Computational Prediction Of RNA Secondary Structure Research Articles

ShapeSorter: a fully probabilistic method for detecting conserved RNAstructure features supported by SHAPE evidence.

There is an increased interest in the determination of RNA structures in vivo as it is now possible to probe them in a high-throughput manner, e.g. using SHAPE protocols. By now, there exist a range of computational methods that integrate experimental SHAPE-probing evidence into computational RNA secondary structure prediction. The state-of-the-art in this field is currently provided by computational methods that employ the minimum-free energy strategy for prediction RNA secondary structures with SHAPE-probing evidence. These methods, however, rely on the assumption that transcripts in vivo fold into the thermodynamically most stable configuration and ignore evolutionary evidence for conserved RNA structure features. We here present a new computational method, ShapeSorter, that predicts RNA structure features without employing the thermodynamic strategy. Instead, ShapeSorter employs a fully probabilistic framework to identify RNA structure features that are supported by evolutionary and SHAPE-probing evidence. Our method can capture RNA structure heterogeneity, pseudo-knotted RNA structures as well as transient and mutually exclusive RNA structure features. Moreover, it estimates P-values for the predicted RNA structure features which allows for easy filtering and ranking. We investigate the merits of our method in a comprehensive performance benchmarking and conclude that ShapeSorter has a significantly superior performance for predicting base-pairs than the existing state-of-the-art methods.

Estimating Energy Parameters for RNA Secondary Structure Predictions Using Both Experimental and Computational Data.

Computational RNA secondary structure prediction depends on a large number of nearest-neighbor free-energy parameters, including 10 parameters for Watson-Crick stacked base pairs that were estimated from experimental measurements of the free energies of 90 RNA duplexes. These experimental data are provided by time-consuming and cost-intensive experiments. In contrast, various modified nucleotides in RNAs, which would affect not only their structures but also functions, have been found, and rapid determination of energy parameters for a such modified nucleotides is needed. To reduce the high cost of determining energy parameters, we propose a novel method to estimate energy parameters from both experimental and computational data, where the computational data are provided by a recently developed molecular dynamics simulation protocol. We evaluate our method for Watson-Crick stacked base pairs, and show that parameters estimated from 10 experimental data items and 10 computational data items can predict RNA secondary structures with accuracy comparable to that using conventional parameters. The results indicate that the combination of experimental free-energy measurements and molecular dynamics simulations is capable of estimating the thermodynamic properties of RNA secondary structures at lower cost.

RNA-SSPT: RNA Secondary Structure Prediction Tools.

The prediction of RNA structure is useful for understanding evolution for both in silico and in vitro studies. Physical methods like NMR studies to predict RNA secondary structure are expensive and difficult. Computational RNA secondary structure prediction is easier. Comparative sequence analysis provides the best solution. But secondary structure prediction of a single RNA sequence is challenging. RNA-SSPT is a tool that computationally predicts secondary structure of a single RNA sequence. Most of the RNA secondary structure prediction tools do not allow pseudoknots in the structure or are unable to locate them. Nussinov dynamic programming algorithm has been implemented in RNA-SSPT. The current studies shows only energetically most favorable secondary structure is required and the algorithm modification is also available that produces base pairs to lower the total free energy of the secondary structure. For visualization of RNA secondary structure, NAVIEW in C language is used and modified in C# for tool requirement. RNA-SSPT is built in C# using Dot Net 2.0 in Microsoft Visual Studio 2005 Professional edition. The accuracy of RNA-SSPT is tested in terms of Sensitivity and Positive Predicted Value. It is a tool which serves both secondary structure prediction and secondary structure visualization purposes.

CyloFold: secondary structure prediction including pseudoknots

Computational RNA secondary structure prediction approaches differ by the way RNA pseudoknot interactions are handled. For reasons of computational efficiency, most approaches only allow a limited class of pseudoknot interactions or are not considering them at all. Here we present a computational method for RNA secondary structure prediction that is not restricted in terms of pseudoknot complexity. The approach is based on simulating a folding process in a coarse-grained manner by choosing helices based on established energy rules. The steric feasibility of the chosen set of helices is checked during the folding process using a highly coarse-grained 3D model of the RNA structures. Using two data sets of 26 and 241 RNA sequences we find that this approach is competitive compared to the existing RNA secondary structure prediction programs pknotsRG, HotKnots and UnaFold. The key advantages of the new method are that there is no algorithmic restriction in terms of pseudoknot complexity and a test is made for steric feasibility. Availability: The program is available as web server at the site: http://cylofold.abcc.ncifcrf.gov.

Correlation between the secondary structure of pre-mRNA introns and the efficiency of splicing in Saccharomyces cerevisiae

BackgroundSecondary structure interactions within introns have been shown to be essential for efficient splicing of several yeast genes. The nature of these base-pairing interactions and their effect on splicing efficiency were most extensively studied in ribosomal protein gene RPS17B (previously known as RP51B). It was determined that complementary pairing between two sequence segments located downstream of the 5' splice site and upstream of the branchpoint sequence promotes efficient splicing of the RPS17B pre-mRNA, presumably by shortening the branchpoint distance. However, no attempts were made to compute a shortened, 'structural' branchpoint distance and thus the functional relationship between this distance and the splicing efficiency remains unknown.ResultsIn this paper we use computational RNA secondary structure prediction to analyze the secondary structure of the RPS17B intron. We show that it is necessary to consider suboptimal structure predictions and to compute the structural branchpoint distances in order to explain previously published splicing efficiency results. Our study reveals that there is a tight correlation between this distance and splicing efficiency levels of intron mutants described in the literature. We experimentally test this correlation on additional RPS17B mutants and intron mutants within two other yeast genes.ConclusionThe proposed model of secondary structure requirements for efficient splicing is the first attempt to specify the functional relationship between pre-mRNA secondary structure and splicing. Our findings provide further insights into the role of pre-mRNA secondary structure in gene splicing in yeast and also offer basis for improvement of computational methods for splice site identification and gene-finding.

A statistical analysis of RNA folding algorithms through thermodynamic parameter perturbation

Computational RNA secondary structure prediction is rather well established. However, such prediction algorithms always depend on a large number of experimentally measured parameters. Here, we study how sensitive structure prediction algorithms are to changes in these parameters. We found already that for changes corresponding to the actual experimental error to which these parameters have been determined, 30% of the structure are falsely predicted whereas the ground state structure is preserved under parameter perturbation in only 5% of all the cases. We establish that base-pairing probabilities calculated in a thermal ensemble are viable although not a perfect measure for the reliability of the prediction of individual structure elements. Here, a new measure of stability using parameter perturbation is proposed, and its limitations are discussed.