7,021 research outputs found
Topology of RNA-RNA interaction structures
The topological filtration of interacting RNA complexes is studied and the
role is analyzed of certain diagrams called irreducible shadows, which form
suitable building blocks for more general structures. We prove that for two
interacting RNAs, called interaction structures, there exist for fixed genus
only finitely many irreducible shadows. This implies that for fixed genus there
are only finitely many classes of interaction structures. In particular the
simplest case of genus zero already provides the formalism for certain types of
structures that occur in nature and are not covered by other filtrations. This
case of genus zero interaction structures is already of practical interest, is
studied here in detail and found to be expressed by a multiple context-free
grammar extending the usual one for RNA secondary structures. We show that in
time and space complexity, this grammar for genus zero
interaction structures provides not only minimum free energy solutions but also
the complete partition function and base pairing probabilities.Comment: 40 pages 15 figure
Stochastic sampling of the RNA structural alignment space
A novel method is presented for predicting the common secondary structures and alignment of two homologous RNA sequences by sampling the ‘structural alignment’ space, i.e. the joint space of their alignments and common secondary structures. The structural alignment space is sampled according to a pseudo-Boltzmann distribution based on a pseudo-free energy change that combines base pairing probabilities from a thermodynamic model and alignment probabilities from a hidden Markov model. By virtue of the implicit comparative analysis between the two sequences, the method offers an improvement over single sequence sampling of the Boltzmann ensemble. A cluster analysis shows that the samples obtained from joint sampling of the structural alignment space cluster more closely than samples generated by the single sequence method. On average, the representative (centroid) structure and alignment of the most populated cluster in the sample of structures and alignments generated by joint sampling are more accurate than single sequence sampling and alignment based on sequence alone, respectively. The ‘best’ centroid structure that is closest to the known structure among all the centroids is, on average, more accurate than structure predictions of other methods. Additionally, cluster analysis identifies, on average, a few clusters, whose centroids can be presented as alternative candidates. The source code for the proposed method can be downloaded at http://rna.urmc.rochester.edu
RNA-RNA interaction prediction based on multiple sequence alignments
Many computerized methods for RNA-RNA interaction structure prediction have
been developed. Recently, time and space dynamic programming
algorithms have become available that compute the partition function of RNA-RNA
interaction complexes. However, few of these methods incorporate the knowledge
concerning related sequences, thus relevant evolutionary information is often
neglected from the structure determination. Therefore, it is of considerable
practical interest to introduce a method taking into consideration both
thermodynamic stability and sequence covariation. We present the \emph{a
priori} folding algorithm \texttt{ripalign}, whose input consists of two
(given) multiple sequence alignments (MSA). \texttt{ripalign} outputs (1) the
partition function, (2) base-pairing probabilities, (3) hybrid probabilities
and (4) a set of Boltzmann-sampled suboptimal structures consisting of
canonical joint structures that are compatible to the alignments. Compared to
the single sequence-pair folding algorithm \texttt{rip}, \texttt{ripalign}
requires negligible additional memory resource. Furthermore, we incorporate
possible structure constraints as input parameters into our algorithm. The
algorithm described here is implemented in C as part of the \texttt{rip}
package. The supplemental material, source code and input/output files can
freely be downloaded from \url{http://www.combinatorics.cn/cbpc/ripalign.html}.
\section{Contact} Christian Reidys \texttt{[email protected]}Comment: 8 pages, 9 figure
SimulFold: Simultaneously Inferring RNA Structures Including Pseudoknots, Alignments, and Trees Using a Bayesian MCMC Framework
Computational methods for predicting evolutionarily conserved rather than thermodynamic RNA structures have recently attracted increased interest. These methods are indispensable not only for elucidating the regulatory roles of known RNA transcripts, but also for predicting RNA genes. It has been notoriously difficult to devise them to make the best use of the available data and to predict high-quality RNA structures that may also contain pseudoknots. We introduce a novel theoretical framework for co-estimating an RNA secondary structure including pseudoknots, a multiple sequence alignment, and an evolutionary tree, given several RNA input sequences. We also present an implementation of the framework in a new computer program, called SimulFold, which employs a Bayesian Markov chain Monte Carlo method to sample from the joint posterior distribution of RNA structures, alignments, and trees. We use the new framework to predict RNA structures, and comprehensively evaluate the quality of our predictions by comparing our results to those of several other programs. We also present preliminary data that show SimulFold's potential as an alignment and phylogeny prediction method. SimulFold overcomes many conceptual limitations that current RNA structure prediction methods face, introduces several new theoretical techniques, and generates high-quality predictions of conserved RNA structures that may include pseudoknots. It is thus likely to have a strong impact, both on the field of RNA structure prediction and on a wide range of data analyses
Statistical modeling of RNA structure profiling experiments enables parsimonious reconstruction of structure landscapes.
RNA plays key regulatory roles in diverse cellular processes, where its functionality often derives from folding into and converting between structures. Many RNAs further rely on co-existence of alternative structures, which govern their response to cellular signals. However, characterizing heterogeneous landscapes is difficult, both experimentally and computationally. Recently, structure profiling experiments have emerged as powerful and affordable structure characterization methods, which improve computational structure prediction. To date, efforts have centered on predicting one optimal structure, with much less progress made on multiple-structure prediction. Here, we report a probabilistic modeling approach that predicts a parsimonious set of co-existing structures and estimates their abundances from structure profiling data. We demonstrate robust landscape reconstruction and quantitative insights into structural dynamics by analyzing numerous data sets. This work establishes a framework for data-directed characterization of structure landscapes to aid experimentalists in performing structure-function studies
RNAstructure: software for RNA secondary structure prediction and analysis
<p>Abstract</p> <p>Background</p> <p>To understand an RNA sequence's mechanism of action, the structure must be known. Furthermore, target RNA structure is an important consideration in the design of small interfering RNAs and antisense DNA oligonucleotides. RNA secondary structure prediction, using thermodynamics, can be used to develop hypotheses about the structure of an RNA sequence.</p> <p>Results</p> <p>RNAstructure is a software package for RNA secondary structure prediction and analysis. It uses thermodynamics and utilizes the most recent set of nearest neighbor parameters from the Turner group. It includes methods for secondary structure prediction (using several algorithms), prediction of base pair probabilities, bimolecular structure prediction, and prediction of a structure common to two sequences. This contribution describes new extensions to the package, including a library of C++ classes for incorporation into other programs, a user-friendly graphical user interface written in JAVA, and new Unix-style text interfaces. The original graphical user interface for Microsoft Windows is still maintained.</p> <p>Conclusion</p> <p>The extensions to RNAstructure serve to make RNA secondary structure prediction user-friendly. The package is available for download from the Mathews lab homepage at <url>http://rna.urmc.rochester.edu/RNAstructure.html</url>.</p
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