RNAmute: RNA secondary structure mutation analysis tool

Background  

RNAMute is an interactive Java application that calculates the secondary structure of all single point mutations, given an RNA sequence, and organizes them into categories according to their similarity with respect to the wild type predicted structure. The secondary structure predictions are performed using the Vienna RNA package. Several alternatives are used for the categorization of single point mutations: Vienna's RNAdistance based on dot-bracket representation, as well as tree edit distance and second eigenvalue of the Laplacian matrix based on Shapiro's coarse grain tree graph representation.     

Multithreaded comparative RNA secondary structure prediction using stochastic context-free grammars

Background  

The prediction of the structure of large RNAs remains a particular challenge in bioinformatics, due to the computational complexity and low levels of accuracy of state-of-the-art algorithms. The pfold model couples a stochastic context-free grammar to phylogenetic analysis for a high accuracy in predictions, but the time complexity of the algorithm and underflow errors have prevented its use for long alignments. Here we present PPfold, a multithreaded version of pfold, which is capable of predicting the structure of large RNA alignments accurately on practical timescales.     

Predicting RNA secondary structure by the comparative approach: how to select the homologous sequences

Background  

The secondary structure of an RNA must be known before the relationship between its structure and function can be determined. One way to predict the secondary structure of an RNA is to identify covarying residues that maintain the pairings (Watson-Crick, Wobble and non-canonical pairings). This "comparative approach" consists of identifying mutations from homologous sequence alignments. The sequences must covary enough for compensatory mutations to be revealed, but comparison is difficult if they are too different. Thus the choice of homologous sequences is critical. While many possible combinations of homologous sequences may be used for prediction, only a few will give good structure predictions. This can be due to poor quality alignment in stems or to the variability of certain sequences. This problem of sequence selection is currently unsolved.     

RNAstructure: software for RNA secondary structure prediction and analysis

Background  

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.     

Global analysis of RNA secondary structure in two metazoans

1. Download : Download full-size image

Highlights? RNA folding in miRNA target sites is distinct between animals ? There is a negative correlation between miRNA target-site structure and miRISC binding ? Conserved secondary structure features demarcate protein-coding regions of animal mRNAs ? There are conserved features of RNA secondary structure in animals     

Phylogenetic comparative analysis of RNA structure on Macintosh computers

A Macintosh Hypertalk program (Hypercard ‘stack’)for use in phylogenetic comparative analysis of RNA structureis described. The program identifies covariations and compensatorychanges in RNA sequence alignments, for use in the constructionof secondary structure models or the identification of tertiaryinteractions. The results of an analysis are presented eitheras a list of positions in the alignment which covary, or asa 2-dimensional matrix in which potential helices in the secondarystructure appear as diagonal patterns. Received on January 7, 1991; accepted on March 19, 1991     

New insight into RNase P RNA structure from comparative analysis of the archaeal RNA

A detailed comparative analysis of archaeal RNase P RNA structure and a comparison of the resulting structural information with that of the bacterial RNA reveals that the archaeal RNase P RNAs are strikingly similar to those of Bacteria. The differences between the secondary structure models of archaeal and bacterial RNase P RNA have largely disappeared, and even variation in the sequence and structure of the RNAs are similar in extent and type. The structure of the cruciform (P7-11) has been reevaluated on the basis of a total of 321 bacterial and archaeal sequences, leading to a model for the structure of this region of the RNA that includes an extension to P11 that consistently organizes the cruciform and adjacent highly-conserved sequences.     

Boltzmann ensemble features of RNA secondary structures: a comparative analysis of biological RNA sequences and random shuffles

Ensemble-based approaches to RNA secondary structure prediction have become increasingly appreciated in recent years. Here, we utilize sampling and clustering of the Boltzmann ensemble of RNA secondary structures to investigate whether biological sequences exhibit ensemble features that are distinct from their random shuffles. Representative messenger RNAs (mRNAs), structural RNAs, and precursor microRNAs (miRNAs) are analyzed for nine ensemble features. These include structure clustering features, the energy gap between the minimum free energy (MFE) and the ensemble, the numbers of high-frequency base pairs in the ensemble and in clusters, the average base-pair distance between the MFE structure and the ensemble, and between-cluster and within-cluster sums of squares. For each of the features, we observe a lack of significant distinction between mRNAs and their random shuffles. For five features, significant differences are found between structural RNAs and random counterparts. For seven features including the five for structural RNAs, much greater differences are observed between precursor miRNAs and random shuffles. These findings reveal differences in the Boltzmann structure ensemble among different types of functional RNAs. In addition, for two ensemble features, we observe distinctive, non-overlapping distributions for precursor miRNAs and random shuffles. A distributional separation can be particularly useful for the prediction of miRNA genes.     

RNA secondary structure and translation inhibition: analysis of mutants in the rplJ leader

We have carried out measurements of the stable binding of the ribosomal protein (r-protein) complex L10-L7/L12 to mutant forms of the mRNA leader of the rplJ operon of Escherichia coli. One of the point mutations, base 1548, which lies within the L10-L7/L12-protected region, almost completely abolishes in vitro formation of a stable complex of L10-L7/L12 with rplJ mRNA leader, and a second point mutation, base 1634, strongly reduces it. These observations constitute strong support for the proposition that L10-L7/L12 binds to the rplJ leader in bringing about translational feedback. To account for the action of these and other mutations, and to explain the mechanism of translation feedback inhibition, we suggest a secondary structure model involving alternate forms of the rplJ mRNA leader.     

Phylogenetic comparative analysis and the secondary structure of ribonuclease P RNA--a review

The most incisive a priori approach to inferring the higher order structure of large RNAs has proven to be the use of phylogenetic comparisons. This article provides guidelines to the method, using as an illustration the elucidation of the secondary structure of the catalytic RNA subunit of ribonuclease P (RNase P). The resultant structure is compared to the possibilities that are predicted thermodynamically for the RNase P RNA sequences of nine eubacteria.     

Ribosomal RNA secondary structure: compensatory mutations and implications for phylogenetic analysis

Using sequence data from the 28S ribosomal RNA (rRNA) genes of selected vertebrates, we investigated the effects that constraints imposed by secondary structure have on the phylogenetic analysis of rRNA sequence data. Our analysis indicates that characters from both base-pairing regions (stems) and non-base-pairing regions (loops) contain phylogenetic information, as judged by the level of support of the phylogenetic results compared with a well-established tree based on both morphological and molecular data. The best results (the greatest level of support of well-accepted nodes) were obtained when the complete data set was used. However, some previously supported nodes were resolved using either the stem or loop bases alone. Stem bases sustain a greater number of compensatory mutations than would be expected at random, but the number is < 40% of that expected under a hypothesis of perfect compensation to maintain secondary structure. Therefore, we suggest that in phylogenetic analyses, the weighting of stem characters be reduced by no more than 20%, relative to that of loop characters. In contrast to previous suggestions, we do not recommend weighting of stem positions by one-half, compared with that of loop positions, because this overcompensates for the constraints that selection imposes on the secondary structure of rRNA.      

Spectral decomposition for the search and analysis of RNA secondary structure.

Scales in RNA, based on geometrical considerations, can be exploited for the analysis and prediction of RNA structures. By using spectral decomposition, geometric information that relates to a given RNA fold can be reduced to a single positive scalar number, the second eigenvalue of the Laplacian matrix corresponding to the tree-graph representation of the RNA secondary structure. Along with the free energy of the structure, being the most important scalar number in the prediction of RNA folding by energy minimization methods, the second eigenvalue of the Laplacian matrix can be used as an effective signature for locating a target folded structure given a set of RNA folds. Furthermore, the second eigenvector of the Laplacian matrix can be used to partition large RNA structures into smaller fragments. An illustrative example is given for the use of the second eigenvalue to predict mutations that may cause structural rearrangements, thereby disrupting stable motifs.     

An RNA secondary structure workbench

A multiple approach to the study of RNA secondary structure is described which provides for the independent drawing of structures using base-pairing lists, for the generation of local structures in the form of hairpins, and for the generation of global structures by both Monte Carlo and dynamic programming methodologies. User-adjustable parameters provide for limiting the size of hairpin loops, bulges and inner loops, and constraints can be imposed relative to position-dependent base pairing.     

Vienna RNA secondary structure server

The Vienna RNA secondary structure server provides a web interface to the most frequently used functions of the Vienna RNA software package for the analysis of RNA secondary structures. It currently offers prediction of secondary structure from a single sequence, prediction of the consensus secondary structure for a set of aligned sequences and the design of sequences that will fold into a predefined structure. All three services can be accessed via the Vienna RNA web server at http://rna.tbi.univie.ac.at/.     

Simplified RNA secondary structure mapping by automation of SHAPE data analysis

SHAPE (Selective 2'-hydroxyl acylation analysed by primer extension) technology has emerged as one of the leading methods of determining RNA secondary structure at the nucleotide level. A significant bottleneck in using SHAPE is the complex and time-consuming data processing that is required. We present here a modified data collection method and a series of algorithms, embodied in a program entitled Fast Analysis of SHAPE traces (FAST), which significantly reduces processing time. We have used this method to resolve the secondary structure of the first ~900 nt of the hepatitis C virus (HCV) genome, including the entire core gene. We have also demonstrated the ability of SHAPE/FAST to detect the binding of a small molecule inhibitor to the HCV internal ribosomal entry site (IRES). In conclusion, FAST allows for high-throughput data processing to match the current high-throughput generation of data possible with SHAPE, reducing the barrier to determining the structure of RNAs of interest.     

General combinatorics of RNA secondary structure

The total number of RNA secondary structures of a given length with minimal hairpin loop length m(m>0) and with minimal stack length l(l>0) is computed, under the assumption that all base pairs can occur. Asymptotics are derived from the determination of recurrence relations of decomposition properties.     

Measuring the thermodynamics of RNA secondary structure formation

The thermodynamics of RNA secondary structure formation in small model systems provides a database for predicting RNA structure from sequence. Methods for making these measurements are reviewed with emphasis on optical methods and treatment of experimental errors. Analysis of experimental results in terms of simple nearest-neighbor models is presented. Some measured sequence dependences of non-Watson-Crick motifs are discussed. © 1998 John Wiley & Sons, Inc. Biopoly 44: 309–319, 1997