Topological classification of RNA structures.

@article{Bon2008TopologicalCO,
  title={Topological classification of RNA structures.},
  author={Micha{\"e}l Bon and G. Vernizzi and Henri Orland and Anthony Zee},
  journal={Journal of molecular biology},
  year={2008},
  volume={379 4},
  pages={
          900-11
        }
}
Topological Classification of RNA Structures via Intersection Graph
TLDR
An abstract algebraic representation of RNA secondary structures as a composition of hairpins, considered as basic loops, and a novel methodology to classify RNA structures based on two topological invariants, the genus and the crossing number are proposed.
On Topological RNA Interaction Structures
TLDR
The theoretical foundations for the folding of the two backbone analogues of γ structures: the RNA γ-interaction structures and the generating function is algebraic, which implies that the numbers of interaction structures can be computed recursively.
On RNA-RNA interaction structures of fixed topological genus.
New models and algorithms for RNA pseudoknot order assignment
TLDR
A novel graph coloring-based model for the problem of pseudoknot order assignment is introduced and a specialized heuristic operating on the proposed model and an alternative integer programming algorithm are shown that are compared with that of state-of-the-art algorithms.
Statistics of topological RNA structures
TLDR
A new bivariate generating function is derived whose singular expansion allows for analysis of the distributions of arcs, stacks, hairpin- , interior- and multi-loops and H-type pseudoknots, kissing hairpins and their respective expectation values.
Conformational Features of Topologically Classified RNA Secondary Structures
TLDR
Results from topological classification suggest that complex pseudoknots are usually some well-known motifs that are themselves complex or the interaction results of some special motifs, even if the required thermodynamic parameters are currently unknown.
Topology and prediction of RNA pseudoknots
TLDR
Gfold admits a topology-dependent parametrization of pseudoknot penalties that increases the sensitivity and positive predictive value of predicted base pairs by 10-20% compared with earlier approaches.
Genus trace reveals the topological complexity and domain structure of biomolecules
TLDR
The genus trace turns out to be a useful and versatile tool, with many potential applications, and gives a way to quantify how much more complicated a biomolecule is than its nested secondary structure alone would indicate.
Persistent Homology Analysis of RNA
TLDR
The application of persistent homology, a topological data analysis tool, is introduced for computing persistent features (loops) of the RNA folding space to discover persistent structural features, which are the set of smallest components to which the RNA fold space can be reduced.
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A folding algorithm to predict the structure of an RNA from its sequence is suggested, but to solve the RNA folding problem one needs thermodynamic data on tertiary structure interactions, and identification and characterization of metal-ion binding sites.
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TLDR
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TLDR
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TLDR
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