scholarly journals Counting RNA pseudoknotted structures (extended abstract)

2010 ◽  
Vol DMTCS Proceedings vol. AN,... (Proceedings) ◽  
Author(s):  
Cédric Saule ◽  
Mireille Regnier ◽  
Jean-Marc Steyaert ◽  
Alain Denise
Keyword(s):  
Computational Complexity ◽  
Rna Structure ◽  
Structure Prediction ◽  
Hierarchical Classification ◽  
Asymptotic Formulas ◽  
Rna Structure Prediction ◽  
Prediction Algorithms ◽  
International Audience

International audience In 2004, Condon and coauthors gave a hierarchical classification of exact RNA structure prediction algorithms according to the generality of structure classes that they handle. We complete this classification by adding two recent prediction algorithms. More importantly, we precisely quantify the hierarchy by giving closed or asymptotic formulas for the theoretical number of structures of given size n in all the classes but one. This allows to assess the tradeoff between the expressiveness and the computational complexity of RNA structure prediction algorithms. \par En 2004, Condon et ses coauteurs ont défini une classification des algorithmes exacts de prédiction de structure d'ARN, selon le degré de généralité des classes de structures qu'ils sont capables de prédire. Nous complétons cette classification en y ajoutant deux algorithmes récents. Chose plus importante, nous quantifions la hiérarchie des algorithmes, en donnant des formules closes ou asymptotiques pour le nombre théorique de structures de taille donnée n dans chacune des classes, sauf une. Ceci fournit un moyen d'évaluer, pour chaque algorithme, le compromis entre son degré de généralité et sa complexité.

scholarly journals A Method for RNA Structure Prediction Shows Evidence for Structure in lncRNAs

2018 ◽  
Vol 5 ◽  
Author(s):  
Riccardo Delli Ponti ◽  
Alexandros Armaos ◽  
Stefanie Marti ◽  
Gian Gaetano Tartaglia
Keyword(s):  
Rna Structure ◽  
Structure Prediction ◽  
Rna Structure Prediction

scholarly journals A method for RNA structure prediction shows evidence for structure in lncRNAs

2018 ◽  
Author(s):  
Riccardo Delli ponti ◽  
Alexandros Armaos ◽  
Stefanie Marti ◽  
Gian Gaetano Tartaglia
Keyword(s):  
Secondary Structure ◽  
Rna Structure ◽  
Structure Prediction ◽  
Sequence Information ◽  
Time Warping ◽  
Single Nucleotide ◽  
Rna Molecules ◽  
Rna Structure Prediction ◽  
Dynamic Time ◽  
Nucleotide Resolution

AbstractTo compare the secondary structures of RNA molecules we developed the CROSSalign method. CROSSalign is based on the combination of the Computational Recognition Of Secondary Structure (CROSS) algorithm to predict the RNA secondary structure at single-nucleotide resolution using sequence information, and the Dynamic Time Warping (DTW) method to align profiles of different lengths. We applied CROSSalign to investigate the structural conservation of long non-coding RNAs such as XIST and HOTAIR as well as ssRNA viruses including HIV. In a pool of sequences with the same secondary structure CROSSalign accurately recognizes repeat A of XIST and domain D2 of HOTAIR and outperforms other methods based on covariance modelling. CROSSalign can be applied to perform pair-wise comparisons and is able to find homologues between thousands of matches identifying the exact regions of similarity between profiles of different lengths. The algorithm is freely available at the webpage http://service.tartaglialab.com//new_submission/CROSSalign.

Predicting RNA Secondary Structure Using a Improved BPSO Based on Stem Combination

2013 ◽  
Vol 325-326 ◽  
pp. 1551-1554
Author(s):  
Yi Qi
Keyword(s):  
Secondary Structure ◽  
Sensitivity And Specificity ◽  
Rna Structure ◽  
Structure Prediction ◽  
Rna Secondary Structure ◽  
Experimental Results ◽  
New Method ◽  
Rna Structure Prediction ◽  
New Strategies

In this paper, we present an improved BPSO to predict RNA secondary structure to improve the performance with two new strategies. First one is to reduce the searching space of PSO through super stem set construction. Second is to modify the general BPSO updating process to settle stem permutation and combination problems. The experimental results show that the new method is effective for RNA structure prediction in terms of sensitivity and specificity by different sequence datasets including simple pseudoknot.

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