2,171 research outputs found
Prediction of RNA pseudoknots by Monte Carlo simulations
In this paper we consider the problem of RNA folding with pseudoknots. We use
a graphical representation in which the secondary structures are described by
planar diagrams. Pseudoknots are identified as non-planar diagrams. We analyze
the non-planar topologies of RNA structures and propose a classification of RNA
pseudoknots according to the minimal genus of the surface on which the RNA
structure can be embedded. This classification provides a simple and natural
way to tackle the problem of RNA folding prediction in presence of pseudoknots.
Based on that approach, we describe a Monte Carlo algorithm for the prediction
of pseudoknots in an RNA molecule.Comment: 22 pages, 14 figure
Improved RNA pseudoknots prediction and classification using a new topological invariant
We propose a new topological characterization of RNA secondary structures
with pseudoknots based on two topological invariants. Starting from the classic
arc-representation of RNA secondary structures, we consider a model that
couples both I) the topological genus of the graph and II) the number of
crossing arcs of the corresponding primitive graph. We add a term proportional
to these topological invariants to the standard free energy of the RNA
molecule, thus obtaining a novel free energy parametrization which takes into
account the abundance of topologies of RNA pseudoknots observed in RNA
databases.Comment: 9 pages, 6 figure
A Seeded Genetic Algorithm for RNA Secondary Structural Prediction with Pseudoknots
This work explores a new approach in using genetic algorithm to predict RNA secondary structures with pseudoknots. Since only a small portion of most RNA structures is comprised of pseudoknots, the majority of structural elements from an optimal pseudoknot-free structure are likely to be part of the true structure. Thus seeding the genetic algorithm with optimal pseudoknot-free structures will more likely lead it to the true structure than a randomly generated population. The genetic algorithm uses the known energy models with an additional augmentation to allow complex pseudoknots. The nearest-neighbor energy model is used in conjunction with Turner’s thermodynamic parameters for pseudoknot-free structures, and the H-type pseudoknot energy estimation for simple pseudoknots. Testing with known pseudoknot sequences from PseudoBase shows that it out performs some of the current popular algorithms
Prediction and statistics of pseudoknots in RNA structures using exactly clustered stochastic simulations
Ab initio RNA secondary structure predictions have long dismissed helices
interior to loops, so-called pseudoknots, despite their structural importance.
Here, we report that many pseudoknots can be predicted through long time scales
RNA folding simulations, which follow the stochastic closing and opening of
individual RNA helices. The numerical efficacy of these stochastic simulations
relies on an O(n^2) clustering algorithm which computes time averages over a
continously updated set of n reference structures. Applying this exact
stochastic clustering approach, we typically obtain a 5- to 100-fold simulation
speed-up for RNA sequences up to 400 bases, while the effective acceleration
can be as high as 100,000-fold for short multistable molecules (<150 bases). We
performed extensive folding statistics on random and natural RNA sequences, and
found that pseudoknots are unevenly distributed amongst RNAstructures and
account for up to 30% of base pairs in G+C rich RNA sequences (Online RNA
folding kinetics server including pseudoknots : http://kinefold.u-strasbg.fr/
).Comment: 6 pages, 5 figure
Uniform and non-uniform random generation of RNA secondary structures with pseudoknots
International audienceWe give an efficient algorithm to generate random RNA secondary structures with pseudoknots, either uniformly or non uniformly in a controllable fashion. Although we consider a restrictive class of pseudoknots, the class of {\em simple recursive pseudoknots}, it turns out that most of the known real RNA pseudoknotted secondary structures in the biological databases belong to this class
Uniform and non-uniform random generation of RNA secondary structures with pseudoknots
International audienceWe give an efficient algorithm to generate random RNA secondary structures with pseudoknots, either uniformly or non uniformly in a controllable fashion. Although we consider a restrictive class of pseudoknots, the class of {\em simple recursive pseudoknots}, it turns out that most of the known real RNA pseudoknotted secondary structures in the biological databases belong to this class
PseudoBase++: an extension of PseudoBase for easy searching, formatting and visualization of pseudoknots
Pseudoknots have been recognized to be an important type of RNA secondary structures responsible for many biological functions. PseudoBase, a widely used database of pseudoknot secondary structures developed at Leiden University, contains over 250 records of pseudoknots obtained in the past 25 years through crystallography, NMR, mutational experiments and sequence comparisons. To promptly address the growing analysis requests of the researchers on RNA structures and bring together information from multiple sources across the Internet to a single platform, we designed and implemented PseudoBase++, an extension of PseudoBase for easy searching, formatting and visualization of pseudoknots. PseudoBase++ (http://pseudobaseplusplus.utep.edu) maps the PseudoBase dataset into a searchable relational database including additional functionalities such as pseudoknot type. PseudoBase++ links each pseudoknot in PseudoBase to the GenBank record of the corresponding nucleotide sequence and allows scientists to automatically visualize RNA secondary structures with PseudoViewer. It also includes the capabilities of fine-grained reference searching and collecting new pseudoknot information
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