Dynamic programming algorithms for RNA secondary structure prediction with pseudoknots
Discrete Applied Mathematics - Special volume on combinatorial molecular biology
Finding Common Sequence and Structure Motifs in a Set of RNA Sequences
Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology
Implementation of algorithms for maximum matching on nonbipartite graphs.
Implementation of algorithms for maximum matching on nonbipartite graphs.
Local Similarity in RNA Secondary Structures
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Classifying RNA pseudoknotted structures
Theoretical Computer Science
Alignment of RNA base pairing probability matrices
Bioinformatics
A graphical criterion of planarity for RNA secondary structures with pseudoknots in Rivas–Eddy class
Theoretical Computer Science
A rule-based approach for RNA pseudoknot prediction
International Journal of Data Mining and Bioinformatics
Computational prediction of nucleic acid secondary structure: Methods, applications, and challenges
Theoretical Computer Science
Approximation Algorithms for Predicting RNA Secondary Structures with Arbitrary Pseudoknots
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
K-partite RNA secondary structures
WABI'09 Proceedings of the 9th international conference on Algorithms in bioinformatics
HFold: RNA pseudoknotted secondary structure prediction using hierarchical folding
WABI'07 Proceedings of the 7th international conference on Algorithms in Bioinformatics
ChainKnot: a comparative H-type pseudoknot prediction tool using multiple ab initio folding tools
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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Most functional RNA molecules have characteristic structures that are highly conserved in evolution. Many of them contain pseudoknots. Here, we present a method for computing the consensus structures including pseudoknots based on alignments of a few sequences. The algorithm combines thermodynamic and covariation information to assign scores to all possible base pairs, the base pairs are chosen with the help of the maximum weighted matching algorithm. We applied our algorithm to a number of different types of RNA known to contain pseudoknots. All pseudoknots were predicted correctly and more than 85 percent of the base pairs were identified.