RNA Secondary Structure Prediction Research Articles

Competitive Influence of Alkali Metals in the Ion Atmosphere on Nucleic Acid Duplex Stability.

The nonspecific atmosphere around nucleic acids, often termed the ion atmosphere, encompasses a collection of weak ion-nucleic acid interactions. Although nonspecific, the ion atmosphere has been shown to influence nucleic acid folding and structural stability. Studies investigating the composition of the ion atmosphere have shown competitive occupancy of the atmosphere between metal ions in the same solution. Many studies have investigated single ion effects on nucleic acid secondary structure stability; however, no comprehensive studies have investigated how the competitive occupancy of mixed ions in the ion atmosphere influences nucleic acid secondary structure stability. Here, six oligonucleotides were optically melted in buffers containing molar quantities, or mixtures, of either XCl (X = Li, K, Rb, or Cs) or NaCl. A correction factor was developed to better predict RNA duplex stability in solutions containing mixed XCl/NaCl. For solutions containing a 1:1 mixture of XCl/NaCl, one alkali metal chloride contributed more to duplex stability than the other. Overall, there was a 54% improvement in predictive capabilities with the correction factor compared with the standard 1.0 M NaCl nearest-neighbor models. This correction factor can be used in models to better predict RNA secondary structure in solutions containing mixed XCl/NaCl.

Accelerating prediction of RNA secondary structure using parallelization on multicore architecture

Accelerating prediction of RNA secondary structure using parallelization on multicore architecture

Automated design of dynamic programming schemes for RNA folding with pseudoknots

Although RNA secondary structure prediction is a textbook application of dynamic programming (DP) and routine task in RNA structure analysis, it remains challenging whenever pseudoknots come into play. Since the prediction of pseudoknotted structures by minimizing (realistically modelled) energy is NP-hard, specialized algorithms have been proposed for restricted conformation classes that capture the most frequently observed configurations. To achieve good performance, these methods rely on specific and carefully hand-crafted DP schemes. In contrast, we generalize and fully automatize the design of DP pseudoknot prediction algorithms. For this purpose, we formalize the problem of designing DP algorithms for an (infinite) class of conformations, modeled by (a finite number of) fatgraphs, and automatically build DP schemes minimizing their algorithmic complexity. We propose an algorithm for the problem, based on the tree-decomposition of a well-chosen representative structure, which we simplify and reinterpret as a DP scheme. The algorithm is fixed-parameter tractable for the treewidth tw of the fatgraph, and its output represents a O(ntw+1)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {O}}(n^{tw+1})$$\\end{document} algorithm (and even possibly O(ntw)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {O}}(n^{tw})$$\\end{document} in simple energy models) for predicting the MFE folding of an RNA of length n. We demonstrate, for the most common pseudoknot classes, that our automatically generated algorithms achieve the same complexities as reported in the literature for hand-crafted schemes. Our framework supports general energy models, partition function computations, recursive substructures and partial folding, and could pave the way for algebraic dynamic programming beyond the context-free case.

RNAknot2S: a New RNA Secondary Structure Prediction Method that Combines the Comparative Approach with Genetic Algorithms

RNAknot2S: a New RNA Secondary Structure Prediction Method that Combines the Comparative Approach with Genetic Algorithms

AliNA - a deep learning program for RNA secondary structure prediction.

Nowadays there are numerous discovered natural RNA variations participating in different cellular processes and artificial RNA, e.g., aptamers, riboswitches. One of the required tasks in the investigation of their functions and mechanism of influence on cells and interaction with targets is the prediction of RNA secondary structures. The classic thermodynamic-based prediction algorithms do not consider the specificity of biological folding and deep learning methods that were designed to resolve this issue suffer from homology-based methods problems. Herein, we present a method for RNA secondary structure prediction based on deep learning - AliNA (ALIgned Nucleic Acids). Our method successfully predicts secondary structures for non-homologous to train-data RNA families thanks to usage of the data augmentation techniques. Augmentation extends existing datasets with easily-accessible simulated data. The proposed method shows a high quality of prediction across different benchmarks including pseudoknots. The method is available on GitHub for free (https://github.com/Arty40m/AliNA).

In vivo-like nearest neighbor parameters improve prediction of fractional RNA base-pairing in cells.

We conducted a thermodynamic analysis of RNA stability in Eco80 artificial cytoplasm, which mimics in vivo conditions, and compared it to transcriptome-wide probing of mRNA. Eco80 contains 80% of Escherichia coli metabolites, with biological concentrations of metal ions, including 2 mM free Mg2+ and 29 mM metabolite-chelated Mg2+. Fluorescence-detected binding isotherms (FDBI) were used to conduct a thermodynamic analysis of 24 RNA helices and found that these helices, which have an average stability of -12.3 kcal/mol, are less stable by ΔΔGo37 ∼1 kcal/mol. The FDBI data was used to determine a set of Watson-Crick free energy nearestneighbor parameters (NNPs), which revealed that Eco80 reduces the stability of three NNPs. This information was used to adjust the NN model using the RNAstructure package. The in vivo-like adjustments have minimal effects on the prediction of RNA secondary structures determined in vitro and in silico, but markedly improve prediction of fractional RNA base pairing in E. coli, as benchmarked with our in vivo DMS and EDC RNA chemical probing data. In summary, our thermodynamic and chemical probing analyses of RNA helices indicate that RNA secondary structures are less stable in cells than in artificially stable in vitro buffer conditions.

Automatic recognition of complementary strands: lessons regarding machine learning abilities in RNA folding.

Introduction: Prediction of RNA secondary structure from single sequences still needs substantial improvements. The application of machine learning (ML) to this problem has become increasingly popular. However, ML algorithms are prone to overfitting, limiting the ability to learn more about the inherent mechanisms governing RNA folding. It is natural to use high-capacity models when solving such a difficult task, but poor generalization is expected when too few examples are available. Methods: Here, we report the relation between capacity and performance on a fundamental related problem: determining whether two sequences are fully complementary. Our analysis focused on the impact of model architecture and capacity as well as dataset size and nature on classification accuracy. Results: We observed that low-capacity models are better suited for learning with mislabelled training examples, while large capacities improve the ability to generalize to structurally dissimilar data. It turns out that neural networks struggle to grasp the fundamental concept of base complementarity, especially in lengthwise extrapolation context. Discussion: Given a more complex task like RNA folding, it comes as no surprise that the scarcity of useable examples hurdles the applicability of machine learning techniques to this field.

StemP: A fast and deterministic Stem-graph approach for RNA secondary structure prediction.

We propose a new deterministic methodology to predict the secondary structure of RNA sequences. What information of stem is important for structure prediction, and is it enough ? The proposed simple deterministic algorithm uses minimum stem length, Stem-Loop score, and co-existence of stems, to give good structure predictions for short RNA and tRNA sequences. The main idea is to consider all possible stem with certain stem loop energy and strength to predict RNA secondary structure. We use graph notation, where stems are represented as vertexes, and co-existence between stems as edges. This full Stem-graph presents all possible folding structure, and we pick sub-graph(s) which give the best matching energy for structure prediction. Stem-Loop score adds structure information and speeds up the computation. The proposed method can predict secondary structure even with pseudo knots. One of the strengths of this approach is the simplicity and flexibility of the algorithm, and it gives a deterministic answer. Numerical experiments are done on various sequences from Protein Data Bank and the Gutell Lab using a laptop and results take only a few seconds.

New evidence of consistency between phylogeny and morphology for two taxa in ciliated protists, the subclasses Oligotrichia and Choreotrichia (Protista, Ciliophora)

Marine planktonic ciliates are largely oligotrichs and choreotrichs, which are two subclasses of the class Spirotrichea. The current phylogenetic assignments of oligotrichs and choreotrichs are inconsistent with previous results based on morphological features, probably hindered by the limited information from a single gene locus. Here we provide 53 new sequences from small subunit ribosomal RNA (SSU rDNA), ITS1-5.8S rDNA-ITS2, and large subunit ribosomal RNA (LSU rDNA) gene loci in 25 oligotrich and choreotrich species. We also predict RNA secondary structures for the ITS2 regions in 55 species, 48 species of which are reported for the first time. Based on these novel data, we make a more comprehensive phylogenetic reconstruction, revealing consistency between morphological taxonomy and an updated phylogenetic system for oligotrichs and choreotrichs. With the addition of data from ciliature patterns and genes, the phylogenetic analysis of the subclass Oligotrichia suggests three evolutionary trajectories, among which: 1) Novistrombidium asserts an ancestral ciliary pattern in Oligotrichia; 2) the subgenera division of Novistrombidium and Parallelostrombidium are fully supported; 3) the three families (Tontoniidae, Pelagostrombidiidae and Cyrtostrombidiidae) all evolved from the most diverse family Strombidiidae, which explains why strombidiids consistently form polyphyletic clades. In the subclass Choreotrichia, Strombidinopsis likely possesses an ancestral position to other choreotrichs, and both phylogenetic analysis and RNA secondary structure prediction support the hypothesis that tintinnids may have evolved from Strombidinopsis. The results presented here offer an updated hypothesis for the evolutionary history of oligotrichs and choreotrichs based on new evidence obtained by expanding sampling of molecular information across multiple gene loci.

Deciphering phenotyping, DNA barcoding, and RNA secondary structure predictions in eggplant wild relatives provide insights for their future breeding strategies

Eggplant or aubergine (Solanum melongena L.) and its wild cousins, comprising 13 clades with 1500 species, have an unprecedented demand across the globe. Cultivated eggplant has a narrow molecular diversity that hinders eggplant breeding advancements. Wild eggplants need resurgent attention to broaden eggplant breeding resources. In this study, we emphasized phenotypic and genotypic discriminations among 13 eggplant species deploying chloroplast–plastid (Kim matK) and nuclear (ITS2) short gene sequences (400–800 bp) at DNA barcode region followed by ITS2 secondary structure predictions. The identification efficiency at the Kim matK region was higher (99–100%) than in the ITS2 region (80–90%). The eggplant species showed 13 unique secondary structures with a central ring with various helical orientations. Principal component analysis (PCoA) provides the descriptor–wise phenotypic clustering, which is essential for trait–specific breeding. Groups I and IV are categorized under scarlet complexes S. aethiopicum, S. trilobatum, and S. melongena (wild and cultivated). Group II represented the gboma clade (S. macrocarpon, S. wrightii, S. sisymbriifolium, and S. aculeatissimum), and group III includes S. mammosum, and S. torvum with unique fruit shape and size. The present study would be helpful in genetic discrimination, biodiversity conservation, and the safe utilization of wild eggplants.

VarGibbs Usage in the Optimization of Nearest-Neighbor Parameters and Prediction of Melting Temperature of RNA Duplexes.

The nearest-neighbor (NN) model is a general tool for the evaluation for oligonucleotide thermodynamic stability. It is primarily used for the prediction of melting temperatures but has also found use in RNA secondary structure prediction and theoretical models of hybridization kinetics. One of the key problems is to obtain the NN parameters from melting temperatures, and VarGibbs was designed to obtain those parameters directly from melting temperatures. Here we will describe the basic workflow from RNA melting temperatures to NN parameters with the use of VarGibbs. We start by a brief revision of the basic concepts of RNA hybridization and of the NN model and then show how to prepare the data files, run the parameter optimization, and interpret the results.

Mono-valent salt corrections for RNA secondary structures in the ViennaRNA package

BackgroundRNA features a highly negatively charged phosphate backbone that attracts a cloud of counter-ions that reduce the electrostatic repulsion in a concentration dependent manner. Ion concentrations thus have a large influence on folding and stability of RNA structures. Despite their well-documented effects, salt effects are not handled consistently by currently available secondary structure prediction algorithms. Combining Debye-Hückel potentials for line charges and Manning’s counter-ion condensation theory, Einert et al. (Biophys J 100: 2745-2753, 2011) modeled the energetic contributions of monovalent cations on loops and helices.ResultsThe model of Einert et al. is adapted to match the structure of the dynamic programming recursion of RNA secondary structure prediction algorithms. An empirical term describing the salt dependence of the duplex initiation energy is added to improve co-folding predictions for two or more RNA strands. The slightly modified model is implemented in the ViennaRNA package in such way that only the energy parameters but not the algorithmic structure is affected. A comparison with data from the literature show that predicted free energies and melting temperatures are in reasonable agreement with experiments.ConclusionThe new feature in the ViennaRNA package makes it possible to study effects of salt concentrations on RNA folding in a systematic manner. Strictly speaking, the model pertains only to mono-valent cations, and thus covers the most important parameter, i.e., the NaCl concentration. It remains a question for future research to what extent unspecific effects of bi- and tri-valent cations can be approximated in a similar manner.AvailabilityCorrections for the concentration of monovalent cations are available in the ViennaRNA package starting from version 2.6.0.

Local RNA folding revisited.

Most of the functional RNA elements located within large transcripts are local. Local folding therefore serves a practically useful approximation to global structure prediction. Due to the sensitivity of RNA secondary structure prediction to the exact definition of sequence ends, accuracy can be increased by averaging local structure predictions over multiple, overlapping sequence windows. These averages can be computed efficiently by dynamic programming. Here we revisit the local folding problem, present a concise mathematical formalization that generalizes previous approaches and show that correct Boltzmann samples can be obtained by local stochastic backtracing in McCaskill's algorithms but not from local folding recursions. Corresponding new features are implemented in the ViennaRNA package to improve the support of local folding. Applications include the computation of maximum expected accuracy structures from RNAplfold data and a mutual information measure to quantify the sensitivity of individual sequence positions.

GCNfold: A novel lightweight model with valid extractors for RNA secondary structure prediction

RNA secondary structure is essential for predicting the tertiary structure and understanding RNA function. Recent research tends to stack numerous modules to design large deep-learning models. This can increase the accuracy to more than 70%, as well as significant training costs and prediction efficiency. We proposed a model with three feature extractors called GCNfold. Structure Extractor utilizes a three-layer Graph Convolutional Network (GCN) to mine the structural information of RNA, such as stems, hairpin, and internal loops. Structure and Sequence Fusion embeds structural information into sequences with Transformer Encoders. Long-distance Dependency Extractor captures long-range pairwise relationships by UNet. The experiments indicate that GCNfold has a small number of parameters, a fast inference speed, and a high accuracy among all models with over 80% accuracy. Additionally, GCNfold-Small takes only 90ms to infer an RNA secondary structure and can achieve close to 90% accuracy on average. The GCNfold code is available on Github https://github.com/EnbinYang/GCNfold.

RNA Secondary Structure Analysis Using RNAstructure.

RNAstructure is a user-friendly program for the prediction and analysis of RNA secondary structure. It is available as a web server, a program with a graphical user interface, or a set of command line tools. The programs are available for Microsoft Windows, macOS, or Linux. This article provides protocols for prediction of RNA secondary structure (using the web server, the graphical user interface, or the command line) and high-affinity oligonucleotide binding sites to a structured RNA target (using the graphical user interface). © 2023 Wiley Periodicals LLC. Basic Protocol 1: Predicting RNA secondary structure using the RNAstructure web server Alternate Protocol 1: Predicting secondary structure and base pair probabilities using the RNAstructure graphical user interface Alternate Protocol 2: Predicting secondary structure and base pair probabilities using the RNAstructure command line interface Basic Protocol 2: Predicting binding affinities of oligonucleotides complementary to an RNA target using OligoWalk.

Comparative Analysis of CRISPR-Cas Systems in Pseudomonas Genomes.

Pseudomonas is a bacterial genus with some saprophytic species from land and others associated with opportunistic infections in humans and animals. Factors such as pathogenicity or metabolic aspects have been related to CRISPR-Cas, and in silico studies into it have focused more on the clinical and non-environmental setting. This work aimed to perform an in silico analysis of the CRISPR-Cas systems present in Pseudomonas genomes. It analyzed 275 complete genomic sequences of Pseudomonas taken from the NCBI database. CRISPR loci were obtained from CRISPRdb. The genes associated with CRISPR (cas) and CAS proteins, and the origin and diversity of spacer sequences, were identified and compared by BLAST. The presence of self-targeting sequences, PAMs, and the conservation of DRs were visualized using WebLogo 3.6. The CRISPR-like RNA secondary structure prediction was analyzed using RNAFold and MFold. CRISPR structures were identified in 19.6% of Pseudomonas species. In all, 113 typical CRISPR arrays with 18 putative cas were found, as were 2050 spacers, of which 52% showed homology to bacteriophages, 26% to chromosomes, and 22% to plasmids. No potential self-targeting was detected within the CRISPR array. All the found DRs can form thermodynamically stable secondary RNA structures. The comparison of the CRISPR/Cas system can help understand the environmental adaptability of each evolutionary lineage of clinically and environmentally relevant species, providing data support for bacterial typing, traceability, analysis, and exploration of unconventional CRISPR.

Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery.

Computational analysis of RNA sequences constitutes a crucial step in the field of RNA biology. As in other domains of the life sciences, the incorporation of artificial intelligence and machine learning techniques into RNA sequence analysis has gained significant traction in recent years. Historically, thermodynamics-based methods were widely employed for the prediction of RNA secondary structures; however, machine learning-based approaches have demonstrated remarkable advancements in recent years, enabling more accurate predictions. Consequently, the precision of sequence analysis pertaining to RNA secondary structures, such as RNA-protein interactions, has also been enhanced, making a substantial contribution to the field of RNA biology. Additionally, artificial intelligence and machine learning are also introducing technical innovations in the analysis of RNA-small molecule interactions for RNA-targeted drug discovery and in the design of RNA aptamers, where RNA serves as its own ligand. This review will highlight recent trends in the prediction of RNA secondary structure, RNA aptamers and RNA drug discovery using machine learning, deep learning and related technologies, and will also discuss potential future avenues in the field of RNA informatics.

A rapid and efficient platform for antiviral crRNA screening using CRISPR-Cas13a-based nucleic acid detection.

Rapid and high-throughput screening of antiviral clustered regularly interspaced short palindromic repeat (CRISPR) RNAs (crRNAs) is urgently required for the CRISPR-Cas13a antiviral system. Based on the same principle, we established an efficient screening platform for antiviral crRNA through CRISPR-Cas13a nucleic acid detection. In this study, crRNAs targeting PA, PB1, NP, and PB2 of the influenza A virus (H1N1) were screened using CRISPR-Cas13a nucleic acid detection, and their antiviral effects were confirmed by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The RNA secondary structures were predicted by bioinformatics methods. The results showed that crRNAs screened by CRISPR-Cas13a nucleic acid detection could effectively inhibit viral RNA in mammalian cells. Besides, we found that this platform for antiviral crRNA screening was more accurate than RNA secondary structure prediction. In addition, we validated the feasibility of the platform by screening crRNAs targeting NS of the influenza A virus (H1N1). This study provides a new approach for screening antiviral crRNAs and contributes to the rapid advancement of the CRISPR-Cas13a antiviral system.

E-RNA: a collection of web-servers for the prediction and visualisation of RNA secondary structure and their functional features.

e-RNA is a collection of web-servers for the prediction and visualisation of RNA secondary structures and their functional features, including in particular RNA-RNA interactions. In this updated version, we have added novel tools for RNA secondary structure prediction and have significantly updated the visualisation functionality. The new method CoBold can identify transient RNA structure features and their potential functional effects on a known RNA structure during co-transcriptional structure formation. New tool ShapeSorter can predict evolutionarily conserved RNA secondary structure features while simultaneously taking experimental SHAPE probing evidence into account. The web-server R-Chie which visualises RNA secondary structure information in terms of arc diagrams, can now be used to also visualise and intuitively compare RNA-RNA, RNA-DNA and DNA-DNA interactions alongside multiple sequence alignments and quantitative information. The prediction generated by any method in e-RNA can be readily visualised on the web-server. For completed tasks, users can download their results and readily visualise them later on with R-Chie without having to re-run the predictions. e-RNA can be found at http://www.e-rna.org.

Machine learning for RNA 2D structure prediction benchmarked on experimental data

Since the 1980s, dozens of computational methods have addressed the problem of predicting RNA secondary structure. Among them are those that follow standard optimization approaches and, more recently, machine learning (ML) algorithms. The former were repeatedly benchmarked on various datasets. The latter, on the other hand, have not yet undergone extensive analysis that could suggest to the user which algorithm best fits the problem to be solved. In this review, we compare 15 methods that predict the secondary structure of RNA, of which 6 are based on deep learning (DL), 3 on shallow learning (SL) and 6 control methods on non-ML approaches. We discuss the ML strategies implemented and perform three experiments in which we evaluate the prediction of (I) representatives of the RNA equivalence classes, (II) selected Rfam sequences and (III) RNAs from new Rfam families. We show that DL-based algorithms (such as SPOT-RNA and UFold) can outperform SL and traditional methods if the data distribution is similar in the training and testing set. However, when predicting 2D structures for new RNA families, the advantage of DL is no longer clear, and its performance is inferior or equal to that of SL and non-ML methods.