534 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
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
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
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
Ab initio RNA folding
RNA molecules are essential cellular machines performing a wide variety of
functions for which a specific three-dimensional structure is required. Over
the last several years, experimental determination of RNA structures through
X-ray crystallography and NMR seems to have reached a plateau in the number of
structures resolved each year, but as more and more RNA sequences are being
discovered, need for structure prediction tools to complement experimental data
is strong. Theoretical approaches to RNA folding have been developed since the
late nineties when the first algorithms for secondary structure prediction
appeared. Over the last 10 years a number of prediction methods for 3D
structures have been developed, first based on bioinformatics and data-mining,
and more recently based on a coarse-grained physical representation of the
systems. In this review we are going to present the challenges of RNA structure
prediction and the main ideas behind bioinformatic approaches and physics-based
approaches. We will focus on the description of the more recent physics-based
phenomenological models and on how they are built to include the specificity of
the interactions of RNA bases, whose role is critical in folding. Through
examples from different models, we will point out the strengths of
physics-based approaches, which are able not only to predict equilibrium
structures, but also to investigate dynamical and thermodynamical behavior, and
the open challenges to include more key interactions ruling RNA folding.Comment: 28 pages, 18 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
Shapes of topological RNA structures
A topological RNA structure is derived from a diagram and its shape is
obtained by collapsing the stacks of the structure into single arcs and by
removing any arcs of length one. Shapes contain key topological, information
and for fixed topological genus there exist only finitely many such shapes. We
shall express topological RNA structures as unicellular maps, i.e. graphs
together with a cyclic ordering of their half-edges. In this paper we prove a
bijection of shapes of topological RNA structures. We furthermore derive a
linear time algorithm generating shapes of fixed topological genus. We derive
explicit expressions for the coefficients of the generating polynomial of these
shapes and the generating function of RNA structures of genus . Furthermore
we outline how shapes can be used in order to extract essential information of
RNA structure databases.Comment: 27 pages, 11 figures, 2 tables. arXiv admin note: text overlap with
arXiv:1304.739
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