Analytic models and ambiguity of context-free languages
Theoretical Computer Science
A calculus for the random generation of labelled combinatorial structures
Theoretical Computer Science
Dynamic programming algorithms for RNA secondary structure prediction with pseudoknots
Discrete Applied Mathematics - Special volume on combinatorial molecular biology
A Grammar-Based Unification of Several Alignment and Folding Algorithms
Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology
Estimating Seed Sensitivity on Homogeneous Alignments
BIBE '04 Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering
Classifying RNA pseudoknotted structures
Theoretical Computer Science
Bioinformatics
Algorithms and software for nucleic acid sequences
Algorithms and software for nucleic acid sequences
Controlled non-uniform random generation of decomposable structures
Theoretical Computer Science
Prediction of RNA secondary structure including kissing hairpin motifs
WABI'10 Proceedings of the 10th international conference on Algorithms in bioinformatics
Topology and prediction of RNA pseudoknots
Bioinformatics
RNA-RNA interaction prediction and antisense RNA target search
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
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We extend an hypergraph representation, introduced by Finkelstein and Roytberg, to unify dynamic programming algorithms in the context of RNA folding with pseudoknots. Classic applications of RNA dynamic programming (Energy minimization, partition function, base-pair probabilities...) are reformulated within this framework, giving rise to very simple algorithms. This reformulation allows one to conceptually detach the conformation space/energy model - captured by the hypergraph model - from the specific application, assuming unambiguity of the decomposition. To ensure the latter property, we propose a new combinatorial methodology based on generating functions. We extend the set of generic applications by proposing an exact algorithm for extracting generalized moments in weighted distribution, generalizing a prior contribution by Miklos and al. Finally, we illustrate our full-fledged programme on three exemplary conformation spaces (secondary structures, Akutsu's simple type pseudoknots and kissing hairpins). This readily gives sets of algorithms that are either novel or have complexity comparable to classic implementations for minimization and Boltzmann ensemble applications of dynamic programming.