404 research outputs found
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
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
Computational identification and analysis of noncoding RNAs - Unearthing the buried treasures in the genome
The central dogma of molecular biology states that the genetic information flows from DNA to RNA to protein. This dogma has exerted a substantial influence on our understanding of the genetic activities in the cells. Under this influence, the prevailing assumption until the recent past was that genes are basically repositories for protein coding information, and proteins are responsible for most of the important biological functions in all cells. In the meanwhile, the importance of RNAs has remained rather obscure, and RNA was mainly viewed as a passive intermediary that bridges the gap between DNA and protein. Except for classic examples such as tRNAs (transfer RNAs) and rRNAs (ribosomal RNAs), functional noncoding RNAs were considered to be rare.
However, this view has experienced a dramatic change during the last decade, as systematic screening of various genomes identified myriads of noncoding RNAs (ncRNAs), which are RNA molecules that function without being translated into proteins [11], [40]. It has been realized that many ncRNAs play important roles in various biological processes. As RNAs can interact with other RNAs and DNAs in a sequence-specific manner, they are especially useful in tasks that require highly specific nucleotide recognition [11]. Good examples are the miRNAs (microRNAs) that regulate gene expression by targeting mRNAs (messenger RNAs) [4], [20], and the siRNAs (small interfering RNAs) that take part in the RNAi (RNA interference) pathways for gene silencing [29], [30]. Recent developments show that ncRNAs are extensively involved in many gene regulatory mechanisms [14], [17].
The roles of ncRNAs known to this day are truly diverse. These include transcription and translation control, chromosome replication, RNA processing and modification, and protein degradation and translocation [40], just to name a few. These days, it is even claimed that ncRNAs dominate the genomic output of the higher organisms such as mammals, and it is being suggested that the greater portion of their genome (which does not encode proteins) is dedicated to the control and regulation of cell development [27]. As more and more evidence piles up, greater attention is paid to ncRNAs, which have been neglected for a long time. Researchers began to realize that the vast majority of the genome that was regarded as “junk,” mainly because it was not well understood, may indeed hold the key for the best kept secrets in life, such as the mechanism of alternative splicing, the control of epigenetic variations and so forth [27]. The complete range and extent of the role of ncRNAs are not so obvious at this point, but it is certain that a comprehensive understanding of cellular processes is not possible without understanding the functions of ncRNAs [47]
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
Structural Alignment of RNAs Using Profile-csHMMs and Its Application to RNA Homology Search: Overview and New Results
Systematic research on noncoding RNAs (ncRNAs) has revealed that many ncRNAs are actively involved in various biological networks. Therefore, in order to fully understand the mechanisms of these networks, it is crucial to understand the roles of ncRNAs. Unfortunately, the annotation of ncRNA genes that give rise to functional RNA molecules has begun only recently, and it is far from being complete. Considering the huge amount of genome sequence data, we need efficient computational methods for finding ncRNA genes. One effective way of finding ncRNA genes is to look for regions that are similar to known ncRNA genes. As many ncRNAs have well-conserved secondary structures, we need statistical models that can represent such structures for this purpose. In this paper, we propose a new method for representing RNA sequence profiles and finding structural alignment of RNAs based on profile context-sensitive hidden Markov models (profile-csHMMs). Unlike existing models, the proposed approach can handle any kind of RNA secondary structures, including pseudoknots. We show that profile-csHMMs can provide an effective framework for the computational analysis of RNAs and the identification of ncRNA genes
Pseudoknots in a Homopolymer
After a discussion of the definition and number of pseudoknots, we reconsider
the self-attracting homopolymer paying particular attention to the scaling of
the number of pseudoknots at different temperature regimes in two and three
dimensions. Although the total number of pseudoknots is extensive at all
temperatures, we find that the number of pseudoknots forming between the two
halves of the chain diverges logarithmically at (in both dimensions) and below
(in 2d only) the theta-temparature. We later introduce a simple model that is
sensitive to pseudoknot formation during collapse. The resulting phase diagram
involves swollen, branched and collapsed homopolymer phases with transitions
between each pair.Comment: submitted to PR
Target prediction and a statistical sampling algorithm for RNA-RNA interaction
It has been proven that the accessibility of the target sites has a critical
influence for miRNA and siRNA. In this paper, we present a program, rip2.0, not
only the energetically most favorable targets site based on the
hybrid-probability, but also a statistical sampling structure to illustrate the
statistical characterization and representation of the Boltzmann ensemble of
RNA-RNA interaction structures. The outputs are retrieved via backtracing an
improved dynamic programming solution for the partition function based on the
approach of Huang et al. (Bioinformatics). The time and space
algorithm is implemented in C (available from
\url{http://www.combinatorics.cn/cbpc/rip2.html})Comment: 7 pages, 10 figure
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