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TT2NE: a novel algorithm to predict RNA secondary structures with pseudoknots
 Nucleic Acids Res
, 2011
"... We present TT2NE, a new algorithm to predict RNA secondary structures with pseudoknots. The method is based on a classification of RNA structures according to their topological genus. TT2NE is guaranteed to find the minimum free energy structure regardless of pseudoknot topology. This unique profic ..."
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We present TT2NE, a new algorithm to predict RNA secondary structures with pseudoknots. The method is based on a classification of RNA structures according to their topological genus. TT2NE is guaranteed to find the minimum free energy structure regardless of pseudoknot topology. This unique proficiency is obtained at the expense of the maximum length of sequences that can be treated, but comparison with stateoftheart algorithms shows that TT2NE significantly improves the quality of predictions. Analysis of TT2NE’s incorrect predictions sheds light on the need to study how sterical constraints limit the range of pseudoknotted structures that can be formed from a given sequence. An implementation of TT2NE on a public server can be found at
New Width Parameters of Graphs
, 2012
"... The main focus of this thesis is on using the divide and conquer technique to efficiently solve graph problems that are in general intractable. We work in the field of parameterized algorithms, using width parameters of graphs that indicate the complexity inherent in the structure of the input graph ..."
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The main focus of this thesis is on using the divide and conquer technique to efficiently solve graph problems that are in general intractable. We work in the field of parameterized algorithms, using width parameters of graphs that indicate the complexity inherent in the structure of the input graph. We use the notion of branch decompositions of a set function introduced by Robertson and Seymour to define three new graph parameters, booleanwidth, maximum matchingwidth (MMwidth) and maximum induced matchingwidth (MIMwidth). We compare these new graph width parameters to existing graph parameters by defining partial orders of width parameters. We focus on treewidth, branchwidth, cliquewidth, modulewidth and rankwidth, and include a Hasse diagram of these orders containing 32 graph parameters. We use the size of a maximum matching in a bipartite graph as a set function to define MMwidth and show that MMwidth never differs by more than a multiplicative factor 3 from treewidth. The main reason for introduc
doi:10.1093/bioinformatics/btq218 Thermodynamics of RNA structures by Wang–Landau sampling
"... Vol. 26 ISMB 2010, pages i278–i286 ..."
The Extragalactic Reference
 International Celestial Reference
, 1995
"... observed in the atenolol group could be related to more favourable changes in confounding factors, as compared with the control group. Interestingly, although we did not assess triglyceride levels (a potentially modifiable confounding variable) at followup, the latter might be expected to increas ..."
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observed in the atenolol group could be related to more favourable changes in confounding factors, as compared with the control group. Interestingly, although we did not assess triglyceride levels (a potentially modifiable confounding variable) at followup, the latter might be expected to increase only in the betablockade group2, which, in contrast with Yildiz et al.’s fears, might have limited the favourable autonomic mediated effect on CRP serum levels shown in atenolol treated patients.
On the Page Number of Secondary Structures with Pseudoknots
, 2011
"... Let S denote the set of (possibly noncanonical) base pairs {i, j} of an RNA tertiary structure; i.e. {i, j} ∈ S if there is a hydrogen bond between the ith and jth nucleotide. The page number of S, denoted π(S), is the minimum number k such that S can be decomposed into a disjoint union of k second ..."
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Let S denote the set of (possibly noncanonical) base pairs {i, j} of an RNA tertiary structure; i.e. {i, j} ∈ S if there is a hydrogen bond between the ith and jth nucleotide. The page number of S, denoted π(S), is the minimum number k such that S can be decomposed into a disjoint union of k secondary structures. Here, we show that computing the page number is NPcomplete; we describe an exact computation of page number, using constraint programming, and determine the page number of a collection of RNA tertiary structures, for which the topological genus is known. We describe two greedy algorithms, and show by an example that neither is optimal. We describe an algorithm running in time O(n log n) that produces a decomposition of an RNA structure S on n bases into at most ω(S)·log n disjoint secondary structures, where ω(S) denotes the maximum number of base pairs that may cross a given base pair. It follows that ω(S) ≤ π(S) ≤ ω(S) · log n, where π(S) denotes the page number of S. We give an O(n 3/2) time algorithm for finding a 2page decomposition of bisecondary structures for RNA sequences of size n, and we provide bounds on the expected page number of random structures having pseudoknots.
OF PROCESSES
"... Proteomics techniques have been used to generate comprehensive lists of protein interactions in a number of species. However, relatively little is known about how these interactions result in functional multiprotein complexes. This gap can be bridged by combining data from proteomics experiments w ..."
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Proteomics techniques have been used to generate comprehensive lists of protein interactions in a number of species. However, relatively little is known about how these interactions result in functional multiprotein complexes. This gap can be bridged by combining data from proteomics experiments with data from established structure determination techniques. Correspondingly, integrative computational methods are being developed to provide descriptions of protein complexes at varying levels of accuracy and resolution, ranging from complex compositions to detailed atomic structures. Molecular & Cellular Proteomics 9:1689–1702, 2010. A 3D enhanced version of this article is available. The text is identical to this version but includes interactive figures. Viewing the enhanced version of this article requires the use of a browser plugin. Please install the plugin when prompted.
Impact Of The Energy Model On The Complexity Of RNA Folding With Pseudoknots
"... Abstract. Predicting the folding of an RNA sequence, while allowing general pseudoknots (PK), consists in finding a minimal freeenergy matching of its n positions. Assuming independently contributing basepairs, the problem can be solved in Θ(n3)time using a variant of the maximal weighted matchi ..."
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Abstract. Predicting the folding of an RNA sequence, while allowing general pseudoknots (PK), consists in finding a minimal freeenergy matching of its n positions. Assuming independently contributing basepairs, the problem can be solved in Θ(n3)time using a variant of the maximal weighted matching. By contrast, the problem was previously proven NPHard in the more realistic nearestneighbor energy model. In this work, we consider an intermediate model, called the stackingpairs energy model. We extend a result by Lyngsø, showing that RNA folding with PK is NPHard within a large class of parametrization for the model. We also show the approximability of the problem, by giving a practical Θ(n3) algorithm that achieves at least a 5approximation for any parametrization of the stacking model. This contrasts nicely with the nearestneighbor version of the problem, which we prove cannot be approximated within any positive ratio, unless P = NP.