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Backofen R: Exact pattern matching for RNA structure ensembles
 In Proceedings of the 16th International Conference on Research in Computational Molecular Biology (RECOMB 2012), Volume 7262 of LNCS. Edited by
"... Abstract. ExpaRNA’s core algorithm computes, for two fixed RNA structures, a maximal nonoverlapping set of maximal exact matchings. We introduce an algorithm ExpaRNAP that solves the lifted problem of finding such sets of exact matchings in entire Boltzmanndistributed structure ensembles of two R ..."
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Abstract. ExpaRNA’s core algorithm computes, for two fixed RNA structures, a maximal nonoverlapping set of maximal exact matchings. We introduce an algorithm ExpaRNAP that solves the lifted problem of finding such sets of exact matchings in entire Boltzmanndistributed structure ensembles of two RNAs. Due to a novel kind of structural sparsification, the new algorithm maintains the time and space complexity of the algorithm for fixed input structures. Furthermore, we generalized the chaining algorithm of ExpaRNA in order to compute a compatible subset of ExpaRNAP’s exact matchings. We show that ExpaRNAP outperforms ExpaRNA in BRAliBase 2.1 benchmarks, where we pass the chained exact matchings as anchor constraints to the RNA alignment tool LocARNA. Compared to LocARNA, this novel approach shows similar accuracy but is six times faster. 1
PETcofold: predicting conserved interactions and structures of two multiple alignments of RNA sequences
, 2011
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Reducing the Worst Case Running Times of a Family of RNA and CFG Problems, Using Valiant’s Approach
"... Abstract. We study Valiant’s classical algorithm for Context Free Grammar recognition in subcubic time, and extract features that are common to problems on which Valiant’s approach can be applied. Based on this, we describe several problem templates, and formulate generic algorithms that use Valian ..."
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Abstract. We study Valiant’s classical algorithm for Context Free Grammar recognition in subcubic time, and extract features that are common to problems on which Valiant’s approach can be applied. Based on this, we describe several problem templates, and formulate generic algorithms that use Valiant’s technique and can be applied to all problems which abide by these templates. These algorithms obtain new worst case running time bounds for a large family of important problems within the world of RNA Secondary Structures and Context Free Grammars. 1
RESEARCH Sparsification of RNA structure prediction including pseudoknots
"... 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 sparsificat ..."
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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 RNArelated 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 RivasEddy algorithm) and the fastest one (ReederGiegerich algorithm). In all algorithms the number of “candidate ” substructures to be considered is reduced. Conclusions: Our experimental results on the sparsified ReederGiegerich algorithm suggest a linear speedup over the unsparsified implementation. Background
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"... BIOINFORMATICS Vol. 26 ECCB 2010, pages i460–i466doi:10.1093/bioinformatics/btq372 RactIP: fast and accurate prediction of RNARNA interaction using integer programming ..."
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BIOINFORMATICS Vol. 26 ECCB 2010, pages i460–i466doi:10.1093/bioinformatics/btq372 RactIP: fast and accurate prediction of RNARNA interaction using integer programming
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"... Vol. 26 ECCB 2010, pages i460–i466 doi:10.1093/bioinformatics/btq372 RactIP: fast and accurate prediction of RNARNA interaction using integer programming ..."
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Vol. 26 ECCB 2010, pages i460–i466 doi:10.1093/bioinformatics/btq372 RactIP: fast and accurate prediction of RNARNA interaction using integer programming
Sparsification in Algebraic Dynamic Programming
"... Sparsification is a technique to speed up dynamic programming algorithms which has been successfully applied to RNA structure prediction, RNARNAinteraction prediction, simultaneous alignment and folding, and pseudoknot prediction. So far, sparsification has been more a collection of loosely relate ..."
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Sparsification is a technique to speed up dynamic programming algorithms which has been successfully applied to RNA structure prediction, RNARNAinteraction prediction, simultaneous alignment and folding, and pseudoknot prediction. So far, sparsification has been more a collection of loosely related examples and no general, well understood theory. In this work we propose a general theory to describe and implement sparsification in dynamic programming algorithms. The approach is formalized as an extension of Algebraic Dynamic Programming (ADP) which makes it applicable to a variety of algorithms and scoring schemes. In particular, this is the first approach that shows how to sparsify algorithms with scoring schemes that go beyond simple minimization or maximization, like enumeration of suboptimal solutions and approximation of the partition function. As an example, we show how to sparsify different variants of RNA structure prediction algorithms. 1
ABSTRACT
, 1994
"... We present a number of arguments in favor of the suggestion that the MarshallPeierls sign rule survives the frustration in the squarelattice Heisenberg antiferromagnet with frustrating nextnearestneighbor (diagonal) bonds (J1 − J2 model) for relatively large values of the parameter J2/J1. Both t ..."
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We present a number of arguments in favor of the suggestion that the MarshallPeierls sign rule survives the frustration in the squarelattice Heisenberg antiferromagnet with frustrating nextnearestneighbor (diagonal) bonds (J1 − J2 model) for relatively large values of the parameter J2/J1. Both the spinwave analysis and the exactdiagonalization data concerning the weight of Marshall states support the above suggestion.
#.c The Authors DOI: 10.1142/S0219720012410077 PRACTICALITY AND TIME COMPLEXITY OF A SPARSIFIED RNA FOLDING ALGORITHM
, 2012
"... Commonly used RNA folding programs compute the minimum free energy structure of a sequence under the pseudoknot exclusion constraint. They are based on Zuker’s algorithm which runs in time Oðn 3 Þ. Recently, it has been claimed that RNA folding can be achieved in average time Oðn 2 Þ using a sparsif ..."
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Commonly used RNA folding programs compute the minimum free energy structure of a sequence under the pseudoknot exclusion constraint. They are based on Zuker’s algorithm which runs in time Oðn 3 Þ. Recently, it has been claimed that RNA folding can be achieved in average time Oðn 2 Þ using a sparsification technique. A proof of quadratic time complexity was based on the assumption that computational RNA folding obeys the \polymerzeta property". Several variants of sparse RNA folding algorithms were later developed. Here, we present our own version, which is readily applicable to existing RNA folding programs, as it is extremely simple and does not require any new data structure. We applied it to the widely used Vienna RNAfold program, to create sibRNAfold, the first public sparsified version of a standard RNA folding program. To gain a better understanding of the time complexity of sparsified RNA folding in general, we carried out a thorough run time analysis with synthetic random sequences, both in the context of energy minimization and base pairing maximization. Contrary to previous claims, the asymptotic time complexity of a sparsified RNA folding algorithm using standard energy parameters remains Oðn 3 Þ under a wide variety of conditions. Consistent with our runtime analysis, we found that RNA folding does not obey the \polymerzeta