Recent advances in RNA folding

Sequence disorder-induced first order phase transition in confined polyelectrolytes

We consider a statistical mechanical model of a generic flexible polyelectrolyte, comprised of identically charged monomers with long-range electrostatic interactions and short-range interactions quantified by a disorder field along the polymer contour sequence, which is randomly quenched. The free energy and the monomer density profile of the system for no electrolyte screening are calculated in the case of a system composed of two infinite planar bounding surfaces with an intervening oppositely charged polyelectrolyte chain. We show that the effect of the contour sequence disorder, mediated by short-range interactions, leads to an enhanced localization of the polyelectrolyte chain and a first order phase transition at a critical value of the inter-surface spacing. This phase transition results in an abrupt change of the pressure from negative to positive values, effectively eliminating polyelectrolyte mediated bridging attraction.

siRNADiscovery: a graph neural network for siRNA efficacy prediction via deep RNA sequence analysis

The clinical adoption of small interfering RNAs (siRNAs) has prompted the development of various computational strategies for siRNA design, from traditional data analysis to advanced machine learning techniques. However, previous studies have inadequately considered the full complexity of the siRNA silencing mechanism, neglecting critical elements such as siRNA positioning on mRNA, RNA base-pairing probabilities, and RNA-AGO2 interactions, thereby limiting the insight and accuracy of existing models. Here, we introduce siRNADiscovery, a Graph Neural Network (GNN) framework that leverages both non-empirical and empirical rule-based features of siRNA and mRNA to effectively capture the complex dynamics of gene silencing. On multiple internal datasets, siRNADiscovery achieves state-of-the-art performance. Significantly, siRNADiscovery also outperforms existing methodologies in in vitro studies and on an externally validated dataset. Additionally, we develop a new data-splitting methodology that addresses the data leakage issue, a frequently overlooked problem in previous studies, ensuring the robustness and stability of our model under various experimental settings. Through rigorous testing, siRNADiscovery has demonstrated remarkable predictive accuracy and robustness, making significant contributions to the field of gene silencing. Furthermore, our approach to redefining data-splitting standards aims to set new benchmarks for future research in the domain of predictive biological modeling for siRNA.

Targeting non-coding RNAs: Perspectives and challenges of in-silico approaches

The growing information currently available on the central role of non-coding RNAs (ncRNAs) including microRNAs (miRNAS) and long non-coding RNAs (lncRNAs) for chronic and degenerative human diseases makes them attractive therapeutic targets. RNAs carry out different functional roles in human biology and are deeply deregulated in several diseases. So far, different attempts to therapeutically target the 3D RNA structures with small molecules have been reported. In this scenario, the development of computational tools suitable for describing RNA structures and their potential interactions with small molecules is gaining more and more interest. Here, we describe the most suitable strategies to study ncRNAs through computational tools. We focus on methods capable of predicting 2D and 3D ncRNA structures. Furthermore, we describe computational tools to identify, design and optimize small molecule ncRNA binders. This review aims to outline the state of the art and perspectives of computational methods for ncRNAs over the past decade.

TFOFinder: Python program for identifying purine-only double-stranded stretches in the predicted secondary structure(s) of RNA targets

Nucleic acid probes are valuable tools in biology and chemistry and are indispensable for PCR amplification of DNA, RNA quantification and visualization, and downregulation of gene expression. Recently, triplex-forming oligonucleotides (TFO) have received increased attention due to their improved selectivity and sensitivity in recognizing purine-rich double-stranded RNA regions at physiological pH by incorporating backbone and base modifications. For example, triplex-forming peptide nucleic acid (PNA) oligomers have been used for imaging a structured RNA in cells and inhibiting influenza A replication. Although a handful of programs are available to identify triplex target sites (TTS) in DNA, none are available that find such regions in structured RNAs. Here, we describe TFOFinder, a Python program that facilitates the identification of intramolecular purine-only RNA duplexes that are amenable to forming parallel triple helices (pyrimidine/purine/pyrimidine) and the design of the corresponding TFO(s). We performed genome- and transcriptome-wide analyses of TTS in Drosophila melanogaster and found that only 0.3% (123) of total unique transcripts (35,642) show the potential of forming 12-purine long triplex forming sites that contain at least one guanine. Using minimization algorithms, we predicted the secondary structure(s) of these transcripts, and using TFOFinder, we found that 97 (79%) of the identified 123 transcripts are predicted to fold to form at least one TTS for parallel triple helix formation. The number of transcripts with potential purine TTS increases when the strict search conditions are relaxed by decreasing the length of the probe or by allowing up to two pyrimidine inversions or 1-nucleotide bulge in the target site. These results are encouraging for the use of modified triplex forming probes for live imaging of endogenous structured RNA targets, such as pre-miRNAs, and inhibition of target-specific translation and viral replication.

Sequence similarity governs generalizability of de novo deep learning models for RNA secondary structure prediction

Making no use of physical laws or co-evolutionary information, de novo deep learning (DL) models for RNA secondary structure prediction have achieved far superior performances than traditional algorithms. However, their statistical underpinning raises the crucial question of generalizability. We present a quantitative study of the performance and generalizability of a series of de novo DL models, with a minimal two-module architecture and no post-processing, under varied similarities between seen and unseen sequences. Our models demonstrate excellent expressive capacities and outperform existing methods on common benchmark datasets. However, model generalizability, i.e., the performance gap between the seen and unseen sets, degrades rapidly as the sequence similarity decreases. The same trends are observed from several recent DL and machine learning models. And an inverse correlation between performance and generalizability is revealed collectively across all learning-based models with wide-ranging architectures and sizes. We further quantitate how generalizability depends on sequence and structure identity scores via pairwise alignment, providing unique quantitative insights into the limitations of statistical learning. Generalizability thus poses a major hurdle for deploying de novo DL models in practice and various pathways for future advances are discussed.

Ultrasensitive Hybridization Chain Reaction-Assisted Multisite Exonuclease III Amplification Strategy Combined with a Direct Quantitative Fluorescence Lateral Flow Technique for Multiple Bacterial 16S rRNA Detection

Accurate and in-time detection of bacteria conduces to preventing their rapid spread around the environment, while a nucleic acid test (NAT) is a powerful tool for early diagnosis of pathogens. Herein, we propose a hybridization chain reaction (HCR)-mediated multisite exonuclease III (Exo-III) amplification strategy (HCR/Exo-III amplifier) to achieve the one-pot and ultrasensitive isothermal amplification of bacterial 16S rRNA and a portable fluorescence detection device (PFD) to directly read signals in a lateral flow assay (LFA). In detail, the target-initiated HCR products present multiple binding sites for triggering the Exo-III amplifier that produces numerous target amplicons. Following that, the target amplicons travel up on the strip and bridge between the DNA-CdTe/CdS probes and the capture DNA to form a positive fluorescence line. After that, the strip is inserted into the PFD to accomplish the fluorescence signal reading. The constructed HCR/Exo-III amplifier-based PFD-LFA implemented the simultaneous and specific detection of three bacteria with a detection limit of a few tenths of fM for synthetic 16S rRNA fragments and dozens of CFU/mL for Staphylococcus aureus, Listeria monocytogenes, and Salmonella typhimurium in pure cultures. The sensing platform features isothermal amplification, convenient operation, and good economy, displaying great potential for on-site testing toward multiple nucleic acid analytes.

DNA Nanomachine (DNM) Biplex Assay for Differentiating Bacillus cereus Species

Conventional methods for the detection and differentiation of Bacillus cereus group species have drawbacks mostly due to the complexity of genetic discrimination between the Bacillus cereus species. Here, we describe a simple and straightforward assay based on the detected unamplified bacterial 16S rRNA by DNA nanomachine (DNM). The assay uses a universal fluorescent reporter and four all-DNA binding fragments, three of which are responsible for "opening up" the folded rRNA while the fourth stand is responsible for detecting single nucleotide variation (SNV) with high selectivity. Binding of the DNM to 16S rRNA results in the formation of the 10-23 deoxyribozyme catalytic core that cleaves the fluorescent reporter and produces a signal, which is amplified over time due to catalytic turnover. This developed biplex assay enables the detection of B. thuringiensis 16S rRNA at fluorescein and B. mycoides at Cy5 channels with a limit of detection of 30 × 10³ and 35 × 10³ CFU/mL, respectively, after 1.5 h with a hands-on time of ~10 min. The new assay may simplify the analysis of biological RNA samples and might be useful for environmental monitoring as a simple and inexpensive alternative to amplification-based nucleic acid analysis. The DNM proposed here may become an advantageous tool for detecting SNV in clinically significant DNA or RNA samples and can easily differentiate SNV under broadly variable experimental conditions and without prior amplification.

Computer Aided Development of Nucleic Acid Applications in Nanotechnologies

Utilization of nucleic acids (NAs) in nanotechnologies and nanotechnology-related applications is a growing field with broad application potential, ranging from biosensing up to targeted cell delivery. Computer simulations are useful techniques that can aid design and speed up development in this field. This review focuses on computer simulations of hybrid nanomaterials composed of NAs and other components. Current state-of-the-art molecular dynamics simulations, empirical force fields (FFs), and coarse-grained approaches for the description of deoxyribonucleic acid and ribonucleic acid are critically discussed. Challenges in combining biomacromolecular and nanomaterial FFs are emphasized. Recent applications of simulations for modeling NAs and their interactions with nano- and biomaterials are overviewed in the fields of sensing applications, targeted delivery, and NA templated materials. Future perspectives of development are also highlighted.

RAFFT: Efficient prediction of RNA folding pathways using the fast Fourier transform

We propose a novel heuristic to predict RNA secondary structure formation pathways that has two components: (i) a folding algorithm and (ii) a kinetic ansatz. This heuristic is inspired by the kinetic partitioning mechanism, by which molecules follow alternative folding pathways to their native structure, some much faster than others. Similarly, our algorithm RAFFT starts by generating an ensemble of concurrent folding pathways ending in multiple metastable structures, which is in contrast with traditional thermodynamic approaches that find single structures with minimal free energies. When we constrained the algorithm to predict only 50 structures per sequence, near-native structures were found for RNA molecules of length ≤ 200 nucleotides. Our heuristic has been tested on the coronavirus frameshifting stimulation element (CFSE): an ensemble of 68 distinct structures allowed us to produce complete folding kinetic trajectories, whereas known methods require evaluating millions of sub-optimal structures to achieve this result. Thanks to the fast Fourier transform on which RAFFT (RNA folding Algorithm wih Fast Fourier Transform) is based, these computations are efficient, with complexity O ( L 2logL ).

Secondary structure prediction of long noncoding RNA: review and experimental comparison of existing approaches

MOTIVATION

In contrast to messenger RNAs, the function of the wide range of existing long noncoding RNAs (lncRNAs) largely depends on their structure, which determines interactions with partner molecules. Thus, the determination or prediction of the secondary structure of lncRNAs is critical to uncover their function. Classical approaches for predicting RNA secondary structure have been based on dynamic programming and thermodynamic calculations. In the last 4 years, a growing number of machine learning (ML)-based models, including deep learning (DL), have achieved breakthrough performance in structure prediction of biomolecules such as proteins and have outperformed classical methods in short transcripts folding. Nevertheless, the accurate prediction for lncRNA still remains far from being effectively solved. Notably, the myriad of new proposals has not been systematically and experimentally evaluated.

RESULTS

In this work, we compare the performance of the classical methods as well as the most recently proposed approaches for secondary structure prediction of RNA sequences using a unified and consistent experimental setup. We use the publicly available structural profiles for 3023 yeast RNA sequences, and a novel benchmark of well-characterized lncRNA structures from different species. Moreover, we propose a novel metric to assess the predictive performance of methods, exclusively based on the chemical probing data commonly used for profiling RNA structures, avoiding any potential bias incorporated by computational predictions when using dot-bracket references. Our results provide a comprehensive comparative assessment of existing methodologies, and a novel and public benchmark resource to aid in the development and comparison of future approaches.

AVAILABILITY

Full source code and benchmark datasets are available at: https://github.com/sinc-lab/lncRNA-folding.

CONTACT

lbugnon@sinc.unl.edu.ar.

Fast free-energy-based neutral set size estimates for the RNA genotype-phenotype map

The genotype-phenotype (GP) map of RNA secondary structure links each RNA sequence to its corresponding secondary structure. Previous research has shown that the large-scale structural properties of GP maps, such as the size of neutral sets in genotype space, can influence evolutionary outcomes. In order to use neutral set sizes, efficient and accurate computational methods are needed to compute them. Here, we propose a new method, which is based on free energy estimates and is much faster than existing sample-based methods. Moreover, this approach can give insight into the reasons behind neutral set size variations, for example, why structures with fewer stacks tend to have larger neutral set sizes. In addition, we generalize neutral set size calculations from the previously studied many-to-one framework, where each sequence folds into a single energetically preferred structure, to a fuller many-to-many framework, where several low-energy structures are included. We find that structures with high neutral sets in one framework also tend to have large neutral sets in the other framework for a range of parameters and thus the choice of GP map does not fundamentally affect which structures have the largest neutral set sizes.

The helper oligonucleotides enable detection of folded single-stranded DNA by lateral flow immunoassay after HCR signal amplification

A combination of Hybridization Chain Reaction (HCR) and Lateral Flow Immunoassay (LFIA) is an attractive strategy for a simple signal amplification DNA/RNA detection. The present study aimed to report a strategy used to solve a problem encountered when the target DNA contained folded secondary structure during HCR, enabling HCR hairpin probes to easily access the target site. The 24-nt conserved sequence within 3'-UTR, present only in dengue virus genome but not in other species, is an ideal target to use as a probe binding site for pan-dengue virus detection. Thus, the 105-nt target containing the 24-nt target sequence was chosen as a target with secondary structures. The 24-nucleotide (nt) synthetic target DNA successfully induced HCR reaction within 5 min at room temperature. However, the HCR detection of the 105-nt synthetic target DNA with secondary structures was problematic. The probe hybridization was prevented by the secondary structures of the target, resulting in a failure to generate HCR product. To solve this problem, two helper oligonucleotides (helper1 and helper2) were designed to linearize the folded structure of the 105-nt target through strand-displacement mechanism, allowing the HCR hairpin probes to easily access the target site. The HCR product with the labeled helper oligonucleotides and the labeled probes were successfully detected by LFIA. With this strategy, the combination of the helper-enhanced HCR and LFIA exhibited a limit of detection (LOD) in a nanomolar range of the 105-nt DENV synthetic target DNA. Our study demonstrated that signal amplification by the combination of HCR and LFIA could successfully detect the target DNA with secondary structure, but not target RNA with secondary structure. In summary, this work provided a proof of concept of two main issues including probe hybridization enhancement by helper oligonucleotide for the target with complicated secondary structure and the advantage of a combination of labeled helper and HCR probes design for LFIA to overcome the false positive result from HCR probe leakage. Our findings on the use of helper oligonucleotides may be beneficial for the development of other isothermal amplification, since the secondary structure of the target is one of the major obstacles among hybridization-based methods.

NMR-derived secondary structure of the full-length Ox40 mRNA 3'UTR and its multivalent binding to the immunoregulatory RBP Roquin

Control of posttranscriptional mRNA decay is a crucial determinant of cell homeostasis and differentiation. mRNA lifetime is governed by cis-regulatory elements in their 3' untranslated regions (UTR). Despite ongoing progress in the identification of cis elements we have little knowledge about the functional and structural integration of multiple elements in 3'UTR regulatory hubs and their recognition by mRNA-binding proteins (RBPs). Structural analyses are complicated by inconsistent mapping and prediction of RNA fold, by dynamics, and size. We here, for the first time, provide the secondary structure of a complete mRNA 3'UTR. We use NMR spectroscopy in a divide-and-conquer strategy complemented with SAXS, In-line probing and SHAPE-seq applied to the 3'UTR of Ox40 mRNA, which encodes a T-cell co-receptor repressed by the protein Roquin. We provide contributions of RNA elements to Roquin-binding. The protein uses its extended bi-modal ROQ domain to sequentially engage in a 2:1 stoichiometry with a 3'UTR core motif. We observe differential binding of Roquin to decay elements depending on their structural embedment. Our data underpins the importance of studying RNA regulation in a full sequence and structural context. This study serves as a paradigm for an approach in analysing structured RNA-regulatory hubs and their binding by RBPs.

RNA folding using quantum computers

The 3-dimensional fold of an RNA molecule is largely determined by patterns of intramolecular hydrogen bonds between bases. Predicting the base pairing network from the sequence, also referred to as RNA secondary structure prediction or RNA folding, is a nondeterministic polynomial-time (NP)-complete computational problem. The structure of the molecule is strongly predictive of its functions and biochemical properties, and therefore the ability to accurately predict the structure is a crucial tool for biochemists. Many methods have been proposed to efficiently sample possible secondary structure patterns. Classic approaches employ dynamic programming, and recent studies have explored approaches inspired by evolutionary and machine learning algorithms. This work demonstrates leveraging quantum computing hardware to predict the secondary structure of RNA. A Hamiltonian written in the form of a Binary Quadratic Model (BQM) is derived to drive the system toward maximizing the number of consecutive base pairs while jointly maximizing the average length of the stems. A Quantum Annealer (QA) is compared to a Replica Exchange Monte Carlo (REMC) algorithm programmed with the same objective function, with the QA being shown to be highly competitive at rapidly identifying low energy solutions. The method proposed in this study was compared to three algorithms from literature and, despite its simplicity, was found to be competitive on a test set containing known structures with pseudoknots.

The recent FDA approval of mRNA-based vaccines has increased public interest in synthetically designed RNA molecules. RNA molecules fold into complex secondary structures which determine their molecular properties and in part their efficacy. Determining the folded structure of an RNA molecule is a computationally challenging task with exponential scaling that is intractable to solve exactly, and therefore approximate methods are used. Quantum computing technology offers a new approach to finding approximate solutions to problems with exponential scaling. We formulate a simplistic, yet effective, model of RNA folding that can easily be mapped to quantum computers and we show that currently available quantum computing hardware is competitive with classical methods.

UFold: fast and accurate RNA secondary structure prediction with deep learning

For many RNA molecules, the secondary structure is essential for the correct function of the RNA. Predicting RNA secondary structure from nucleotide sequences is a long-standing problem in genomics, but the prediction performance has reached a plateau over time. Traditional RNA secondary structure prediction algorithms are primarily based on thermodynamic models through free energy minimization, which imposes strong prior assumptions and is slow to run. Here, we propose a deep learning-based method, called UFold, for RNA secondary structure prediction, trained directly on annotated data and base-pairing rules. UFold proposes a novel image-like representation of RNA sequences, which can be efficiently processed by Fully Convolutional Networks (FCNs). We benchmark the performance of UFold on both within- and cross-family RNA datasets. It significantly outperforms previous methods on within-family datasets, while achieving a similar performance as the traditional methods when trained and tested on distinct RNA families. UFold is also able to predict pseudoknots accurately. Its prediction is fast with an inference time of about 160 ms per sequence up to 1500 bp in length. An online web server running UFold is available at https://ufold.ics.uci.edu. Code is available at https://github.com/uci-cbcl/UFold.

Review of machine learning methods for RNA secondary structure prediction

Secondary structure plays an important role in determining the function of noncoding RNAs. Hence, identifying RNA secondary structures is of great value to research. Computational prediction is a mainstream approach for predicting RNA secondary structure. Unfortunately, even though new methods have been proposed over the past 40 years, the performance of computational prediction methods has stagnated in the last decade. Recently, with the increasing availability of RNA structure data, new methods based on machine learning (ML) technologies, especially deep learning, have alleviated the issue. In this review, we provide a comprehensive overview of RNA secondary structure prediction methods based on ML technologies and a tabularized summary of the most important methods in this field. The current pending challenges in the field of RNA secondary structure prediction and future trends are also discussed.

Motif Discovery from CLIP Experiments

RNA primary and secondary motif discovery is an important step in the annotation and characterization of unknown interaction dynamics between RNAs and RNA-Binding Proteins, and several methods have been developed to meet the need of fast and efficient discovery of interaction motifs. Recent advances have increased the amount of data produced by experimental assays and there is no available method suitable for the analysis of all type of results. Here we present a simple workflow to help choosing the more appropriate method, depending on the starting situation, among the three algorithms that best cover the landscape of approaches. A detailed analysis is presented to highlight the need for different algorithms in different working settings. In conclusion, the proposed workflow depends on the nature of the starting data and on the availability of RNA annotations.

Alignments of biomolecular contact maps

Alignments of discrete objects can be constructed in a very general setting as super-objects from which the constituent objects are recovered by means of projections. Here, we focus on contact maps, i.e. undirected graphs with an ordered set of vertices. These serve as natural discretizations of RNA and protein structures. In the general case, the alignment problem for vertex-ordered graphs is NP-complete. In the special case of RNA secondary structures, i.e. crossing-free matchings, however, the alignments have a recursive structure. The alignment problem then can be solved by a variant of the Sankoff algorithm in polynomial time. Moreover, the tree or forest alignments of RNA secondary structure can be understood as the alignments of ordered edge sets.

Cotranscriptional Kinetic Folding of RNA Secondary Structures Including Pseudoknots

Computational prediction of ribonucleic acid (RNA) structures is an important problem in computational structural biology. Studies of RNA structure formation often assume that the process starts from a fully synthesized sequence. Experimental evidence, however, has shown that RNA folds concurrently with its elongation. We investigate RNA secondary structure formation, including pseudoknots, that takes into account the cotranscriptional effects. We propose a single-nucleotide resolution kinetic model of the folding process of RNA molecules, where the polymerase-driven elongation of an RNA strand by a new nucleotide is included as a primitive operation, together with a stochastic simulation method that implements this folding concurrently with the transcriptional synthesis. Numerical case studies show that our cotranscriptional RNA folding model can predict the formation of conformations that are favored in actual biological systems. Our new computational tool can thus provide quantitative predictions and offer useful insights into the kinetics of RNA folding.

How We Think about Targeting RNA with Small Molecules

RNA offers nearly unlimited potential as a target for small molecule chemical probes and lead medicines. Many RNAs fold into structures that can be selectively targeted with small molecules. This Perspective discusses molecular recognition of RNA by small molecules and highlights key enabling technologies and properties of bioactive interactions. Sequence-based design of ligands targeting RNA has established rules for affecting RNA targets and provided a potentially general platform for the discovery of bioactive small molecules. The RNA targets that contain preferred small molecule binding sites can be identified from sequence, allowing identification of off-targets and prediction of bioactive interactions by nature of ligand recognition of functional sites. Small molecule targeted degradation of RNA targets (ribonuclease-targeted chimeras, RIBOTACs) and direct cleavage by small molecules have also been developed. These growing technologies suggest that the time is right to provide small molecule chemical probes to target functionally relevant RNAs throughout the human transcriptome.

Aptamer-Based Affinity Chromatography for Protein Extraction and Purification

Aptamers are oligonucleotide molecules able to recognize very specifically proteins. Among the possible applications, aptamers have been used for affinity chromatography with effective results and advantages over most advanced protein separation technologies. This chapter first discusses the context of the affinity chromatography with aptamer ligands. With the adaptation of SELEX, the chemical modifications of aptamers to comply with the covalent coupling and the separation process are then extensively presented. A focus is then made about the most important applications for protein separation with real-life examples and the comparison with immunoaffinity chromatography. In spite of well-advanced demonstrations and the extraordinary potential developments, a significant optimization work is still due to deserve large-scale applications with all necessary validations. Graphical Abstract Aptamer-protein complexes by X-ray crystallography.

RNA substructure as a random matrix ensemble

Combinatorial analysis of a certain abstraction of RNA structures has been studied to investigate their statistics. Our approach regards the backbone of secondary structures as an alternate sequence of paired and unpaired sets of nucleotides, which can be described by a random matrix model. We obtain the generating function of the structures using the Hermitian matrix model with the Chebyshev polynomial of the second kind and analyze the statistics with respect to the number of stems. To match the experimental findings of the statistical behavior, we consider the structures in a grand canonical ensemble and find a fugacity value corresponding to an appropriate number of stems.

RNAconTest: comparing tools for noncoding RNA multiple sequence alignment based on structural consistency

The importance of noncoding RNA sequences has become increasingly clear over the past decade. New RNA families are often detected and analyzed using comparative methods based on multiple sequence alignments. Accordingly, a number of programs have been developed for aligning and deriving secondary structures from sets of RNA sequences. Yet, the best tools for these tasks remain unclear because existing benchmarks contain too few sequences belonging to only a small number of RNA families. RNAconTest (RNA consistency test) is a new benchmarking approach relying on the observation that secondary structure is often conserved across highly divergent RNA sequences from the same family. RNAconTest scores multiple sequence alignments based on the level of consistency among known secondary structures belonging to reference sequences in their output alignment. Similarly, consensus secondary structure predictions are scored according to their agreement with one or more known structures in a family. Comparing the performance of 10 popular alignment programs using RNAconTest revealed that DAFS, DECIPHER, LocARNA, and MAFFT created the most structurally consistent alignments. The best consensus secondary structure predictions were generated by DAFS and LocARNA (via RNAalifold). Many of the methods specific to noncoding RNAs exhibited poor scalability as the number or length of input sequences increased, and several programs displayed substantial declines in score as more sequences were aligned. Overall, RNAconTest provides a means of testing and improving tools for comparative RNA analysis, as well as highlighting the best available approaches. RNAconTest is available from the DECIPHER website (http://DECIPHER.codes/Downloads.html).

Viral RNA structure analysis using DMS-MaPseq

RNA structure is critically important to RNA viruses in every part of the replication cycle. RNA structure is also utilized by DNA viruses in order to regulate gene expression and interact with host factors. Advances in next-generation sequencing have greatly enhanced the utility of chemical probing in order to analyze RNA structure. This review will cover some recent viral RNA structural studies using chemical probing and next-generation sequencing as well as the advantages of dimethyl sulfate (DMS)-mutational profiling and sequencing (MaPseq). DMS-MaPseq is a robust assay that can easily modify RNA in vitro, in cell and in virion. A detailed protocol for whole-genome DMS-MaPseq from cells transfected with HIV-1 and the structure of TAR as determined by DMS-MaPseq is presented. DMS-MaPseq has the ability to answer a variety of integral questions about viral RNA, including how they change in different environments and when interacting with different host factors.

rPredictorDB: a predictive database of individual secondary structures of RNAs and their formatted plots

Secondary data structure of RNA molecules provides insights into the identity and function of RNAs. With RNAs readily sequenced, the question of their structural characterization is increasingly important. However, RNA structure is difficult to acquire. Its experimental identification is extremely technically demanding, while computational prediction is not accurate enough, especially for large structures of long sequences. We address this difficult situation with rPredictorDB, a predictive database of RNA secondary structures that aims to form a middle ground between experimentally identified structures in PDB and predicted consensus secondary structures in Rfam. The database contains individual secondary structures predicted using a tool for template-based prediction of RNA secondary structure for the homologs of the RNA families with at least one homolog with experimentally solved structure. Experimentally identified structures are used as the structural templates and thus the prediction has higher reliability than de novo predictions in Rfam. The sequences are downloaded from public resources. So far rPredictorDB covers 7365 RNAs with their secondary structures. Plots of the secondary structures use the Traveler package for readable display of RNAs with long sequences and complex structures, such as ribosomal RNAs. The RNAs in the output of rPredictorDB are extensively annotated and can be viewed, browsed, searched and downloaded according to taxonomic, sequence and structure data. Additionally, structure of user-provided sequences can be predicted using the templates stored in rPredictorDB.

Reporter-recruiting bifunctional aptasensor for bioluminescent analytical assays

A novel structure-switching bioluminescent 2′-F-RNA aptasensor consists of analyte-binding and obelin-recruiting modules, joined into a bi-specific aptamer construct.

It's a small, small world: unravelling the role and evolution of small RNAs in organelle and endosymbiont genomes

Organelles and host-restricted bacterial symbionts are characterized by having highly reduced genomes that lack many key regulatory genes and elements. Thus, it has been hypothesized that the eukaryotic nuclear genome is primarily responsible for regulating these symbioses. However, with the discovery of organelle- and symbiont-expressed small RNAs (sRNAs) there is emerging evidence that these sRNAs may play a role in gene regulation as well. Here, we compare the diversity of organelle and bacterial symbiont sRNAs recently identified using genome-enabled '-omic' technologies and discuss their potential role in gene regulation. We also discuss how the genome architecture of small genomes may influence the evolution of these sRNAs and their potential function. Additionally, these new studies suggest that some sRNAs are conserved within organelle and symbiont taxa and respond to changes in the environment and/or their hosts. In summary, these results suggest that organelle and symbiont sRNAs may play a role in gene regulation in addition to nuclear-encoded host mechanisms.

The de.NBI / ELIXIR-DE training platform - Bioinformatics training in Germany and across Europe within ELIXIR

The German Network for Bioinformatics Infrastructure (de.NBI) is a national and academic infrastructure funded by the German Federal Ministry of Education and Research (BMBF). The de.NBI provides (i) service, (ii) training, and (iii) cloud computing to users in life sciences research and biomedicine in Germany and Europe and (iv) fosters the cooperation of the German bioinformatics community with international network structures. The de.NBI members also run the German node (ELIXIR-DE) within the European ELIXIR infrastructure. The de.NBI / ELIXIR-DE training platform, also known as special interest group 3 (SIG 3) ‘Training & Education’, coordinates the bioinformatics training of de.NBI and the German ELIXIR node. The network provides a high-quality, coherent, timely, and impactful training program across its eight service centers. Life scientists learn how to handle and analyze biological big data more effectively by applying tools, standards and compute services provided by de.NBI. Since 2015, more than 300 training courses were carried out with about 6,000 participants and these courses received recommendation rates of almost 90% (status as of July 2020). In addition to face-to-face training courses, online training was introduced on the de.NBI website in 2016 and guidelines for the preparation of e-learning material were established in 2018. In 2016, ELIXIR-DE joined the ELIXIR training platform. Here, the de.NBI / ELIXIR-DE training platform collaborates with ELIXIR in training activities, advertising training courses via TeSS and discussions on the exchange of data for training events essential for quality assessment on both the technical and administrative levels. The de.NBI training program trained thousands of scientists from Germany and beyond in many different areas of bioinformatics.

Cell-Free Prototyping of AND-Logic Gates Based on Heterogeneous RNA Activators

RNA-based devices controlling gene expression bear great promise for synthetic biology, as they offer many advantages such as short response times and light metabolic burden compared to protein-circuits. However, little work has been done regarding their integration to multilevel regulated circuits. In this work, we combined a variety of small transcriptional activator RNAs (STARs) and toehold switches to build highly effective AND-gates. To characterize the components and their dynamic range, we used an Escherichia coli (E. coli) cell-free transcription-translation (TX-TL) system dispensed via nanoliter droplets. We analyzed a prototype gate in vitro as well as in silico, employing parametrized ordinary differential equations (ODEs), for which parameters were inferred via parallel tempering, a Markov chain Monte Carlo (MCMC) method. On the basis of this analysis, we created nine additional AND-gates and tested them in vitro. The functionality of the gates was found to be highly dependent on the concentration of the activating RNA for either the STAR or the toehold switch. All gates were successfully implemented in vivo, offering a dynamic range comparable to the level of protein circuits. This study shows the potential of a rapid prototyping approach for RNA circuit design, using cell-free systems in combination with a model prediction.

Evolution of Hybridization Probes to DNA Machines and Robots

Hybridization probes are RNA or DNA oligonucleotides or their analogs that bind to specific nucleotide sequences in targeted nucleic acids (analytes) via Watson-Crick base pairs to form probe-analyte hybrids. Formation of a stable hybrid would indicate the presence of a DNA or RNA fragment complementary to the known probe sequence. Some of the well-known technologies that rely on nucleic acid hybridization are TaqMan and molecular beacon (MB) probes, fluorescent in situ hybridization (FISH), polymerase chain reaction (PCR), antisense, siRNA, and CRISPR/cas9, among others. Although invaluable tools for DNA and RNA recognition, hybridization probes suffer from several common disadvantages including low selectivity under physiological conditions, low affinity to folded single-stranded RNA and double-stranded DNA, and high cost of dye-labeled and chemically modified probes. Hybridization probes are evolving into multifunctional molecular devices (dubbed here "multicomponent probes", "DNA machines", and "DNA robots") to satisfy complex and often contradictory requirements of modern biomedical applications. In the definition used here, "multicomponent probes" are DNA probes that use more than one oligonucleotide complementary to an analyzed sequence. A "DNA machine" is an association of a discrete number of DNA strands that undergoes structural rearrangements in response to the presence of a specific analyte. Unlike multicomponent probes, DNA machines unify several functional components in a single association even in the absence of a target. DNA robots are DNA machines equipped with computational (analytic) capabilities. This Account is devoted to an overview of the ongoing evolution of hybridization probes to DNA machines and robots. The Account starts with a brief excursion to historically significant and currently used instantaneous probes. The majority of the text is devoted to the design of (i) multicomponent probes and (ii) DNA machines for nucleic acid recognition and analysis. The fundamental advantage of both designs is their ability to simultaneously address multiple problems of RNA/DNA analysis. This is achieved by modular design, in which several specialized functional components are used simultaneously for recognition of RNA or DNA analytes. The Account is concluded with the analysis of perspectives for further evolution of DNA machines into DNA robots.

The RNA encoding the microtubule-associated protein tau has extensive structure that affects its biology

Tauopathies are neurodegenerative diseases that affect millions of people worldwide including those with Alzheimer’s disease. While many efforts have focused on understanding the role of tau protein in neurodegeneration, there has been little done to systematically analyze and study the structures within tau’s encoding RNA and their connection to disease pathology. Knowledge of RNA structure can provide insights into disease mechanisms and how to affect protein production for therapeutic benefit. Using computational methods based on thermodynamic stability and evolutionary conservation, we identified structures throughout the tau pre-mRNA, especially at exon-intron junctions and within the 5′ and 3′ untranslated regions (UTRs). In particular, structures were identified at twenty exon-intron junctions. The 5′ UTR contains one structured region, which lies within a known internal ribosome entry site. The 3′ UTR contains eight structured regions, including one that contains a polyadenylation signal. A series of functional experiments were carried out to assess the effects of mutations associated with mis-regulation of alternative splicing of exon 10 and to identify regions of the 3′ UTR that contain cis-regulatory elements. These studies defined novel structural regions within the mRNA that affect stability and pre-mRNA splicing and may lead to new therapeutic targets for treating tau-associated diseases.

Insights into Telomerase/hTERT Alternative Splicing Regulation Using Bioinformatics and Network Analysis in Cancer

The reactivation of telomerase in cancer cells remains incompletely understood. The catalytic component of telomerase, hTERT, is thought to be the limiting component in cancer cells for the formation of active enzymes. hTERT gene expression is regulated at several levels including chromatin, DNA methylation, transcription factors, and RNA processing events. Of these regulatory events, RNA processing has received little attention until recently. RNA processing and alternative splicing regulation have been explored to understand how hTERT is regulated in cancer cells. The cis- and trans-acting factors that regulate the alternative splicing choice of hTERT in the reverse transcriptase domain have been investigated. Further, it was discovered that the splicing factors that promote the production of full-length hTERT were also involved in cancer cell growth and survival. The goals are to review telomerase regulation via alternative splicing and the function of hTERT splicing variants and to point out how bioinformatics approaches are leading the way in elucidating the networks that regulate hTERT splicing choice and ultimately cancer growth.

Computational approaches for the discovery of splicing regulatory RNA structures

Global RNA structure and local functional motifs mediate interactions important in determining the rates and patterns of mRNA splicing. In this review, we overview approaches for the computational prediction of RNA secondary structure with a special emphasis on the discovery of motifs important to RNA splicing. The process of identifying and modeling potential splicing regulatory structures is illustrated using a recently-developed approach for RNA structural motif discovery, the ScanFold pipeline, which is applied to the identification of a known splicing regulatory structure in influenza virus.

An algebraic language for RNA pseudoknots comparison

BACKGROUND

RNA secondary structure comparison is a fundamental task for several studies, among which are RNA structure prediction and evolution. The comparison can currently be done efficiently only for pseudoknot-free structures due to their inherent tree representation.

RESULTS

In this work, we introduce an algebraic language to represent RNA secondary structures with arbitrary pseudoknots. Each structure is associated with a unique algebraic RNA tree that is derived from a tree grammar having concatenation, nesting and crossing as operators. From an algebraic RNA tree, an abstraction is defined in which the primary structure is neglected. The resulting structural RNA tree allows us to define a new measure of similarity calculated exploiting classical tree alignment.

CONCLUSIONS

The tree grammar with its operators permit to uniquely represent any RNA secondary structure as a tree. Structural RNA trees allow us to perform comparison of RNA secondary structures with arbitrary pseudoknots without taking into account the primary structure.

The RNA Base-Pairing Problem and Base-Pairing Solutions

RNA molecules are folded into structures and complexes to perform a wide variety of functions. Determination of RNA structures and their interactions is a fundamental problem in RNA biology. Most RNA molecules in living cells are large and dynamic, posing unique challenges to structure analysis. Here we review progress in RNA structure analysis, focusing on methods that use the "cross-link, proximally ligate, and sequence" principle for high-throughput detection of base-pairing interactions in living cells. Beginning with a comparison of commonly used methods in structure determination and a brief historical account of psoralen cross-linking studies, we highlight the important features of cross-linking methods and new biological insights into RNA structures and interactions from recent studies. Further improvement of these cross-linking methods and application to previously intractable problems will shed new light on the mechanisms of the "modern RNA world."

Challenges and approaches to predicting RNA with multiple functional structures

The revolution in sequencing technology demands new tools to interpret the genetic code. As in vivo transcriptome-wide chemical probing techniques advance, new challenges emerge in the RNA folding problem. The emphasis on one sequence folding into a single minimum free energy structure is fading as a new focus develops on generating RNA structural ensembles and identifying functional structural features in ensembles. This review describes an efficient combinatorially complete method and three free energy minimization approaches to predicting RNA structures with more than one functional fold, as well as two methods for analysis of a thermodynamics-based Boltzmann ensemble of structures. The review then highlights two examples of viral RNA 3'-UTR regions that fold into more than one conformation and have been characterized by single molecule fluorescence energy resonance transfer or NMR spectroscopy. These examples highlight the different approaches and challenges in predicting structure and function from sequence for RNA with multiple biological roles and folds. More well-defined examples and new metrics for measuring differences in RNA structures will guide future improvements in prediction of RNA structure and function from sequence.

Kinetics coming into focus: single-molecule microscopy of riboswitch dynamics

Riboswitches are dynamic RNA motifs that are mostly embedded in the 5'-untranslated regions of bacterial mRNAs, where they regulate gene expression transcriptionally or translationally by undergoing conformational changes upon binding of a small metabolite or ion. Due to the small size of typical ligands, relatively little free energy is available from ligand binding to overcome the often high energetic barrier of reshaping RNA structure. Instead, most riboswitches appear to take advantage of the directional and hierarchical folding of RNA by employing the ligand as a structural 'linchpin' to adjust the kinetic partitioning between alternate folds. In this model, even small, local structural and kinetic effects of ligand binding can cascade into global RNA conformational changes affecting gene expression. Single-molecule (SM) microscopy tools are uniquely suited to study such kinetically controlled RNA folding since they avoid the ensemble averaging of bulk techniques that loses sight of unsynchronized, transient, and/or multi-state kinetic behavior. This review summarizes how SM methods have begun to unravel riboswitch-mediated gene regulation.

Prediction of secondary and tertiary structures of human BC200 RNA (BCYRN1) based on experimental and bioinformatic cross-validation

Based on experimental and bioinformatic approaches, we present the first empirically established complete secondary structure of human BC200 RNA. BC200 RNA is a brain-specific non-messenger RNA with a confirmed regulatory role in dendritic translation in neurons. Although the involvement of human BC200 RNA in various types of tumour and Alzheimer's disease has been repeatedly confirmed, the exact secondary structure remains not fully elucidated. To determine the secondary structure of BC200 RNA in vitro, we performed partial hydrolysis with sequence-specific nucleases and lead-induced cleavage. We also examined the availabilities of putative single-stranded regions and base-pairing interactions via specific DNAzymes and RNase H assay. To determine the complete spatial folding of BC200 RNA, we used experimental data as constraints in structure prediction programs and performed a comparison of results obtained by several algorithms using different criteria. Based on the experimental-derived secondary structure of BC200 RNA, we also predicted the tertiary structure of BC200 RNA. The presented combination of experimental and bioinformatic approaches not only enabled the determination of the most reliable secondary and tertiary structures of human BC200 RNA (largely in agreement with the previous phylogenetic model), but also verified the compatibility and potential disadvantages of utilizing in silico structure prediction programs.

The Ever-Evolving Concept of the Gene: The Use of RNA/Protein Experimental Techniques to Understand Genome Functions

The completion of the human genome sequence together with advances in sequencing technologies have shifted the paradigm of the genome, as composed of discrete and hereditable coding entities, and have shown the abundance of functional noncoding DNA. This part of the genome, previously dismissed as “junk” DNA, increases proportionally with organismal complexity and contributes to gene regulation beyond the boundaries of known protein-coding genes. Different classes of functionally relevant nonprotein-coding RNAs are transcribed from noncoding DNA sequences. Among them are the long noncoding RNAs (lncRNAs), which are thought to participate in the basal regulation of protein-coding genes at both transcriptional and post-transcriptional levels. Although knowledge of this field is still limited, the ability of lncRNAs to localize in different cellular compartments, to fold into specific secondary structures and to interact with different molecules (RNA or proteins) endows them with multiple regulatory mechanisms. It is becoming evident that lncRNAs may play a crucial role in most biological processes such as the control of development, differentiation and cell growth. This review places the evolution of the concept of the gene in its historical context, from Darwin's hypothetical mechanism of heredity to the post-genomic era. We discuss how the original idea of protein-coding genes as unique determinants of phenotypic traits has been reconsidered in light of the existence of noncoding RNAs. We summarize the technological developments which have been made in the genome-wide identification and study of lncRNAs and emphasize the methodologies that have aided our understanding of the complexity of lncRNA-protein interactions in recent years.

Know Your Enemy: Successful Bioinformatic Approaches to Predict Functional RNA Structures in Viral RNAs

Structured RNA elements may control virus replication, transcription and translation, and their distinct features are being exploited by novel antiviral strategies. Viral RNA elements continue to be discovered using combinations of experimental and computational analyses. However, the wealth of sequence data, notably from deep viral RNA sequencing, viromes, and metagenomes, necessitates computational approaches being used as an essential discovery tool. In this review, we describe practical approaches being used to discover functional RNA elements in viral genomes. In addition to success stories in new and emerging viruses, these approaches have revealed some surprising new features of well-studied viruses e.g., human immunodeficiency virus, hepatitis C virus, influenza, and dengue viruses. Some notable discoveries were facilitated by new comparative analyses of diverse viral genome alignments. Importantly, comparative approaches for finding RNA elements embedded in coding and non-coding regions differ. With the exponential growth of computer power we have progressed from stem-loop prediction on single sequences to cutting edge 3D prediction, and from command line to user friendly web interfaces. Despite these advances, many powerful, user friendly prediction tools and resources are underutilized by the virology community.