1,136 research outputs found
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
On the combinatorics of sparsification
Background: We study the sparsification of dynamic programming folding
algorithms of RNA structures. Sparsification applies to the mfe-folding of RNA
structures and can lead to a significant reduction of time complexity. Results:
We analyze the sparsification of a particular decomposition rule, ,
that splits an interval for RNA secondary and pseudoknot structures of fixed
topological genus. Essential for quantifying the sparsification is the size of
its so called candidate set. We present a combinatorial framework which allows
by means of probabilities of irreducible substructures to obtain the expected
size of the set of -candidates. We compute these expectations for
arc-based energy models via energy-filtered generating functions (GF) for RNA
secondary structures as well as RNA pseudoknot structures. For RNA secondary
structures we also consider a simplified loop-energy model. This combinatorial
analysis is then compared to the expected number of -candidates
obtained from folding mfe-structures. In case of the mfe-folding of RNA
secondary structures with a simplified loop energy model our results imply that
sparsification provides a reduction of time complexity by a constant factor of
91% (theory) versus a 96% reduction (experiment). For the "full" loop-energy
model there is a reduction of 98% (experiment).Comment: 27 pages, 12 figure
TT2NE: A novel algorithm to predict RNA secondary structures with pseudoknots
We present TT2NE, a new algorithm to predict RNA secondary structures with
pseudoknots. The method is based on a classification of RNA structures
according to their topological genus. TT2NE guarantees to find the minimum free
energy structure irrespectively of pseudoknot topology. This unique proficiency
is obtained at the expense of the maximum length of sequence that can be
treated but comparison with state-of-the-art algorithms shows that TT2NE is a
very powerful tool within its limits. Analysis of TT2NE's wrong predictions
sheds light on the need to study how sterical constraints limit the range of
pseudoknotted structures that can be formed from a given sequence. An
implementation of TT2NE on a public server can be found at
http://ipht.cea.fr/rna/tt2ne.php
McGenus: A Monte Carlo algorithm to predict RNA secondary structures with pseudoknots
We present McGenus, an algorithm to predict RNA secondary structures with
pseudoknots. The method is based on a classification of RNA structures
according to their topological genus. McGenus can treat sequences of up to 1000
bases and performs an advanced stochastic search of their minimum free energy
structure allowing for non trivial pseudoknot topologies. Specifically, McGenus
employs a multiple Markov chain scheme for minimizing a general scoring
function which includes not only free energy contributions for pair stacking,
loop penalties, etc. but also a phenomenological penalty for the genus of the
pairing graph. The good performance of the stochastic search strategy was
successfully validated against TT2NE which uses the same free energy
parametrization and performs exhaustive or partially exhaustive structure
search, albeit for much shorter sequences (up to 200 bases). Next, the method
was applied to other RNA sets, including an extensive tmRNA database, yielding
results that are competitive with existing algorithms. Finally, it is shown
that McGenus highlights possible limitations in the free energy scoring
function. The algorithm is available as a web-server at
http://ipht.cea.fr/rna/mcgenus.php .Comment: 6 pages, 1 figur
An O(n^5) algorithm for MFE prediction of kissing hairpins and 4-chains in nucleic acids
Efficient methods for prediction of minimum free energy (MFE) nucleic secondary structures are widely used, both to better understand structure and function of biological RNAs and to design novel nano-structures. Here, we present a new algorithm for MFE secondary structure prediction, which significantly expands the class of structures that can be handled in O(n^5) time. Our algorithm can handle H-type pseudoknotted structures, kissing hairpins, and chains of four overlapping stems, as well as nested substructures of these types
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