceedings of the 10th international conference on Algorithms in bioinformatics
Reducing the worst case running times of a family of RNA and CFG problems, using Valiant's approach
WABI'10 Proceedings of the 10th international conference on Algorithms in bioinformatics
Rich parameterization improves RNA structure prediction
RECOMB'11 Proceedings of the 15th Annual international conference on Research in computational molecular biology
Exact pattern matching for RNA structure ensembles
RECOMB'12 Proceedings of the 16th Annual international conference on Research in Computational Molecular Biology
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In the past years, a large set of new regulatory ncRNAs have been identified, but the number of experimentally verified targets is considerably low Thus, computational target prediction methods are on high demand Whereas all previous approaches for predicting a general joint structure have a complexity of O(n6) running time and O(n4) space, a more time and space efficient interaction prediction that is able to handle complex joint structures is necessary for genome-wide target prediction problems In this paper we show how to reduce both the time and space complexity of the RNA-RNA interaction prediction problem as described by Alkan et al [1] via dynamic programming sparsification - which allows to discard large portions of DP tables without loosing optimality Applying sparsification techniques reduces the complexity of the original algorithm from O(n6) time and O(n4) space to O(n4ψ(n)) time and O(n2ψ(n)+n3) space for some function ψ(n), which turns out to have small values for the range of n that we encounter in practice Under the assumption that the polymer-zeta property holds for RNA-structures, we demonstrate that ψ(n)=O(n) on average, resulting in a linear time and space complexity improvement over the original algorithm We evaluate our sparsified algorithm for RNA-RNA interaction prediction by total free energy minimization, based on the energy model of Chitsaz et al.[2], on a set of known interactions Our results confirm the significant reduction of time and space requirements in practice.