Consensus Structure Prediction Research Articles

LinearAlifold: Linear-time consensus structure prediction for RNA alignments

Predicting the consensus structure of a set of aligned RNA homologs is a convenient method to find conserved structures in an RNA genome, which has many applications including viral diagnostics and therapeutics. However, the most commonly used tool for this task, RNAalifold, is prohibitively slow for long sequences, due to a cubic scaling with the sequence length, taking over a day on 400 SARS-CoV-2 and SARS-related genomes (∼30,000nt). We present LinearAlifold, a much faster alternative that scales linearly with both the sequence length and the number of sequences, based on our work LinearFold that folds a single RNA in linear time. Our work is orders of magnitude faster than RNAalifold (0.7 h on the above 400 genomes, or ∼36× speedup) and achieves higher accuracies when compared to a database of known structures. More interestingly, LinearAlifold’s prediction on SARS-CoV-2 correlates well with experimentally determined structures, substantially outperforming RNAalifold. Finally, LinearAlifold supports two energy models (Vienna and BL*) and four modes: minimum free energy (MFE), maximum expected accuracy (MEA), ThreshKnot, and stochastic sampling, each of which takes under an hour for hundreds of SARS-CoV variants. Our resource is at:https://github.com/LinearFold/LinearAlifold (code) and http://linearfold.org/linear-alifold (server).

Riboswitches in Archaea.

Riboswitches are cis-acting, non-coding RNA elements found in the 5'UTR of bacterial mRNA and 3' UTR of eukaryotic mRNA, that fold in a complex manner to act as receptors for specific metabolites hence altering their conformation in response to the change in concentrations of a ligand or metabolite. Riboswitches function as gene regulators in numerous bacteria, archaea, fungi, algae and plants. This study identifies different classes of riboswitches in the Archaeal domain of life. Previous studies have suggested that riboswitches carry a conserved aptameric domain in different domains of life. Since Archaea are considered to be the most idiosyncratic organisms it was interesting to look for the conservation pattern of riboswitches in these obviously strange microorganisms. Completely sequenced Archaeal Genomes present in the NCBI repository were used for studying riboswitches and other ncRNAs. The sequence files in FASTA format were downloaded from NCBI Genome database and information related to these genomes was retrieved from GenBank. Three bioinformatics approaches were used namely, ab initio, consensus structure prediction and statistical model-based prediction for identifying riboswitches. Archaeal genomes have a sporadic distribution of putative riboswitches like the TPP, FMN, Guanidine, Lysine and c-di-AMP riboswitches, which are known to occur in bacteria. Also, a class of riboswitch sensing c-di-GMP, a second messenger, has been identified in a few Archaeal organisms. This study clearly reveals that bioinformatics methods are likely to play a major role in identifying conserved riboswitches and in establishing how widespread these classes are in all domains of life, even though the final confirmation may come from wet lab methods.

Incorporating phylogenetic-based covarying mutations into RNAalifold for RNA consensus structure prediction

BackgroundRNAalifold, a popular computational method for RNA consensus structure prediction, incorporates covarying mutations into a thermodynamic model to fold the aligned RNA sequences. When quantifying covariance, it evaluates conserved signals of two aligned columns with base-pairing rules. This scoring scheme performs better than some other approaches, such as mutual information. However it ignores the phylogenetic history of the aligned sequences, which is an important criterion to evaluate the level of sequence covariance.ResultsIn this article, in order to improve the accuracy of consensus structure folding, we propose a novel approach named PhyloRNAalifold. It incorporates the number of covarying mutations on the phylogenetic tree of the aligned sequences into the covariance scoring of RNAalifold. The benchmarking results show that the new scoring scheme of PhyloRNAalifold can improve the consensus structure detection of RNAalifold.ConclusionIncorporating additional phylogenetic information of aligned sequences into the covariance scoring of RNAalifold can improve its performance of consensus structures folding. This improvement is correlated with alignment characteristics, such as pair-wise identity and the number of sequences in the alignment.

From consensus structure prediction to RNA gene finding

Reliable structure prediction is a prerequisite for most types of bioinformatical analysis of RNA. Since the accuracy of structure prediction from single sequences is limited, one often resorts to computing the consensus structure for a set of related RNA sequences. Since functionally important RNA structures are expected to evolve much more slowly than the underlying sequences, the pattern of sequence (co-)variation can be exploited to dramatically improve structure prediction. Since a conserved common structure is only expected when the RNA structure is under selective pressure, consensus structure prediction also provides an ideal starting point for the de novo detection of structured non-coding RNAs. Here, we review different strategies for the prediction of consensus secondary structures, and show how these approaches can be used to predict non-coding RNA genes.

RNAalifold: improved consensus structure prediction for RNA alignments

BackgroundThe prediction of a consensus structure for a set of related RNAs is an important first step for subsequent analyses. RNAalifold, which computes the minimum energy structure that is simultaneously formed by a set of aligned sequences, is one of the oldest and most widely used tools for this task. In recent years, several alternative approaches have been advocated, pointing to several shortcomings of the original RNAalifold approach.ResultsWe show that the accuracy of RNAalifold predictions can be improved substantially by introducing a different, more rational handling of alignment gaps, and by replacing the rather simplistic model of covariance scoring with more sophisticated RIBOSUM-like scoring matrices. These improvements are achieved without compromising the computational efficiency of the algorithm. We show here that the new version of RNAalifold not only outperforms the old one, but also several other tools recently developed, on different datasets.ConclusionThe new version of RNAalifold not only can replace the old one for almost any application but it is also competitive with other approaches including those based on SCFGs, maximum expected accuracy, or hierarchical nearest neighbor classifiers.

ConStruct: Improved construction of RNA consensus structures

BackgroundAligning homologous non-coding RNAs (ncRNAs) correctly in terms of sequence and structure is an unresolved problem, due to both mathematical complexity and imperfect scoring functions. High quality alignments, however, are a prerequisite for most consensus structure prediction approaches, homology searches, and tools for phylogeny inference. Automatically created ncRNA alignments often need manual corrections, yet this manual refinement is tedious and error-prone.ResultsWe present an extended version of CONSTRUCT, a semi-automatic, graphical tool suitable for creating RNA alignments correct in terms of both consensus sequence and consensus structure. To this purpose CONSTRUCT combines sequence alignment, thermodynamic data and various measures of covariation.One important feature is that the user is guided during the alignment correction step by a consensus dotplot, which displays all thermodynamically optimal base pairs and the corresponding covariation. Once the initial alignment is corrected, optimal and suboptimal secondary structures as well as tertiary interaction can be predicted. We demonstrate CONSTRUCT's ability to guide the user in correcting an initial alignment, and show an example for optimal secondary consensus structure prediction on very hard to align SECIS elements. Moreover we use CONSTRUCT to predict tertiary interactions from sequences of the internal ribosome entry site of CrP-like viruses. In addition we show that alignments specifically designed for benchmarking can be easily be optimized using CONSTRUCT, although they share very little sequence identity.ConclusionCONSTRUCT's graphical interface allows for an easy alignment correction based on and guided by predicted and known structural constraints. It combines several algorithms for prediction of secondary consensus structure and even tertiary interactions. The CONSTRUCT package can be downloaded from the URL listed in the Availability and requirements section of this article.

Strategies for measuring evolutionary conservation of RNA secondary structures

BackgroundEvolutionary conservation of RNA secondary structure is a typical feature of many functional non-coding RNAs. Since almost all of the available methods used for prediction and annotation of non-coding RNA genes rely on this evolutionary signature, accurate measures for structural conservation are essential.ResultsWe systematically assessed the ability of various measures to detect conserved RNA structures in multiple sequence alignments. We tested three existing and eight novel strategies that are based on metrics of folding energies, metrics of single optimal structure predictions, and metrics of structure ensembles. We find that the folding energy based SCI score used in the RNAz program and a simple base-pair distance metric are by far the most accurate. The use of more complex metrics like for example tree editing does not improve performance. A variant of the SCI performed particularly well on highly conserved alignments and is thus a viable alternative when only little evolutionary information is available. Surprisingly, ensemble based methods that, in principle, could benefit from the additional information contained in sub-optimal structures, perform particularly poorly. As a general trend, we observed that methods that include a consensus structure prediction outperformed equivalent methods that only consider pairwise comparisons.ConclusionStructural conservation can be measured accurately with relatively simple and intuitive metrics. They have the potential to form the basis of future RNA gene finders, that face new challenges like finding lineage specific structures or detecting mis-aligned sequences.

Can Clustal-style progressive pairwise alignment of multiple sequences be used in RNA secondary structure prediction?

BackgroundIn ribonucleic acid (RNA) molecules whose function depends on their final, folded three-dimensional shape (such as those in ribosomes or spliceosome complexes), the secondary structure, defined by the set of internal basepair interactions, is more consistently conserved than the primary structure, defined by the sequence of nucleotides.ResultsThe research presented here investigates the possibility of applying a progressive, pairwise approach to the alignment of multiple RNA sequences by simultaneously predicting an energy-optimized consensus secondary structure. We take an existing algorithm for finding the secondary structure common to two RNA sequences, Dynalign, and alter it to align profiles of multiple sequences. We then explore the relative successes of different approaches to designing the tree that will guide progressive alignments of sequence profiles to create a multiple alignment and prediction of conserved structure.ConclusionWe have found that applying a progressive, pairwise approach to the alignment of multiple ribonucleic acid sequences produces highly reliable predictions of conserved basepairs, and we have shown how these predictions can be used as constraints to improve the results of a single-sequence structure prediction algorithm. However, we have also discovered that the amount of detail included in a consensus structure prediction is highly dependent on the order in which sequences are added to the alignment (the guide tree), and that if a consensus structure does not have sufficient detail, it is less likely to provide useful constraints for the single-sequence method.

Structural analysis of aligned RNAs

The knowledge about classes of non-coding RNAs (ncRNAs) is growing very fast and it is mainly the structure which is the common characteristic property shared by members of the same class. For correct characterization of such classes it is therefore of great importance to analyse the structural features in great detail. In this manuscript I present RNAlishapes which combines various secondary structure analysis methods, such as suboptimal folding and shape abstraction, with a comparative approach known as RNA alignment folding. RNAlishapes makes use of an extended thermodynamic model and covariance scoring, which allows to reward covariation of paired bases. Applying the algorithm to a set of bacterial trp-operon leaders using shape abstraction it was able to identify the two alternating conformations of this attenuator. Besides providing in-depth analysis methods for aligned RNAs, the tool also shows a fairly well prediction accuracy. Therefore, RNAlishapes provides the community with a powerful tool for structural analysis of classes of RNAs and is also a reasonable method for consensus structure prediction based on sequence alignments. RNAlishapes is available for online use and download at .

StrAl: progressive alignment of non-coding RNA using base pairing probability vectors in quadratic time

Alignment of RNA has a wide range of applications, for example in phylogeny inference, consensus structure prediction and homology searches. Yet aligning structural or non-coding RNAs (ncRNAs) correctly is notoriously difficult as these RNA sequences may evolve by compensatory mutations, which maintain base pairing but destroy sequence homology. Ideally, alignment programs would take RNA structure into account. The Sankoff algorithm for the simultaneous solution of RNA structure prediction and RNA sequence alignment was proposed 20 years ago but suffers from its exponential complexity. A number of programs implement lightweight versions of the Sankoff algorithm by restricting its application to a limited type of structure and/or only pairwise alignment. Thus, despite recent advances, the proper alignment of multiple structural RNA sequences remains a problem. Here we present StrAl, a heuristic method for alignment of ncRNA that reduces sequence-structure alignment to a two-dimensional problem similar to standard multiple sequence alignment. The scoring function takes into account sequence similarity as well as up- and downstream pairing probability. To test the robustness of the algorithm and the performance of the program, we scored alignments produced by StrAl against a large set of published reference alignments. The quality of alignments predicted by StrAl is far better than that obtained by standard sequence alignment programs, especially when sequence homologies drop below approximately 65%; nevertheless StrAl's runtime is comparable to that of ClustalW.

Consensus shapes: an alternative to the Sankoff algorithm for RNA consensus structure prediction

The well-known Sankoff algorithm for simultaneous RNA sequence alignment and folding is currently considered an ideal, but computationally over-expensive method. Available tools implement this algorithm under various pragmatic restrictions. They are still expensive to use, and it is difficult to judge if the moderate quality of results is because of the underlying model or to its imperfect implementation. We propose to redefine the consensus structure prediction problem in a way that does not imply a multiple sequence alignment step. For a family of RNA sequences, our method explicitly and independently enumerates the near-optimal abstract shape space, and predicts as the consensus an abstract shape common to all sequences. For each sequence, it delivers the thermodynamically best structure which has this common shape. Since the shape space is much smaller than the structure space, and identification of common shapes can be done in linear time (in the number of shapes considered), the method is essentially linear in the number of sequences. Our evaluation shows that the new method compares favorably with available alternatives. The new method has been implemented in the program RNAcast and is available on the Bielefeld Bioinformatics Server. jreeder@TechFak.Uni-Bielefeld.DE, robert@TechFak.Uni-Bielefeld.DE SUPPLEMENTARY INFORMATION: Available at http://bibiserv.techfak.uni-bielefeld.de/rnacast/supplementary.html

MARNA: multiple alignment and consensus structure prediction of RNAs based on sequence structure comparisons

Due to the importance of considering secondary structures in aligning functional RNAs, several pairwise sequence-structure alignment methods have been developed. They use extended alignment scores that evaluate secondary structure information in addition to sequence information. However, two problems for the multiple alignment step remain. First, how to combine pairwise sequence-structure alignments into a multiple alignment and second, how to generate secondary structure information for sequences whose explicit structural information is missing. We describe a novel approach for multiple alignment of RNAs (MARNA) taking into consideration both the primary and the secondary structures. It is based on pairwise sequence-structure comparisons of RNAs. From these sequence-structure alignments, libraries of weighted alignment edges are generated. The weights reflect the sequential and structural conservation. For sequences whose secondary structures are missing, the libraries are generated by sampling low energy conformations. The libraries are then processed by the T-Coffee system, which is a consistency based multiple alignment method. Furthermore, we are able to extract a consensus-sequence and -structure from a multiple alignment. We have successfully tested MARNA on several datasets taken from the Rfam database.

3D-Jury: a simple approach to improve protein structure predictions

Consensus structure prediction methods (meta-predictors) have higher accuracy than individual structure prediction algorithms (their components). The goal for the development of the 3D-Jury system is to create a simple but powerful procedure for generating meta-predictions using variable sets of models obtained from diverse sources. The resulting protocol should help to improve the quality of structural annotations of novel proteins. The 3D-Jury system generates meta-predictions from sets of models created using variable methods. It is not necessary to know prior characteristics of the methods. The system is able to utilize immediately new components (additional prediction providers). The accuracy of the system is comparable with other well-tuned prediction servers. The algorithm resembles methods of selecting models generated using ab initio folding simulations. It is simple and offers a portable solution to improve the accuracy of other protein structure prediction protocols. The 3D-Jury system is available via the Structure Prediction Meta Server (http://BioInfo.PL/Meta/) to the academic community. 3D-Jury is coupled to the continuous online server evaluation program, LiveBench (http://BioInfo.PL/LiveBench/)